Using AMQ Streams on OpenShift

Red Hat AMQ 7.5

For use with AMQ Streams 1.3 on OpenShift Container Platform

Abstract

This guide describes how to install, configure, and manage Red Hat AMQ Streams to build a large-scale messaging network.

Chapter 1. Overview of AMQ Streams

AMQ Streams is based on Apache Kafka, a popular platform for streaming data delivery and processing. AMQ Streams makes it easy to run Apache Kafka on OpenShift.

AMQ Streams provides three operators:

Cluster Operator
Responsible for deploying and managing Apache Kafka clusters within an OpenShift cluster.
Topic Operator
Responsible for managing Kafka topics within a Kafka cluster running within an OpenShift cluster.
User Operator
Responsible for managing Kafka users within a Kafka cluster running within an OpenShift cluster.
Note

The Cluster Operator can deploy the Topic Operator and User Operator (as part of an Entity Operator configuration) at the same time as a Kafka cluster.

Operators within the AMQ Streams architecture

Operators

1.1. Kafka Key Features

  • Designed for horizontal scalability
  • Message ordering guarantee at the partition level
  • Message rewind/replay

    • "Long term" storage allows the reconstruction of an application state by replaying the messages
    • Combines with compacted topics to use Kafka as a key-value store

Additional resources

1.2. Document Conventions

Replaceables

In this document, replaceable text is styled in monospace and italics.

For example, in the following code, you will want to replace my-namespace with the name of your namespace:

sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

Chapter 2. Getting started with AMQ Streams

AMQ Streams works on all types of clusters, from public and private clouds to local deployments intended for development. This guide assumes that an OpenShift cluster is available and the oc command-line tool is installed and configured to connect to the running cluster.

AMQ Streams is based on Strimzi 0.14.x. This chapter describes the procedures to deploy AMQ Streams on OpenShift 3.11 and later.

Note

To run the commands in this guide, your cluster user must have the rights to manage role-based access control (RBAC) and CRDs.

For more information about OpenShift and setting up OpenShift cluster, see OpenShift documentation.

2.1. Installing AMQ Streams and deploying components

To install AMQ Streams, download and extract the amq-streams-x.y.z-ocp-install-examples.zip file from the AMQ Streams download site.

The folder contains several YAML files to help you deploy the components of AMQ Streams to OpenShift, perform common operations, and configure your Kafka cluster. The YAML files are referenced throughout this documentation.

The remainder of this chapter provides an overview of each component and instructions for deploying the components to OpenShift using the YAML files provided.

Note

Although container images for AMQ Streams are available in the Red Hat Container Catalog, we recommend that you use the YAML files provided instead.

2.2. Custom resources

Custom resource definitions (CRDs) extend the Kubernetes API, providing definitions to create and modify custom resources to an OpenShift cluster. Custom resources are created as instances of CRDs.

In AMQ Streams, CRDs introduce custom resources specific to AMQ Streams to an OpenShift cluster, such as Kafka, Kafka Connect, Kafka Mirror Maker, and users and topics custom resources. CRDs provide configuration instructions, defining the schemas used to instantiate and manage the AMQ Streams-specific resources. CRDs also allow AMQ Streams resources to benefit from native OpenShift features like CLI accessibility and configuration validation.

CRDs require a one-time installation in a cluster. Depending on the cluster setup, installation typically requires cluster admin privileges.

Note

Access to manage custom resources is limited to AMQ Streams administrators.

CRDs and custom resources are defined as YAML files.

A CRD defines a new kind of resource, such as kind:Kafka, within an OpenShift cluster.

The Kubernetes API server allows custom resources to be created based on the kind and understands from the CRD how to validate and store the custom resource when it is added to the OpenShift cluster.

Warning

When CRDs are deleted, custom resources of that type are also deleted. Additionally, the resources created by the custom resource, such as pods and statefulsets are also deleted.

2.2.1. AMQ Streams custom resource example

Each AMQ Streams-specific custom resource conforms to the schema defined by the CRD for the resource’s kind.

To understand the relationship between a CRD and a custom resource, let’s look at a sample of the CRD for a Kafka topic.

Kafka topic CRD

apiVersion: kafka.strimzi.io/v1beta1
kind: CustomResourceDefinition
metadata: 1
  name: kafkatopics.kafka.strimzi.io
  labels:
    app: strimzi
spec: 2
  group: kafka.strimzi.io
  versions:
    v1beta1
  scope: Namespaced
  names:
    # ...
    singular: kafkatopic
    plural: kafkatopics
    shortNames:
    - kt 3
  additionalPrinterColumns: 4
      # ...
  subresources:
    status: {} 5
  validation: 6
    openAPIV3Schema:
      properties:
        spec:
          type: object
          properties:
            partitions:
              type: integer
              minimum: 1
            replicas:
              type: integer
              minimum: 1
              maximum: 32767
      # ...

1
The metadata for the topic CRD, its name and a label to identify the CRD.
2
The specification for this CRD, including the group (domain) name, the plural name and the supported schema version, which are used in the URL to access the API of the topic. The other names are used to identify instance resources in the CLI. For example, oc get kafkatopic my-topic or oc get kafkatopics.
3
The shortname can be used in CLI commands. For example, oc get kt can be used as an abbreviation instead of oc get kafkatopic.
4
The information presented when using a get command on the custom resource.
5
The current status of the CRD as described in the schema reference for the resource.
6
openAPIV3Schema validation provides validation for the creation of topic custom resources. For example, a topic requires at least one partition and one replica.
Note

You can identify the CRD YAML files supplied with the AMQ Streams installation files, because the file names contain an index number followed by ‘Crd’.

Here is a corresponding example of a KafkaTopic custom resource.

Kafka topic custom resource

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic 1
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster 2
spec: 3
  partitions: 1
  replicas: 1
  config:
    retention.ms: 7200000
    segment.bytes: 1073741824
status:
  conditions: 4
    lastTransitionTime: "2019-08-20T11:37:00.706Z"
    status: "True"
    type: Ready
  observedGeneration: 1
  / ...

1
The kind and apiVersion identify the CRD of which the custom resource is an instance.
2
A label, applicable only to KafkaTopic and KafkaUser resources, that defines the name of the Kafka cluster (which is same as the name of the Kafka resource) to which a topic or user belongs.

The name is used by the Topic Operator and User Operator to identify the Kafka cluster when creating a topic or user.

3
The spec shows the number of partitions and replicas for the topic as well as the configuration parameters for the topic itself. In this example, the retention period for a message to remain in the topic and the segment file size for the log are specified.
4
Status conditions for the KafkaTopic resource. The type condition changed to Ready at the lastTransitionTime.

Custom resources can be applied to a cluster through the platform CLI. When the custom resource is created, it uses the same validation as the built-in resources of the Kubernetes API.

After a KafkaTopic custom resource is created, the Topic Operator is notified and corresponding Kafka topics are created in AMQ Streams.

2.2.2. AMQ Streams custom resource status

The status property of a AMQ Streams custom resource publishes information about the resource to users and tools that need it.

Several resources have a status property, as described in the following table.

AMQ Streams resourceSchema referencePublishes status information on…​

Kafka

Section C.58, “KafkaStatus schema reference”

The Kafka cluster.

KafkaConnect

Section C.77, “KafkaConnectStatus schema reference”

The Kafka Connect cluster, if deployed.

KafkaConnectS2I

Section C.80, “KafkaConnectS2Istatus schema reference”

The Kafka Connect cluster with Source-to-Image support, if deployed.

KafkaMirrorMaker

Section C.101, “KafkaMirrorMakerStatus schema reference”

The Kafka MirrorMaker tool, if deployed.

KafkaTopic

Section C.83, “KafkaTopicStatus schema reference”

Kafka topics in your Kafka cluster.

KafkaUser

Section C.94, “KafkaUserStatus schema reference”

Kafka users in your Kafka cluster.

KafkaBridge

Section C.109, “KafkaBridgeStatus schema reference”

The AMQ Streams Kafka Bridge, if deployed.

The status property of a resource provides information on the resource’s:

  • Current state, in the status.conditions property
  • Last observed generation, in the status.observedGeneration property

The status property also provides resource-specific information. For example:

  • KafkaConnectStatus provides the REST API endpoint for Kafka Connect connectors.
  • KafkaUserStatus provides the user name of the Kafka user and the Secret in which their credentials are stored.
  • KafkaBridgeStatus provides the HTTP address at which external client applications can access the Bridge service.

A resource’s current state is useful for tracking progress related to the resource achieving its desired state, as defined by the spec property. The status conditions provide the time and reason the state of the resource changed and details of events preventing or delaying the operator from realizing the resource’s desired state.

The last observed generation is the generation of the resource that was last reconciled by the Cluster Operator. If the value of observedGeneration is different from the value of metadata.generation, the operator has not yet processed the latest update to the resource. If these values are the same, the status information reflects the most recent changes to the resource.

AMQ Streams creates and maintains the status of custom resources, periodically evaluating the current state of the custom resource and updating its status accordingly. When performing an update on a custom resource using oc edit, for example, its status is not editable. Moreover, changing the status would not affect the configuration of the Kafka cluster.

Here we see the status property specified for a Kafka custom resource.

Kafka custom resource with status

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
spec:
  # ...
status:
  conditions: 1
  - lastTransitionTime: 2019-07-23T23:46:57+0000
    status: "True"
    type: Ready 2
  observedGeneration: 4 3
  listeners: 4
  - addresses:
    - host: my-cluster-kafka-bootstrap.myproject.svc
      port: 9092
    type: plain
  - addresses:
    - host: my-cluster-kafka-bootstrap.myproject.svc
      port: 9093
    type: tls
  - addresses:
    - host: 172.29.49.180
      port: 9094
    type: external
    # ...

1
Status conditions describe criteria related to the status that cannot be deduced from the existing resource information, or are specific to the instance of a resource.
2
The Ready condition indicates whether the Cluster Operator currently considers the Kafka cluster able to handle traffic.
3
The observedGeneration indicates the generation of the Kafka custom resource that was last reconciled by the Cluster Operator.
4
The listeners describe the current Kafka bootstrap addresses by type.
Important

The address in the custom resource status for external listeners with type nodeport is currently not supported.

Note

The Kafka bootstrap addresses listed in the status do not signify that those endpoints or the Kafka cluster is in a ready state.

Accessing status information

You can access status information for a resource from the command line. For more information, see Chapter 16, Checking the status of a custom resource.

2.3. Cluster Operator

The Cluster Operator is responsible for deploying and managing Apache Kafka clusters within an OpenShift cluster.

2.3.1. Overview of the Cluster Operator component

AMQ Streams uses the Cluster Operator to deploy and manage clusters for:

  • Kafka (including Zookeeper, Entity Operator and Kafka Exporter)
  • Kafka Connect
  • Kafka Mirror Maker
  • Kafka Bridge

Custom resources are used to deploy the clusters.

For example, to deploy a Kafka cluster:

  • A Kafka resource with the cluster configuration is created within the OpenShift cluster.
  • The Cluster Operator deploys a corresponding Kafka cluster, based on what is declared in the Kafka resource.

The Cluster Operator can also deploy (through Entity Operator configuration of the Kafka resource):

  • A Topic Operator to provide operator-style topic management through KafkaTopic custom resources
  • A User Operator to provide operator-style user management through KafkaUser custom resources

For more information on the configuration options supported by the Kafka resource, see Section 3.1, “Kafka cluster configuration”.

Note

On OpenShift, a Kafka Connect deployment can incorporate a Source2Image feature to provides a convenient way to include connectors.

Example architecture for the Cluster Operator

Cluster Operator

2.3.2. Watch options for a Cluster Operator deployment

When the Cluster Operator is running, it starts to watch for updates of Kafka resources.

Depending on the deployment, the Cluster Operator can watch Kafka resources from:

Note

AMQ Streams provides example YAML files to make the deployment process easier.

The Cluster Operator watches the following resources:

  • Kafka for the Kafka cluster.
  • KafkaConnect for the Kafka Connect cluster.
  • KafkaConnectS2I for the Kafka Connect cluster with Source2Image support.
  • KafkaMirrorMaker for the Kafka Mirror Maker instance.
  • KafkaBridge for the Kafka Bridge instance

When one of these resources is created in the OpenShift cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary OpenShift resources, such as StatefulSets, Services and ConfigMaps.

Each time a Kafka resource is updated, the operator performs corresponding updates on the OpenShift resources that make up the cluster for the resource.

Resources are either patched or deleted, and then recreated in order to make the cluster for the resource reflect the desired state of the cluster. This operation might cause a rolling update that might lead to service disruption.

When a resource is deleted, the operator undeploys the cluster and deletes all related OpenShift resources.

2.3.3. Deploying the Cluster Operator to watch a single namespace

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.
  • Modify the installation files according to the namespace the Cluster Operator is going to be installed in.

    On Linux, use:

    sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

Procedure

  • Deploy the Cluster Operator:

    oc apply -f install/cluster-operator -n my-namespace

2.3.4. Deploying the Cluster Operator to watch multiple namespaces

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.
  • Edit the installation files according to the namespace the Cluster Operator is going to be installed in.

    On Linux, use:

    sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

Procedure

  1. Edit the file install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml and in the environment variable STRIMZI_NAMESPACE list all the namespaces where Cluster Operator should watch for resources. For example:

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq7/amq-streams-operator:1.3.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: watched-namespace-1,watched-namespace-2,watched-namespace-3
  2. For all namespaces which should be watched by the Cluster Operator (watched-namespace-1, watched-namespace-2, watched-namespace-3 in the above example), install the RoleBindings. Replace the watched-namespace with the namespace used in the previous step.

    This can be done using oc apply:

    oc apply -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace
    oc apply -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace
    oc apply -f install/cluster-operator/032-RoleBinding-strimzi-cluster-operator-topic-operator-delegation.yaml -n watched-namespace
  3. Deploy the Cluster Operator

    This can be done using oc apply:

    oc apply -f install/cluster-operator -n my-namespace

2.3.5. Deploying the Cluster Operator to watch all namespaces

You can configure the Cluster Operator to watch AMQ Streams resources across all namespaces in your OpenShift cluster. When running in this mode, the Cluster Operator automatically manages clusters in any new namespaces that are created.

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.
  • Your OpenShift cluster is running.

Procedure

  1. Configure the Cluster Operator to watch all namespaces:

    1. Edit the 050-Deployment-strimzi-cluster-operator.yaml file.
    2. Set the value of the STRIMZI_NAMESPACE environment variable to *.

      apiVersion: apps/v1
      kind: Deployment
      spec:
        # ...
        template:
          spec:
            # ...
            serviceAccountName: strimzi-cluster-operator
            containers:
            - name: strimzi-cluster-operator
              image: registry.redhat.io/amq7/amq-streams-operator:1.3.0
              imagePullPolicy: IfNotPresent
              env:
              - name: STRIMZI_NAMESPACE
                value: "*"
              # ...
  2. Create ClusterRoleBindings that grant cluster-wide access to all namespaces to the Cluster Operator.

    Use the oc create clusterrolebinding command:

    oc create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-namespace:strimzi-cluster-operator
    oc create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-namespace:strimzi-cluster-operator
    oc create clusterrolebinding strimzi-cluster-operator-topic-operator-delegation --clusterrole=strimzi-topic-operator --serviceaccount my-namespace:strimzi-cluster-operator

    Replace my-namespace with the namespace in which you want to install the Cluster Operator.

  3. Deploy the Cluster Operator to your OpenShift cluster.

    Use the oc apply command:

    oc apply -f install/cluster-operator -n my-namespace

2.4. Kafka cluster

You can use AMQ Streams to deploy an ephemeral or persistent Kafka cluster to OpenShift. When installing Kafka, AMQ Streams also installs a Zookeeper cluster and adds the necessary configuration to connect Kafka with Zookeeper.

You can also use it to deploy Kafka Exporter.

Ephemeral cluster
In general, an ephemeral (that is, temporary) Kafka cluster is suitable for development and testing purposes, not for production. This deployment uses emptyDir volumes for storing broker information (for Zookeeper) and topics or partitions (for Kafka). Using an emptyDir volume means that its content is strictly related to the pod life cycle and is deleted when the pod goes down.
Persistent cluster
A persistent Kafka cluster uses PersistentVolumes to store Zookeeper and Kafka data. The PersistentVolume is acquired using a PersistentVolumeClaim to make it independent of the actual type of the PersistentVolume. For example, it can use Amazon EBS volumes in Amazon AWS deployments without any changes in the YAML files. The PersistentVolumeClaim can use a StorageClass to trigger automatic volume provisioning.

AMQ Streams includes two templates for deploying a Kafka cluster:

  • kafka-ephemeral.yaml deploys an ephemeral cluster, named my-cluster by default.
  • kafka-persistent.yaml deploys a persistent cluster, named my-cluster by default.

The cluster name is defined by the name of the resource and cannot be changed after the cluster has been deployed. To change the cluster name before you deploy the cluster, edit the Kafka.metadata.name property of the resource in the relevant YAML file.

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
# ...

2.4.1. Deploying the Kafka cluster

You can deploy an ephemeral or persistent Kafka cluster to OpenShift on the command line.

Prerequisites

  • The Cluster Operator is deployed.

Procedure

  1. If you plan to use the cluster for development or testing purposes, you can create and deploy an ephemeral cluster using oc apply.

    oc apply -f examples/kafka/kafka-ephemeral.yaml
  2. If you plan to use the cluster in production, create and deploy a persistent cluster using oc apply.

    oc apply -f examples/kafka/kafka-persistent.yaml

Additional resources

2.5. Kafka Connect

Kafka Connect is a tool for streaming data between Apache Kafka and external systems. It provides a framework for moving large amounts of data into and out of your Kafka cluster while maintaining scalability and reliability. Kafka Connect is typically used to integrate Kafka with external databases and storage and messaging systems.

You can use Kafka Connect to:

  • Build connector plug-ins (as JAR files) for your Kafka cluster
  • Run connectors

Kafka Connect includes the following built-in connectors for moving file-based data into and out of your Kafka cluster.

File ConnectorDescription

FileStreamSourceConnector

Transfers data to your Kafka cluster from a file (the source).

FileStreamSinkConnector

Transfers data from your Kafka cluster to a file (the sink).

In AMQ Streams, you can use the Cluster Operator to deploy a Kafka Connect to your OpenShift cluster. OpenShift also supports Kafka Connect Source-2-Image (S2I) clusters.

A Kafka Connect cluster is implemented as a Deployment with a configurable number of workers. The Kafka Connect REST API is available on port 8083, as the <connect-cluster-name>-connect-api service.

For more information on deploying a Kafka Connect S2I cluster on OpenShift, see Creating a container image using OpenShift builds and Source-to-Image.

2.5.1. Deploying Kafka Connect to your cluster

You can deploy a Kafka Connect cluster to your OpenShift cluster by using the Cluster Operator.

Procedure

  • Use the oc apply command to create a KafkaConnect resource based on the kafka-connect.yaml file:

    oc apply -f examples/kafka-connect/kafka-connect.yaml

2.5.2. Extending Kafka Connect with connector plug-ins

The AMQ Streams container images for Kafka Connect include the two built-in file connectors: FileStreamSourceConnector and FileStreamSinkConnector. You can add your own connectors by:

  • Creating a container image from the Kafka Connect base image (manually or using your CI (continuous integration), for example).
  • Creating a container image using OpenShift builds and Source-to-Image (S2I) - available only on OpenShift.

2.5.2.1. Creating a Docker image from the Kafka Connect base image

You can use the Kafka container image on Red Hat Container Catalog as a base image for creating your own custom image with additional connector plug-ins.

The following procedure explains how to create your custom image and add it to the /opt/kafka/plugins directory. At startup, the AMQ Streams version of Kafka Connect loads any third-party connector plug-ins contained in the /opt/kafka/plugins directory.

Procedure

  1. Create a new Dockerfile using registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 as the base image:

    FROM registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0
    USER root:root
    COPY ./my-plugins/ /opt/kafka/plugins/
    USER jboss:jboss
  2. Build the container image.
  3. Push your custom image to your container registry.
  4. Point to the new container image.

    You can either:

    • Edit the KafkaConnect.spec.image property of the KafkaConnect custom resource.

      If set, this property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable in the Cluster Operator.

      apiVersion: kafka.strimzi.io/v1beta1
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
      spec:
        #...
        image: my-new-container-image

      or

    • In the install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml file, edit the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable to point to the new container image and reinstall the Cluster Operator.

Additional resources

2.5.2.2. Creating a container image using OpenShift builds and Source-to-Image

You can use OpenShift builds and the Source-to-Image (S2I) framework to create new container images. An OpenShift build takes a builder image with S2I support, together with source code and binaries provided by the user, and uses them to build a new container image. Once built, container images are stored in OpenShift’s local container image repository and are available for use in deployments.

A Kafka Connect builder image with S2I support is provided on the Red Hat Container Catalog as part of the registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 image. This S2I image takes your binaries (with plug-ins and connectors) and stores them in the /tmp/kafka-plugins/s2i directory. It creates a new Kafka Connect image from this directory, which can then be used with the Kafka Connect deployment. When started using the enhanced image, Kafka Connect loads any third-party plug-ins from the /tmp/kafka-plugins/s2i directory.

Procedure

  1. On the command line, use the oc apply command to create and deploy a Kafka Connect S2I cluster:

    oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
  2. Create a directory with Kafka Connect plug-ins:

    $ tree ./my-plugins/
    ./my-plugins/
    ├── debezium-connector-mongodb
    │   ├── bson-3.4.2.jar
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mongodb-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mongodb-driver-3.4.2.jar
    │   ├── mongodb-driver-core-3.4.2.jar
    │   └── README.md
    ├── debezium-connector-mysql
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mysql-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mysql-binlog-connector-java-0.13.0.jar
    │   ├── mysql-connector-java-5.1.40.jar
    │   ├── README.md
    │   └── wkb-1.0.2.jar
    └── debezium-connector-postgres
        ├── CHANGELOG.md
        ├── CONTRIBUTE.md
        ├── COPYRIGHT.txt
        ├── debezium-connector-postgres-0.7.1.jar
        ├── debezium-core-0.7.1.jar
        ├── LICENSE.txt
        ├── postgresql-42.0.0.jar
        ├── protobuf-java-2.6.1.jar
        └── README.md
  3. Use the oc start-build command to start a new build of the image using the prepared directory:

    oc start-build my-connect-cluster-connect --from-dir ./my-plugins/
    Note

    The name of the build is the same as the name of the deployed Kafka Connect cluster.

  4. Once the build has finished, the new image is used automatically by the Kafka Connect deployment.

2.6. Kafka Mirror Maker

The Cluster Operator deploys one or more Kafka Mirror Maker replicas to replicate data between Kafka clusters. This process is called mirroring to avoid confusion with the Kafka partitions replication concept. The Mirror Maker consumes messages from the source cluster and republishes those messages to the target cluster.

For information about example resources and the format for deploying Kafka Mirror Maker, see Kafka Mirror Maker configuration.

2.6.1. Deploying Kafka Mirror Maker

Prerequisites

  • Before deploying Kafka Mirror Maker, the Cluster Operator must be deployed.

Procedure

  • Create a Kafka Mirror Maker cluster from the command-line:

    oc apply -f examples/kafka-mirror-maker/kafka-mirror-maker.yaml

Additional resources

2.7. Kafka Bridge

The Cluster Operator deploys one or more Kafka bridge replicas to send data between Kafka clusters and clients via HTTP API.

For information about example resources and the format for deploying Kafka Bridge, see Kafka Bridge configuration.

2.7.1. Deploying Kafka Bridge to your OpenShift cluster

You can deploy a Kafka Bridge cluster to your OpenShift cluster by using the Cluster Operator.

Procedure

  • Use the oc apply command to create a KafkaBridge resource based on the kafka-bridge.yaml file:

    oc apply -f examples/kafka-bridge/kafka-bridge.yaml

Additional resources

2.8. Deploying example clients

Prerequisites

  • An existing Kafka cluster for the client to connect to.

Procedure

  1. Deploy the producer.

    Use oc run:

    oc run kafka-producer -ti --image=registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 --rm=true --restart=Never -- bin/kafka-console-producer.sh --broker-list cluster-name-kafka-bootstrap:9092 --topic my-topic
  2. Type your message into the console where the producer is running.
  3. Press Enter to send the message.
  4. Deploy the consumer.

    Use oc run:

    oc run kafka-consumer -ti --image=registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 --rm=true --restart=Never -- bin/kafka-console-consumer.sh --bootstrap-server cluster-name-kafka-bootstrap:9092 --topic my-topic --from-beginning
  5. Confirm that you see the incoming messages in the consumer console.

2.9. Topic Operator

The Topic Operator is responsible for managing Kafka topics within a Kafka cluster running within an OpenShift cluster.

2.9.1. Overview of the Topic Operator component

The Topic Operator provides a way of managing topics in a Kafka cluster via OpenShift resources.

Example architecture for the Topic Operator

Topic Operator

The role of the Topic Operator is to keep a set of KafkaTopic OpenShift resources describing Kafka topics in-sync with corresponding Kafka topics.

Specifically, if a KafkaTopic is:

  • Created, the operator will create the topic it describes
  • Deleted, the operator will delete the topic it describes
  • Changed, the operator will update the topic it describes

And also, in the other direction, if a topic is:

  • Created within the Kafka cluster, the operator will create a KafkaTopic describing it
  • Deleted from the Kafka cluster, the operator will delete the KafkaTopic describing it
  • Changed in the Kafka cluster, the operator will update the KafkaTopic describing it

This allows you to declare a KafkaTopic as part of your application’s deployment and the Topic Operator will take care of creating the topic for you. Your application just needs to deal with producing or consuming from the necessary topics.

If the topic is reconfigured or reassigned to different Kafka nodes, the KafkaTopic will always be up to date.

For more details about creating, modifying and deleting topics, see Chapter 5, Using the Topic Operator.

2.9.2. Deploying the Topic Operator using the Cluster Operator

This procedure describes how to deploy the Topic Operator using the Cluster Operator. If you want to use the Topic Operator with a Kafka cluster that is not managed by AMQ Streams, you must deploy the Topic Operator as a standalone component. For more information, see Section 4.2.6, “Deploying the standalone Topic Operator”.

Prerequisites

  • A running Cluster Operator
  • A Kafka resource to be created or updated

Procedure

  1. Ensure that the Kafka.spec.entityOperator object exists in the Kafka resource. This configures the Entity Operator.

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the Topic Operator using the fields described in Section C.52, “EntityTopicOperatorSpec schema reference”.
  3. Create or update the Kafka resource in OpenShift.

    Use oc apply:

    oc apply -f your-file

Additional resources

2.10. User Operator

The User Operator is responsible for managing Kafka users within a Kafka cluster running within an OpenShift cluster.

2.10.1. Overview of the User Operator component

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser resources that describe Kafka users and ensuring that they are configured properly in the Kafka cluster. For example:

  • if a KafkaUser is created, the User Operator will create the user it describes
  • if a KafkaUser is deleted, the User Operator will delete the user it describes
  • if a KafkaUser is changed, the User Operator will update the user it describes

Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the OpenShift resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.

The User Operator allows you to declare a KafkaUser as part of your application’s deployment. When the user is created, the user credentials will be created in a Secret. Your application needs to use the user and its credentials for authentication and to produce or consume messages.

In addition to managing credentials for authentication, the User Operator also manages authorization rules by including a description of the user’s rights in the KafkaUser declaration.

2.10.2. Deploying the User Operator using the Cluster Operator

Prerequisites

  • A running Cluster Operator
  • A Kafka resource to be created or updated.

Procedure

  1. Edit the Kafka resource ensuring it has a Kafka.spec.entityOperator.userOperator object that configures the User Operator how you want.
  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

2.11. Strimzi Administrators

AMQ Streams includes several custom resources. By default, permission to create, edit, and delete these resources is limited to OpenShift cluster administrators. If you want to allow non-cluster administators to manage AMQ Streams resources, you must assign them the Strimzi Administrator role.

2.11.1. Designating Strimzi Administrators

Prerequisites

  • AMQ Streams CustomResourceDefinitions are installed.

Procedure

  1. Create the strimzi-admin cluster role in OpenShift.

    Use oc apply:

    oc apply -f install/strimzi-admin
  2. Assign the strimzi-admin ClusterRole to one or more existing users in the OpenShift cluster.

    Use oc create:

    oc create clusterrolebinding strimzi-admin --clusterrole=strimzi-admin --user=user1 --user=user2

2.12. Container images

Container images for AMQ Streams are available in the Red Hat Container Catalog. The installation YAML files provided by AMQ Streams will pull the images directly from the Red Hat Container Catalog.

If you do not have access to the Red Hat Container Catalog or want to use your own container repository:

  1. Pull all container images listed here
  2. Push them into your own registry
  3. Update the image names in the installation YAML files
Note

Each Kafka version supported for the release has a separate image.

Container imageNamespace/RepositoryDescription

Kafka

  • registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0
  • registry.redhat.io/amq7/amq-streams-kafka-22:1.3.0

AMQ Streams image for running Kafka, including:

  • Kafka Broker
  • Kafka Connect / S2I
  • Kafka Mirror Maker
  • Zookeeper
  • TLS Sidecars

Operator

  • registry.redhat.io/amq7/amq-streams-operator:1.3.0

AMQ Streams image for running the operators:

  • Cluster Operator
  • Topic Operator
  • User Operator
  • Kafka Initializer

Kafka Bridge

  • registry.redhat.io/amq7/amq-streams-bridge:1.3.0

AMQ Streams image for running the AMQ Streams Kafka Bridge

Chapter 3. Deployment configuration

This chapter describes how to configure different aspects of the supported deployments:

  • Kafka clusters
  • Kafka Connect clusters
  • Kafka Connect clusters with Source2Image support
  • Kafka Mirror Maker
  • Kafka Bridge
  • OAuth 2.0 token based authentication

3.1. Kafka cluster configuration

The full schema of the Kafka resource is described in the Section C.1, “Kafka schema reference”. All labels that are applied to the desired Kafka resource will also be applied to the OpenShift resources making up the Kafka cluster. This provides a convenient mechanism for resources to be labeled as required.

3.1.1. Sample Kafka YAML configuration

For help in understanding the configuration options available for your Kafka deployment, refer to sample YAML file provided here.

The sample shows only some of the possible configuration options, but those that are particularly important include:

  • Resource requests (CPU / Memory)
  • JVM options for maximum and minimum memory allocation
  • Listeners (and authentication)
  • Authentication
  • Storage
  • Rack awareness
  • Metrics
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    replicas: 3 1
    version: 1.3 2
    resources: 3
      requests:
        memory: 64Gi
        cpu: "8"
      limits: 4
        memory: 64Gi
        cpu: "12"
    jvmOptions: 5
      -Xms: 8192m
      -Xmx: 8192m
    listeners: 6
      tls:
        authentication:7
          type: tls
      external: 8
        type: route
        authentication:
          type: tls
    authorization: 9
      type: simple
    config: 10
      auto.create.topics.enable: "false"
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
    storage: 11
      type: persistent-claim 12
      size: 10000Gi 13
    rack: 14
      topologyKey: failure-domain.beta.kubernetes.io/zone
    metrics: 15
      lowercaseOutputName: true
      rules: 16
      # Special cases and very specific rules
      - pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value
        name: kafka_server_$1_$2
        type: GAUGE
        labels:
          clientId: "$3"
          topic: "$4"
          partition: "$5"
        # ...
  zookeeper: 17
    replicas: 3
    resources:
      requests:
        memory: 8Gi
        cpu: "2"
      limits:
        memory: 8Gi
        cpu: "2"
    jvmOptions:
      -Xms: 4096m
      -Xmx: 4096m
    storage:
      type: persistent-claim
      size: 1000Gi
    metrics:
      # ...
  entityOperator: 18
    topicOperator:
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
    userOperator:
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
  kafkaExporter: 19
    # ...
1
2
3
4
Resource limits specify the maximum resources that can be consumed by a container.
5
6
Listeners configure how clients connect to the Kafka cluster via bootstrap addresses. Listeners are configured as plain (without encryption), tls or external.
7
Listener authentication mechanisms may be configured for each listener, and specified as mutual TLS or SCRAM-SHA.
8
9
10
11
12
13
Persistent storage has additional configuration options, such as a storage id and class for dynamic volume provisioning.
14
Rack awareness is configured to spread replicas across different racks. A topology key must match the label of a cluster node.
15
16
Kafka rules for exporting metrics to a Grafana dashboard through the JMX Exporter. A set of rules provided with AMQ Streams may be copied to your Kafka resource configuration.
17
Zookeeper-specific configuration, which contains properties similar to the Kafka configuration.
18
19
Kafka Exporter configuration, which is used to expose data as Prometheus metrics.

3.1.2. Data storage considerations

An efficient data storage infrastructure is essential to the optimal performance of AMQ Streams.

AMQ Streams requires block storage and is designed to work optimally with cloud-based block storage solutions, including Amazon Elastic Block Store (EBS). The use of file storage (for example, NFS) is not recommended.

Choose local storage (local persistent volumes) when possible. If local storage is not available, you can use a Storage Area Network (SAN) accessed by a protocol such as Fibre Channel or iSCSI.

3.1.2.1. Apache Kafka and Zookeeper storage

Use separate disks for Apache Kafka and Zookeeper.

Three types of data storage are supported:

  • Ephemeral (Recommended for development only)
  • Persistent
  • JBOD (Just a Bunch of Disks, suitable for Kafka only)

For more information, see Kafka and Zookeeper storage.

Solid-state drives (SSDs), though not essential, can improve the performance of Kafka in large clusters where data is sent to and received from multiple topics asynchronously. SSDs are particularly effective with Zookeeper, which requires fast, low latency data access.

Note

You do not need to provision replicated storage because Kafka and Zookeeper both have built-in data replication.

3.1.2.2. File systems

It is recommended that you configure your storage system to use the XFS file system. AMQ Streams is also compatible with the ext4 file system, but this might require additional configuration for best results.

3.1.3. Kafka and Zookeeper storage types

As stateful applications, Kafka and Zookeeper need to store data on disk. AMQ Streams supports three storage types for this data:

  • Ephemeral
  • Persistent
  • JBOD storage
Note

JBOD storage is supported only for Kafka, not for Zookeeper.

When configuring a Kafka resource, you can specify the type of storage used by the Kafka broker and its corresponding Zookeeper node. You configure the storage type using the storage property in the following resources:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper

The storage type is configured in the type field.

Warning

The storage type cannot be changed after a Kafka cluster is deployed.

Additional resources

3.1.3.1. Ephemeral storage

Ephemeral storage uses the `emptyDir` volumes to store data. To use ephemeral storage, the type field should be set to ephemeral.

Important

EmptyDir volumes are not persistent and the data stored in them will be lost when the Pod is restarted. After the new pod is started, it has to recover all data from other nodes of the cluster. Ephemeral storage is not suitable for use with single node Zookeeper clusters and for Kafka topics with replication factor 1, because it will lead to data loss.

An example of Ephemeral storage

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: ephemeral
    # ...
  zookeeper:
    # ...
    storage:
      type: ephemeral
    # ...

3.1.3.1.1. Log directories

The ephemeral volume will be used by the Kafka brokers as log directories mounted into the following path:

/var/lib/kafka/data/kafka-log_idx_
Where idx is the Kafka broker pod index. For example /var/lib/kafka/data/kafka-log0.

3.1.3.2. Persistent storage

Persistent storage uses Persistent Volume Claims to provision persistent volumes for storing data. Persistent Volume Claims can be used to provision volumes of many different types, depending on the Storage Class which will provision the volume. The data types which can be used with persistent volume claims include many types of SAN storage as well as Local persistent volumes.

To use persistent storage, the type has to be set to persistent-claim. Persistent storage supports additional configuration options:

id (optional)
Storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration. Default is 0.
size (required)
Defines the size of the persistent volume claim, for example, "1000Gi".
class (optional)
The OpenShift Storage Class to use for dynamic volume provisioning.
selector (optional)
Allows selecting a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume.
deleteClaim (optional)
Boolean value which specifies if the Persistent Volume Claim has to be deleted when the cluster is undeployed. Default is false.
Warning

Increasing the size of persistent volumes in an existing AMQ Streams cluster is only supported in OpenShift versions that support persistent volume resizing. The persistent volume to be resized must use a storage class that supports volume expansion. For other versions of OpenShift and storage classes which do not support volume expansion, you must decide the necessary storage size before deploying the cluster. Decreasing the size of existing persistent volumes is not possible.

Example fragment of persistent storage configuration with 1000Gi size

# ...
storage:
  type: persistent-claim
  size: 1000Gi
# ...

The following example demonstrates the use of a storage class.

Example fragment of persistent storage configuration with specific Storage Class

# ...
storage:
  type: persistent-claim
  size: 1Gi
  class: my-storage-class
# ...

Finally, a selector can be used to select a specific labeled persistent volume to provide needed features such as an SSD.

Example fragment of persistent storage configuration with selector

# ...
storage:
  type: persistent-claim
  size: 1Gi
  selector:
    hdd-type: ssd
  deleteClaim: true
# ...

3.1.3.2.1. Storage class overrides

You can specify a different storage class for one or more Kafka brokers, instead of using the default storage class. This is useful if, for example, storage classes are restricted to different availability zones or data centers. You can use the overrides field for this purpose.

In this example, the default storage class is named my-storage-class:

Example AMQ Streams cluster using storage class overrides

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  labels:
    app: my-cluster
  name: my-cluster
  namespace: myproject
spec:
  # ...
  kafka:
    replicas: 3
    storage:
      deleteClaim: true
      size: 100Gi
      type: persistent-claim
      class: my-storage-class
      overrides:
        - broker: 0
          class: my-storage-class-zone-1a
        - broker: 1
          class: my-storage-class-zone-1b
        - broker: 2
          class: my-storage-class-zone-1c
  # ...

As a result of the configured overrides property, the broker volumes use the following storage classes:

  • The persistent volumes of broker 0 will use my-storage-class-zone-1a.
  • The persistent volumes of broker 1 will use my-storage-class-zone-1b.
  • The persistent volumes of broker 2 will use my-storage-class-zone-1c.

The overrides property is currently used only to override storage class configurations. Overriding other storage configuration fields is not currently supported. Other fields from the storage configuration are currently not supported.

3.1.3.2.2. Persistent Volume Claim naming

When persistent storage is used, it creates Persistent Volume Claims with the following names:

data-cluster-name-kafka-idx
Persistent Volume Claim for the volume used for storing data for the Kafka broker pod idx.
data-cluster-name-zookeeper-idx
Persistent Volume Claim for the volume used for storing data for the Zookeeper node pod idx.
3.1.3.2.3. Log directories

The persistent volume will be used by the Kafka brokers as log directories mounted into the following path:

/var/lib/kafka/data/kafka-log_idx_
Where idx is the Kafka broker pod index. For example /var/lib/kafka/data/kafka-log0.

3.1.3.3. Resizing persistent volumes

You can provision increased storage capacity by increasing the size of the persistent volumes used by an existing AMQ Streams cluster. Resizing persistent volumes is supported in clusters that use either a single persistent volume or multiple persistent volumes in a JBOD storage configuration.

Note

You can increase but not decrease the size of persistent volumes. Decreasing the size of persistent volumes is not currently supported in OpenShift.

Prerequisites

  • An OpenShift cluster with support for volume resizing.
  • The Cluster Operator is running.
  • A Kafka cluster using persistent volumes created using a storage class that supports volume expansion.

Procedure

  1. In a Kafka resource, increase the size of the persistent volume allocated to the Kafka cluster, the Zookeeper cluster, or both.

    • To increase the volume size allocated to the Kafka cluster, edit the spec.kafka.storage property.
    • To increase the volume size allocated to the Zookeeper cluster, edit the spec.zookeeper.storage property.

      For example, to increase the volume size from 1000Gi to 2000Gi:

      apiVersion: kafka.strimzi.io/v1beta1
      kind: Kafka
      metadata:
        name: my-cluster
      spec:
        kafka:
          # ...
          storage:
            type: persistent-claim
            size: 2000Gi
            class: my-storage-class
          # ...
        zookeeper:
          # ...
  2. Create or update the resource.

    Use oc apply:

    oc apply -f your-file

    OpenShift increases the capacity of the selected persistent volumes in response to a request from the Cluster Operator. When the resizing is complete, the Cluster Operator restarts all pods that use the resized persistent volumes. This happens automatically.

Additional resources

For more information about resizing persistent volumes in OpenShift, see Resizing Persistent Volumes using Kubernetes.

3.1.3.4. JBOD storage overview

You can configure AMQ Streams to use JBOD, a data storage configuration of multiple disks or volumes. JBOD is one approach to providing increased data storage for Kafka brokers. It can also improve performance.

A JBOD configuration is described by one or more volumes, each of which can be either ephemeral or persistent. The rules and constraints for JBOD volume declarations are the same as those for ephemeral and persistent storage. For example, you cannot change the size of a persistent storage volume after it has been provisioned.

3.1.3.4.1. JBOD configuration

To use JBOD with AMQ Streams, the storage type must be set to jbod. The volumes property allows you to describe the disks that make up your JBOD storage array or configuration. The following fragment shows an example JBOD configuration:

# ...
storage:
  type: jbod
  volumes:
  - id: 0
    type: persistent-claim
    size: 100Gi
    deleteClaim: false
  - id: 1
    type: persistent-claim
    size: 100Gi
    deleteClaim: false
# ...

The ids cannot be changed once the JBOD volumes are created.

Users can add or remove volumes from the JBOD configuration.

3.1.3.4.2. JBOD and Persistent Volume Claims

When persistent storage is used to declare JBOD volumes, the naming scheme of the resulting Persistent Volume Claims is as follows:

data-id-cluster-name-kafka-idx
Where id is the ID of the volume used for storing data for Kafka broker pod idx.
3.1.3.4.3. Log directories

The JBOD volumes will be used by the Kafka brokers as log directories mounted into the following path:

/var/lib/kafka/data-id/kafka-log_idx_
Where id is the ID of the volume used for storing data for Kafka broker pod idx. For example /var/lib/kafka/data-0/kafka-log0.

3.1.3.5. Adding volumes to JBOD storage

This procedure describes how to add volumes to a Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type.

Note

When adding a new volume under an id which was already used in the past and removed, you have to make sure that the previously used PersistentVolumeClaims have been deleted.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • A Kafka cluster with JBOD storage

Procedure

  1. Edit the spec.kafka.storage.volumes property in the Kafka resource. Add the new volumes to the volumes array. For example, add the new volume with id 2:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        storage:
          type: jbod
          volumes:
          - id: 0
            type: persistent-claim
            size: 100Gi
            deleteClaim: false
          - id: 1
            type: persistent-claim
            size: 100Gi
            deleteClaim: false
          - id: 2
            type: persistent-claim
            size: 100Gi
            deleteClaim: false
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file
  3. Create new topics or reassign existing partitions to the new disks.

Additional resources

For more information about reassigning topics, see Section 3.1.24.2, “Partition reassignment”.

3.1.3.6. Removing volumes from JBOD storage

This procedure describes how to remove volumes from Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type. The JBOD storage always has to contain at least one volume.

Important

To avoid data loss, you have to move all partitions before removing the volumes.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • A Kafka cluster with JBOD storage with two or more volumes

Procedure

  1. Reassign all partitions from the disks which are you going to remove. Any data in partitions still assigned to the disks which are going to be removed might be lost.
  2. Edit the spec.kafka.storage.volumes property in the Kafka resource. Remove one or more volumes from the volumes array. For example, remove the volumes with ids 1 and 2:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        storage:
          type: jbod
          volumes:
          - id: 0
            type: persistent-claim
            size: 100Gi
            deleteClaim: false
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

For more information about reassigning topics, see Section 3.1.24.2, “Partition reassignment”.

3.1.4. Kafka broker replicas

A Kafka cluster can run with many brokers. You can configure the number of brokers used for the Kafka cluster in Kafka.spec.kafka.replicas. The best number of brokers for your cluster has to be determined based on your specific use case.

3.1.4.1. Configuring the number of broker nodes

This procedure describes how to configure the number of Kafka broker nodes in a new cluster. It only applies to new clusters with no partitions. If your cluster already has topics defined, see Section 3.1.24, “Scaling clusters”.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • A Kafka cluster with no topics defined yet

Procedure

  1. Edit the replicas property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        replicas: 3
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

If your cluster already has topics defined, see Section 3.1.24, “Scaling clusters”.

3.1.5. Kafka broker configuration

AMQ Streams allows you to customize the configuration of the Kafka brokers in your Kafka cluster. You can specify and configure most of the options listed in the "Broker Configs" section of the Apache Kafka documentation. You cannot configure options that are related to the following areas:

  • Security (Encryption, Authentication, and Authorization)
  • Listener configuration
  • Broker ID configuration
  • Configuration of log data directories
  • Inter-broker communication
  • Zookeeper connectivity

These options are automatically configured by AMQ Streams.

3.1.5.1. Kafka broker configuration

The config property in Kafka.spec.kafka contains Kafka broker configuration options as keys with values in one of the following JSON types:

  • String
  • Number
  • Boolean

You can specify and configure all of the options in the "Broker Configs" section of the Apache Kafka documentation apart from those managed directly by AMQ Streams. Specifically, you are prevented from modifying all configuration options with keys equal to or starting with one of the following strings:

  • listeners
  • advertised.
  • broker.
  • listener.
  • host.name
  • port
  • inter.broker.listener.name
  • sasl.
  • ssl.
  • security.
  • password.
  • principal.builder.class
  • log.dir
  • zookeeper.connect
  • zookeeper.set.acl
  • authorizer.
  • super.user

If the config property specifies a restricted option, it is ignored and a warning message is printed to the Cluster Operator log file. All other supported options are passed to Kafka.

An example Kafka broker configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    config:
      num.partitions: 1
      num.recovery.threads.per.data.dir: 1
      default.replication.factor: 3
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 1
      log.retention.hours: 168
      log.segment.bytes: 1073741824
      log.retention.check.interval.ms: 300000
      num.network.threads: 3
      num.io.threads: 8
      socket.send.buffer.bytes: 102400
      socket.receive.buffer.bytes: 102400
      socket.request.max.bytes: 104857600
      group.initial.rebalance.delay.ms: 0
    # ...

3.1.5.2. Configuring Kafka brokers

You can configure an existing Kafka broker, or create a new Kafka broker with a specified configuration.

Prerequisites

  • An OpenShift cluster is available.
  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.
  2. In the spec.kafka.config property in the Kafka resource, enter one or more Kafka configuration settings. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        config:
          default.replication.factor: 3
          offsets.topic.replication.factor: 3
          transaction.state.log.replication.factor: 3
          transaction.state.log.min.isr: 1
        # ...
      zookeeper:
        # ...
  3. Apply the new configuration to create or update the resource.

    Use oc apply:

    oc apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

3.1.6. Kafka broker listeners

You can configure the listeners enabled in Kafka brokers. The following types of listener are supported:

  • Plain listener on port 9092 (without encryption)
  • TLS listener on port 9093 (with encryption)
  • External listener on port 9094 for access from outside of OpenShift

OAuth 2.0

If you are using OAuth 2.0 token based authentication, you can configure the listeners to connect to your authorization server. For more information, see Using OAuth 2.0 token based authentication.

3.1.6.1. Kafka listeners

You can configure Kafka broker listeners using the listeners property in the Kafka.spec.kafka resource. The listeners property contains three sub-properties:

  • plain
  • tls
  • external

Each listener will only be defined when the listeners object has the given property.

An example of listeners property with all listeners enabled

# ...
listeners:
  plain: {}
  tls: {}
  external:
    type: loadbalancer
# ...

An example of listeners property with only the plain listener enabled

# ...
listeners:
  plain: {}
# ...

3.1.6.2. Configuring Kafka listeners

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the listeners property in the Kafka.spec.kafka resource.

    An example configuration of the plain (unencrypted) listener without authentication:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          plain: {}
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.6.3. Listener authentication

The listener authentication property is used to specify an authentication mechanism specific to that listener:

  • Mutual TLS authentication (only on the listeners with TLS encryption)
  • SCRAM-SHA authentication

If no authentication property is specified then the listener does not authenticate clients which connect through that listener.

Authentication must be configured when using the User Operator to manage KafkaUsers.

3.1.6.3.1. Authentication configuration for a listener

The following example shows:

  • A plain listener configured for SCRAM-SHA authentication
  • A tls listener with mutual TLS authentication
  • An external listener with mutual TLS authentication

An example showing listener authentication configuration

# ...
listeners:
  plain:
    authentication:
      type: scram-sha-512
  tls:
    authentication:
      type: tls
  external:
    type: loadbalancer
    tls: true
    authentication:
      type: tls
# ...

3.1.6.3.2. Mutual TLS authentication

Mutual TLS authentication is always used for the communication between Kafka brokers and Zookeeper pods.

Mutual authentication or two-way authentication is when both the server and the client present certificates. AMQ Streams can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. When you configure mutual authentication, the broker authenticates the client and the client authenticates the broker.

Note

TLS authentication is more commonly one-way, with one party authenticating the identity of another. For example, when HTTPS is used between a web browser and a web server, the server obtains proof of the identity of the browser.

3.1.6.3.2.1. When to use mutual TLS authentication for clients

Mutual TLS authentication is recommended for authenticating Kafka clients when:

  • The client supports authentication using mutual TLS authentication
  • It is necessary to use the TLS certificates rather than passwords
  • You can reconfigure and restart client applications periodically so that they do not use expired certificates.
3.1.6.3.3. SCRAM-SHA authentication

SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. AMQ Streams can configure Kafka to use SASL (Simple Authentication and Security Layer) SCRAM-SHA-512 to provide authentication on both unencrypted and TLS-encrypted client connections. TLS authentication is always used internally between Kafka brokers and Zookeeper nodes. When used with a TLS client connection, the TLS protocol provides encryption, but is not used for authentication.

The following properties of SCRAM make it safe to use SCRAM-SHA even on unencrypted connections:

  • The passwords are not sent in the clear over the communication channel. Instead the client and the server are each challenged by the other to offer proof that they know the password of the authenticating user.
  • The server and client each generate a new challenge for each authentication exchange. This means that the exchange is resilient against replay attacks.
3.1.6.3.3.1. Supported SCRAM credentials

AMQ Streams supports SCRAM-SHA-512 only. When a KafkaUser.spec.authentication.type is configured with scram-sha-512 the User Operator will generate a random 12 character password consisting of upper and lowercase ASCII letters and numbers.

3.1.6.3.3.2. When to use SCRAM-SHA authentication for clients

SCRAM-SHA is recommended for authenticating Kafka clients when:

  • The client supports authentication using SCRAM-SHA-512
  • It is necessary to use passwords rather than the TLS certificates
  • Authentication for unencrypted communication is required

3.1.6.4. External listeners

Use an external listener to expose your AMQ Streams Kafka cluster to a client outside an OpenShift environment.

Additional resources

3.1.6.4.1. Customizing advertised addresses on external listeners

By default, AMQ Streams tries to automatically determine the hostnames and ports that your Kafka cluster advertises to its clients. This is not sufficient in all situations, because the infrastructure on which AMQ Streams is running might not provide the right hostname or port through which Kafka can be accessed. You can customize the advertised hostname and port in the overrides property of the external listener. AMQ Streams will then automatically configure the advertised address in the Kafka brokers and add it to the broker certificates so it can be used for TLS hostname verification. Overriding the advertised host and ports is available for all types of external listeners.

Example of an external listener configured with overrides for advertised addresses

# ...
listeners:
  external:
    type: route
    authentication:
      type: tls
    overrides:
      brokers:
      - broker: 0
        advertisedHost: example.hostname.0
        advertisedPort: 12340
      - broker: 1
        advertisedHost: example.hostname.1
        advertisedPort: 12341
      - broker: 2
        advertisedHost: example.hostname.2
        advertisedPort: 12342
# ...

Additionally, you can specify the name of the bootstrap service. This name will be added to the broker certificates and can be used for TLS hostname verification. Adding the additional bootstrap address is available for all types of external listeners.

Example of an external listener configured with an additional bootstrap address

# ...
listeners:
  external:
    type: route
    authentication:
      type: tls
    overrides:
      bootstrap:
        address: example.hostname
# ...

3.1.6.4.2. Route external listeners

An external listener of type route exposes Kafka using OpenShift Routes and the HAProxy router.

Note

route is only supported on OpenShift

3.1.6.4.2.1. Exposing Kafka using OpenShift Routes

When exposing Kafka using OpenShift Routes and the HAProxy router, a dedicated Route is created for every Kafka broker pod. An additional Route is created to serve as a Kafka bootstrap address. Kafka clients can use these Routes to connect to Kafka on port 443.

TLS encryption is always used with Routes.

By default, the route hosts are automatically assigned by OpenShift. However, you can override the assigned route hosts by specifying the requested hosts in the overrides property. AMQ Streams will not perform any validation that the requested hosts are available; you must ensure that they are free and can be used.

Example of an external listener of type routes configured with overrides for OpenShift route hosts

# ...
listeners:
  external:
    type: route
    authentication:
      type: tls
    overrides:
      bootstrap:
        host: bootstrap.myrouter.com
      brokers:
      - broker: 0
        host: broker-0.myrouter.com
      - broker: 1
        host: broker-1.myrouter.com
      - broker: 2
        host: broker-2.myrouter.com
# ...

For more information on using Routes to access Kafka, see Section 3.1.6.4.2.2, “Accessing Kafka using OpenShift routes”.

3.1.6.4.2.2. Accessing Kafka using OpenShift routes

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Deploy Kafka cluster with an external listener enabled and configured to the type route.

    An example configuration with an external listener configured to use Routes:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          external:
            type: route
            # ...
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    oc apply -f your-file
  3. Find the address of the bootstrap Route.

    oc get routes _cluster-name_-kafka-bootstrap -o=jsonpath='{.status.ingress[0].host}{"\n"}'

    Use the address together with port 443 in your Kafka client as the bootstrap address.

  4. Extract the public certificate of the broker certification authority

    oc get secret _<cluster-name>_-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

    Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

Additional resources

3.1.6.4.3. Loadbalancer external listeners

External listeners of type loadbalancer expose Kafka by using Loadbalancer type Services.

3.1.6.4.3.1. Exposing Kafka using loadbalancers

When exposing Kafka using Loadbalancer type Services, a new loadbalancer service is created for every Kafka broker pod. An additional loadbalancer is created to serve as a Kafka bootstrap address. Loadbalancers listen to connections on port 9094.

By default, TLS encryption is enabled. To disable it, set the tls field to false.

Example of an external listener of type loadbalancer

# ...
listeners:
  external:
    type: loadbalancer
    authentication:
      type: tls
# ...

For more information on using loadbalancers to access Kafka, see Section 3.1.6.4.3.4, “Accessing Kafka using loadbalancers”.

3.1.6.4.3.2. Customizing the DNS names of external loadbalancer listeners

On loadbalancer listeners, you can use the dnsAnnotations property to add additional annotations to the loadbalancer services. You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the loadbalancer services.

Example of an external listener of type loadbalancer using dnsAnnotations

# ...
listeners:
  external:
    type: loadbalancer
    authentication:
      type: tls
    overrides:
      bootstrap:
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-bootstrap.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      brokers:
      - broker: 0
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-0.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 1
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-1.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 2
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-2.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
# ...

3.1.6.4.3.3. Customizing the loadbalancer IP addresses

On loadbalancer listeners, you can use the loadBalancerIP property to request a specific IP address when creating a loadbalancer. Use this property when you need to use a loadbalancer with a specific IP address. The loadBalancerIP field is ignored if the cloud provider does not support the feature.

Example of an external listener of type loadbalancer with specific loadbalancer IP address requests

# ...
listeners:
  external:
    type: loadbalancer
    authentication:
      type: tls
    overrides:
      bootstrap:
        loadBalancerIP: 172.29.3.10
      brokers:
      - broker: 0
        loadBalancerIP: 172.29.3.1
      - broker: 1
        loadBalancerIP: 172.29.3.2
      - broker: 2
        loadBalancerIP: 172.29.3.3
# ...

3.1.6.4.3.4. Accessing Kafka using loadbalancers

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Deploy Kafka cluster with an external listener enabled and configured to the type loadbalancer.

    An example configuration with an external listener configured to use loadbalancers:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          external:
            type: loadbalancer
            authentication:
              type: tls
            # ...
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file
  3. Find the hostname of the bootstrap loadbalancer.

    This can be done using oc get:

    oc get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.status.loadBalancer.ingress[0].hostname}{"\n"}'

    If no hostname was found (nothing was returned by the command), use the loadbalancer IP address.

    This can be done using oc get:

    oc get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.status.loadBalancer.ingress[0].ip}{"\n"}'

    Use the hostname or IP address together with port 9094 in your Kafka client as the bootstrap address.

  4. Unless TLS encryption was disabled, extract the public certificate of the broker certification authority.

    This can be done using oc get:

    oc get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

    Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

Additional resources

3.1.6.4.4. Node Port external listeners

External listeners of type nodeport expose Kafka by using NodePort type Services.

3.1.6.4.4.1. Exposing Kafka using node ports

When exposing Kafka using NodePort type Services, Kafka clients connect directly to the nodes of OpenShift. You must enable access to the ports on the OpenShift nodes for each client (for example, in firewalls or security groups). Each Kafka broker pod is then accessible on a separate port. Additional NodePort type Service is created to serve as a Kafka bootstrap address.

When configuring the advertised addresses for the Kafka broker pods, AMQ Streams uses the address of the node on which the given pod is running. When selecting the node address, the different address types are used with the following priority:

  1. ExternalDNS
  2. ExternalIP
  3. Hostname
  4. InternalDNS
  5. InternalIP

By default, TLS encryption is enabled. To disable it, set the tls field to false.

Note

TLS hostname verification is not currently supported when exposing Kafka clusters using node ports.

By default, the port numbers used for the bootstrap and broker services are automatically assigned by OpenShift. However, you can override the assigned node ports by specifying the requested port numbers in the overrides property. AMQ Streams does not perform any validation on the requested ports; you must ensure that they are free and available for use.

Example of an external listener configured with overrides for node ports

# ...
listeners:
  external:
    type: nodeport
    tls: true
    authentication:
      type: tls
    overrides:
      bootstrap:
        nodePort: 32100
      brokers:
      - broker: 0
        nodePort: 32000
      - broker: 1
        nodePort: 32001
      - broker: 2
        nodePort: 32002
# ...

For more information on using node ports to access Kafka, see Section 3.1.6.4.4.3, “Accessing Kafka using node ports”.

3.1.6.4.4.2. Customizing the DNS names of external node port listeners

On nodeport listeners, you can use the dnsAnnotations property to add additional annotations to the nodeport services. You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the cluster nodes.

Example of an external listener of type nodeport using dnsAnnotations

# ...
listeners:
  external:
    type: nodeport
    tls: true
    authentication:
      type: tls
    overrides:
      bootstrap:
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-bootstrap.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      brokers:
      - broker: 0
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-0.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 1
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-1.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 2
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-2.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
# ...

3.1.6.4.4.3. Accessing Kafka using node ports

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Deploy Kafka cluster with an external listener enabled and configured to the type nodeport.

    An example configuration with an external listener configured to use node ports:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          external:
            type: nodeport
            tls: true
            # ...
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file
  3. Find the port number of the bootstrap service.

    This can be done using oc get:

    oc get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.spec.ports[0].nodePort}{"\n"}'

    The port should be used in the Kafka bootstrap address.

  4. Find the address of the OpenShift node.

    This can be done using oc get:

    oc get node node-name -o=jsonpath='{range .status.addresses[*]}{.type}{"\t"}{.address}{"\n"}'

    If several different addresses are returned, select the address type you want based on the following order:

    1. ExternalDNS
    2. ExternalIP
    3. Hostname
    4. InternalDNS
    5. InternalIP

      Use the address with the port found in the previous step in the Kafka bootstrap address.

  5. Unless TLS encryption was disabled, extract the public certificate of the broker certification authority.

    This can be done using oc get:

    oc get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

    Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

Additional resources

3.1.6.4.5. OpenShift Ingress external listeners

External listeners of type ingress exposes Kafka by using Kubernetes Ingress and the NGINX Ingress Controller for Kubernetes.

3.1.6.4.5.1. Exposing Kafka using Kubernetes Ingress

When exposing Kafka using using Kubernetes Ingress and the NGINX Ingress Controller for Kubernetes, a dedicated Ingress resource is created for every Kafka broker pod. An additional Ingress resource is created to serve as a Kafka bootstrap address. Kafka clients can use these Ingress resources to connect to Kafka on port 443.

Note

External listeners using Ingress have been currently tested only with the NGINX Ingress Controller for Kubernetes.

AMQ Streams uses the TLS passthrough feature of the NGINX Ingress Controller for Kubernetes. Make sure TLS passthrough is enabled in your NGINX Ingress Controller for Kubernetes deployment. For more information about enabling TLS passthrough see TLS passthrough documentation. Because it is using the TLS passthrough functionality, TLS encryption cannot be disabled when exposing Kafka using Ingress.

The Ingress controller does not assign any hostnames automatically. You have to specify the hostnames which should be used by the bootstrap and per-broker services in the spec.kafka.listeners.external.configuration section. You also have to make sure that the hostnames resolve to the Ingress endpoints. AMQ Streams will not perform any validation that the requested hosts are available and properly routed to the Ingress endpoints.

Example of an external listener of type ingress

# ...
listeners:
  external:
    type: ingress
    authentication:
      type: tls
    configuration:
      bootstrap:
        host: bootstrap.myingress.com
      brokers:
      - broker: 0
        host: broker-0.myingress.com
      - broker: 1
        host: broker-1.myingress.com
      - broker: 2
        host: broker-2.myingress.com
# ...

For more information on using Ingress to access Kafka, see Section 3.1.6.4.5.4, “Accessing Kafka using ingress”.

3.1.6.4.5.2. Configuring the Ingress class

By default, the Ingress class is set to nginx. You can change the Ingress class using the class property.

Example of an external listener of type ingress using Ingress class nginx-internal

# ...
listeners:
  external:
    type: ingress
    class: nginx-internal
    # ...
# ...

3.1.6.4.5.3. Customizing the DNS names of external ingress listeners

On ingress listeners, you can use the dnsAnnotations property to add additional annotations to the ingress resources. You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the ingress resources.

Example of an external listener of type ingress using dnsAnnotations

# ...
listeners:
  external:
    type: ingress
    authentication:
      type: tls
    configuration:
      bootstrap:
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: bootstrap.myingress.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
        host: bootstrap.myingress.com
      brokers:
      - broker: 0
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: broker-0.myingress.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
        host: broker-0.myingress.com
      - broker: 1
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: broker-1.myingress.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
        host: broker-1.myingress.com
      - broker: 2
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: broker-2.myingress.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
        host: broker-2.myingress.com
# ...

3.1.6.4.5.4. Accessing Kafka using ingress

This procedure shows how to access AMQ Streams Kafka clusters from outside of OpenShift using Ingress.

Prerequisites

Procedure

  1. Deploy Kafka cluster with an external listener enabled and configured to the type ingress.

    An example configuration with an external listener configured to use Ingress:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          external:
            type: ingress
            authentication:
              type: tls
            configuration:
              bootstrap:
                host: bootstrap.myingress.com
              brokers:
              - broker: 0
                host: broker-0.myingress.com
              - broker: 1
                host: broker-1.myingress.com
              - broker: 2
                host: broker-2.myingress.com
        # ...
      zookeeper:
        # ...
  2. Make sure the hosts in the configuration section properly resolve to the Ingress endpoints.
  3. Create or update the resource.

    oc apply -f your-file
  4. Extract the public certificate of the broker certificate authority

    oc get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
  5. Use the extracted certificate in your Kafka client to configure the TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication. Connect with your client to the host you specified in the configuration on port 443.

Additional resources

3.1.6.5. Network policies

AMQ Streams automatically creates a NetworkPolicy resource for every listener that is enabled on a Kafka broker. By default, a NetworkPolicy grants access to a listener to all applications and namespaces.

If you want to restrict access to a listener at the network level to only selected applications or namespaces, use the networkPolicyPeers field.

Use network policies in conjunction with authentication and authorization.

Each listener can have a different networkPolicyPeers configuration.

3.1.6.5.1. Network policy configuration for a listener

The following example shows a networkPolicyPeers configuration for a plain and a tls listener:

# ...
listeners:
  plain:
    authentication:
      type: scram-sha-512
    networkPolicyPeers:
      - podSelector:
          matchLabels:
            app: kafka-sasl-consumer
      - podSelector:
          matchLabels:
            app: kafka-sasl-producer
  tls:
    authentication:
      type: tls
    networkPolicyPeers:
      - namespaceSelector:
          matchLabels:
            project: myproject
      - namespaceSelector:
          matchLabels:
            project: myproject2
# ...

In the example:

  • Only application pods matching the labels app: kafka-sasl-consumer and app: kafka-sasl-producer can connect to the plain listener. The application pods must be running in the same namespace as the Kafka broker.
  • Only application pods running in namespaces matching the labels project: myproject and project: myproject2 can connect to the tls listener.

The syntax of the networkPolicyPeers field is the same as the from field in NetworkPolicy resources. For more information about the schema, see NetworkPolicyPeer API reference and the KafkaListeners schema reference.

Note

Your configuration of OpenShift must support ingress NetworkPolicies in order to use network policies in AMQ Streams.

3.1.6.5.2. Restricting access to Kafka listeners using networkPolicyPeers

You can restrict access to a listener to only selected applications by using the networkPolicyPeers field.

Prerequisites

  • An OpenShift cluster with support for Ingress NetworkPolicies.
  • The Cluster Operator is running.

Procedure

  1. Open the Kafka resource.
  2. In the networkPolicyPeers field, define the application pods or namespaces that will be allowed to access the Kafka cluster.

    For example, to configure a tls listener to allow connections only from application pods with the label app set to kafka-client:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          tls:
            networkPolicyPeers:
              - podSelector:
                  matchLabels:
                    app: kafka-client
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    Use oc apply:

    oc apply -f your-file

Additional resources

3.1.7. Authentication and Authorization

AMQ Streams supports authentication and authorization. Authentication can be configured independently for each listener. Authorization is always configured for the whole Kafka cluster.

3.1.7.1. Authentication

Authentication is configured as part of the listener configuration in the authentication property. The authentication mechanism is defined by the type field.

When the authentication property is missing, no authentication is enabled on a given listener. The listener will accept all connections without authentication.

Supported authentication mechanisms:

3.1.7.1.1. TLS client authentication

TLS Client authentication is enabled by specifying the type as tls. The TLS client authentication is supported only on the tls listener.

An example of authentication with type tls

# ...
authentication:
  type: tls
# ...

3.1.7.2. Configuring authentication in Kafka brokers

Prerequisites

  • An OpenShift cluster is available.
  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.
  2. In the spec.kafka.listeners property in the Kafka resource, add the authentication field to the listeners for which you want to enable authentication. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          tls:
            authentication:
              type: tls
        # ...
      zookeeper:
        # ...
  3. Apply the new configuration to create or update the resource.

    Use oc apply:

    oc apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

Additional resources

3.1.7.3. Authorization

You configure authorization for Kafka brokers using the authorization property in the Kafka.spec.kafka resource. If the authorization property is missing, no authorization is enabled. When enabled, authorization is applied to all enabled listeners. The authorization method is defined in the type field; only Simple authorization is currently supported.

You can optionally designate a list of super users in the superUsers field.

3.1.7.3.1. Simple authorization

Simple authorization in AMQ Streams uses the SimpleAclAuthorizer plugin, the default Access Control Lists (ACLs) authorization plugin provided with Apache Kafka. ACLs allow you to define which users have access to which resources at a granular level. To enable simple authorization, set the type field to simple.

An example of Simple authorization

# ...
authorization:
  type: simple
# ...

3.1.7.3.2. Super users

Super users can access all resources in your Kafka cluster regardless of any access restrictions defined in ACLs. To designate super users for a Kafka cluster, enter a list of user principles in the superUsers field. If a user uses TLS Client Authentication, the username will be the common name from their certificate subject prefixed with CN=.

An example of designating super users

# ...
authorization:
  type: simple
  superUsers:
    - CN=fred
    - sam
    - CN=edward
# ...

Note

The super.user configuration option in the config property in Kafka.spec.kafka is ignored. Designate super users in the authorization property instead. For more information, see Kafka broker configuration.

3.1.7.4. Configuring authorization in Kafka brokers

Configure authorization and designate super users for a particular Kafka broker.

Prerequisites

  • An OpenShift cluster
  • The Cluster Operator is running

Procedure

  1. Add or edit the authorization property in the Kafka.spec.kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        authorization:
          type: simple
          superUsers:
            - CN=fred
            - sam
            - CN=edward
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.8. Zookeeper replicas

Zookeeper clusters or ensembles usually run with an odd number of nodes, typically three, five, or seven.

The majority of nodes must be available in order to maintain an effective quorum. If the Zookeeper cluster loses its quorum, it will stop responding to clients and the Kafka brokers will stop working. Having a stable and highly available Zookeeper cluster is crucial for AMQ Streams.

Three-node cluster
A three-node Zookeeper cluster requires at least two nodes to be up and running in order to maintain the quorum. It can tolerate only one node being unavailable.
Five-node cluster
A five-node Zookeeper cluster requires at least three nodes to be up and running in order to maintain the quorum. It can tolerate two nodes being unavailable.
Seven-node cluster
A seven-node Zookeeper cluster requires at least four nodes to be up and running in order to maintain the quorum. It can tolerate three nodes being unavailable.
Note

For development purposes, it is also possible to run Zookeeper with a single node.

Having more nodes does not necessarily mean better performance, as the costs to maintain the quorum will rise with the number of nodes in the cluster. Depending on your availability requirements, you can decide for the number of nodes to use.

3.1.8.1. Number of Zookeeper nodes

The number of Zookeeper nodes can be configured using the replicas property in Kafka.spec.zookeeper.

An example showing replicas configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    replicas: 3
    # ...

3.1.8.2. Changing the number of Zookeeper replicas

Prerequisites

  • An OpenShift cluster is available.
  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.
  2. In the spec.zookeeper.replicas property in the Kafka resource, enter the number of replicated Zookeeper servers. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        replicas: 3
        # ...
  3. Apply the new configuration to create or update the resource.

    Use oc apply:

    oc apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

3.1.9. Zookeeper configuration

AMQ Streams allows you to customize the configuration of Apache Zookeeper nodes. You can specify and configure most of the options listed in the Zookeeper documentation.

Options which cannot be configured are those related to the following areas:

  • Security (Encryption, Authentication, and Authorization)
  • Listener configuration
  • Configuration of data directories
  • Zookeeper cluster composition

These options are automatically configured by AMQ Streams.

3.1.9.1. Zookeeper configuration

Zookeeper nodes are configured using the config property in Kafka.spec.zookeeper. This property contains the Zookeeper configuration options as keys. The values can be described using one of the following JSON types:

  • String
  • Number
  • Boolean

Users can specify and configure the options listed in Zookeeper documentation with the exception of those options which are managed directly by AMQ Streams. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • server.
  • dataDir
  • dataLogDir
  • clientPort
  • authProvider
  • quorum.auth
  • requireClientAuthScheme

When one of the forbidden options is present in the config property, it is ignored and a warning message is printed to the Custer Operator log file. All other options are passed to Zookeeper.

Important

The Cluster Operator does not validate keys or values in the provided config object. When invalid configuration is provided, the Zookeeper cluster might not start or might become unstable. In such cases, the configuration in the Kafka.spec.zookeeper.config object should be fixed and the Cluster Operator will roll out the new configuration to all Zookeeper nodes.

Selected options have default values:

  • timeTick with default value 2000
  • initLimit with default value 5
  • syncLimit with default value 2
  • autopurge.purgeInterval with default value 1

These options will be automatically configured when they are not present in the Kafka.spec.zookeeper.config property.

An example showing Zookeeper configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    config:
      autopurge.snapRetainCount: 3
      autopurge.purgeInterval: 1
    # ...

3.1.9.2. Configuring Zookeeper

Prerequisites

  • An OpenShift cluster is available.
  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.
  2. In the spec.zookeeper.config property in the Kafka resource, enter one or more Zookeeper configuration settings. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        config:
          autopurge.snapRetainCount: 3
          autopurge.purgeInterval: 1
        # ...
  3. Apply the new configuration to create or update the resource.

    Use oc apply:

    oc apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

3.1.10. Zookeeper connection

Zookeeper services are secured with encryption and authentication and are not intended to be used by external applications that are not part of AMQ Streams.

However, if you want to use Kafka CLI tools that require a connection to Zookeeper, such as the kafka-topics tool, you can use a terminal inside a Kafka container and connect to the local end of the TLS tunnel to Zookeeper by using localhost:2181 as the Zookeeper address.

3.1.10.1. Connecting to Zookeeper from a terminal

Open a terminal inside a Kafka container to use Kafka CLI tools that require a Zookeeper connection.

Prerequisites

  • An OpenShift cluster is available.
  • A kafka cluster is running.
  • The Cluster Operator is running.

Procedure

  1. Open the terminal using the OpenShift console or run the exec command from your CLI.

    For example:

    oc exec -ti my-cluster-kafka-0 -- bin/kafka-topics.sh --list --zookeeper localhost:2181

    Be sure to use localhost:2181.

    You can now run Kafka commands to Zookeeper.

3.1.11. Entity Operator

The Entity Operator is responsible for managing Kafka-related entities in a running Kafka cluster.

The Entity Operator comprises the:

Through Kafka resource configuration, the Cluster Operator can deploy the Entity Operator, including one or both operators, when deploying a Kafka cluster.

Note

When deployed, the Entity Operator contains the operators according to the deployment configuration.

The operators are automatically configured to manage the topics and users of the Kafka cluster.

3.1.11.1. Configuration

The Entity Operator can be configured using the entityOperator property in Kafka.spec

The entityOperator property supports several sub-properties:

  • tlsSidecar
  • topicOperator
  • userOperator
  • template

The tlsSidecar property can be used to configure the TLS sidecar container which is used to communicate with Zookeeper. For more details about configuring the TLS sidecar, see Section 3.1.19, “TLS sidecar”.

The template property can be used to configure details of the Entity Operator pod, such as labels, annotations, affinity, tolerations and so on.

The topicOperator property contains the configuration of the Topic Operator. When this option is missing, the Entity Operator is deployed without the Topic Operator.

The userOperator property contains the configuration of the User Operator. When this option is missing, the Entity Operator is deployed without the User Operator.

Example of basic configuration enabling both operators

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}

When both topicOperator and userOperator properties are missing, the Entity Operator will be not deployed.

3.1.11.1.1. Topic Operator

Topic Operator deployment can be configured using additional options inside the topicOperator object. The following options are supported:

watchedNamespace
The OpenShift namespace in which the topic operator watches for KafkaTopics. Default is the namespace where the Kafka cluster is deployed.
reconciliationIntervalSeconds
The interval between periodic reconciliations in seconds. Default 90.
zookeeperSessionTimeoutSeconds
The Zookeeper session timeout in seconds. Default 20.
topicMetadataMaxAttempts
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation could take more time due to the number of partitions or replicas. Default 6.
image
The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 3.1.18, “Container images”.
resources
The resources property configures the amount of resources allocated to the Topic Operator. For more details about resource request and limit configuration, see Section 3.1.12, “CPU and memory resources”.
logging

The logging property configures the logging of the Topic Operator.

The Topic Operator has its own configurable logger:

  • rootLogger.level

Example of Topic Operator configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
    # ...

3.1.11.1.2. User Operator

User Operator deployment can be configured using additional options inside the userOperator object. The following options are supported:

watchedNamespace
The OpenShift namespace in which the topic operator watches for KafkaUsers. Default is the namespace where the Kafka cluster is deployed.
reconciliationIntervalSeconds
The interval between periodic reconciliations in seconds. Default 120.
zookeeperSessionTimeoutSeconds
The Zookeeper session timeout in seconds. Default 6.
image
The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 3.1.18, “Container images”.
resources
The resources property configures the amount of resources allocated to the User Operator. For more details about resource request and limit configuration, see Section 3.1.12, “CPU and memory resources”.
logging

The logging property configures the logging of the User Operator.

The User Operator has its own configurable logger:

  • rootLogger.level

Example of Topic Operator configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60
    # ...

3.1.11.2. Configuring Entity Operator

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the entityOperator property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
      entityOperator:
        topicOperator:
          watchedNamespace: my-topic-namespace
          reconciliationIntervalSeconds: 60
        userOperator:
          watchedNamespace: my-user-namespace
          reconciliationIntervalSeconds: 60
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.12. CPU and memory resources

For every deployed container, AMQ Streams allows you to request specific resources and define the maximum consumption of those resources.

AMQ Streams supports two types of resources:

  • CPU
  • Memory

AMQ Streams uses the OpenShift syntax for specifying CPU and memory resources.

3.1.12.1. Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.entityOperator.tlsSidecar
  • Kafka.spec.KafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaBridge.spec

Additional resources

3.1.12.1.1. Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important

If the resource request is for more than the available free resources in the OpenShift cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by AMQ Streams:

  • cpu
  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources

# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...

3.1.12.1.2. Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by AMQ Streams:

  • cpu
  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration

# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...

3.1.12.1.3. Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).
  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units

# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...

Note

The computing power of 1 CPU core may differ depending on the platform where OpenShift is deployed.

Additional resources

3.1.12.1.4. Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.
  • To specify memory in gigabytes, use the G suffix. For example 1G.
  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.
  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units

# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...

Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

3.1.12.2. Configuring resource requests and limits

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the resources property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: "8"
            memory: 64Gi
          limits:
            cpu: "12"
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.13. Logging

This section provides information on loggers and how to configure log levels.

You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.

3.1.13.1. Kafka and Zookeeper loggers

Kafka has its own configurable loggers:

  • kafka.root.logger.level
  • log4j.logger.org.I0Itec.zkclient.ZkClient
  • log4j.logger.org.apache.zookeeper
  • log4j.logger.kafka
  • log4j.logger.org.apache.kafka
  • log4j.logger.kafka.request.logger
  • log4j.logger.kafka.network.Processor
  • log4j.logger.kafka.server.KafkaApis
  • log4j.logger.kafka.network.RequestChannel$
  • log4j.logger.kafka.controller
  • log4j.logger.kafka.log.LogCleaner
  • log4j.logger.state.change.logger
  • log4j.logger.kafka.authorizer.logger

Zookeeper has its own logger as well:

  • zookeeper.root.logger

3.1.13.2. Specifying inline logging

Procedure

  1. Edit the YAML file to specify the loggers and logging level for the required components.

    For example, the logging level here is set to INFO:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: inline
          loggers:
            logger.name: "INFO"
        # ...
      zookeeper:
        # ...
        logging:
          type: inline
          loggers:
            logger.name: "INFO"
        # ...
      entityOperator:
        # ...
        topicOperator:
          # ...
          logging:
            type: inline
            loggers:
              logger.name: "INFO"
        # ...
        # ...
        userOperator:
          # ...
          logging:
            type: inline
            loggers:
              logger.name: "INFO"
        # ...

    You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    For more information about the log levels, see the log4j manual.

  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

3.1.13.3. Specifying an external ConfigMap for logging

Procedure

  1. Edit the YAML file to specify the name of the ConfigMap to use for the required components. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: external
          name: customConfigMap
        # ...

    Remember to place your custom ConfigMap under the log4j.properties or log4j2.properties key.

  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see Section 3.1.17.1, “JVM configuration”

3.1.14. Kafka rack awareness

The rack awareness feature in AMQ Streams helps to spread the Kafka broker pods and Kafka topic replicas across different racks. Enabling rack awareness helps to improve availability of Kafka brokers and the topics they are hosting.

Note

"Rack" might represent an availability zone, data center, or an actual rack in your data center.

3.1.14.1. Configuring rack awareness in Kafka brokers

Kafka rack awareness can be configured in the rack property of Kafka.spec.kafka. The rack object has one mandatory field named topologyKey. This key needs to match one of the labels assigned to the OpenShift cluster nodes. The label is used by OpenShift when scheduling the Kafka broker pods to nodes. If the OpenShift cluster is running on a cloud provider platform, that label should represent the availability zone where the node is running. Usually, the nodes are labeled with failure-domain.beta.kubernetes.io/zone that can be easily used as the topologyKey value. This has the effect of spreading the broker pods across zones, and also setting the brokers' broker.rack configuration parameter inside Kafka broker.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Consult your OpenShift administrator regarding the node label that represents the zone / rack into which the node is deployed.
  2. Edit the rack property in the Kafka resource using the label as the topology key.

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        rack:
          topologyKey: failure-domain.beta.kubernetes.io/zone
        # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.15. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, OpenShift assumes that the application is not healthy and attempts to fix it.

OpenShift supports two types of Healthcheck probes:

  • Liveness probes
  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in AMQ Streams components.

Users can configure selected options for liveness and readiness probes.

3.1.15.1. Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.KafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMaker.spec
  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds
  • timeoutSeconds
  • periodSeconds
  • successThreshold
  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Section C.34, “Probe schema reference”.

An example of liveness and readiness probe configuration

# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...

3.1.15.2. Configuring healthchecks

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        readinessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        livenessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.16. Prometheus metrics

AMQ Streams supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and Zookeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.

For more information about configuring Prometheus and Grafana, see Metrics.

3.1.16.1. Metrics configuration

Prometheus metrics are enabled by configuring the metrics property in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

When the metrics property is not defined in the resource, the Prometheus metrics will be disabled. To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

Example of enabling metrics without any further configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics: {}
    # ...
  zookeeper:
    # ...

The metrics property might contain additional configuration for the Prometheus JMX exporter.

Example of enabling metrics with additional Prometheus JMX Exporter configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics:
      lowercaseOutputName: true
      rules:
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
          name: "kafka_server_$1_$2_total"
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
          name: "kafka_server_$1_$2_total"
          labels:
            topic: "$3"
    # ...
  zookeeper:
    # ...

3.1.16.2. Configuring Prometheus metrics

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the metrics property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.17. JVM Options

The following components of AMQ Streams run inside a Virtual Machine (VM):

  • Apache Kafka
  • Apache Zookeeper
  • Apache Kafka Connect
  • Apache Kafka Mirror Maker
  • AMQ Streams Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. AMQ Streams allows you to configure some of these options.

3.1.17.1. JVM configuration

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMaker.spec
  • KafkaBridge.spec

Only a selected subset of available JVM options can be configured. The following options are supported:

-Xms and -Xmx

-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.

Note

The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the OpenShift convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:

  • If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
  • If there is no memory limit then the JVM’s minimum memory will be set to 128M and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.
  • If -Xmx is set without also setting an appropriate OpenShift memory limit, it is possible that the container will be killed should the OpenShift node experience memory pressure (from other Pods running on it).
  • If -Xmx is set without also setting an appropriate OpenShift memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • set the memory request and the memory limit to the same value,
  • use a memory request that is at least 4.5 × the -Xmx,
  • consider setting -Xms to the same value as -Xmx.
Important

Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.

Example fragment configuring -Xmx and -Xms

# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and Zookeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server

# ...
jvmOptions:
  "-server": true
# ...

Note

When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.

-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object

jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true
    "UseParNewGC": false

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note

When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.

3.1.17.1.1. Garbage collector logging

The jvmOptions section also allows you to enable and disable garbage collector (GC) logging. GC logging is enabled by default. To disable it, set the gcLoggingEnabled property as follows:

Example of disabling GC logging

# ...
jvmOptions:
  gcLoggingEnabled: false
# ...

3.1.17.2. Configuring JVM options

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.18. Container images

AMQ Streams allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such a case, you should either copy the AMQ Streams images or build them from the source. If the configured image is not compatible with AMQ Streams images, it might not work properly.

3.1.18.1. Container image configurations

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.entityOperator.tlsSidecar
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaBridge.spec
3.1.18.1.1. Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because AMQ Streams supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.
  • If Kafka.spec.kafka.image is given but Kafka.spec.kafka.version is not then the given image will be used and the version will be assumed to be the Cluster Operator’s default Kafka version.
  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.
  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.
Warning

It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.

3.1.18.1.2. Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Kafka broker TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Zookeeper nodes:

    1. Container image specified in the STRIMZI_DEFAULT_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Zookeeper node TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-operator:1.3.0 container image.
  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-operator:1.3.0 container image.
  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Kafka Connect:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
Warning

Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such case, you should either copy the AMQ Streams images or build them from source. In case the configured image is not compatible with AMQ Streams images, it might not work properly.

Example of container image configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

3.1.18.2. Configuring container images

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.19. TLS sidecar

A sidecar is a container that runs in a pod but serves a supporting purpose. In AMQ Streams, the TLS sidecar uses TLS to encrypt and decrypt all communication between the various components and Zookeeper. Zookeeper does not have native TLS support.

The TLS sidecar is used in:

  • Kafka brokers
  • Zookeeper nodes
  • Entity Operator

3.1.19.1. TLS sidecar configuration

The TLS sidecar can be configured using the tlsSidecar property in:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • Kafka.spec.entityOperator

The TLS sidecar supports the following additional options:

  • image
  • resources
  • logLevel
  • readinessProbe
  • livenessProbe

The resources property can be used to specify the memory and CPU resources allocated for the TLS sidecar.

The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 3.1.18, “Container images”.

The logLevel property is used to specify the logging level. Following logging levels are supported:

  • emerg
  • alert
  • crit
  • err
  • warning
  • notice
  • info
  • debug

The default value is notice.

For more information about configuring the readinessProbe and livenessProbe properties for the healthchecks, see Section 3.1.15.1, “Healthcheck configurations”.

Example of TLS sidecar configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    tlsSidecar:
      image: my-org/my-image:latest
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
      logLevel: debug
      readinessProbe:
        initialDelaySeconds: 15
        timeoutSeconds: 5
      livenessProbe:
        initialDelaySeconds: 15
        timeoutSeconds: 5
    # ...
  zookeeper:
    # ...

3.1.19.2. Configuring TLS sidecar

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the tlsSidecar property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        tlsSidecar:
          resources:
            requests:
              cpu: 200m
              memory: 64Mi
            limits:
              cpu: 500m
              memory: 128Mi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.20. Configuring pod scheduling

Important

When two applications are scheduled to the same OpenShift node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.

3.1.20.1. Scheduling pods based on other applications

3.1.20.1.1. Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

3.1.20.1.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.1.20.1.3. Configuring pod anti-affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            affinity:
              podAntiAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  - labelSelector:
                      matchExpressions:
                        - key: application
                          operator: In
                          values:
                            - postgresql
                            - mongodb
                    topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.20.2. Scheduling pods to specific nodes

3.1.20.2.1. Node scheduling

The OpenShift cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of AMQ Streams components to use the right nodes.

OpenShift uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

3.1.20.2.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.1.20.2.3. Configuring node affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Label the nodes where AMQ Streams components should be scheduled.

    This can be done using oc label:

    oc label node your-node node-type=fast-network

    Alternatively, some of the existing labels might be reused.

  2. Edit the affinity property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            affinity:
              nodeAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  nodeSelectorTerms:
                    - matchExpressions:
                      - key: node-type
                        operator: In
                        values:
                        - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.20.3. Using dedicated nodes

3.1.20.3.1. Dedicated nodes

Cluster administrators can mark selected OpenShift nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

3.1.20.3.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.1.20.3.3. Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.

3.1.20.3.4. Setting up dedicated nodes and scheduling pods on them

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.
  2. Make sure there are no workloads scheduled on these nodes.
  3. Set the taints on the selected nodes:

    This can be done using oc adm taint:

    oc adm taint node your-node dedicated=Kafka:NoSchedule
  4. Additionally, add a label to the selected nodes as well.

    This can be done using oc label:

    oc label node your-node dedicated=Kafka
  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            tolerations:
              - key: "dedicated"
                operator: "Equal"
                value: "Kafka"
                effect: "NoSchedule"
            affinity:
              nodeAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  nodeSelectorTerms:
                  - matchExpressions:
                    - key: dedicated
                      operator: In
                      values:
                      - Kafka
        # ...
      zookeeper:
        # ...
  6. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.1.21. Kafka Exporter

You can configure the Kafka resource to automatically deploy Kafka Exporter in your cluster.

Kafka Exporter extracts data for analysis as Prometheus metrics, primarily data relating to offsets, consumer groups, consumer lag and topics.

For information on Kafka Exporter and why it is important to monitor consumer lag for performance, see Kafka Exporter.

3.1.21.1. Configuring Kafka Exporter

Configure Kafka Exporter in the Kafka resource through KafkaExporter properties.

Refer to the sample Kafka YAML configuration for an overview of the Kafka resource and its properties.

The properties relevant to the Kafka Exporter configuration are shown in this procedure.

You can configure these properties as part of a deployment or redeployment of the Kafka cluster.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the KafkaExporter properties for the Kafka resource.

    The properties you can configure are shown in this example configuration:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      # ...
      kafkaExporter:
        image: my-org/my-image:latest 1
        groupRegex: ".*" 2
        topicRegex: ".*" 3
        resources: 4
          requests:
            cpu: 200m
            memory: 64Mi
          limits:
            cpu: 500m
            memory: 128Mi
        logging: debug 5
        enableSaramaLogging: true 6
        template: 7
          pod:
            metadata:
              labels:
                label1: value1
            imagePullSecrets:
              - name: my-docker-credentials
            securityContext:
              runAsUser: 1000001
              fsGroup: 0
            terminationGracePeriodSeconds: 120
        readinessProbe: 8
          initialDelaySeconds: 15
          timeoutSeconds: 5
        livenessProbe: 9
          initialDelaySeconds: 15
          timeoutSeconds: 5
    # ...
    1
    ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
    2
    A regular expression to specify the consumer groups to include in the metrics.
    3
    A regular expression to specify the topics to include in the metrics.
    4
    5
    Logging configuration, to log messages with a given severity (debug, info, warn, error, fatal) or above.
    6
    Boolean to enable Sarama logging, a Go client library used by Kafka Exporter.
    7
    8
    9
  2. Create or update the resource:

    oc apply -f kafka.yaml

What to do next

After configuring and deploying Kafka Exporter, you can enable Grafana to present the Kafka Exporter dashboards.

3.1.22. Performing a rolling update of a Kafka cluster

This procedure describes how to manually trigger a rolling update of an existing Kafka cluster by using an OpenShift annotation.

Prerequisites

  • A running Kafka cluster.
  • A running Cluster Operator.

Procedure

  1. Find the name of the StatefulSet that controls the Kafka pods you want to manually update.

    For example, if your Kafka cluster is named my-cluster, the corresponding StatefulSet is named my-cluster-kafka.

  2. Annotate the StatefulSet resource in OpenShift. For example, using oc annotate:

    oc annotate statefulset cluster-name-kafka strimzi.io/manual-rolling-update=true
  3. Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated StatefulSet is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from the StatefulSet.

Additional resources

3.1.23. Performing a rolling update of a Zookeeper cluster

This procedure describes how to manually trigger a rolling update of an existing Zookeeper cluster by using an OpenShift annotation.

Prerequisites

  • A running Zookeeper cluster.
  • A running Cluster Operator.

Procedure

  1. Find the name of the StatefulSet that controls the Zookeeper pods you want to manually update.

    For example, if your Kafka cluster is named my-cluster, the corresponding StatefulSet is named my-cluster-zookeeper.

  2. Annotate the StatefulSet resource in OpenShift. For example, using oc annotate:

    oc annotate statefulset cluster-name-zookeeper strimzi.io/manual-rolling-update=true
  3. Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated StatefulSet is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from the StatefulSet.

Additional resources

3.1.24. Scaling clusters

3.1.24.1. Scaling Kafka clusters

3.1.24.1.1. Adding brokers to a cluster

The primary way of increasing throughput for a topic is to increase the number of partitions for that topic. That works because the extra partitions allow the load of the topic to be shared between the different brokers in the cluster. However, in situations where every broker is constrained by a particular resource (typically I/O) using more partitions will not result in increased throughput. Instead, you need to add brokers to the cluster.

When you add an extra broker to the cluster, Kafka does not assign any partitions to it automatically. You must decide which partitions to move from the existing brokers to the new broker.

Once the partitions have been redistributed between all the brokers, the resource utilization of each broker should be reduced.

3.1.24.1.2. Removing brokers from a cluster

Because AMQ Streams uses StatefulSets to manage broker pods, you cannot remove any pod from the cluster. You can only remove one or more of the highest numbered pods from the cluster. For example, in a cluster of 12 brokers the pods are named cluster-name-kafka-0 up to cluster-name-kafka-11. If you decide to scale down by one broker, the cluster-name-kafka-11 will be removed.

Before you remove a broker from a cluster, ensure that it is not assigned to any partitions. You should also decide which of the remaining brokers will be responsible for each of the partitions on the broker being decommissioned. Once the broker has no assigned partitions, you can scale the cluster down safely.

3.1.24.2. Partition reassignment

The Topic Operator does not currently support reassigning replicas to different brokers, so it is necessary to connect directly to broker pods to reassign replicas to brokers.

Within a broker pod, the kafka-reassign-partitions.sh utility allows you to reassign partitions to different brokers.

It has three different modes:

--generate
Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you just need to reassign some of the partitions of some topics.
--execute
Takes a reassignment JSON file and applies it to the partitions and brokers in the cluster. Brokers that gain partitions as a result become followers of the partition leader. For a given partition, once the new broker has caught up and joined the ISR (in-sync replicas) the old broker will stop being a follower and will delete its replica.
--verify
Using the same reassignment JSON file as the --execute step, --verify checks whether all of the partitions in the file have been moved to their intended brokers. If the reassignment is complete, --verify also removes any throttles that are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.

It is only possible to have one reassignment running in a cluster at any given time, and it is not possible to cancel a running reassignment. If you need to cancel a reassignment, wait for it to complete and then perform another reassignment to revert the effects of the first reassignment. The kafka-reassign-partitions.sh will print the reassignment JSON for this reversion as part of its output. Very large reassignments should be broken down into a number of smaller reassignments in case there is a need to stop in-progress reassignment.

3.1.24.2.1. Reassignment JSON file

The reassignment JSON file has a specific structure:

{
  "version": 1,
  "partitions": [
    <PartitionObjects>
  ]
}

Where <PartitionObjects> is a comma-separated list of objects like:

{
  "topic": <TopicName>,
  "partition": <Partition>,
  "replicas": [ <AssignedBrokerIds> ]
}
Note

Although Kafka also supports a "log_dirs" property this should not be used in Red Hat AMQ Streams.

The following is an example reassignment JSON file that assigns topic topic-a, partition 4 to brokers 2, 4 and 7, and topic topic-b partition 2 to brokers 1, 5 and 7:

{
  "version": 1,
  "partitions": [
    {
      "topic": "topic-a",
      "partition": 4,
      "replicas": [2,4,7]
    },
    {
      "topic": "topic-b",
      "partition": 2,
      "replicas": [1,5,7]
    }
  ]
}

Partitions not included in the JSON are not changed.

3.1.24.2.2. Reassigning partitions between JBOD volumes

When using JBOD storage in your Kafka cluster, you can choose to reassign the partitions between specific volumes and their log directories (each volume has a single log directory). To reassign a partition to a specific volume, add the log_dirs option to <PartitionObjects> in the reassignment JSON file.

{
  "topic": <TopicName>,
  "partition": <Partition>,
  "replicas": [ <AssignedBrokerIds> ],
  "log_dirs": [ <AssignedLogDirs> ]
}

The log_dirs object should contain the same number of log directories as the number of replicas specified in the replicas object. The value should be either an absolute path to the log directory, or the any keyword.

For example:

{
      "topic": "topic-a",
      "partition": 4,
      "replicas": [2,4,7].
      "log_dirs": [ "/var/lib/kafka/data-0/kafka-log2", "/var/lib/kafka/data-0/kafka-log4", "/var/lib/kafka/data-0/kafka-log7" ]
}

3.1.24.3. Generating reassignment JSON files

This procedure describes how to generate a reassignment JSON file that reassigns all the partitions for a given set of topics using the kafka-reassign-partitions.sh tool.

Prerequisites

  • A running Cluster Operator
  • A Kafka resource
  • A set of topics to reassign the partitions of

Procedure

  1. Prepare a JSON file named topics.json that lists the topics to move. It must have the following structure:

    {
      "version": 1,
      "topics": [
        <TopicObjects>
      ]
    }

    where <TopicObjects> is a comma-separated list of objects like:

    {
      "topic": <TopicName>
    }

    For example if you want to reassign all the partitions of topic-a and topic-b, you would need to prepare a topics.json file like this:

    {
      "version": 1,
      "topics": [
        { "topic": "topic-a"},
        { "topic": "topic-b"}
      ]
    }
  2. Copy the topics.json file to one of the broker pods:

    cat topics.json | oc exec -c kafka <BrokerPod> -i -- \
      /bin/bash -c \
      'cat > /tmp/topics.json'
  3. Use the kafka-reassign-partitions.sh` command to generate the reassignment JSON.

    oc exec <BrokerPod> -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --topics-to-move-json-file /tmp/topics.json \
      --broker-list <BrokerList> \
      --generate

    For example, to move all the partitions of topic-a and topic-b to brokers 4 and 7

    oc exec <BrokerPod> -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --topics-to-move-json-file /tmp/topics.json \
      --broker-list 4,7 \
      --generate

3.1.24.4. Creating reassignment JSON files manually

You can manually create the reassignment JSON file if you want to move specific partitions.

3.1.24.5. Reassignment throttles

Partition reassignment can be a slow process because it involves transferring large amounts of data between brokers. To avoid a detrimental impact on clients, you can throttle the reassignment process. This might cause the reassignment to take longer to complete.

  • If the throttle is too low then the newly assigned brokers will not be able to keep up with records being published and the reassignment will never complete.
  • If the throttle is too high then clients will be impacted.

For example, for producers, this could manifest as higher than normal latency waiting for acknowledgement. For consumers, this could manifest as a drop in throughput caused by higher latency between polls.

3.1.24.6. Scaling up a Kafka cluster

This procedure describes how to increase the number of brokers in a Kafka cluster.

Prerequisites

  • An existing Kafka cluster.
  • A reassignment JSON file named reassignment.json that describes how partitions should be reassigned to brokers in the enlarged cluster.

Procedure

  1. Add as many new brokers as you need by increasing the Kafka.spec.kafka.replicas configuration option.
  2. Verify that the new broker pods have started.
  3. Copy the reassignment.json file to the broker pod on which you will later execute the commands:

    cat reassignment.json | \
      oc exec broker-pod -c kafka -i -- /bin/bash -c \
      'cat > /tmp/reassignment.json'

    For example:

    cat reassignment.json | \
      oc exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \
      'cat > /tmp/reassignment.json'
  4. Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool from the same broker pod.

    oc exec broker-pod -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --execute

    If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:

    oc exec my-cluster-kafka-0 -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 5000000 \
      --execute

    This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

  5. If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:

    oc exec my-cluster-kafka-0 -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 10000000 \
      --execute
  6. Periodically verify whether the reassignment has completed using the kafka-reassign-partitions.sh command line tool from any of the broker pods. This is the same command as the previous step but with the --verify option instead of the --execute option.

    oc exec broker-pod -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --verify

    For example,

    oc exec my-cluster-kafka-0 -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --verify
  7. The reassignment has finished when the --verify command reports each of the partitions being moved as completed successfully. This final --verify will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.

3.1.24.7. Scaling down a Kafka cluster

Additional resources

This procedure describes how to decrease the number of brokers in a Kafka cluster.

Prerequisites

  • An existing Kafka cluster.
  • A reassignment JSON file named reassignment.json describing how partitions should be reassigned to brokers in the cluster once the broker(s) in the highest numbered Pod(s) have been removed.

Procedure

  1. Copy the reassignment.json file to the broker pod on which you will later execute the commands:

    cat reassignment.json | \
      oc exec broker-pod -c kafka -i -- /bin/bash -c \
      'cat > /tmp/reassignment.json'

    For example:

    cat reassignment.json | \
      oc exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \
      'cat > /tmp/reassignment.json'
  2. Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool from the same broker pod.

    oc exec broker-pod -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --execute

    If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:

    oc exec my-cluster-kafka-0 -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 5000000 \
      --execute

    This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

  3. If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:

    oc exec my-cluster-kafka-0 -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 10000000 \
      --execute
  4. Periodically verify whether the reassignment has completed using the kafka-reassign-partitions.sh command line tool from any of the broker pods. This is the same command as the previous step but with the --verify option instead of the --execute option.

    oc exec broker-pod -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --verify

    For example,

    oc exec my-cluster-kafka-0 -c kafka -it -- \
      bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
      --reassignment-json-file /tmp/reassignment.json \
      --verify
  5. The reassignment has finished when the --verify command reports each of the partitions being moved as completed successfully. This final --verify will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.
  6. Once all the partition reassignments have finished, the broker(s) being removed should not have responsibility for any of the partitions in the cluster. You can verify this by checking that the broker’s data log directory does not contain any live partition logs. If the log directory on the broker contains a directory that does not match the extended regular expression \.[a-z0-9]-delete$ then the broker still has live partitions and it should not be stopped.

    You can check this by executing the command:

    oc exec my-cluster-kafka-0 -c kafka -it -- \
      /bin/bash -c \
      "ls -l /var/lib/kafka/kafka-log_<N>_ | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'"

    where N is the number of the Pod(s) being deleted.

    If the above command prints any output then the broker still has live partitions. In this case, either the reassignment has not finished, or the reassignment JSON file was incorrect.

  7. Once you have confirmed that the broker has no live partitions you can edit the Kafka.spec.kafka.replicas of your Kafka resource, which will scale down the StatefulSet, deleting the highest numbered broker Pod(s).

3.1.25. Deleting Kafka nodes manually

Additional resources

This procedure describes how to delete an existing Kafka node by using an OpenShift annotation. Deleting a Kafka node consists of deleting both the Pod on which the Kafka broker is running and the related PersistentVolumeClaim (if the cluster was deployed with persistent storage). After deletion, the Pod and its related PersistentVolumeClaim are recreated automatically.

Warning

Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.

Prerequisites

  • A running Kafka cluster.
  • A running Cluster Operator.

Procedure

  1. Find the name of the Pod that you want to delete.

    For example, if the cluster is named cluster-name, the pods are named cluster-name-kafka-index, where index starts at zero and ends at the total number of replicas.

  2. Annotate the Pod resource in OpenShift.

    Use oc annotate:

    oc annotate pod cluster-name-kafka-index strimzi.io/delete-pod-and-pvc=true
  3. Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.

Additional resources

3.1.26. Deleting Zookeeper nodes manually

This procedure describes how to delete an existing Zookeeper node by using an OpenShift annotation. Deleting a Zookeeper node consists of deleting both the Pod on which Zookeeper is running and the related PersistentVolumeClaim (if the cluster was deployed with persistent storage). After deletion, the Pod and its related PersistentVolumeClaim are recreated automatically.

Warning

Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.

Prerequisites

  • A running Zookeeper cluster.
  • A running Cluster Operator.

Procedure

  1. Find the name of the Pod that you want to delete.

    For example, if the cluster is named cluster-name, the pods are named cluster-name-zookeeper-index, where index starts at zero and ends at the total number of replicas.

  2. Annotate the Pod resource in OpenShift.

    Use oc annotate:

    oc annotate pod cluster-name-zookeeper-index strimzi.io/delete-pod-and-pvc=true
  3. Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.

Additional resources

3.1.27. Maintenance time windows for rolling updates

Maintenance time windows allow you to schedule certain rolling updates of your Kafka and Zookeeper clusters to start at a convenient time.

3.1.27.1. Maintenance time windows overview

In most cases, the Cluster Operator only updates your Kafka or Zookeeper clusters in response to changes to the corresponding Kafka resource. This enables you to plan when to apply changes to a Kafka resource to minimize the impact on Kafka client applications.

However, some updates to your Kafka and Zookeeper clusters can happen without any corresponding change to the Kafka resource. For example, the Cluster Operator will need to perform a rolling restart if a CA (Certificate Authority) certificate that it manages is close to expiry.

While a rolling restart of the pods should not affect availability of the service (assuming correct broker and topic configurations), it could affect performance of the Kafka client applications. Maintenance time windows allow you to schedule such spontaneous rolling updates of your Kafka and Zookeeper clusters to start at a convenient time. If maintenance time windows are not configured for a cluster then it is possible that such spontaneous rolling updates will happen at an inconvenient time, such as during a predictable period of high load.

3.1.27.2. Maintenance time window definition

You configure maintenance time windows by entering an array of strings in the Kafka.spec.maintenanceTimeWindows property. Each string is a cron expression interpreted as being in UTC (Coordinated Universal Time, which for practical purposes is the same as Greenwich Mean Time).

The following example configures a single maintenance time window that starts at midnight and ends at 01:59am (UTC), on Sundays, Mondays, Tuesdays, Wednesdays, and Thursdays:

# ...
maintenanceTimeWindows:
  - "* * 0-1 ? * SUN,MON,TUE,WED,THU *"
# ...

In practice, maintenance windows should be set in conjunction with the Kafka.spec.clusterCa.renewalDays and Kafka.spec.clientsCa.renewalDays properties of the Kafka resource, to ensure that the necessary CA certificate renewal can be completed in the configured maintenance time windows.

Note

AMQ Streams does not schedule maintenance operations exactly according to the given windows. Instead, for each reconciliation, it checks whether a maintenance window is currently "open". This means that the start of maintenance operations within a given time window can be delayed by up to the Cluster Operator reconciliation interval. Maintenance time windows must therefore be at least this long.

Additional resources

3.1.27.3. Configuring a maintenance time window

You can configure a maintenance time window for rolling updates triggered by supported processes.

Prerequisites

  • An OpenShift cluster.
  • The Cluster Operator is running.

Procedure

  1. Add or edit the maintenanceTimeWindows property in the Kafka resource. For example to allow maintenance between 0800 and 1059 and between 1400 and 1559 you would set the maintenanceTimeWindows as shown below:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
      maintenanceTimeWindows:
        - "* * 8-10 * * ?"
        - "* * 14-15 * * ?"
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.28. Renewing CA certificates manually

Unless the Kafka.spec.clusterCa.generateCertificateAuthority and Kafka.spec.clientsCa.generateCertificateAuthority objects are set to false, the cluster and clients CA certificates will auto-renew at the start of their respective certificate renewal periods. You can manually renew one or both of these certificates before the certificate renewal period starts, if required for security reasons. A renewed certificate uses the same private key as the old certificate.

Prerequisites

  • The Cluster Operator is running.
  • A Kafka cluster in which CA certificates and private keys are installed.

Procedure

  • Apply the strimzi.io/force-renew annotation to the Secret that contains the CA certificate that you want to renew.

    CertificateSecretAnnotate command

    Cluster CA

    <cluster-name>-cluster-ca-cert

    oc annotate secret <cluster-name>-cluster-ca-cert strimzi.io/force-renew=true

    Clients CA

    <cluster-name>-clients-ca-cert

    oc annotate secret <cluster-name>-clients-ca-cert strimzi.io/force-renew=true

At the next reconciliation the Cluster Operator will generate a new CA certificate for the Secret that you annotated. If maintenance time windows are configured, the Cluster Operator will generate the new CA certificate at the first reconciliation within the next maintenance time window.

Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.

3.1.29. Replacing private keys

You can replace the private keys used by the cluster CA and clients CA certificates. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.

Prerequisites

  • The Cluster Operator is running.
  • A Kafka cluster in which CA certificates and private keys are installed.

Procedure

  • Apply the strimzi.io/force-replace annotation to the Secret that contains the private key that you want to renew.

    Private key forSecretAnnotate command

    Cluster CA

    <cluster-name>-cluster-ca

    oc annotate secret <cluster-name>-cluster-ca strimzi.io/force-replace=true

    Clients CA

    <cluster-name>-clients-ca

    oc annotate secret <cluster-name>-clients-ca strimzi.io/force-replace=true

At the next reconciliation the Cluster Operator will:

  • Generate a new private key for the Secret that you annotated
  • Generate a new CA certificate

If maintenance time windows are configured, the Cluster Operator will generate the new private key and CA certificate at the first reconciliation within the next maintenance time window.

Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.

3.1.30. List of resources created as part of Kafka cluster

The following resources will created by the Cluster Operator in the OpenShift cluster:

cluster-name-kafka
StatefulSet which is in charge of managing the Kafka broker pods.
cluster-name-kafka-brokers
Service needed to have DNS resolve the Kafka broker pods IP addresses directly.
cluster-name-kafka-bootstrap
Service can be used as bootstrap servers for Kafka clients.
cluster-name-kafka-external-bootstrap
Bootstrap service for clients connecting from outside of the OpenShift cluster. This resource will be created only when external listener is enabled.
cluster-name-kafka-pod-id
Service used to route traffic from outside of the OpenShift cluster to individual pods. This resource will be created only when external listener is enabled.
cluster-name-kafka-external-bootstrap
Bootstrap route for clients connecting from outside of the OpenShift cluster. This resource will be created only when external listener is enabled and set to type route.
cluster-name-kafka-pod-id
Route for traffic from outside of the OpenShift cluster to individual pods. This resource will be created only when external listener is enabled and set to type route.
cluster-name-kafka-config
ConfigMap which contains the Kafka ancillary configuration and is mounted as a volume by the Kafka broker pods.
cluster-name-kafka-brokers
Secret with Kafka broker keys.
cluster-name-kafka
Service account used by the Kafka brokers.
cluster-name-kafka
Pod Disruption Budget configured for the Kafka brokers.
strimzi-namespace-name-cluster-name-kafka-init
Cluster role binding used by the Kafka brokers.
cluster-name-zookeeper
StatefulSet which is in charge of managing the Zookeeper node pods.
cluster-name-zookeeper-nodes
Service needed to have DNS resolve the Zookeeper pods IP addresses directly.
cluster-name-zookeeper-client
Service used by Kafka brokers to connect to Zookeeper nodes as clients.
cluster-name-zookeeper-config
ConfigMap which contains the Zookeeper ancillary configuration and is mounted as a volume by the Zookeeper node pods.
cluster-name-zookeeper-nodes
Secret with Zookeeper node keys.
cluster-name-zookeeper
Pod Disruption Budget configured for the Zookeeper nodes.
cluster-name-entity-operator
Deployment with Topic and User Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-topic-operator-config
Configmap with ancillary configuration for Topic Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-user-operator-config
Configmap with ancillary configuration for User Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-operator-certs
Secret with Entitiy operators keys for communication with Kafka and Zookeeper. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-operator
Service account used by the Entity Operator.
strimzi-cluster-name-topic-operator
Role binding used by the Entity Operator.
strimzi-cluster-name-user-operator
Role binding used by the Entity Operator.
cluster-name-cluster-ca
Secret with the Cluster CA used to encrypt the cluster communication.
cluster-name-cluster-ca-cert
Secret with the Cluster CA public key. This key can be used to verify the identity of the Kafka brokers.
cluster-name-clients-ca
Secret with the Clients CA used to encrypt the communication between Kafka brokers and Kafka clients.
cluster-name-clients-ca-cert
Secret with the Clients CA public key. This key can be used to verify the identity of the Kafka brokers.
cluster-name-cluster-operator-certs
Secret with Cluster operators keys for communication with Kafka and Zookeeper.
data-cluster-name-kafka-idx
Persistent Volume Claim for the volume used for storing data for the Kafka broker pod idx. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.
data-id-cluster-name-kafka-idx
Persistent Volume Claim for the volume id used for storing data for the Kafka broker pod idx. This resource is only created if persistent storage is selected for JBOD volumes when provisioning persistent volumes to store data.
data-cluster-name-zookeeper-idx
Persistent Volume Claim for the volume used for storing data for the Zookeeper node pod idx. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.

3.2. Kafka Connect cluster configuration

The full schema of the KafkaConnect resource is described in the Section C.62, “KafkaConnect schema reference”. All labels that are applied to the desired KafkaConnect resource will also be applied to the OpenShift resources making up the Kafka Connect cluster. This provides a convenient mechanism for resources to be labeled as required.

3.2.1. Replicas

Kafka Connect clusters can run multiple of nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running a Kafka Connect cluster with multiple nodes can provide better availability and scalability. However, when running Kafka Connect on OpenShift it is not absolutely necessary to run multiple nodes of Kafka Connect for high availability. If a node where Kafka Connect is deployed to crashes, OpenShift will automatically reschedule the Kafka Connect pod to a different node. However, running Kafka Connect with multiple nodes can provide faster failover times, because the other nodes will be up and running already.

3.2.1.1. Configuring the number of nodes

The number of Kafka Connect nodes is configured using the replicas property in KafkaConnect.spec and KafkaConnectS2I.spec.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the replicas property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnectS2I
    metadata:
      name: my-cluster
    spec:
      # ...
      replicas: 3
      # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.2. Bootstrap servers

A Kafka Connect cluster always works in combination with a Kafka cluster. A Kafka cluster is specified as a list of bootstrap servers. On OpenShift, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap, and a port of 9092 for plain traffic or 9093 for encrypted traffic.

The list of bootstrap servers is configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_ pairs.

When using Kafka Connect with a Kafka cluster not managed by AMQ Streams, you can specify the bootstrap servers list according to the configuration of the cluster.

3.2.2.1. Configuring bootstrap servers

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the bootstrapServers property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-cluster
    spec:
      # ...
      bootstrapServers: my-cluster-kafka-bootstrap:9092
      # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.3. Connecting to Kafka brokers using TLS

By default, Kafka Connect tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.

3.2.3.1. TLS support in Kafka Connect

TLS support is configured in the tls property in KafkaConnect.spec and KafkaConnectS2I.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates must be stored in X509 format.

An example showing TLS configuration with multiple certificates

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-secret
        certificate: ca.crt
      - secretName: my-other-secret
        certificate: certificate.crt
  # ...

When multiple certificates are stored in the same secret, it can be listed multiple times.

An example showing TLS configuration with multiple certificates from the same secret

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
metadata:
  name: my-cluster
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-secret
        certificate: ca.crt
      - secretName: my-secret
        certificate: ca2.crt
  # ...

3.2.3.2. Configuring TLS in Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • If they exist, the name of the Secret for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a Secret.

    Note

    The secrets created by the Cluster Operator for Kafka cluster may be used directly.

    This can be done using oc create:

    oc create secret generic my-secret --from-file=my-file.crt
  2. Edit the tls property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      tls:
        trustedCertificates:
          - secretName: my-cluster-cluster-cert
            certificate: ca.crt
      # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.4. Connecting to Kafka brokers with Authentication

By default, Kafka Connect will try to connect to Kafka brokers without authentication. Authentication is enabled through the KafkaConnect and KafkaConnectS2I resources.

3.2.4.1. Authentication support in Kafka Connect

Authentication is configured through the authentication property in KafkaConnect.spec and KafkaConnectS2I.spec. The authentication property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism. The supported authentication types are:

3.2.4.1.1. TLS Client Authentication

To use TLS client authentication, set the type property to the value tls. TLS client authentication uses a TLS certificate to authenticate. The certificate is specified in the certificateAndKey property and is always loaded from an OpenShift secret. In the secret, the certificate must be stored in X509 format under two different keys: public and private.

Note

TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Connect see Section 3.2.3, “Connecting to Kafka brokers using TLS”.

An example TLS client authentication configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # ...

3.2.4.1.2. SASL based SCRAM-SHA-512 authentication

To configure Kafka Connect to use SASL-based SCRAM-SHA-512 authentication, set the type property to scram-sha-512. This authentication mechanism requires a username and password.

  • Specify the username in the username property.
  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of the Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Important

Do not specify the actual password in the password field.

An example SASL based SCRAM-SHA-512 client authentication configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: my-connect-user
    passwordSecret:
      secretName: my-connect-user
      password: my-connect-password-key
  # ...

3.2.4.1.3. SASL based PLAIN authentication

To configure Kafka Connect to use SASL-based PLAIN authentication, set the type property to plain. This authentication mechanism requires a username and password.

Warning

The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled.

  • Specify the username in the username property.
  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of such a Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Important

Do not specify the actual password in the password field.

An example showing SASL based PLAIN client authentication configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: plain
    username: my-connect-user
    passwordSecret:
      secretName: my-connect-user
      password: my-connect-password-key
  # ...

3.2.4.2. Configuring TLS client authentication in Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • If they exist, the name of the Secret with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the keys used for authentication in a file and create the Secret.

    Note

    Secrets created by the User Operator may be used.

    This can be done using oc create:

    oc create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key
  2. Edit the authentication property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      authentication:
        type: tls
        certificateAndKey:
          secretName: my-secret
          certificate: my-public.crt
          key: my-private.key
      # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.4.3. Configuring SCRAM-SHA-512 authentication in Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • Username of the user which should be used for authentication
  • If they exist, the name of the Secret with the password used for authentication and the key under which the password is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare a file with the password used in authentication and create the Secret.

    Note

    Secrets created by the User Operator may be used.

    This can be done using oc create:

    echo -n '<password>' > <my-password.txt>
    oc create secret generic <my-secret> --from-file=<my-password.txt>
  2. Edit the authentication property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      authentication:
        type: scram-sha-512
        username: _<my-username>_
        passwordSecret:
          secretName: _<my-secret>_
          password: _<my-password.txt>_
      # ...
  3. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.2.5. Kafka Connect configuration

AMQ Streams allows you to customize the configuration of Apache Kafka Connect nodes by editing certain options listed in Apache Kafka documentation.

Configuration options that cannot be configured relate to:

  • Kafka cluster bootstrap address
  • Security (Encryption, Authentication, and Authorization)
  • Listener / REST interface configuration
  • Plugin path configuration

These options are automatically configured by AMQ Streams.

3.2.5.1. Kafka Connect configuration

Kafka Connect is configured using the config property in KafkaConnect.spec and KafkaConnectS2I.spec. This property contains the Kafka Connect configuration options as keys. The values can be one of the following JSON types:

  • String
  • Number
  • Boolean

You can specify and configure the options listed in the Apache Kafka documentation with the exception of those options that are managed directly by AMQ Streams. Specifically, configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.
  • sasl.
  • security.
  • listeners
  • plugin.path
  • rest.
  • bootstrap.servers

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Custer Operator log file. All other options are passed to Kafka Connect.

Important

The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object, then the Cluster Operator can roll out the new configuration to all Kafka Connect nodes.

Certain options have default values:

  • group.id with default value connect-cluster
  • offset.storage.topic with default value connect-cluster-offsets
  • config.storage.topic with default value connect-cluster-configs
  • status.storage.topic with default value connect-cluster-status
  • key.converter with default value org.apache.kafka.connect.json.JsonConverter
  • value.converter with default value org.apache.kafka.connect.json.JsonConverter

These options are automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

Example 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
    key.converter: org.apache.kafka.connect.json.JsonConverter
    value.converter: org.apache.kafka.connect.json.JsonConverter
    key.converter.schemas.enable: true
    value.converter.schemas.enable: true
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  # ...

3.2.5.2. Kafka Connect configuration for multiple instances

If you are running multiple instances of Kafka Connect, pay attention to the default configuration of the following properties:

# ...
  group.id: connect-cluster 1
  offset.storage.topic: connect-cluster-offsets 2
  config.storage.topic: connect-cluster-configs 3
  status.storage.topic: connect-cluster-status  4
# ...
1
Kafka Connect cluster group the instance belongs to.
2
Kafka topic that stores connector offsets.
3
Kafka topic that stores connector and task status configurations.
4
Kafka topic that stores connector and task status updates.
Note

Values for the three topics must be the same for all Kafka Connect instances with the same group.id.

Unless you change the default settings, each Kafka Connect instance connecting to the same Kafka cluster is deployed with the same values. What happens, in effect, is all instances are coupled to run in a cluster and use the same topics.

If multiple Kafka Connect clusters try to use the same topics, Kafka Connect will not work as expected and generate errors.

If you wish to run multiple Kafka Connect instances, change the values of these properties for each instance.

3.2.5.3. Configuring Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the config property in the KafkaConnect or KafkaConnectS2I resource. For example:

    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
        key.converter: org.apache.kafka.connect.json.JsonConverter
        value.converter: org.apache.kafka.connect.json.JsonConverter
        key.converter.schemas.enable: true
        value.converter.schemas.enable: true
        config.storage.replication.factor: 3
        offset.storage.replication.factor: 3
        status.storage.replication.factor: 3
      # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.6. CPU and memory resources

For every deployed container, AMQ Streams allows you to request specific resources and define the maximum consumption of those resources.

AMQ Streams supports two types of resources:

  • CPU
  • Memory

AMQ Streams uses the OpenShift syntax for specifying CPU and memory resources.

3.2.6.1. Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.entityOperator.tlsSidecar
  • Kafka.spec.KafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaBridge.spec

Additional resources

3.2.6.1.1. Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important

If the resource request is for more than the available free resources in the OpenShift cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by AMQ Streams:

  • cpu
  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources

# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...

3.2.6.1.2. Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by AMQ Streams:

  • cpu
  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration

# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...

3.2.6.1.3. Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).
  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units

# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...

Note

The computing power of 1 CPU core may differ depending on the platform where OpenShift is deployed.

Additional resources

3.2.6.1.4. Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.
  • To specify memory in gigabytes, use the G suffix. For example 1G.
  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.
  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units

# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...

Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

3.2.6.2. Configuring resource requests and limits

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the resources property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: "8"
            memory: 64Gi
          limits:
            cpu: "12"
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

3.2.7. Logging

This section provides information on loggers and how to configure log levels.

You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.

3.2.7.1. Kafka Connect loggers

Kafka Connect has its own configurable loggers:

  • connect.root.logger.level
  • log4j.logger.org.apache.zookeeper
  • log4j.logger.org.I0Itec.zkclient
  • log4j.logger.org.reflections

3.2.7.2. Specifying inline logging

Procedure

  1. Edit the YAML file to specify the loggers and logging level for the required components.

    For example, the logging level here is set to INFO:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    spec:
      # ...
      logging:
        type: inline
        loggers:
          logger.name: "INFO"
      # ...

    You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    For more information about the log levels, see the log4j manual.

  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

3.2.7.3. Specifying an external ConfigMap for logging

Procedure

  1. Edit the YAML file to specify the name of the ConfigMap to use for the required components. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    spec:
      # ...
      logging:
        type: external
        name: customConfigMap
      # ...

    Remember to place your custom ConfigMap under the log4j.properties or log4j2.properties key.

  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see Section 3.1.17.1, “JVM configuration”

3.2.8. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, OpenShift assumes that the application is not healthy and attempts to fix it.

OpenShift supports two types of Healthcheck probes:

  • Liveness probes
  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in AMQ Streams components.

Users can configure selected options for liveness and readiness probes.

3.2.8.1. Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.KafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMaker.spec
  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds
  • timeoutSeconds
  • periodSeconds
  • successThreshold
  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Section C.34, “Probe schema reference”.

An example of liveness and readiness probe configuration

# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...

3.2.8.2. Configuring healthchecks

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        readinessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        livenessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.9. Prometheus metrics

AMQ Streams supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and Zookeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.

For more information about configuring Prometheus and Grafana, see Metrics.

3.2.9.1. Metrics configuration

Prometheus metrics are enabled by configuring the metrics property in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

When the metrics property is not defined in the resource, the Prometheus metrics will be disabled. To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

Example of enabling metrics without any further configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics: {}
    # ...
  zookeeper:
    # ...

The metrics property might contain additional configuration for the Prometheus JMX exporter.

Example of enabling metrics with additional Prometheus JMX Exporter configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics:
      lowercaseOutputName: true
      rules:
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
          name: "kafka_server_$1_$2_total"
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
          name: "kafka_server_$1_$2_total"
          labels:
            topic: "$3"
    # ...
  zookeeper:
    # ...

3.2.9.2. Configuring Prometheus metrics

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the metrics property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.10. JVM Options

The following components of AMQ Streams run inside a Virtual Machine (VM):

  • Apache Kafka
  • Apache Zookeeper
  • Apache Kafka Connect
  • Apache Kafka Mirror Maker
  • AMQ Streams Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. AMQ Streams allows you to configure some of these options.

3.2.10.1. JVM configuration

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMaker.spec
  • KafkaBridge.spec

Only a selected subset of available JVM options can be configured. The following options are supported:

-Xms and -Xmx

-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.

Note

The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the OpenShift convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:

  • If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
  • If there is no memory limit then the JVM’s minimum memory will be set to 128M and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.
  • If -Xmx is set without also setting an appropriate OpenShift memory limit, it is possible that the container will be killed should the OpenShift node experience memory pressure (from other Pods running on it).
  • If -Xmx is set without also setting an appropriate OpenShift memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • set the memory request and the memory limit to the same value,
  • use a memory request that is at least 4.5 × the -Xmx,
  • consider setting -Xms to the same value as -Xmx.
Important

Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.

Example fragment configuring -Xmx and -Xms

# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and Zookeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server

# ...
jvmOptions:
  "-server": true
# ...

Note

When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.

-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object

jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true
    "UseParNewGC": false

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note

When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.

3.2.10.1.1. Garbage collector logging

The jvmOptions section also allows you to enable and disable garbage collector (GC) logging. GC logging is enabled by default. To disable it, set the gcLoggingEnabled property as follows:

Example of disabling GC logging

# ...
jvmOptions:
  gcLoggingEnabled: false
# ...

3.2.10.2. Configuring JVM options

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.11. Container images

AMQ Streams allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such a case, you should either copy the AMQ Streams images or build them from the source. If the configured image is not compatible with AMQ Streams images, it might not work properly.

3.2.11.1. Container image configurations

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.entityOperator.tlsSidecar
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaBridge.spec
3.2.11.1.1. Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because AMQ Streams supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.
  • If Kafka.spec.kafka.image is given but Kafka.spec.kafka.version is not then the given image will be used and the version will be assumed to be the Cluster Operator’s default Kafka version.
  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.
  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.
Warning

It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.

3.2.11.1.2. Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Kafka broker TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Zookeeper nodes:

    1. Container image specified in the STRIMZI_DEFAULT_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Zookeeper node TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-operator:1.3.0 container image.
  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-operator:1.3.0 container image.
  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Kafka Connect:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
Warning

Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such case, you should either copy the AMQ Streams images or build them from source. In case the configured image is not compatible with AMQ Streams images, it might not work properly.

Example of container image configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

3.2.11.2. Configuring container images

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.12. Configuring pod scheduling

Important

When two applications are scheduled to the same OpenShift node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.

3.2.12.1. Scheduling pods based on other applications

3.2.12.1.1. Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

3.2.12.1.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.2.12.1.3. Configuring pod anti-affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            affinity:
              podAntiAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  - labelSelector:
                      matchExpressions:
                        - key: application
                          operator: In
                          values:
                            - postgresql
                            - mongodb
                    topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.12.2. Scheduling pods to specific nodes

3.2.12.2.1. Node scheduling

The OpenShift cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of AMQ Streams components to use the right nodes.

OpenShift uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

3.2.12.2.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.2.12.2.3. Configuring node affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Label the nodes where AMQ Streams components should be scheduled.

    This can be done using oc label:

    oc label node your-node node-type=fast-network

    Alternatively, some of the existing labels might be reused.

  2. Edit the affinity property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            affinity:
              nodeAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  nodeSelectorTerms:
                    - matchExpressions:
                      - key: node-type
                        operator: In
                        values:
                        - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.12.3. Using dedicated nodes

3.2.12.3.1. Dedicated nodes

Cluster administrators can mark selected OpenShift nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

3.2.12.3.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.2.12.3.3. Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.

3.2.12.3.4. Setting up dedicated nodes and scheduling pods on them

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.
  2. Make sure there are no workloads scheduled on these nodes.
  3. Set the taints on the selected nodes:

    This can be done using oc adm taint:

    oc adm taint node your-node dedicated=Kafka:NoSchedule
  4. Additionally, add a label to the selected nodes as well.

    This can be done using oc label:

    oc label node your-node dedicated=Kafka
  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            tolerations:
              - key: "dedicated"
                operator: "Equal"
                value: "Kafka"
                effect: "NoSchedule"
            affinity:
              nodeAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  nodeSelectorTerms:
                  - matchExpressions:
                    - key: dedicated
                      operator: In
                      values:
                      - Kafka
        # ...
      zookeeper:
        # ...
  6. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.2.13. Using external configuration and secrets

Kafka Connect connectors are configured using an HTTP REST interface. The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself.

Some parts of the configuration of a Kafka Connect connector can be externalized using ConfigMaps or Secrets. You can then reference the configuration values in HTTP REST commands (this keeps the configuration separate and more secure, if needed). This method applies especially to confidential data, such as usernames, passwords, or certificates.

ConfigMaps and Secrets are standard OpenShift resources used for storing of configurations and confidential data.

3.2.13.1. Storing connector configurations externally

You can mount ConfigMaps or Secrets into a Kafka Connect pod as volumes or environment variables. Volumes and environment variables are configured in the externalConfiguration property in KafkaConnect.spec and KafkaConnectS2I.spec.

3.2.13.1.1. External configuration as environment variables

The env property is used to specify one or more environment variables. These variables can contain a value from either a ConfigMap or a Secret.

Note

The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_.

To mount a value from a Secret to an environment variable, use the valueFrom property and the secretKeyRef as shown in the following example.

Example of an environment variable set to a value from a Secret

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          secretKeyRef:
            name: my-secret
            key: my-key

A common use case for mounting Secrets to environment variables is when your connector needs to communicate with Amazon AWS and needs to read the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables with credentials.

To mount a value from a ConfigMap to an environment variable, use configMapKeyRef in the valueFrom property as shown in the following example.

Example of an environment variable set to a value from a ConfigMap

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          configMapKeyRef:
            name: my-config-map
            key: my-key

3.2.13.1.2. External configuration as volumes

You can also mount ConfigMaps or Secrets to a Kafka Connect pod as volumes. Using volumes instead of environment variables is useful in the following scenarios:

  • Mounting truststores or keystores with TLS certificates
  • Mounting a properties file that is used to configure Kafka Connect connectors

In the volumes property of the externalConfiguration resource, list the ConfigMaps or Secrets that will be mounted as volumes. Each volume must specify a name in the name property and a reference to ConfigMap or Secret.

Example of volumes with external configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    volumes:
      - name: connector1
        configMap:
          name: connector1-configuration
      - name: connector1-certificates
        secret:
          secretName: connector1-certificates

The volumes will be mounted inside the Kafka Connect containers in the path /opt/kafka/external-configuration/<volume-name>. For example, the files from a volume named connector1 would appear in the directory /opt/kafka/external-configuration/connector1.

The FileConfigProvider has to be used to read the values from the mounted properties files in connector configurations.

3.2.13.2. Mounting Secrets as environment variables

You can create an OpenShift Secret and mount it to Kafka Connect as an environment variable.

Prerequisites

  • A running Cluster Operator.

Procedure

  1. Create a secret containing the information that will be mounted as an environment variable. For example:

    apiVersion: v1
    kind: Secret
    metadata:
      name: aws-creds
    type: Opaque
    data:
      awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg=
      awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=
  2. Create or edit the Kafka Connect resource. Configure the externalConfiguration section of the KafkaConnect or KafkaConnectS2I custom resource to reference the secret. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      externalConfiguration:
        env:
          - name: AWS_ACCESS_KEY_ID
            valueFrom:
              secretKeyRef:
                name: aws-creds
                key: awsAccessKey
          - name: AWS_SECRET_ACCESS_KEY
            valueFrom:
              secretKeyRef:
                name: aws-creds
                key: awsSecretAccessKey
  3. Apply the changes to your Kafka Connect deployment.

    Use oc apply:

    oc apply -f your-file

The environment variables are now available for use when developing your connectors.

Additional resources

3.2.13.3. Mounting Secrets as volumes

You can create an OpenShift Secret, mount it as a volume to Kafka Connect, and then use it to configure a Kafka Connect connector.

Prerequisites

  • A running Cluster Operator.

Procedure

  1. Create a secret containing a properties file that defines the configuration options for your connector configuration. For example:

    apiVersion: v1
    kind: Secret
    metadata:
      name: mysecret
    type: Opaque
    stringData:
      connector.properties: |-
        dbUsername: my-user
        dbPassword: my-password
  2. Create or edit the Kafka Connect resource. Configure the FileConfigProvider in the config section and the externalConfiguration section of the KafkaConnect or KafkaConnectS2I custom resource to reference the secret. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      config:
        config.providers: file
        config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider
      #...
      externalConfiguration:
        volumes:
          - name: connector-config
            secret:
              secretName: mysecret
  3. Apply the changes to your Kafka Connect deployment.

    Use oc apply:

    oc apply -f your-file
  4. Use the values from the mounted properties file in your JSON payload with connector configuration. For example:

    {
       "name":"my-connector",
       "config":{
          "connector.class":"MyDbConnector",
          "tasks.max":"3",
          "database": "my-postgresql:5432"
          "username":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbUsername}",
          "password":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbPassword}",
          # ...
       }
    }

Additional resources

3.2.14. List of resources created as part of Kafka Connect cluster

The following resources will created by the Cluster Operator in the OpenShift cluster:

connect-cluster-name-connect
Deployment which is in charge to create the Kafka Connect worker node pods.
connect-cluster-name-connect-api
Service which exposes the REST interface for managing the Kafka Connect cluster.
connect-cluster-name-config
ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
connect-cluster-name-connect
Pod Disruption Budget configured for the Kafka Connect worker nodes.

3.3. Kafka Connect cluster with Source2Image support

The full schema of the KafkaConnectS2I resource is described in the Section C.78, “KafkaConnectS2I schema reference”. All labels that are applied to the desired KafkaConnectS2I resource will also be applied to the OpenShift resources making up the Kafka Connect cluster with Source2Image support. This provides a convenient mechanism for resources to be labeled as required.

3.3.1. Replicas

Kafka Connect clusters can run multiple of nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running a Kafka Connect cluster with multiple nodes can provide better availability and scalability. However, when running Kafka Connect on OpenShift it is not absolutely necessary to run multiple nodes of Kafka Connect for high availability. If a node where Kafka Connect is deployed to crashes, OpenShift will automatically reschedule the Kafka Connect pod to a different node. However, running Kafka Connect with multiple nodes can provide faster failover times, because the other nodes will be up and running already.

3.3.1.1. Configuring the number of nodes

The number of Kafka Connect nodes is configured using the replicas property in KafkaConnect.spec and KafkaConnectS2I.spec.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the replicas property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnectS2I
    metadata:
      name: my-cluster
    spec:
      # ...
      replicas: 3
      # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.2. Bootstrap servers

A Kafka Connect cluster always works in combination with a Kafka cluster. A Kafka cluster is specified as a list of bootstrap servers. On OpenShift, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap, and a port of 9092 for plain traffic or 9093 for encrypted traffic.

The list of bootstrap servers is configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_ pairs.

When using Kafka Connect with a Kafka cluster not managed by AMQ Streams, you can specify the bootstrap servers list according to the configuration of the cluster.

3.3.2.1. Configuring bootstrap servers

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the bootstrapServers property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-cluster
    spec:
      # ...
      bootstrapServers: my-cluster-kafka-bootstrap:9092
      # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.3. Connecting to Kafka brokers using TLS

By default, Kafka Connect tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.

3.3.3.1. TLS support in Kafka Connect

TLS support is configured in the tls property in KafkaConnect.spec and KafkaConnectS2I.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates must be stored in X509 format.

An example showing TLS configuration with multiple certificates

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-secret
        certificate: ca.crt
      - secretName: my-other-secret
        certificate: certificate.crt
  # ...

When multiple certificates are stored in the same secret, it can be listed multiple times.

An example showing TLS configuration with multiple certificates from the same secret

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
metadata:
  name: my-cluster
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-secret
        certificate: ca.crt
      - secretName: my-secret
        certificate: ca2.crt
  # ...

3.3.3.2. Configuring TLS in Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • If they exist, the name of the Secret for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a Secret.

    Note

    The secrets created by the Cluster Operator for Kafka cluster may be used directly.

    This can be done using oc create:

    oc create secret generic my-secret --from-file=my-file.crt
  2. Edit the tls property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      tls:
        trustedCertificates:
          - secretName: my-cluster-cluster-cert
            certificate: ca.crt
      # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.4. Connecting to Kafka brokers with Authentication

By default, Kafka Connect will try to connect to Kafka brokers without authentication. Authentication is enabled through the KafkaConnect and KafkaConnectS2I resources.

3.3.4.1. Authentication support in Kafka Connect

Authentication is configured through the authentication property in KafkaConnect.spec and KafkaConnectS2I.spec. The authentication property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism. The supported authentication types are:

3.3.4.1.1. TLS Client Authentication

To use TLS client authentication, set the type property to the value tls. TLS client authentication uses a TLS certificate to authenticate. The certificate is specified in the certificateAndKey property and is always loaded from an OpenShift secret. In the secret, the certificate must be stored in X509 format under two different keys: public and private.

Note

TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Connect see Section 3.3.3, “Connecting to Kafka brokers using TLS”.

An example TLS client authentication configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # ...

3.3.4.1.2. SASL based SCRAM-SHA-512 authentication

To configure Kafka Connect to use SASL-based SCRAM-SHA-512 authentication, set the type property to scram-sha-512. This authentication mechanism requires a username and password.

  • Specify the username in the username property.
  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of the Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Important

Do not specify the actual password in the password field.

An example SASL based SCRAM-SHA-512 client authentication configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: my-connect-user
    passwordSecret:
      secretName: my-connect-user
      password: my-connect-password-key
  # ...

3.3.4.1.3. SASL based PLAIN authentication

To configure Kafka Connect to use SASL-based PLAIN authentication, set the type property to plain. This authentication mechanism requires a username and password.

Warning

The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled.

  • Specify the username in the username property.
  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of such a Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Important

Do not specify the actual password in the password field.

An example showing SASL based PLAIN client authentication configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: plain
    username: my-connect-user
    passwordSecret:
      secretName: my-connect-user
      password: my-connect-password-key
  # ...

3.3.4.2. Configuring TLS client authentication in Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • If they exist, the name of the Secret with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the keys used for authentication in a file and create the Secret.

    Note

    Secrets created by the User Operator may be used.

    This can be done using oc create:

    oc create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key
  2. Edit the authentication property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      authentication:
        type: tls
        certificateAndKey:
          secretName: my-secret
          certificate: my-public.crt
          key: my-private.key
      # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.4.3. Configuring SCRAM-SHA-512 authentication in Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • Username of the user which should be used for authentication
  • If they exist, the name of the Secret with the password used for authentication and the key under which the password is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare a file with the password used in authentication and create the Secret.

    Note

    Secrets created by the User Operator may be used.

    This can be done using oc create:

    echo -n '<password>' > <my-password.txt>
    oc create secret generic <my-secret> --from-file=<my-password.txt>
  2. Edit the authentication property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      authentication:
        type: scram-sha-512
        username: _<my-username>_
        passwordSecret:
          secretName: _<my-secret>_
          password: _<my-password.txt>_
      # ...
  3. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.3.5. Kafka Connect configuration

AMQ Streams allows you to customize the configuration of Apache Kafka Connect nodes by editing certain options listed in Apache Kafka documentation.

Configuration options that cannot be configured relate to:

  • Kafka cluster bootstrap address
  • Security (Encryption, Authentication, and Authorization)
  • Listener / REST interface configuration
  • Plugin path configuration

These options are automatically configured by AMQ Streams.

3.3.5.1. Kafka Connect configuration

Kafka Connect is configured using the config property in KafkaConnect.spec and KafkaConnectS2I.spec. This property contains the Kafka Connect configuration options as keys. The values can be one of the following JSON types:

  • String
  • Number
  • Boolean

You can specify and configure the options listed in the Apache Kafka documentation with the exception of those options that are managed directly by AMQ Streams. Specifically, configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.
  • sasl.
  • security.
  • listeners
  • plugin.path
  • rest.
  • bootstrap.servers

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Custer Operator log file. All other options are passed to Kafka Connect.

Important

The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object, then the Cluster Operator can roll out the new configuration to all Kafka Connect nodes.

Certain options have default values:

  • group.id with default value connect-cluster
  • offset.storage.topic with default value connect-cluster-offsets
  • config.storage.topic with default value connect-cluster-configs
  • status.storage.topic with default value connect-cluster-status
  • key.converter with default value org.apache.kafka.connect.json.JsonConverter
  • value.converter with default value org.apache.kafka.connect.json.JsonConverter

These options are automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

Example 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
    key.converter: org.apache.kafka.connect.json.JsonConverter
    value.converter: org.apache.kafka.connect.json.JsonConverter
    key.converter.schemas.enable: true
    value.converter.schemas.enable: true
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  # ...

3.3.5.2. Kafka Connect configuration for multiple instances

If you are running multiple instances of Kafka Connect, pay attention to the default configuration of the following properties:

# ...
  group.id: connect-cluster 1
  offset.storage.topic: connect-cluster-offsets 2
  config.storage.topic: connect-cluster-configs 3
  status.storage.topic: connect-cluster-status  4
# ...
1
Kafka Connect cluster group the instance belongs to.
2
Kafka topic that stores connector offsets.
3
Kafka topic that stores connector and task status configurations.
4
Kafka topic that stores connector and task status updates.
Note

Values for the three topics must be the same for all Kafka Connect instances with the same group.id.

Unless you change the default settings, each Kafka Connect instance connecting to the same Kafka cluster is deployed with the same values. What happens, in effect, is all instances are coupled to run in a cluster and use the same topics.

If multiple Kafka Connect clusters try to use the same topics, Kafka Connect will not work as expected and generate errors.

If you wish to run multiple Kafka Connect instances, change the values of these properties for each instance.

3.3.5.3. Configuring Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the config property in the KafkaConnect or KafkaConnectS2I resource. For example:

    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
        key.converter: org.apache.kafka.connect.json.JsonConverter
        value.converter: org.apache.kafka.connect.json.JsonConverter
        key.converter.schemas.enable: true
        value.converter.schemas.enable: true
        config.storage.replication.factor: 3
        offset.storage.replication.factor: 3
        status.storage.replication.factor: 3
      # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.6. CPU and memory resources

For every deployed container, AMQ Streams allows you to request specific resources and define the maximum consumption of those resources.

AMQ Streams supports two types of resources:

  • CPU
  • Memory

AMQ Streams uses the OpenShift syntax for specifying CPU and memory resources.

3.3.6.1. Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.entityOperator.tlsSidecar
  • Kafka.spec.KafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaBridge.spec

Additional resources

3.3.6.1.1. Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important

If the resource request is for more than the available free resources in the OpenShift cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by AMQ Streams:

  • cpu
  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources

# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...

3.3.6.1.2. Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by AMQ Streams:

  • cpu
  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration

# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...

3.3.6.1.3. Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).
  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units

# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...

Note

The computing power of 1 CPU core may differ depending on the platform where OpenShift is deployed.

Additional resources

3.3.6.1.4. Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.
  • To specify memory in gigabytes, use the G suffix. For example 1G.
  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.
  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units

# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...

Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

3.3.6.2. Configuring resource requests and limits

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the resources property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: "8"
            memory: 64Gi
          limits:
            cpu: "12"
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

3.3.7. Logging

This section provides information on loggers and how to configure log levels.

You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.

3.3.7.1. Kafka Connect with Source2Image loggers

Kafka Connect with Source2Image support has its own configurable loggers:

  • connect.root.logger.level
  • log4j.logger.org.apache.zookeeper
  • log4j.logger.org.I0Itec.zkclient
  • log4j.logger.org.reflections

3.3.7.2. Specifying inline logging

Procedure

  1. Edit the YAML file to specify the loggers and logging level for the required components.

    For example, the logging level here is set to INFO:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnectS2I
    spec:
      # ...
      logging:
        type: inline
        loggers:
          logger.name: "INFO"
      # ...

    You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    For more information about the log levels, see the log4j manual.

  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

3.3.7.3. Specifying an external ConfigMap for logging

Procedure

  1. Edit the YAML file to specify the name of the ConfigMap to use for the required components. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnectS2I
    spec:
      # ...
      logging:
        type: external
        name: customConfigMap
      # ...

    Remember to place your custom ConfigMap under the log4j.properties or log4j2.properties key.

  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see Section 3.1.17.1, “JVM configuration”

3.3.8. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, OpenShift assumes that the application is not healthy and attempts to fix it.

OpenShift supports two types of Healthcheck probes:

  • Liveness probes
  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in AMQ Streams components.

Users can configure selected options for liveness and readiness probes.

3.3.8.1. Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.KafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMaker.spec
  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds
  • timeoutSeconds
  • periodSeconds
  • successThreshold
  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Section C.34, “Probe schema reference”.

An example of liveness and readiness probe configuration

# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...

3.3.8.2. Configuring healthchecks

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        readinessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        livenessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.9. Prometheus metrics

AMQ Streams supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and Zookeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.

For more information about configuring Prometheus and Grafana, see Metrics.

3.3.9.1. Metrics configuration

Prometheus metrics are enabled by configuring the metrics property in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

When the metrics property is not defined in the resource, the Prometheus metrics will be disabled. To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

Example of enabling metrics without any further configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics: {}
    # ...
  zookeeper:
    # ...

The metrics property might contain additional configuration for the Prometheus JMX exporter.

Example of enabling metrics with additional Prometheus JMX Exporter configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics:
      lowercaseOutputName: true
      rules:
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
          name: "kafka_server_$1_$2_total"
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
          name: "kafka_server_$1_$2_total"
          labels:
            topic: "$3"
    # ...
  zookeeper:
    # ...

3.3.9.2. Configuring Prometheus metrics

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the metrics property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.10. JVM Options

The following components of AMQ Streams run inside a Virtual Machine (VM):

  • Apache Kafka
  • Apache Zookeeper
  • Apache Kafka Connect
  • Apache Kafka Mirror Maker
  • AMQ Streams Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. AMQ Streams allows you to configure some of these options.

3.3.10.1. JVM configuration

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMaker.spec
  • KafkaBridge.spec

Only a selected subset of available JVM options can be configured. The following options are supported:

-Xms and -Xmx

-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.

Note

The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the OpenShift convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:

  • If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
  • If there is no memory limit then the JVM’s minimum memory will be set to 128M and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.
  • If -Xmx is set without also setting an appropriate OpenShift memory limit, it is possible that the container will be killed should the OpenShift node experience memory pressure (from other Pods running on it).
  • If -Xmx is set without also setting an appropriate OpenShift memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • set the memory request and the memory limit to the same value,
  • use a memory request that is at least 4.5 × the -Xmx,
  • consider setting -Xms to the same value as -Xmx.
Important

Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.

Example fragment configuring -Xmx and -Xms

# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and Zookeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server

# ...
jvmOptions:
  "-server": true
# ...

Note

When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.

-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object

jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true
    "UseParNewGC": false

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note

When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.

3.3.10.1.1. Garbage collector logging

The jvmOptions section also allows you to enable and disable garbage collector (GC) logging. GC logging is enabled by default. To disable it, set the gcLoggingEnabled property as follows:

Example of disabling GC logging

# ...
jvmOptions:
  gcLoggingEnabled: false
# ...

3.3.10.2. Configuring JVM options

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.11. Container images

AMQ Streams allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such a case, you should either copy the AMQ Streams images or build them from the source. If the configured image is not compatible with AMQ Streams images, it might not work properly.

3.3.11.1. Container image configurations

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.entityOperator.tlsSidecar
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaBridge.spec
3.3.11.1.1. Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because AMQ Streams supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.
  • If Kafka.spec.kafka.image is given but Kafka.spec.kafka.version is not then the given image will be used and the version will be assumed to be the Cluster Operator’s default Kafka version.
  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.
  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.
Warning

It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.

3.3.11.1.2. Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Kafka broker TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Zookeeper nodes:

    1. Container image specified in the STRIMZI_DEFAULT_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Zookeeper node TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-operator:1.3.0 container image.
  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-operator:1.3.0 container image.
  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Kafka Connect:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
Warning

Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such case, you should either copy the AMQ Streams images or build them from source. In case the configured image is not compatible with AMQ Streams images, it might not work properly.

Example of container image configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

3.3.11.2. Configuring container images

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.12. Configuring pod scheduling

Important

When two applications are scheduled to the same OpenShift node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.

3.3.12.1. Scheduling pods based on other applications

3.3.12.1.1. Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

3.3.12.1.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.3.12.1.3. Configuring pod anti-affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            affinity:
              podAntiAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  - labelSelector:
                      matchExpressions:
                        - key: application
                          operator: In
                          values:
                            - postgresql
                            - mongodb
                    topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.12.2. Scheduling pods to specific nodes

3.3.12.2.1. Node scheduling

The OpenShift cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of AMQ Streams components to use the right nodes.

OpenShift uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

3.3.12.2.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.3.12.2.3. Configuring node affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Label the nodes where AMQ Streams components should be scheduled.

    This can be done using oc label:

    oc label node your-node node-type=fast-network

    Alternatively, some of the existing labels might be reused.

  2. Edit the affinity property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            affinity:
              nodeAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  nodeSelectorTerms:
                    - matchExpressions:
                      - key: node-type
                        operator: In
                        values:
                        - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.12.3. Using dedicated nodes

3.3.12.3.1. Dedicated nodes

Cluster administrators can mark selected OpenShift nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

3.3.12.3.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.3.12.3.3. Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.

3.3.12.3.4. Setting up dedicated nodes and scheduling pods on them

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.
  2. Make sure there are no workloads scheduled on these nodes.
  3. Set the taints on the selected nodes:

    This can be done using oc adm taint:

    oc adm taint node your-node dedicated=Kafka:NoSchedule
  4. Additionally, add a label to the selected nodes as well.

    This can be done using oc label:

    oc label node your-node dedicated=Kafka
  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            tolerations:
              - key: "dedicated"
                operator: "Equal"
                value: "Kafka"
                effect: "NoSchedule"
            affinity:
              nodeAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  nodeSelectorTerms:
                  - matchExpressions:
                    - key: dedicated
                      operator: In
                      values:
                      - Kafka
        # ...
      zookeeper:
        # ...
  6. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.3.13. Using external configuration and secrets

Kafka Connect connectors are configured using an HTTP REST interface. The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself.

Some parts of the configuration of a Kafka Connect connector can be externalized using ConfigMaps or Secrets. You can then reference the configuration values in HTTP REST commands (this keeps the configuration separate and more secure, if needed). This method applies especially to confidential data, such as usernames, passwords, or certificates.

ConfigMaps and Secrets are standard OpenShift resources used for storing of configurations and confidential data.

3.3.13.1. Storing connector configurations externally

You can mount ConfigMaps or Secrets into a Kafka Connect pod as volumes or environment variables. Volumes and environment variables are configured in the externalConfiguration property in KafkaConnect.spec and KafkaConnectS2I.spec.

3.3.13.1.1. External configuration as environment variables

The env property is used to specify one or more environment variables. These variables can contain a value from either a ConfigMap or a Secret.

Note

The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_.

To mount a value from a Secret to an environment variable, use the valueFrom property and the secretKeyRef as shown in the following example.

Example of an environment variable set to a value from a Secret

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          secretKeyRef:
            name: my-secret
            key: my-key

A common use case for mounting Secrets to environment variables is when your connector needs to communicate with Amazon AWS and needs to read the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables with credentials.

To mount a value from a ConfigMap to an environment variable, use configMapKeyRef in the valueFrom property as shown in the following example.

Example of an environment variable set to a value from a ConfigMap

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          configMapKeyRef:
            name: my-config-map
            key: my-key

3.3.13.1.2. External configuration as volumes

You can also mount ConfigMaps or Secrets to a Kafka Connect pod as volumes. Using volumes instead of environment variables is useful in the following scenarios:

  • Mounting truststores or keystores with TLS certificates
  • Mounting a properties file that is used to configure Kafka Connect connectors

In the volumes property of the externalConfiguration resource, list the ConfigMaps or Secrets that will be mounted as volumes. Each volume must specify a name in the name property and a reference to ConfigMap or Secret.

Example of volumes with external configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    volumes:
      - name: connector1
        configMap:
          name: connector1-configuration
      - name: connector1-certificates
        secret:
          secretName: connector1-certificates

The volumes will be mounted inside the Kafka Connect containers in the path /opt/kafka/external-configuration/<volume-name>. For example, the files from a volume named connector1 would appear in the directory /opt/kafka/external-configuration/connector1.

The FileConfigProvider has to be used to read the values from the mounted properties files in connector configurations.

3.3.13.2. Mounting Secrets as environment variables

You can create an OpenShift Secret and mount it to Kafka Connect as an environment variable.

Prerequisites

  • A running Cluster Operator.

Procedure

  1. Create a secret containing the information that will be mounted as an environment variable. For example:

    apiVersion: v1
    kind: Secret
    metadata:
      name: aws-creds
    type: Opaque
    data:
      awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg=
      awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=
  2. Create or edit the Kafka Connect resource. Configure the externalConfiguration section of the KafkaConnect or KafkaConnectS2I custom resource to reference the secret. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      externalConfiguration:
        env:
          - name: AWS_ACCESS_KEY_ID
            valueFrom:
              secretKeyRef:
                name: aws-creds
                key: awsAccessKey
          - name: AWS_SECRET_ACCESS_KEY
            valueFrom:
              secretKeyRef:
                name: aws-creds
                key: awsSecretAccessKey
  3. Apply the changes to your Kafka Connect deployment.

    Use oc apply:

    oc apply -f your-file

The environment variables are now available for use when developing your connectors.

Additional resources

3.3.13.3. Mounting Secrets as volumes

You can create an OpenShift Secret, mount it as a volume to Kafka Connect, and then use it to configure a Kafka Connect connector.

Prerequisites

  • A running Cluster Operator.

Procedure

  1. Create a secret containing a properties file that defines the configuration options for your connector configuration. For example:

    apiVersion: v1
    kind: Secret
    metadata:
      name: mysecret
    type: Opaque
    stringData:
      connector.properties: |-
        dbUsername: my-user
        dbPassword: my-password
  2. Create or edit the Kafka Connect resource. Configure the FileConfigProvider in the config section and the externalConfiguration section of the KafkaConnect or KafkaConnectS2I custom resource to reference the secret. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      config:
        config.providers: file
        config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider
      #...
      externalConfiguration:
        volumes:
          - name: connector-config
            secret:
              secretName: mysecret
  3. Apply the changes to your Kafka Connect deployment.

    Use oc apply:

    oc apply -f your-file
  4. Use the values from the mounted properties file in your JSON payload with connector configuration. For example:

    {
       "name":"my-connector",
       "config":{
          "connector.class":"MyDbConnector",
          "tasks.max":"3",
          "database": "my-postgresql:5432"
          "username":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbUsername}",
          "password":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbPassword}",
          # ...
       }
    }

Additional resources

3.3.14. List of resources created as part of Kafka Connect cluster with Source2Image support

The following resources will created by the Cluster Operator in the OpenShift cluster:

connect-cluster-name-connect-source
ImageStream which is used as the base image for the newly-built Docker images.
connect-cluster-name-connect
BuildConfig which is responsible for building the new Kafka Connect Docker images.
connect-cluster-name-connect
ImageStream where the newly built Docker images will be pushed.
connect-cluster-name-connect
DeploymentConfig which is in charge of creating the Kafka Connect worker node pods.
connect-cluster-name-connect-api
Service which exposes the REST interface for managing the Kafka Connect cluster.
connect-cluster-name-config
ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
connect-cluster-name-connect
Pod Disruption Budget configured for the Kafka Connect worker nodes.

3.3.15. Creating a container image using OpenShift builds and Source-to-Image

You can use OpenShift builds and the Source-to-Image (S2I) framework to create new container images. An OpenShift build takes a builder image with S2I support, together with source code and binaries provided by the user, and uses them to build a new container image. Once built, container images are stored in OpenShift’s local container image repository and are available for use in deployments.

A Kafka Connect builder image with S2I support is provided on the Red Hat Container Catalog as part of the registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 image. This S2I image takes your binaries (with plug-ins and connectors) and stores them in the /tmp/kafka-plugins/s2i directory. It creates a new Kafka Connect image from this directory, which can then be used with the Kafka Connect deployment. When started using the enhanced image, Kafka Connect loads any third-party plug-ins from the /tmp/kafka-plugins/s2i directory.

Procedure

  1. On the command line, use the oc apply command to create and deploy a Kafka Connect S2I cluster:

    oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
  2. Create a directory with Kafka Connect plug-ins:

    $ tree ./my-plugins/
    ./my-plugins/
    ├── debezium-connector-mongodb
    │   ├── bson-3.4.2.jar
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mongodb-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mongodb-driver-3.4.2.jar
    │   ├── mongodb-driver-core-3.4.2.jar
    │   └── README.md
    ├── debezium-connector-mysql
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mysql-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mysql-binlog-connector-java-0.13.0.jar
    │   ├── mysql-connector-java-5.1.40.jar
    │   ├── README.md
    │   └── wkb-1.0.2.jar
    └── debezium-connector-postgres
        ├── CHANGELOG.md
        ├── CONTRIBUTE.md
        ├── COPYRIGHT.txt
        ├── debezium-connector-postgres-0.7.1.jar
        ├── debezium-core-0.7.1.jar
        ├── LICENSE.txt
        ├── postgresql-42.0.0.jar
        ├── protobuf-java-2.6.1.jar
        └── README.md
  3. Use the oc start-build command to start a new build of the image using the prepared directory:

    oc start-build my-connect-cluster-connect --from-dir ./my-plugins/
    Note

    The name of the build is the same as the name of the deployed Kafka Connect cluster.

  4. Once the build has finished, the new image is used automatically by the Kafka Connect deployment.

3.4. Kafka Mirror Maker configuration

This chapter describes how to configure the KafkaMirrorMaker resource to support a Kafka Mirror Maker deployment in your cluster.

The following procedures show how the resource is configured:

Supported properties are also described in more detail for your reference:

The full schema of the KafkaMirrorMaker resource is described in the KafkaMirrorMaker schema reference.

Note

Labels applied to a KafkaMirrorMaker resource are also applied to the OpenShift resources comprising Kafka Mirror Maker. This provides a convenient mechanism for resources to be labeled as required.

3.4.1. Configuring Kafka Mirror Maker

Use the properties of the KafkaMirrorMaker resource to configure your Kafka Mirror Maker deployment.

You can configure access control for producers and consumers using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and authentication on the consumer and producer side.

Prerequisites

Procedure

  1. Edit the spec properties for the KafkaMirrorMaker resource.

    The properties you can configure are shown in this example configuration:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      replicas: 3 1
      consumer:
        bootstrapServers: my-source-cluster-kafka-bootstrap:9092 2
        groupId: "my-group" 3
        numStreams: 2 4
        offsetCommitInterval: 120000 5
        tls: 6
          trustedCertificates:
          - secretName: my-source-cluster-ca-cert
            certificate: ca.crt
        authentication: 7
          type: tls
          certificateAndKey:
            secretName: my-source-secret
            certificate: public.crt
            key: private.key
        config: 8
          max.poll.records: 100
          receive.buffer.bytes: 32768
      producer:
        bootstrapServers: my-target-cluster-kafka-bootstrap:9092
        abortOnSendFailure: false 9
        tls:
          trustedCertificates:
          - secretName: my-target-cluster-ca-cert
            certificate: ca.crt
        authentication:
          type: tls
          certificateAndKey:
            secretName: my-target-secret
            certificate: public.crt
            key: private.key
        config:
          compression.type: gzip
          batch.size: 8192
      whitelist: "my-topic|other-topic" 10
      resources: 11
        requests:
          cpu: "1"
          memory: 2Gi
        limits:
          cpu: "2"
          memory: 2Gi
      logging: 12
        type: inline
        loggers:
          mirrormaker.root.logger: "INFO"
      readinessProbe: 13
        initialDelaySeconds: 15
        timeoutSeconds: 5
      livenessProbe:
        initialDelaySeconds: 15
        timeoutSeconds: 5
      metrics: 14
        lowercaseOutputName: true
        rules:
          - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
            name: "kafka_server_$1_$2_total"
          - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*,
            topic=(.+)><>Count"
            name: "kafka_server_$1_$2_total"
            labels:
              topic: "$3"
      jvmOptions: 15
        "-Xmx": "1g"
        "-Xms": "1g"
      image: my-org/my-image:latest 16
      template: 17
          pod:
            affinity:
              podAntiAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  - labelSelector:
                      matchExpressions:
                        - key: application
                          operator: In
                          values:
                            - postgresql
                            - mongodb
                    topologyKey: "kubernetes.io/hostname"
    1
    The number of replica nodes.
    2
    Bootstrap servers for consumer and producer.
    3
    Group ID for the consumer.
    4
    The number of consumer streams.
    5
    The offset auto-commit interval in milliseconds.
    6
    TLS encryption with key names under which TLS certificates are stored in X.509 format for consumer or producer. For more details see KafkaMirrorMakerTls schema reference.
    7
    Authentication for consumer or producer, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.
    8
    Kafka configuration options for consumer and producer.
    9
    If set to true, Kafka Mirror Maker will exit and the container will restart following a send failure for a message.
    10
    Topics mirrored from source to target Kafka cluster.
    11
    Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
    12
    Specified loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties or log4j2.properties key. Mirror Maker has a single logger called mirrormaker.root.logger. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
    13
    Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
    14
    Prometheus metrics, which are enabled with configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using metrics: {}.
    15
    JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka Mirror Maker.
    16
    ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
    17
    Template customization. Here a pod is scheduled based with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
    Warning

    With the abortOnSendFailure property set to false, the producer attempts to send the next message in a topic. The original message might be lost, as there is no attempt to resend a failed message.

  2. Create or update the resource:

    oc apply -f <your-file>

3.4.2. Kafka Mirror Maker configuration properties

Use the spec configuration properties of the KafkaMirrorMaker resource to set up your Mirror Maker deployment.

Supported properties are described here for your reference.

3.4.2.1. Replicas

Use the replicas property to configure replicas.

You can run multiple Mirror Maker replicas to provide better availability and scalability. When running Kafka Mirror Maker on OpenShift it is not absolutely necessary to run multiple replicas of the Kafka Mirror Maker for high availability. When the node where the Kafka Mirror Maker has deployed crashes, OpenShift will automatically reschedule the Kafka Mirror Maker pod to a different node. However, running Kafka Mirror Maker with multiple replicas can provide faster failover times as the other nodes will be up and running.

3.4.2.2. Bootstrap servers

Use the consumer.bootstrapServers and producer.bootstrapServers properties to configure lists of bootstrap servers for the consumer and producer.

Kafka Mirror Maker always works together with two Kafka clusters (source and target). The source and the target Kafka clusters are specified in the form of two lists of comma-separated list of <hostname>:‍<port> pairs. Each comma-separated list contains one or more Kafka brokers or a Service pointing to Kafka brokers specified as a <hostname>:<port> pairs.

The bootstrap server lists can refer to Kafka clusters that do not need to be deployed in the same OpenShift cluster. They can even refer to a Kafka cluster not deployed by AMQ Streams, or deployed by AMQ Streams but on a different OpenShift cluster accessible outside.

If on the same OpenShift cluster, each list must ideally contain the Kafka cluster bootstrap service which is named <cluster-name>-kafka-bootstrap and a port of 9092 for plain traffic or 9093 for encrypted traffic. If deployed by AMQ Streams but on different OpenShift clusters, the list content depends on the approach used for exposing the clusters (routes, nodeports or loadbalancers).

When using Kafka Mirror Maker with a Kafka cluster not managed by AMQ Streams, you can specify the bootstrap servers list according to the configuration of the given cluster.

3.4.2.3. Whitelist

Use the whitelist property to configure a list of topics that Kafka Mirror Maker mirrors from the source to the target Kafka cluster.

The property allows any regular expression from the simplest case with a single topic name to complex patterns. For example, you can mirror topics A and B using "A|B" or all topics using "*". You can also pass multiple regular expressions separated by commas to the Kafka Mirror Maker.

3.4.2.4. Consumer group identifier

Use the consumer.groupId property to configure a consumer group identifier for the consumer.

Kafka Mirror Maker uses a Kafka consumer to consume messages, behaving like any other Kafka consumer client. Messages consumed from the source Kafka cluster are mirrored to a target Kafka cluster. A group identifier is required, as the consumer needs to be part of a consumer group for the assignment of partitions.

3.4.2.5. Consumer streams

Use the consumer.numStreams property to configure the number of streams for the consumer.

You can increase the throughput in mirroring topics by increasing the number of consumer threads. Consumer threads belong to the consumer group specified for Kafka Mirror Maker. Topic partitions are assigned across the consumer threads, which consume messages in parallel.

3.4.2.6. Offset auto-commit interval

Use the consumer.offsetCommitInterval property to configure an offset auto-commit interval for the consumer.

You can specify the regular time interval at which an offset is committed after Kafka Mirror Maker has consumed data from the source Kafka cluster. The time interval is set in milliseconds, with a default value of 60,000.

3.4.2.7. Abort on message send failure

Use the producer.abortOnSendFailure property to configure how to handle message send failure from the producer.

By default, if an error occurs when sending a message from Kafka Mirror Maker to a Kafka cluster:

  • The Kafka Mirror Maker container is terminated in OpenShift.
  • The container is then recreated.

If the abortOnSendFailure option is set to false, message sending errors are ignored.

3.4.2.8. Kafka producer and consumer

Use the consumer.config and producer.config properties to configure Kafka options for the consumer and producer.

The config property contains the Kafka Mirror Maker consumer and producer configuration options as keys, with values set in one of the following JSON types:

  • String
  • Number
  • Boolean

Exceptions

You can specify and configure standard Kafka consumer and producer options:

However, there are exceptions for options automatically configured and managed directly by AMQ Streams related to:

  • Kafka cluster bootstrap address
  • Security (encryption, authentication, and authorization)
  • Consumer group identifier

Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.
  • sasl.
  • security.
  • bootstrap.servers
  • group.id

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Custer Operator log file. All other options are passed to Kafka Mirror Maker.

Important

The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Mirror Maker might not start or might become unstable. In such cases, the configuration in the KafkaMirrorMaker.spec.consumer.config or KafkaMirrorMaker.spec.producer.config object should be fixed and the Cluster Operator will roll out the new configuration for Kafka Mirror Maker.

3.4.2.9. CPU and memory resources

Use the reources.requests and resources.limits properties to configure resource requests and limits.

For every deployed container, AMQ Streams allows you to request specific resources and define the maximum consumption of those resources.

AMQ Streams supports requests and limits for the following types of resources:

  • cpu
  • memory

AMQ Streams uses the OpenShift syntax for specifying these resources.

For more information about managing computing resources on OpenShift, see Managing Compute Resources for Containers.

Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important

If the resource request is for more than the available free resources in the OpenShift cluster, the pod is not scheduled.

A request may be configured for one or more supported resources.

Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

A resource may be configured for one or more supported limits.

Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).
  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.
Note

The computing power of 1 CPU core may differ depending on the platform where OpenShift is deployed.

For more information on CPU specification, see the Meaning of CPU.

Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.
  • To specify memory in gigabytes, use the G suffix. For example 1G.
  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.
  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

For more details about memory specification and additional supported units, see Meaning of memory.

3.4.2.10. Kafka Mirror Maker loggers

Kafka Mirror Maker has its own configurable logger:

  • mirrormaker.root.logger

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap.

Here we see examples of inline and external logging:

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
  # ...
  logging:
    type: inline
    loggers:
      mirrormaker.root.logger: "INFO"
  # ...
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...

Additional resources

3.4.2.11. Healthchecks

Use the livenessProbe and readinessProbe properties to configure healthcheck probes supported in AMQ Streams.

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, OpenShift assumes that the application is not healthy and attempts to fix it.

For more details about the probes, see Configure Liveness and Readiness Probes.

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds
  • timeoutSeconds
  • periodSeconds
  • successThreshold
  • failureThreshold

An example of liveness and readiness probe configuration

# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...

For more information about the livenessProbe and readinessProbe options, see Probe schema reference.

3.4.2.12. Prometheus metrics

Use the metrics property to enable and configure Prometheus metrics.

The metrics property can also contain additional configuration for the Prometheus JMX exporter. AMQ Streams supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and Zookeeper to Prometheus metrics.

To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

When metrics are enabled, they are exposed on port 9404.

When the metrics property is not defined in the resource, the Prometheus metrics are disabled.

For more information about configuring Prometheus and Grafana, see Metrics.

3.4.2.13. JVM Options

Use the jvmOptions property to configure supported options for the JVM on which the component is running.

Supported JVM options help to optimize performance for different platforms and architectures.

For more information on the supported options, see JVM configuration.

3.4.2.14. Container images

Use the image property to configure the container image used by the component.

Overriding container images is recommended only in special situations where you need to use a different container registry or a customized image.

For example, if your network does not allow access to the container repository used by AMQ Streams, you can copy the AMQ Streams images or build them from the source. However, if the configured image is not compatible with AMQ Streams images, it might not work properly.

A copy of the container image might also be customized and used for debugging.

For more information see Container image configurations.

3.4.3. List of resources created as part of Kafka Mirror Maker

The following resources are created by the Cluster Operator in the OpenShift cluster:

<mirror-maker-name>-mirror-maker
Deployment which is responsible for creating the Kafka Mirror Maker pods.
<mirror-maker-name>-config
ConfigMap which contains ancillary configuration for the the Kafka Mirror Maker, and is mounted as a volume by the Kafka broker pods.
<mirror-maker-name>-mirror-maker
Pod Disruption Budget configured for the Kafka Mirror Maker worker nodes.

3.5. Kafka Bridge configuration

The full schema of the KafkaBridge resource is described in the Section C.102, “KafkaBridge schema reference”. All labels that are applied to the desired KafkaBridge resource will also be applied to the OpenShift resources making up the Kafka Bridge cluster. This provides a convenient mechanism for resources to be labeled as required.

3.5.1. Replicas

Kafka Bridge can run multiple nodes. The number of nodes is defined in the KafkaBridge resource. Running a Kafka Bridge with multiple nodes can provide better availability and scalability. However, when running Kafka Bridge on OpenShift it is not absolutely necessary to run multiple nodes of Kafka Bridge for high availability.

Important

If a node where Kafka Bridge is deployed to crashes, OpenShift will automatically reschedule the Kafka Bridge pod to a different node. In order to prevent issues arising when client consumer requests are processed by different Kafka Bridge instances, addressed-based routing must be employed to ensure that requests are routed to the right Kafka Bridge instance. Additionally, each independent Kafka Bridge instance must have a replica. A Kafka Bridge instance has its own state which is not shared with another instances.

3.5.1.1. Configuring the number of nodes

The number of Kafka Bridge nodes is configured using the replicas property in KafkaBridge.spec.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the replicas property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaBridge
    metadata:
      name: my-bridge
    spec:
      # ...
      replicas: 3
      # ...
  2. Create or update the resource.

    oc apply -f your-file

3.5.2. Bootstrap servers

A Kafka Bridge always works in combination with a Kafka cluster. A Kafka cluster is specified as a list of bootstrap servers. On OpenShift, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap, and a port of 9092 for plain traffic or 9093 for encrypted traffic.

The list of bootstrap servers is configured in the bootstrapServers property in KafkaBridge.kafka.spec. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_ pairs.

When using Kafka Bridge with a Kafka cluster not managed by AMQ Streams, you can specify the bootstrap servers list according to the configuration of the cluster.

3.5.2.1. Configuring bootstrap servers

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the bootstrapServers property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaBridge
    metadata:
      name: my-bridge
    spec:
      # ...
      bootstrapServers: my-cluster-kafka-bootstrap:9092
      # ...
  2. Create or update the resource.

    oc apply -f your-file

3.5.3. Connecting to Kafka brokers using TLS

By default, Kafka Bridge tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.

3.5.3.1. TLS support for Kafka connection to the Kafka Bridge

TLS support for Kafka connection is configured in the tls property in KafkaBridge.spec.kafka. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates must be stored in X509 format.

An example showing TLS configuration with multiple certificates

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  tls:
    trustedCertificates:
    - secretName: my-secret
      certificate: ca.crt
    - secretName: my-other-secret
      certificate: certificate.crt
  # ...

When multiple certificates are stored in the same secret, it can be listed multiple times.

An example showing TLS configuration with multiple certificates from the same secret

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  tls:
    trustedCertificates:
    - secretName: my-secret
      certificate: ca.crt
    - secretName: my-secret
      certificate: ca2.crt
  # ...

3.5.3.2. Configuring TLS in Kafka Bridge

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • If they exist, the name of the Secret for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a Secret.

    Note

    The secrets created by the Cluster Operator for Kafka cluster may be used directly.

    oc create secret generic my-secret --from-file=my-file.crt
  2. Edit the tls property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaBridge
    metadata:
      name: my-bridge
    spec:
      # ...
      tls:
    	  trustedCertificates:
    	  - secretName: my-cluster-cluster-cert
    	    certificate: ca.crt
      # ...
  3. Create or update the resource.

    oc apply -f your-file

3.5.4. Connecting to Kafka brokers with Authentication

By default, Kafka Bridge will try to connect to Kafka brokers without authentication. Authentication is enabled through the KafkaBridge resources.

3.5.4.1. Authentication support in Kafka Bridge

Authentication is configured through the authentication property in KafkaBridge.spec.kafka. The authentication property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism. The currently supported authentication types are:

3.5.4.1.1. TLS Client Authentication

To use TLS client authentication, set the type property to the value tls. TLS client authentication uses a TLS certificate to authenticate. The certificate is specified in the certificateAndKey property and is always loaded from an OpenShift secret. In the secret, the certificate must be stored in X509 format under two different keys: public and private.

Note

TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Bridge see Section 3.5.3, “Connecting to Kafka brokers using TLS”.

An example TLS client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # ...

3.5.4.1.2. SCRAM-SHA-512 authentication

To configure Kafka Bridge to use SASL-based SCRAM-SHA-512 authentication, set the type property to scram-sha-512. This authentication mechanism requires a username and password.

  • Specify the username in the username property.
  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of the Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Important

Do not specify the actual password in the password field.

An example SASL based SCRAM-SHA-512 client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: my-bridge-user
    passwordSecret:
      secretName: my-bridge-user
      password: my-bridge-password-key
  # ...

3.5.4.1.3. SASL-based PLAIN authentication

To configure Kafka Bridge to use SASL-based PLAIN authentication, set the type property to plain. This authentication mechanism requires a username and password.

Warning

The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled.

  • Specify the username in the username property.
  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name the Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Important

Do not specify the actual password in the password field.

An example showing SASL based PLAIN client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: plain
    username: my-bridge-user
    passwordSecret:
      secretName: my-bridge-user
      password: my-bridge-password-key
  # ...

3.5.4.2. Configuring TLS client authentication in Kafka Bridge

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • If they exist, the name of the Secret with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the keys used for authentication in a file and create the Secret.

    Note

    Secrets created by the User Operator may be used.

    oc create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key
  2. Edit the authentication property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaBridge
    metadata:
      name: my-bridge
    spec:
      # ...
      authentication:
      type: tls
      certificateAndKey:
        secretName: my-secret
        certificate: my-public.crt
        key: my-private.key
      # ...
  3. Create or update the resource.

    oc apply -f your-file

3.5.4.3. Configuring SCRAM-SHA-512 authentication in Kafka Bridge

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator
  • Username of the user which should be used for authentication
  • If they exist, the name of the Secret with the password used for authentication and the key under which the password is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare a file with the password used in authentication and create the Secret.

    Note

    Secrets created by the User Operator may be used.

    echo -n '<password>' > <my-password.txt>
    oc create secret generic <my-secret> --from-file=<my-password.txt>
  2. Edit the authentication property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaBridge
    metadata:
      name: my-bridge
    spec:
      # ...
      authentication:
        type: scram-sha-512
        username: _<my-username>_
        passwordSecret:
          secretName: _<my-secret>_
          password: _<my-password.txt>_
      # ...
  3. Create or update the resource.

    oc apply -f your-file

3.5.5. Kafka Bridge configuration

AMQ Streams allows you to customize the configuration of Apache Kafka Bridge nodes by editing certain options listed in Apache Kafka configuration documentation for consumers and Apache Kafka configuration documentation for producers.

Configuration options that can be configured relate to:

  • Kafka cluster bootstrap address
  • Security (Encryption, Authentication, and Authorization)
  • Consumer configuration
  • Producer configuration
  • HTTP configuration

3.5.5.1. Kafka Bridge Consumer configuration

Kafka Bridge consumer is configured using the properties in KafkaBridge.spec.consumer. This property contains the Kafka Bridge consumer configuration options as keys. The values can be one of the following JSON types:

  • String
  • Number
  • Boolean

Users can specify and configure the options listed in the Apache Kafka configuration documentation for consumers with the exception of those options which are managed directly by AMQ Streams. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.
  • sasl.
  • security.
  • bootstrap.servers
  • group.id

When one of the forbidden options is present in the config property, it will be ignored and a warning message will be printed to the Custer Operator log file. All other options will be passed to Kafka

Important

The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Bridge cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaBridge.spec.consumer.config object, then the Cluster Operator can roll out the new configuration to all Kafka Bridge nodes.

Example Kafka Bridge consumer configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  consumer:
    config:
      auto.offset.reset: earliest
      enable.auto.commit: true
  # ...

3.5.5.2. Kafka Bridge Producer configuration

Kafka Bridge producer is configured using the properties in KafkaBridge.spec.producer. This property contains the Kafka Bridge producer configuration options as keys. The values can be one of the following JSON types:

  • String
  • Number
  • Boolean

Users can specify and configure the options listed in the Apache Kafka configuration documentation for producers with the exception of those options which are managed directly by AMQ Streams. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.
  • sasl.
  • security.
  • bootstrap.servers
Important

The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Bridge cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaBridge.spec.producer.config object, then the Cluster Operator can roll out the new configuration to all Kafka Bridge nodes.

Example Kafka Bridge producer configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  producer:
    config:
      acks: 1
      delivery.timeout.ms: 300000
  # ...

3.5.5.3. Kafka Bridge HTTP configuration

Kafka Bridge HTTP configuration is set using the properties in KafkaBridge.spec.http. This property contains the Kafka Bridge HTTP configuration options.

  • port

Example Kafka Bridge HTTP configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  http:
    port: 8080
  # ...

3.5.5.4. Configuring Kafka Bridge

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the kafka, http, consumer or producer property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaBridge
    metadata:
      name: my-bridge
    spec:
      # ...
      bootstrapServers: my-cluster-kafka:9092
      http:
        port: 8080
      consumer:
        config:
          auto.offset.reset: earliest
      producer:
        config:
          delivery.timeout.ms: 300000
      # ...
  2. Create or update the resource.

    oc apply -f your-file

3.5.6. CPU and memory resources

For every deployed container, AMQ Streams allows you to request specific resources and define the maximum consumption of those resources.

AMQ Streams supports two types of resources:

  • CPU
  • Memory

AMQ Streams uses the OpenShift syntax for specifying CPU and memory resources.

3.5.6.1. Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.entityOperator.tlsSidecar
  • Kafka.spec.KafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaBridge.spec

Additional resources

3.5.6.1.1. Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important

If the resource request is for more than the available free resources in the OpenShift cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by AMQ Streams:

  • cpu
  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources

# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...

3.5.6.1.2. Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by AMQ Streams:

  • cpu
  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration

# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...

3.5.6.1.3. Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).
  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units

# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...

Note

The computing power of 1 CPU core may differ depending on the platform where OpenShift is deployed.

Additional resources

3.5.6.1.4. Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.
  • To specify memory in gigabytes, use the G suffix. For example 1G.
  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.
  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units

# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...

Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

3.5.6.2. Configuring resource requests and limits

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the resources property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: "8"
            memory: 64Gi
          limits:
            cpu: "12"
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

3.5.7. Logging

This section provides information on loggers and how to configure log levels.

You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.

3.5.7.1. Kafka Bridge loggers

Kafka Bridge has configurable loggers for each OpenAPI operation: Loggers are formatted as follows:

log4j.logger.http.openapi.operation.<operation-id>

Where <operation-id> is the identifier of the specific operation. Following is the list of operations defined by the OpenAPI specification:

  • createConsumer
  • deleteConsumer
  • subscribe
  • unsubscribe
  • poll
  • assign
  • commit
  • send
  • sendToPartition
  • seekToBeginning
  • seekToEnd
  • seek
  • healthy
  • ready
  • openapi

3.5.7.2. Specifying inline logging

Procedure

  1. Edit the YAML file to specify the loggers and logging level for the required components.

    For example, the logging level here is set to INFO:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaBridge
    spec:
      # ...
      logging:
        type: inline
        loggers:
          logger.name: "INFO"
      # ...

    You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    For more information about the log levels, see the log4j manual.

  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

3.5.7.3. Specifying an external ConfigMap for logging

Procedure

  1. Edit the YAML file to specify the name of the ConfigMap to use for the required components. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaBridge
    spec:
      # ...
      logging:
        type: external
        name: customConfigMap
      # ...

    Remember to place your custom ConfigMap under the log4j.properties or log4j2.properties key.

  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see Section 3.1.17.1, “JVM configuration”

3.5.8. JVM Options

The following components of AMQ Streams run inside a Virtual Machine (VM):

  • Apache Kafka
  • Apache Zookeeper
  • Apache Kafka Connect
  • Apache Kafka Mirror Maker
  • AMQ Streams Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. AMQ Streams allows you to configure some of these options.

3.5.8.1. JVM configuration

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMaker.spec
  • KafkaBridge.spec

Only a selected subset of available JVM options can be configured. The following options are supported:

-Xms and -Xmx

-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.

Note

The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the OpenShift convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:

  • If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
  • If there is no memory limit then the JVM’s minimum memory will be set to 128M and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.
  • If -Xmx is set without also setting an appropriate OpenShift memory limit, it is possible that the container will be killed should the OpenShift node experience memory pressure (from other Pods running on it).
  • If -Xmx is set without also setting an appropriate OpenShift memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • set the memory request and the memory limit to the same value,
  • use a memory request that is at least 4.5 × the -Xmx,
  • consider setting -Xms to the same value as -Xmx.
Important

Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.

Example fragment configuring -Xmx and -Xms

# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and Zookeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server

# ...
jvmOptions:
  "-server": true
# ...

Note

When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.

-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object

jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true
    "UseParNewGC": false

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note

When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.

3.5.8.1.1. Garbage collector logging

The jvmOptions section also allows you to enable and disable garbage collector (GC) logging. GC logging is enabled by default. To disable it, set the gcLoggingEnabled property as follows:

Example of disabling GC logging

# ...
jvmOptions:
  gcLoggingEnabled: false
# ...

3.5.8.2. Configuring JVM options

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.5.9. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, OpenShift assumes that the application is not healthy and attempts to fix it.

OpenShift supports two types of Healthcheck probes:

  • Liveness probes
  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in AMQ Streams components.

Users can configure selected options for liveness and readiness probes.

3.5.9.1. Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.KafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMaker.spec
  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds
  • timeoutSeconds
  • periodSeconds
  • successThreshold
  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Section C.34, “Probe schema reference”.

An example of liveness and readiness probe configuration

# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...

3.5.9.2. Configuring healthchecks

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        readinessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        livenessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.5.10. Container images

AMQ Streams allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such a case, you should either copy the AMQ Streams images or build them from the source. If the configured image is not compatible with AMQ Streams images, it might not work properly.

3.5.10.1. Container image configurations

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka
  • Kafka.spec.kafka.tlsSidecar
  • Kafka.spec.zookeeper
  • Kafka.spec.zookeeper.tlsSidecar
  • Kafka.spec.entityOperator.topicOperator
  • Kafka.spec.entityOperator.userOperator
  • Kafka.spec.entityOperator.tlsSidecar
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaBridge.spec
3.5.10.1.1. Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because AMQ Streams supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.
  • If Kafka.spec.kafka.image is given but Kafka.spec.kafka.version is not then the given image will be used and the version will be assumed to be the Cluster Operator’s default Kafka version.
  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.
  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.
Warning

It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.

3.5.10.1.2. Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Kafka broker TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Zookeeper nodes:

    1. Container image specified in the STRIMZI_DEFAULT_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Zookeeper node TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-operator:1.3.0 container image.
  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-operator:1.3.0 container image.
  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Kafka Connect:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.
    2. registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0 container image.
Warning

Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such case, you should either copy the AMQ Streams images or build them from source. In case the configured image is not compatible with AMQ Streams images, it might not work properly.

Example of container image configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

3.5.10.2. Configuring container images

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.5.11. Configuring pod scheduling

Important

When two applications are scheduled to the same OpenShift node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.

3.5.11.1. Scheduling pods based on other applications

3.5.11.1.1. Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

3.5.11.1.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.5.11.1.3. Configuring pod anti-affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            affinity:
              podAntiAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  - labelSelector:
                      matchExpressions:
                        - key: application
                          operator: In
                          values:
                            - postgresql
                            - mongodb
                    topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.5.11.2. Scheduling pods to specific nodes

3.5.11.2.1. Node scheduling

The OpenShift cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of AMQ Streams components to use the right nodes.

OpenShift uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

3.5.11.2.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.5.11.2.3. Configuring node affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Label the nodes where AMQ Streams components should be scheduled.

    This can be done using oc label:

    oc label node your-node node-type=fast-network

    Alternatively, some of the existing labels might be reused.

  2. Edit the affinity property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            affinity:
              nodeAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  nodeSelectorTerms:
                    - matchExpressions:
                      - key: node-type
                        operator: In
                        values:
                        - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.5.11.3. Using dedicated nodes

3.5.11.3.1. Dedicated nodes

Cluster administrators can mark selected OpenShift nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

3.5.11.3.2. Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity
  • Node affinity

The format of the affinity property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

3.5.11.3.3. Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod
  • Kafka.spec.zookeeper.template.pod
  • Kafka.spec.entityOperator.template.pod
  • KafkaConnect.spec.template.pod
  • KafkaConnectS2I.spec.template.pod
  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.

3.5.11.3.4. Setting up dedicated nodes and scheduling pods on them

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.
  2. Make sure there are no workloads scheduled on these nodes.
  3. Set the taints on the selected nodes:

    This can be done using oc adm taint:

    oc adm taint node your-node dedicated=Kafka:NoSchedule
  4. Additionally, add a label to the selected nodes as well.

    This can be done using oc label:

    oc label node your-node dedicated=Kafka
  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      kafka:
        # ...
        template:
          pod:
            tolerations:
              - key: "dedicated"
                operator: "Equal"
                value: "Kafka"
                effect: "NoSchedule"
            affinity:
              nodeAffinity:
                requiredDuringSchedulingIgnoredDuringExecution:
                  nodeSelectorTerms:
                  - matchExpressions:
                    - key: dedicated
                      operator: In
                      values:
                      - Kafka
        # ...
      zookeeper:
        # ...
  6. Create or update the resource.

    This can be done using oc apply:

    oc apply -f your-file

3.5.12. List of resources created as part of Kafka Bridge cluster

The following resources are created by the Cluster Operator in the OpenShift cluster:

bridge-cluster-name-bridge
Deployment which is in charge to create the Kafka Bridge worker node pods.
bridge-cluster-name-bridge-service
Service which exposes the REST interface of the Kafka Bridge cluster.
bridge-cluster-name-bridge-config
ConfigMap which contains the Kafka Bridge ancillary configuration and is mounted as a volume by the Kafka broker pods.
bridge-cluster-name-bridge
Pod Disruption Budget configured for the Kafka Bridge worker nodes.

3.6. Using OAuth 2.0 token based authentication

AMQ Streams supports the use of OAuth 2.0 authentication using the SASL OAUTHBEARER mechanism.

OAuth 2.0 enables standardized token based authentication and authorization between applications, using a central authorization server to issue tokens that grant limited access to resources.

OAuth 2.0 is currently only supported for authentication, with no authorization support. However, OAuth 2.0 authentication can be used in conjunction with ACL-based Kafka authorization.

Using OAuth 2.0 token based authentication, application clients can access resources on application servers (called ‘resource servers’) without exposing account credentials. The client passes an access token as a means of authenticating, which application servers can also use to find more information about the level of access granted. The authorization server handles the granting of access and inquiries about access.

In the context of AMQ Streams:

  • Kafka brokers act as resource servers
  • Kafka clients act as resource clients

The brokers and clients communicate with the OAuth 2.0 authorization server, as necessary, to obtain or validate access tokens.

For a deployment of AMQ Streams, OAuth 2.0 integration provides:

  • Server-side OAuth 2.0 support for Kafka brokers
  • Client-side OAuth 2.0 support for Kafka Mirror Maker, Kafka Connect and the Kafka Bridge

Additional resources

3.6.1. OAuth 2.0 authentication mechanism

Support for OAuth 2.0 is based on the Kafka SASL OAUTHBEARER mechanism, which is used to establish authenticated sessions with a Kafka broker.

A Kafka client initiates a session with the Kafka broker using the SASL OAUTHBEARER mechanism for credentials exchange, where credentials take the form of an access token.

Kafka brokers and clients need to be configured to use OAuth 2.0.

Kafka broker configuration

The Kafka broker must be configured to validate the token received during session initiation. The recommended approach is to create a client definition in an authorization server with:

  • Client ID of kafka-broker
  • Client ID and secret as the authentication mechanism

Kafka client configuration

A Kafka client is configured with either:

  • Credentials required to obtain a valid access token from an authorization server
  • A valid long-lived access token, obtained using tools provided by an authorization server

Credentials are never sent to the Kafka broker. The only information ever sent to the Kafka broker is an access token. When a client obtains an access token, no further communication with the authorization server is needed.

The simplest mechanism, which requires no additional usage of authorization server tools, is authentication with a client ID and secret. Using a long-lived access token, or a long-lived refresh token, is more complex.

Note

If you are using long-lived access tokens, you can set policy in the authorization server to increase the maximum lifetime of the token.

If the Kafka client is not configured with an access token directly, the client exchanges credentials for an access token during Kafka session initiation by contacting the authorization server. The Kafka client uses one of two mechanisms:

  • Client id and secret
  • Client id, refresh token, and (optionally) a secret

3.6.2. OAuth 2.0 client authentication flow

In this section, we explain and visualize the communication flow between Kafka client, Kafka broker, and authorization server during Kafka session initiation. The flow depends on the client and server configuration.

When a Kafka client sends an access token as credentials to a Kafka broker, the token needs to be validated.

Depending on the authorization server used, and the configuration options available, you may prefer to use:

  • Fast local token validation based on JWT signature checking and local token introspection, without contacting the authorization server
  • An OAuth 2.0 introspection endpoint provided by the authorization server

Using fast local token validation requires the authorization server to provide a JWKS endpoint with public certificates that are used to validate signatures on the tokens.

Note

An authorization server might only allow the use of opaque access tokens, which means that local token validation is not possible.

Another option is to use an OAuth 2.0 introspection endpoint on the authorization server. Each time a Kafka broker connection is established, the broker sends the access token it receives to the authorization server, and a response confirming whether or not the token is valid is returned.

Kafka client credentials can also be configured for:

  • Direct local access using a previously generated long-lived access token
  • Contact with the authorization server for a new access token to be issued and sent to the Kafka broker

3.6.2.1. Example client authentication flows

Here you can see the communication flows, for different configurations of Kafka clients and brokers, during Kafka session authentication.

Client using client ID and secret, with broker delegating validation to authorization server

Client using client ID and secret with broker delegating validation to authorization server

  1. Kafka client requests access token from authorization server, using client ID and secret, and optionally a refresh token.
  2. Authorization server generates a new access token.
  3. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
  4. Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.
  5. Kafka client session is established if the token is valid.

Client using client ID and secret, with broker performing fast local token validation

Client using client ID and secret with broker performing fast local token validation

  1. Kafka client authenticates with authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token.
  2. Authorization server generates a new access token.
  3. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
  4. Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.

Client using long-lived access token, with broker delegating validation to authorization server

Client using long-lived access token with broker delegating validation to authorization server

  1. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.
  3. Kafka client session is established if the token is valid.

Client using long-lived access token, with broker performing fast local validation

Client using long-lived access token with broker performing fast local validation

  1. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. Kafka broker validates the access token locally using JWT token signature check, and local token introspection.
Warning

Fast local JWT token signature validation is suitable only for short-lived tokens as there is no check with the authorization server if a token has been revoked. Token expiration is written into the token, but revocation can happen at any time, so cannot be accounted for without contacting the authorization server. Any issued token would be considered valid until it expires.

3.6.3. Configuring OAuth 2.0 authentication

OAuth 2.0 is used for interaction between Kafka clients and AMQ Streams components.

In order to use OAuth 2.0 for AMQ Streams, you must:

3.6.3.1. Configuring Red Hat Single Sign-On as an OAuth 2.0 authorization server

This procedure describes how to deploy Red Hat Single Sign-On as an authorization server and configure it for integration with AMQ Streams.

The authorization server provides a central point for authentication and authorization, and management of users, clients, and permissions. Red Hat Single Sign-On has a concept of realms where a realm represents a separate set of users, clients, permissions, and other configuration. You can use a default master realm, or create a new one. Each realm exposes its own OAuth 2.0 endpoints, which means that application clients and application servers all need to use the same realm.

To use OAuth 2.0 with AMQ Streams, you use a deployment of Red Hat Single Sign-On to create and manage authentication realms.

Note

If you already have Red Hat Single Sign-On deployed, you can skip the deployment step and use your current deployment.

Before you begin

You will need to be familiar with using Red Hat Single Sign-On.

For deployment and administration instructions, see:

Prerequisites

  • AMQ Streams and Kafka is running

For the Red Hat Single Sign-On deployment:

Procedure

  1. Deploy Red Hat Single Sign-On to your OpenShift cluster.

    Check the progress of the deployment in your OpenShift web console.

  2. Log in to the Red Hat Single Sign-On Admin Console to create the OAuth 2.0 policies for AMQ Streams.

    Login details are provided when you deploy Red Hat Single Sign-On.

  3. Create and enable a realm.

    You can use an existing master realm.

  4. Adjust the session and token timeouts for the realm, if required.
  5. Create a client called kafka-broker.
  6. From the Settings tab, set:

    • Access Type to Confidential
    • Standard Flow Enabled to OFF to disable web login for this client
    • Service Accounts Enabled to ON to allow this client to authenticate in its own name
  7. Click Save before continuing.
  8. From the Credentials tab, take a note of the secret for using in your AMQ Streams Kafka cluster configuration.
  9. Repeat the client creation steps for any application client that will connect to your Kafka brokers.

    Create a definition for each new client.

    You will use the names as client IDs in your configuration.

What to do next

After deploying and configuring the authorization server, configure the Kafka brokers to use OAuth 2.0.

3.6.3.2. Configuring OAuth 2.0 support for Kafka brokers

This procedure describes how to configure Kafka brokers so that the broker listeners are enabled to use OAuth 2.0 authentication using an authorization server.

If the authorization server is using certificates signed by the trusted CA and matching the OAuth 2.0 server hostname, TLS connection works using the default settings. Otherwise, you have two connection options for your external listener configuration when delegating token validation to the authorization server:

  1. TLS connection to the authorization server with trusted certificates
  2. Direct connection using an introspection endpoint configuration

Both options are described in this procedure.

Before you start

For more information on the configuration and authentication of Kafka broker listeners, see:

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed

Procedure

  1. Update the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    oc edit kafka my-cluster
  2. Configure the Kafka broker listeners configuration.

    The configuration for each type of listener does not have to be the same, as they are independent.

    For example:

    #...
       authentication:
         type: oauth 1
         validIssuerUri: <https://<authorization-server-address>/auth/realms/master> 2
         jwksEndpointUri: <https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/certs> 3
         userNameClaim: preferred_username 4
    
    #external configuration
     external:
       type: loadbalancer
    
       //Option 1
       authentication: 5
         type: oauth
         validIssuerUri: <https://<authorization-server-address>/auth/realms/external>
         jwksEndpointUri: <https://<authorization-server-address>/auth/realms/external/protocol/openid-connect/certs>
         userNameClaim: preferred_username
         tlsTrustedCertificates: 6
         - secretName: oauth-server-cert
           certificate: ca.crt
       disableTlsHostnameVerification: true 7
    
       //Option 2
       authentication: 8
         type: oauth
         validIssuerUri: <https://<authorization-server-address>/auth/realms/external>
         introspectionEndpointUri: <https://<authorization-server-address>/auth/realms/external/protocol/openid-connect/token/introspect>
         clientId: kafka-broker
         clientSecret:
           secretName: my-cluster-oauth
           key: clientSecret
    1
    Listener type set to oauth.
    2
    URI of the token issuer used for authentication.
    3
    URI of the JWKS certificate endpoint used for local JWT validation.
    4
    The user name profile claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authentication flow and the authorization server used.
    5
    OPTION 1: TLS connection to the authorization server.
    6
    (Optional) Trusted certificates for TLS connection to the authorization server.
    7
    Enable TLS hostname verification. Default is false.
    8
    OPTION 2: Introspection endpoint to connect directly to the authorization server.
  3. Save and exit the editor, then wait for rolling updates to complete.
  4. Check the update in the logs or by watching the pod state transitions:

    oc logs -f ${POD_NAME} -c ${CONTAINER_NAME}
    oc get po -w

    The rolling update configures the brokers to use OAuth 2.0 authentication.

3.6.3.3. Configuring Kafka Java clients to use OAuth 2.0

This procedure describes how to configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers.

Add a client callback plugin to your pom.xml file, and configure the system properties.

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
  • Kafka brokers are configured for OAuth 2.0

Procedure

  1. Add the client library with OAuth 2.0 support to the pom.xml file for the Kafka client:

    <dependency>
     <groupId>io.strimzi</groupId>
     <artifactId>kafka-oauth-client</artifactId>
     <version>0.1.0.redhat-00002</version>
    </dependency>
  2. Configure the system properties for the callback:

    For example:

    System.setProperty(ClientConfig.OAUTH_TOKEN_ENDPOINT_URI, “https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/token”); 1
    System.setProperty(ClientConfig.OAUTH_CLIENT_ID, "<client-name>"); 2
    System.setProperty(ClientConfig.OAUTH_CLIENT_SECRET, "<client-secret>"); 3
    1
    URI of the authorization server token endpoint.
    2
    Client ID, which is the name used when creating the client in the authorization server.
    3
    Client secret created when creating the client in the authorization server.
  3. Enable the SASL OAUTHBEARER mechanism on a TLS encrypted connection in the Kafka client configuration:

    For example:

    props.put("sasl.jaas.config", "org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required;");
    props.put("security.protocol", "SASL_SSL"); 1
    props.put("sasl.mechanism", "OAUTHBEARER");
    props.put("sasl.login.callback.handler.class", "io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler");
    1
    Here we use SASL_SSL for use over TLS connections. Use SASL_PLAINTEXT over unencrypted connections.
  4. Verify that the Kafka client can access the Kafka brokers.

3.6.3.4. Configuring OAuth for Kafka components

This procedure describes how to configure Kafka components to use OAuth 2.0 authentication using an authorization server.

You can configure authentication for:

  • Kafka Connect
  • Kafka Mirror Maker
  • Kafka Bridge

In this scenario, the Kafka component and the authorization server are running in the same cluster.

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
  • Kafka brokers are configured for OAuth 2.0

Procedure

  1. Create a client secret and mount it to the component as an environment variable.

    For example, here we are creating a client Secret for the Kafka Bridge:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Secret
    metadata:
     name: my-bridge-oauth
    type: Opaque
    data:
     clientSecret: MGQ1OTRmMzYtZTllZS00MDY2LWI5OGEtMTM5MzM2NjdlZjQw 1
    1
    The clientSecret key must be in base64 format.
  2. Create or edit the resource for the Kafka component so that OAuth 2.0 authentication is configured for the authentication property.

    For example, here OAuth 2.0 is assigned to the Kafka Bridge client:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaBridge
    metadata:
      name: my-bridge
    spec:
      # ...
      authentication:
       type: oauth 1
       tokenEndpointUri: https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/token 2
       clientId: kafka-bridge
       clientSecret:
         secretName: my-bridge-oauth
         key: clientSecret
       tlsTrustedCertificates: 3
         - secretName: oauth-server-cert
         certificate: tls.crt
    1
    Authentication type set to oauth.
    2
    URI of the token endpoint for authentication.
    3
    Trusted certificates for TLS connection to the authorization server.
  3. Apply the changes to the deployment of your Kafka resource.

    oc apply -f your-file
  4. Check the update in the logs or by watching the pod state transitions:

    oc logs -f ${POD_NAME} -c ${CONTAINER_NAME}
    oc get pod -w

    The rolling updates configure the component for interaction with Kafka brokers using OAuth 2.0 authentication.

3.7. Customizing deployments

AMQ Streams creates several OpenShift resources, such as Deployments, StatefulSets, Pods, and Services, which are managed by OpenShift operators. Only the operator that is responsible for managing a particular OpenShift resource can change that resource. If you try to manually change an operator-managed OpenShift resource, the operator will revert your changes back.

However, changing an operator-managed OpenShift resource can be useful if you want to perform certain tasks, such as:

  • Adding custom labels or annotations that control how Pods are treated by Istio or other services;
  • Managing how Loadbalancer-type Services are created by the cluster.

You can make these types of changes using the template property in the AMQ Streams custom resources.

3.7.1. Template properties

You can use the template property to configure aspects of the resource creation process. You can include it in the following resources and properties:

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper
  • Kafka.spec.entityOperator
  • Kafka.spec.kafkaExporter
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMakerSpec
  • KafkaBridge.spec

In the following example, the template property is used to modify the labels in a Kafka broker’s StatefulSet:

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
  labels:
    app: my-cluster
spec:
  kafka:
    # ...
    template:
      statefulset:
        metadata:
          labels:
            mylabel: myvalue
    # ...

3.7.1.1. Supported template properties for a Kafka cluster

statefulset
Configures the StatefulSet used by the Kafka broker.
pod
Configures the Kafka broker Pods created by the StatefulSet.
bootstrapService
Configures the bootstrap service used by clients running within OpenShift to connect to the Kafka broker.
brokersService
Configures the headless service.
externalBootstrapService
Configures the bootstrap service used by clients connecting to Kafka brokers from outside of OpenShift.
perPodService
Configures the per-Pod services used by clients connecting to the Kafka broker from outside OpenShift to access individual brokers.
externalBootstrapRoute
Configures the bootstrap route used by clients connecting to the Kafka brokers from outside of OpenShift using OpenShift Routes.
perPodRoute
Configures the per-Pod routes used by clients connecting to the Kafka broker from outside OpenShift to access individual brokers using OpenShift Routes.
podDisruptionBudget
Configures the Pod Disruption Budget for Kafka broker StatefulSet.
kafkaContainer
Configures the container used to run the Kafka broker, including custom environment variables.
tlsSidecarContainer
Configures the TLS sidecar container, including custom environment variables.
initContainer
Configures the container used to initialize the brokers.
persistentVolumeClaim
Configures the metadata of the Kafka PersistentVolumeClaims.

3.7.1.2. Supported template properties for a Zookeeper cluster

statefulset
Configures the Zookeeper StatefulSet.
pod
Configures the Zookeeper Pods created by the StatefulSet.
clientsService
Configures the service used by clients to access Zookeeper.
nodesService
Configures the headless service.
podDisruptionBudget
Configures the Pod Disruption Budget for Zookeeper StatefulSet.
zookeeperContainer
Configures the container used to run the Zookeeper Node, including custom environment variables.
tlsSidecarContainer
Configures the TLS sidecar container, including custom environment variables.
persistentVolumeClaim
Configures the metadata of the Zookeeper PersistentVolumeClaims.

3.7.1.3. Supported template properties for Entity Operator

deployment
Configures the Deployment used by the Entity Operator.
pod
Configures the Entity Operator Pod created by the Deployment.
topicOperatorContainer
Configures the container used to run the Topic Operator, including custom environment variables.
userOperatorContainer
Configures the container used to run the User Operator, including custom environment variables.
tlsSidecarContainer
Configures the TLS sidecar container, including custom environment variables.

3.7.1.4. Supported template properties for Kafka Exporter

deployment
Configures the Deployment used by Kafka Exporter.
pod
Configures the Kafka Exporter Pod created by the Deployment.
services
Configures the Kafka Exporter services.
container
Configures the container used to run Kafka Exporter, including custom environment variables.

3.7.1.5. Supported template properties for Kafka Connect and Kafka Connect with Source2Image support

deployment
Configures the Kafka Connect Deployment.
pod
Configures the Kafka Connect Pods created by the Deployment.
apiService
Configures the service used by the Kafka Connect REST API.
podDisruptionBudget
Configures the Pod Disruption Budget for Kafka Connect Deployment.
connectContainer
Configures the container used to run Kafka Connect, including custom environment variables.

3.7.1.6. Supported template properties for Kafka Mirror Maker

deployment
Configures the Kafka Mirror Maker Deployment.
pod
Configures the Kafka Mirror Maker Pods created by the Deployment.
podDisruptionBudget
Configures the Pod Disruption Budget for Kafka Mirror Maker Deployment.
mirrorMakerContainer
Configures the container used to run Kafka Mirror Maker, including custom environment variables.

3.7.2. Labels and Annotations

For every resource, you can configure additional Labels and Annotations. Labels and Annotations are used to identify and organize resources, and are configured in the metadata property.

For example:

# ...
template:
    statefulset:
        metadata:
            labels:
                label1: value1
                label2: value2
            annotations:
                annotation1: value1
                annotation2: value2
# ...

The labels and annotations fields can contain any labels or annotations that do not contain the reserved string strimzi.io. Labels and annotations containing strimzi.io are used internally by AMQ Streams and cannot be configured.

Note

The metadata property is not applicable to container templates, such as the kafkaContainer.

3.7.3. Customizing Pods

In addition to Labels and Annotations, you can customize some other fields on Pods. These fields are described in the following table and affect how the Pod is created.

FieldDescription

terminationGracePeriodSeconds

Defines the period of time, in seconds, by which the Pod must have terminated gracefully. After the grace period, the Pod and its containers are forcefully terminated (killed). The default value is 30 seconds.

NOTE: You might need to increase the grace period for very large Kafka clusters, so that the Kafka brokers have enough time to transfer their work to another broker before they are terminated.

imagePullSecrets

Defines a list of references to OpenShift Secrets that can be used for pulling container images from private repositories. For more information about how to create a Secret with the credentials, see Pull an Image from a Private Registry.

NOTE: When the STRIMZI_IMAGE_PULL_SECRETS environment variable in Cluster Operator and the imagePullSecrets option are specified, only the imagePullSecrets variable is used. The STRIMZI_IMAGE_PULL_SECRETS variable is ignored.

securityContext

Configures pod-level security attributes for containers running as part of a given Pod. For more information about configuring SecurityContext, see Configure a Security Context for a Pod or Container.

priorityClassName

Configures the name of the Priority Class which will be used for given a Pod. For more information about Priority Classes, see Pod Priority and Preemption.

These fields are effective on each type of cluster (Kafka and Zookeeper; Kafka Connect and Kafka Connect with S2I support; and Kafka Mirror Maker).

The following example shows these customized fields on a template property:

# ...
template:
  pod:
    metadata:
      labels:
        label1: value1
    imagePullSecrets:
      - name: my-docker-credentials
    securityContext:
      runAsUser: 1000001
      fsGroup: 0
    terminationGracePeriodSeconds: 120
# ...

Additional resources

3.7.4. Customizing containers with environment variables

You can set custom environment variables for a container by using the relevant template container property. The following table lists the AMQ Streams containers and the relevant template configuration property (defined under spec) for each custom resource.

Table 3.1. Table Container environment variable properties

AMQ Streams ElementContainerConfiguration property

Kafka

Kafka Broker

kafka.template.kafkaContainer.env

Kafka

Kafka Broker TLS Sidecar

kafka.template.tlsSidecarContainer.env

Kafka

Kafka Initialization

kafka.template.initContainer.env

Kafka

Zookeeper Node

zookeeper.template.zookeeperContainer.env

Kafka

Zookeeper TLS Sidecar

zookeeper.template.tlsSidecarContainer.env

Kafka

Topic Operator

entityOperator.template.topicOperatorContainer.env

Kafka

User Operator

entityOperator.template.userOperatorContainer.env

Kafka

Entity Operator TLS Sidecar

entityOperator.template.tlsSidecarContainer.env

KafkaConnect

Connect and ConnectS2I

template.connectContainer.env

KafkaMirrorMaker

Mirror Maker

template.mirrorMakerContainer.env

KafkaBridge

Bridge

template.bridgeContainer.env

The environment variables are defined under the env property as a list of objects with name and value fields. The following example shows two custom environment variables set for the Kafka broker containers:

# ...
kind: Kafka
spec:
    kafka:
        template:
            kafkaContainer:
                env:
                    - name: TEST_ENV_1
                      value: test.env.one
                    - name: TEST_ENV_2
                      value: test.env.two
# ...

Environment variables prefixed with KAFKA_ are internal to AMQ Streams and should be avoided. If you set a custom environment variable that is already in use by AMQ Streams, it is ignored and a warning is recorded in the log.

Additional resources

3.7.5. Customizing the image pull policy

AMQ Streams allows you to customize the image pull policy for containers in all pods deployed by the Cluster Operator. The image pull policy is configured using the environment variable STRIMZI_IMAGE_PULL_POLICY in the Cluster Operator deployment. The STRIMZI_IMAGE_PULL_POLICY environment variable can be set to three different values:

Always
Container images are pulled from the registry every time the pod is started or restarted.
IfNotPresent
Container images are pulled from the registry only when they were not pulled before.
Never
Container images are never pulled from the registry.

The image pull policy can be currently customized only for all Kafka, Kafka Connect, and Kafka Mirror Maker clusters at once. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka Mirror Maker clusters.

Additional resources

3.7.6. Customizing Pod Disruption Budgets

AMQ Streams creates a pod disruption budget for every new StatefulSet or Deployment. By default, these pod disruption budgets only allow a single pod to be unavailable at a given time by setting the maxUnavailable value in the`PodDisruptionBudget.spec` resource to 1. You can change the amount of unavailable pods allowed by changing the default value of maxUnavailable in the pod disruption budget template. This template applies to each type of cluster (Kafka and Zookeeper; Kafka Connect and Kafka Connect with S2I support; and Kafka Mirror Maker).

The following example shows customized podDisruptionBudget fields on a template property:

# ...
template:
    podDisruptionBudget:
        metadata:
            labels:
                key1: label1
                key2: label2
            annotations:
                key1: label1
                key2: label2
        maxUnavailable: 1
# ...

Additional resources

3.7.7. Customizing deployments

This procedure describes how to customize Labels of a Kafka cluster.

Prerequisites

  • An OpenShift cluster.
  • A running Cluster Operator.

Procedure

  1. Edit the template property in the Kafka, KafkaConnect, KafkaConnectS2I, or KafkaMirrorMaker resource. For example, to modify the labels for the Kafka broker StatefulSet, use:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
      labels:
        app: my-cluster
    spec:
      kafka:
        # ...
        template:
          statefulset:
            metadata:
              labels:
                mylabel: myvalue
        # ...
  2. Create or update the resource.

    Use oc apply:

    oc apply -f your-file

    Alternatively, use oc edit:

    oc edit Resource ClusterName

Chapter 4. Operators

4.1. Cluster Operator

4.1.1. Overview of the Cluster Operator component

AMQ Streams uses the Cluster Operator to deploy and manage clusters for:

  • Kafka (including Zookeeper, Entity Operator and Kafka Exporter)
  • Kafka Connect
  • Kafka Mirror Maker
  • Kafka Bridge

Custom resources are used to deploy the clusters.

For example, to deploy a Kafka cluster:

  • A Kafka resource with the cluster configuration is created within the OpenShift cluster.
  • The Cluster Operator deploys a corresponding Kafka cluster, based on what is declared in the Kafka resource.

The Cluster Operator can also deploy (through Entity Operator configuration of the Kafka resource):

  • A Topic Operator to provide operator-style topic management through KafkaTopic custom resources
  • A User Operator to provide operator-style user management through KafkaUser custom resources

For more information on the configuration options supported by the Kafka resource, see Section 3.1, “Kafka cluster configuration”.

Note

On OpenShift, a Kafka Connect deployment can incorporate a Source2Image feature to provides a convenient way to include connectors.

Example architecture for the Cluster Operator

Cluster Operator

4.1.2. Watch options for a Cluster Operator deployment

When the Cluster Operator is running, it starts to watch for updates of Kafka resources.

Depending on the deployment, the Cluster Operator can watch Kafka resources from:

Note

AMQ Streams provides example YAML files to make the deployment process easier.

The Cluster Operator watches the following resources:

  • Kafka for the Kafka cluster.
  • KafkaConnect for the Kafka Connect cluster.
  • KafkaConnectS2I for the Kafka Connect cluster with Source2Image support.
  • KafkaMirrorMaker for the Kafka Mirror Maker instance.
  • KafkaBridge for the Kafka Bridge instance

When one of these resources is created in the OpenShift cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary OpenShift resources, such as StatefulSets, Services and ConfigMaps.

Each time a Kafka resource is updated, the operator performs corresponding updates on the OpenShift resources that make up the cluster for the resource.

Resources are either patched or deleted, and then recreated in order to make the cluster for the resource reflect the desired state of the cluster. This operation might cause a rolling update that might lead to service disruption.

When a resource is deleted, the operator undeploys the cluster and deletes all related OpenShift resources.

4.1.3. Deploying the Cluster Operator to watch a single namespace

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.
  • Modify the installation files according to the namespace the Cluster Operator is going to be installed in.

    On Linux, use:

    sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

Procedure

  • Deploy the Cluster Operator:

    oc apply -f install/cluster-operator -n my-namespace

4.1.4. Deploying the Cluster Operator to watch multiple namespaces

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.
  • Edit the installation files according to the namespace the Cluster Operator is going to be installed in.

    On Linux, use:

    sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

Procedure

  1. Edit the file install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml and in the environment variable STRIMZI_NAMESPACE list all the namespaces where Cluster Operator should watch for resources. For example:

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq7/amq-streams-operator:1.3.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: watched-namespace-1,watched-namespace-2,watched-namespace-3
  2. For all namespaces which should be watched by the Cluster Operator (watched-namespace-1, watched-namespace-2, watched-namespace-3 in the above example), install the RoleBindings. Replace the watched-namespace with the namespace used in the previous step.

    This can be done using oc apply:

    oc apply -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace
    oc apply -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace
    oc apply -f install/cluster-operator/032-RoleBinding-strimzi-cluster-operator-topic-operator-delegation.yaml -n watched-namespace
  3. Deploy the Cluster Operator

    This can be done using oc apply:

    oc apply -f install/cluster-operator -n my-namespace

4.1.5. Deploying the Cluster Operator to watch all namespaces

You can configure the Cluster Operator to watch AMQ Streams resources across all namespaces in your OpenShift cluster. When running in this mode, the Cluster Operator automatically manages clusters in any new namespaces that are created.

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.
  • Your OpenShift cluster is running.

Procedure

  1. Configure the Cluster Operator to watch all namespaces:

    1. Edit the 050-Deployment-strimzi-cluster-operator.yaml file.
    2. Set the value of the STRIMZI_NAMESPACE environment variable to *.

      apiVersion: apps/v1
      kind: Deployment
      spec:
        # ...
        template:
          spec:
            # ...
            serviceAccountName: strimzi-cluster-operator
            containers:
            - name: strimzi-cluster-operator
              image: registry.redhat.io/amq7/amq-streams-operator:1.3.0
              imagePullPolicy: IfNotPresent
              env:
              - name: STRIMZI_NAMESPACE
                value: "*"
              # ...
  2. Create ClusterRoleBindings that grant cluster-wide access to all namespaces to the Cluster Operator.

    Use the oc create clusterrolebinding command:

    oc create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-namespace:strimzi-cluster-operator
    oc create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-namespace:strimzi-cluster-operator
    oc create clusterrolebinding strimzi-cluster-operator-topic-operator-delegation --clusterrole=strimzi-topic-operator --serviceaccount my-namespace:strimzi-cluster-operator

    Replace my-namespace with the namespace in which you want to install the Cluster Operator.

  3. Deploy the Cluster Operator to your OpenShift cluster.

    Use the oc apply command:

    oc apply -f install/cluster-operator -n my-namespace

4.1.6. Reconciliation

Although the operator reacts to all notifications about the desired cluster resources received from the OpenShift cluster, if the operator is not running, or if a notification is not received for any reason, the desired resources will get out of sync with the state of the running OpenShift cluster.

In order to handle failovers properly, a periodic reconciliation process is executed by the Cluster Operator so that it can compare the state of the desired resources with the current cluster deployments in order to have a consistent state across all of them. You can set the time interval for the periodic reconciliations using the [STRIMZI_FULL_RECONCILIATION_INTERVAL_MS] variable.

4.1.7. Cluster Operator Configuration

The Cluster Operator can be configured through the following supported environment variables:

STRIMZI_NAMESPACE

A comma-separated list of namespaces that the operator should operate in. When not set, set to empty string, or to * the Cluster Operator will operate in all namespaces. The Cluster Operator deployment might use the OpenShift Downward API to set this automatically to the namespace the Cluster Operator is deployed in. See the example below:

env:
  - name: STRIMZI_NAMESPACE
    valueFrom:
      fieldRef:
        fieldPath: metadata.namespace
STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
Optional, default is 120000 ms. The interval between periodic reconciliations, in milliseconds.
STRIMZI_LOG_LEVEL
Optional, default INFO. The level for printing logging messages. The value can be set to: ERROR, WARNING, INFO, DEBUG, and TRACE.
STRIMZI_OPERATION_TIMEOUT_MS
Optional, default 300000 ms. The timeout for internal operations, in milliseconds. This value should be increased when using AMQ Streams on clusters where regular OpenShift operations take longer than usual (because of slow downloading of Docker images, for example).
STRIMZI_KAFKA_IMAGES
Required. This provides a mapping from Kafka version to the corresponding Docker image containing a Kafka broker of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.2.1=registry.redhat.io/amq7/amq-streams-kafka-22:1.3.0, 2.3.0=registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0. This is used when a Kafka.spec.kafka.version property is specified but not the Kafka.spec.kafka.image, as described in Section 3.1.18, “Container images”.
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
Optional, default registry.redhat.io/amq7/amq-streams-operator:1.3.0. The image name to use as default for the init container started before the broker for initial configuration work (that is, rack support), if no image is specified as the kafka-init-image in the Section 3.1.18, “Container images”.
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE
Optional, default registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0. The image name to use as the default when deploying the sidecar container which provides TLS support for Kafka, if no image is specified as the Kafka.spec.kafka.tlsSidecar.image in the Section 3.1.18, “Container images”.
STRIMZI_DEFAULT_ZOOKEEPER_IMAGE
Optional, default registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0. The image name to use as the default when deploying Zookeeper, if no image is specified as the Kafka.spec.zookeeper.image in the Section 3.1.18, “Container images”.
STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE
Optional, default registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0. The image name to use as the default when deploying the sidecar container which provides TLS support for Zookeeper, if no image is specified as the Kafka.spec.zookeeper.tlsSidecar.image in the Section 3.1.18, “Container images”.
STRIMZI_KAFKA_CONNECT_IMAGES
Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.2.1=registry.redhat.io/amq7/amq-streams-kafka-22:1.3.0, 2.3.0=registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0. This is used when a KafkaConnect.spec.version property is specified but not the KafkaConnect.spec.image, as described in Section 3.2.11, “Container images”.
STRIMZI_KAFKA_CONNECT_S2I_IMAGES
Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.2.1=registry.redhat.io/amq7/amq-streams-kafka-22:1.3.0, 2.3.0=registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0. This is used when a KafkaConnectS2I.spec.version property is specified but not the KafkaConnectS2I.spec.image, as described in Section 3.3.11, “Container images”.
STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka mirror maker of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.2.1=registry.redhat.io/amq7/amq-streams-kafka-22:1.3.0, 2.3.0=registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0. This is used when a KafkaMirrorMaker.spec.version property is specified but not the KafkaMirrorMaker.spec.image, as described in Section 3.4.2.14, “Container images”.
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
Optional, default registry.redhat.io/amq7/amq-streams-operator:1.3.0. The image name to use as the default when deploying the topic operator, if no image is specified as the Kafka.spec.entityOperator.topicOperator.image in the Section 3.1.18, “Container images” of the Kafka resource.
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
Optional, default registry.redhat.io/amq7/amq-streams-operator:1.3.0. The image name to use as the default when deploying the user operator, if no image is specified as the Kafka.spec.entityOperator.userOperator.image in the Section 3.1.18, “Container images” of the Kafka resource.
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE
Optional, default registry.redhat.io/amq7/amq-streams-kafka-23:1.3.0. The image name to use as the default when deploying the sidecar container which provides TLS support for the Entity Operator, if no image is specified as the Kafka.spec.entityOperator.tlsSidecar.image in the Section 3.1.18, “Container images”.
STRIMZI_IMAGE_PULL_POLICY
Optional. The ImagePullPolicy which will be applied to containers in all pods managed by AMQ Streams Cluster Operator. The valid values are Always, IfNotPresent, and Never. If not specified, the OpenShift defaults will be used. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka Mirror Maker clusters.
STRIMZI_IMAGE_PULL_SECRETS
Optional. A comma-separated list of Secret names. The secrets referenced here contain the credentials to the container registries where the container images are pulled from. The secrets are used in the imagePullSecrets field for all Pods created by the Cluster Operator. Changing this list results in a rolling update of all your Kafka, Kafka Connect, and Kafka Mirror Maker clusters.

4.1.8. Role-Based Access Control (RBAC)

4.1.8.1. Provisioning Role-Based Access Control (RBAC) for the Cluster Operator

For the Cluster Operator to function it needs permission within the OpenShift cluster to interact with resources such as Kafka, KafkaConnect, and so on, as well as the managed resources, such as ConfigMaps, Pods, Deployments, StatefulSets, Services, and so on. Such permission is described in terms of OpenShift role-based access control (RBAC) resources:

  • ServiceAccount,
  • Role and ClusterRole,
  • RoleBinding and ClusterRoleBinding.

In addition to running under its own ServiceAccount with a ClusterRoleBinding, the Cluster Operator manages some RBAC resources for the components that need access to OpenShift resources.

OpenShift also includes privilege escalation protections that prevent components operating under one ServiceAccount from granting other ServiceAccounts privileges that the granting ServiceAccount does not have. Because the Cluster Operator must be able to create the ClusterRoleBindings, and RoleBindings needed by resources it manages, the Cluster Operator must also have those same privileges.

4.1.8.2. Delegated privileges

When the Cluster Operator deploys resources for a desired Kafka resource it also creates ServiceAccounts, RoleBindings, and ClusterRoleBindings, as follows:

  • The Kafka broker pods use a ServiceAccount called cluster-name-kafka

    • When the rack feature is used, the strimzi-cluster-name-kafka-init ClusterRoleBinding is used to grant this ServiceAccount access to the nodes within the cluster via a ClusterRole called strimzi-kafka-broker
    • When the rack feature is not used no binding is created.
  • The Zookeeper pods use the default ServiceAccount, as they do not need access to the OpenShift resources.
  • The Topic Operator pod uses a ServiceAccount called cluster-name-topic-operator

    • The Topic Operator produces OpenShift events with status information, so the ServiceAccount is bound to a ClusterRole called strimzi-topic-operator which grants this access via the strimzi-topic-operator-role-binding RoleBinding.

The pods for KafkaConnect and KafkaConnectS2I resources use the default ServiceAccount, as they do not require access to the OpenShift resources.

4.1.8.3. ServiceAccount

The Cluster Operator is best run using a ServiceAccount:

Example ServiceAccount for the Cluster Operator

apiVersion: v1
kind: ServiceAccount
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi

The Deployment of the operator then needs to specify this in its spec.template.spec.serviceAccountName:

Partial example of Deployment for the Cluster Operator

apiVersion: apps/v1
kind: Deployment
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
spec:
  replicas: 1
  selector:
    matchLabels:
      name: strimzi-cluster-operator
      strimzi.io/kind: cluster-operator
  template:
      # ...

Note line 12, where the the strimzi-cluster-operator ServiceAccount is specified as the serviceAccountName.

4.1.8.4. ClusterRoles

The Cluster Operator needs to operate using ClusterRoles that gives access to the necessary resources. Depending on the OpenShift cluster setup, a cluster administrator might be needed to create the ClusterRoles.

Note

Cluster administrator rights are only needed for the creation of the ClusterRoles. The Cluster Operator will not run under the cluster admin account.

The ClusterRoles follow the principle of least privilege and contain only those privileges needed by the Cluster Operator to operate Kafka, Kafka Connect, and Zookeeper clusters. The first set of assigned privileges allow the Cluster Operator to manage OpenShift resources such as StatefulSets, Deployments, Pods, and ConfigMaps.

Cluster Operator uses ClusterRoles to grant permission at the namespace-scoped resources level and cluster-scoped resources level:

ClusterRole with namespaced resources for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-namespaced
  labels:
    app: strimzi
rules:
- apiGroups:
  - ""
  resources:
  - serviceaccounts
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - rbac.authorization.k8s.io
  resources:
  - rolebindings
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - configmaps
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkas
  - kafkas/status
  - kafkaconnects
  - kafkaconnects/status
  - kafkaconnects2is
  - kafkaconnects2is/status
  - kafkamirrormakers
  - kafkamirrormakers/status
  - kafkabridges
  - kafkabridges/status
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - pods
  verbs:
  - get
  - list
  - watch
  - delete
- apiGroups:
  - ""
  resources:
  - services
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - endpoints
  verbs:
  - get
  - list
  - watch
- apiGroups:
  - extensions
  resources:
  - deployments
  - deployments/scale
  - replicasets
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - apps
  resources:
  - deployments
  - deployments/scale
  - deployments/status
  - statefulsets
  - replicasets
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - events
  verbs:
  - create
- apiGroups:
  - extensions
  resources:
  - replicationcontrollers
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - apps.openshift.io
  resources:
  - deploymentconfigs
  - deploymentconfigs/scale
  - deploymentconfigs/status
  - deploymentconfigs/finalizers
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - build.openshift.io
  resources:
  - buildconfigs
  - builds
  verbs:
  - create
  - delete
  - get
  - list
  - patch
  - watch
  - update
- apiGroups:
  - image.openshift.io
  resources:
  - imagestreams
  - imagestreams/status
  verbs:
  - create
  - delete
  - get
  - list
  - watch
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - replicationcontrollers
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - secrets
  verbs:
  - get
  - list
  - create
  - delete
  - patch
  - update
- apiGroups:
  - extensions
  resources:
  - networkpolicies
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - networking.k8s.io
  resources:
  - networkpolicies
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - route.openshift.io
  resources:
  - routes
  - routes/custom-host
  verbs:
  - get
  - list
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - persistentvolumeclaims
  verbs:
  - get
  - list
  - create
  - delete
  - patch
  - update
- apiGroups:
  - policy
  resources:
  - poddisruptionbudgets
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - extensions
  resources:
  - ingresses
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update

The second includes the permissions needed for cluster-scoped resources.

ClusterRole with cluster-scoped resources for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-global
  labels:
    app: strimzi
rules:
- apiGroups:
  - rbac.authorization.k8s.io
  resources:
  - clusterrolebindings
  verbs:
  - get
  - create
  - delete
  - patch
  - update
  - watch
- apiGroups:
  - storage.k8s.io
  resources:
  - storageclasses
  verbs:
  - get

The strimzi-kafka-broker ClusterRole represents the access needed by the init container in Kafka pods that is used for the rack feature. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.

ClusterRole for the Cluster Operator allowing it to delegate access to OpenShift nodes to the Kafka broker pods

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-kafka-broker
  labels:
    app: strimzi
rules:
- apiGroups:
  - ""
  resources:
  - nodes
  verbs:
  - get

The strimzi-topic-operator ClusterRole represents the access needed by the Topic Operator. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.

ClusterRole for the Cluster Operator allowing it to delegate access to events to the Topic Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-entity-operator
  labels:
    app: strimzi
rules:
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkatopics
  - kafkatopics/status
  verbs:
  - get
  - list
  - watch
  - create
  - patch
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - events
  verbs:
  - create
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkausers
  - kafkausers/status
  verbs:
  - get
  - list
  - watch
  - create
  - patch
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - secrets
  verbs:
  - get
  - list
  - create
  - patch
  - update
  - delete

4.1.8.5. ClusterRoleBindings

The operator needs ClusterRoleBindings and RoleBindings which associates its ClusterRole with its ServiceAccount: ClusterRoleBindings are needed for ClusterRoles containing cluster-scoped resources.

Example ClusterRoleBinding for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-cluster-operator-global
  apiGroup: rbac.authorization.k8s.io

ClusterRoleBindings are also needed for the ClusterRoles needed for delegation:

Examples RoleBinding for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator-kafka-broker-delegation
  labels:
    app: strimzi
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-kafka-broker
  apiGroup: rbac.authorization.k8s.io

ClusterRoles containing only namespaced resources are bound using RoleBindings only.

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-cluster-operator-namespaced
  apiGroup: rbac.authorization.k8s.io
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator-entity-operator-delegation
  labels:
    app: strimzi
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-entity-operator
  apiGroup: rbac.authorization.k8s.io

4.2. Topic Operator

4.2.1. Overview of the Topic Operator component

The Topic Operator provides a way of managing topics in a Kafka cluster via OpenShift resources.

Example architecture for the Topic Operator

Topic Operator

The role of the Topic Operator is to keep a set of KafkaTopic OpenShift resources describing Kafka topics in-sync with corresponding Kafka topics.

Specifically, if a KafkaTopic is:

  • Created, the operator will create the topic it describes
  • Deleted, the operator will delete the topic it describes
  • Changed, the operator will update the topic it describes

And also, in the other direction, if a topic is:

  • Created within the Kafka cluster, the operator will create a KafkaTopic describing it
  • Deleted from the Kafka cluster, the operator will delete the KafkaTopic describing it
  • Changed in the Kafka cluster, the operator will update the KafkaTopic describing it

This allows you to declare a KafkaTopic as part of your application’s deployment and the Topic Operator will take care of creating the topic for you. Your application just needs to deal with producing or consuming from the necessary topics.

If the topic is reconfigured or reassigned to different Kafka nodes, the KafkaTopic will always be up to date.

For more details about creating, modifying and deleting topics, see Chapter 5, Using the Topic Operator.

4.2.2. Identifying a Kafka cluster for topic handling

A KafkaTopic resource includes a label that defines the appropriate name of the Kafka cluster (derived from the name of the Kafka resource) to which it belongs.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster

The label is used by the Topic Operator to identify the KafkaTopic resource and create a new topic, and also in subsequent handling of the topic.

If the label does not match the Kafka cluster, the Topic Operator cannot identify the KafkaTopic and the topic is not created.

4.2.3. Understanding the Topic Operator

A fundamental problem that the operator has to solve is that there is no single source of truth: Both the KafkaTopic resource and the topic within Kafka can be modified independently of the operator. Complicating this, the Topic Operator might not always be able to observe changes at each end in real time (for example, the operator might be down).

To resolve this, the operator maintains its own private copy of the information about each topic. When a change happens either in the Kafka cluster, or in OpenShift, it looks at both the state of the other system and at its private copy in order to determine what needs to change to keep everything in sync. The same thing happens whenever the operator starts, and periodically while it is running.

For example, suppose the Topic Operator is not running, and a KafkaTopic my-topic gets created. When the operator starts it will lack a private copy of "my-topic", so it can infer that the KafkaTopic has been created since it was last running. The operator will create the topic corresponding to "my-topic" and also store a private copy of the metadata for "my-topic".

The private copy allows the operator to cope with scenarios where the topic configuration gets changed both in Kafka and in OpenShift, so long as the changes are not incompatible (for example, both changing the same topic config key, but to different values). In the case of incompatible changes, the Kafka configuration wins, and the KafkaTopic will be updated to reflect that.

The private copy is held in the same Zookeeper ensemble used by Kafka itself. This mitigates availability concerns, because if Zookeeper is not running then Kafka itself cannot run, so the operator will be no less available than it would even if it was stateless.

4.2.4. Deploying the Topic Operator using the Cluster Operator

This procedure describes how to deploy the Topic Operator using the Cluster Operator. If you want to use the Topic Operator with a Kafka cluster that is not managed by AMQ Streams, you must deploy the Topic Operator as a standalone component. For more information, see Section 4.2.6, “Deploying the standalone Topic Operator”.

Prerequisites

  • A running Cluster Operator
  • A Kafka resource to be created or updated

Procedure

  1. Ensure that the Kafka.spec.entityOperator object exists in the Kafka resource. This configures the Entity Operator.

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the Topic Operator using the fields described in Section C.52, “EntityTopicOperatorSpec schema reference”.
  3. Create or update the Kafka resource in OpenShift.

    Use oc apply:

    oc apply -f your-file

Additional resources

4.2.5. Configuring the Topic Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the Topic Operator and set a limit on the amount of resources it can consume.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Update the Kafka cluster configuration in an editor, as required:

    Use oc edit:

    oc edit kafka my-cluster
  2. In the spec.entityOperator.topicOperator.resources property in the Kafka resource, set the resource requests and limits for the Topic Operator.

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      # kafka and zookeeper sections...
      entityOperator:
        topicOperator:
          resources:
            request:
              cpu: "1"
              memory: 500Mi
            limit:
              cpu: "1"
              memory: 500Mi
  3. Apply the new configuration to create or update the resource.

    Use oc apply:

    oc apply -f kafka.yaml

Additional resources

4.2.6. Deploying the standalone Topic Operator

Deploying the Topic Operator as a standalone component is more complicated than installing it using the Cluster Operator, but it is more flexible. For instance, it can operate with any Kafka cluster, not necessarily one deployed by the Cluster Operator.

Prerequisites

  • An existing Kafka cluster for the Topic Operator to connect to.

Procedure

  1. Edit the install/topic-operator/05-Deployment-strimzi-topic-operator.yaml resource. You will need to change the following

    1. The STRIMZI_KAFKA_BOOTSTRAP_SERVERS environment variable in Deployment.spec.template.spec.containers[0].env should be set to a list of bootstrap brokers in your Kafka cluster, given as a comma-separated list of hostname:‍port pairs.
    2. The STRIMZI_ZOOKEEPER_CONNECT environment variable in Deployment.spec.template.spec.containers[0].env should be set to a list of the Zookeeper nodes, given as a comma-separated list of hostname:‍port pairs. This should be the same Zookeeper cluster that your Kafka cluster is using.
    3. The STRIMZI_NAMESPACE environment variable in Deployment.spec.template.spec.containers[0].env should be set to the OpenShift namespace in which you want the operator to watch for KafkaTopic resources.
  2. Deploy the Topic Operator.

    This can be done using oc apply:

    oc apply -f install/topic-operator
  3. Verify that the Topic Operator has been deployed successfully. This can be done using oc describe:

    oc describe deployment strimzi-topic-operator

    The Topic Operator is deployed once the Replicas: entry shows 1 available.

    Note

    This could take some time if you have a slow connection to the OpenShift and the images have not been downloaded before.

Additional resources

4.2.7. Topic Operator environment

When deployed standalone the Topic Operator can be configured using environment variables.

Note

The Topic Operator should be configured using the Kafka.spec.entityOperator.topicOperator property when deployed by the Cluster Operator.

STRIMZI_RESOURCE_LABELS
The label selector used to identify KafkaTopics to be managed by the operator.
STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS
The Zookeeper session timeout, in milliseconds. For example, 10000. Default 20000 (20 seconds).
STRIMZI_KAFKA_BOOTSTRAP_SERVERS
The list of Kafka bootstrap servers. This variable is mandatory.
STRIMZI_ZOOKEEPER_CONNECT
The Zookeeper connection information. This variable is mandatory.
STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
The interval between periodic reconciliations, in milliseconds.
STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation could take more time due to the number of partitions or replicas. Default 6.
STRIMZI_LOG_LEVEL
The level for printing logging messages. The value can be set to: ERROR, WARNING, INFO, DEBUG, and TRACE. Default INFO.
STRIMZI_TLS_ENABLED
For enabling the TLS support so encrypting the communication with Kafka brokers. Default true.
STRIMZI_TRUSTSTORE_LOCATION
The path to the truststore containing certificates for enabling TLS based communication. This variable is mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.
STRIMZI_TRUSTSTORE_PASSWORD
The password for accessing the truststore defined by STRIMZI_TRUSTSTORE_LOCATION. This variable is mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.
STRIMZI_KEYSTORE_LOCATION
The path to the keystore containing private keys for enabling TLS based communication. This variable is mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.
STRIMZI_KEYSTORE_PASSWORD
The password for accessing the keystore defined by STRIMZI_KEYSTORE_LOCATION. This variable is mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.

4.3. User Operator

The User Operator manages Kafka users through custom resources.

4.3.1. Overview of the User Operator component

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser resources that describe Kafka users and ensuring that they are configured properly in the Kafka cluster. For example:

  • if a KafkaUser is created, the User Operator will create the user it describes
  • if a KafkaUser is deleted, the User Operator will delete the user it describes
  • if a KafkaUser is changed, the User Operator will update the user it describes

Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the OpenShift resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.

The User Operator allows you to declare a KafkaUser as part of your application’s deployment. When the user is created, the user credentials will be created in a Secret. Your application needs to use the user and its credentials for authentication and to produce or consume messages.

In addition to managing credentials for authentication, the User Operator also manages authorization rules by including a description of the user’s rights in the KafkaUser declaration.

4.3.2. Identifying a Kafka cluster for user handling

A KafkaUser resource includes a label that defines the appropriate name of the Kafka cluster (derived from the name of the Kafka resource) to which it belongs.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster

The label is used by the User Operator to identify the KafkaUser resource and create a new user, and also in subsequent handling of the user.

If the label does not match the Kafka cluster, the User Operator cannot identify the kafkaUser and the user is not created.

4.3.3. Deploying the User Operator using the Cluster Operator

Prerequisites

  • A running Cluster Operator
  • A Kafka resource to be created or updated.

Procedure

  1. Edit the Kafka resource ensuring it has a Kafka.spec.entityOperator.userOperator object that configures the User Operator how you want.
  2. Create or update the Kafka resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

4.3.4. Configuring the User Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the User Operator and set a limit on the amount of resources it can consume.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Update the Kafka cluster configuration in an editor, as required:

    oc edit kafka my-cluster
  2. In the spec.entityOperator.userOperator.resources property in the Kafka resource, set the resource requests and limits for the User Operator.

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      # kafka and zookeeper sections...
      entityOperator:
        userOperator:
          resources:
            request:
              cpu: "1"
              memory: 500Mi
            limit:
              cpu: "1"
              memory: 500Mi

    Save the file and exit the editor. The Cluster Operator will apply the changes automatically.

Additional resources

4.3.5. Deploying the standalone User Operator

Deploying the User Operator as a standalone component is more complicated than installing it using the Cluster Operator, but it is more flexible. For instance, it can operate with any Kafka cluster, not only the one deployed by the Cluster Operator.

Prerequisites

  • An existing Kafka cluster for the User Operator to connect to.

Procedure

  1. Edit the install/user-operator/05-Deployment-strimzi-user-operator.yaml resource. You will need to change the following

    1. The STRIMZI_CA_CERT_NAME environment variable in Deployment.spec.template.spec.containers[0].env should be set to point to an OpenShift Secret which should contain the public key of the Certificate Authority for signing new user certificates for TLS Client Authentication. The Secret should contain the public key of the Certificate Authority under the key ca.crt.
    2. The STRIMZI_CA_KEY_NAME environment variable in Deployment.spec.template.spec.containers[0].env should be set to point to an OpenShift Secret which should contain the private key of the Certificate Authority for signing new user certificates for TLS Client Authentication. The Secret should contain the private key of the Certificate Authority under the key ca.key.
    3. The STRIMZI_ZOOKEEPER_CONNECT environment variable in Deployment.spec.template.spec.containers[0].env should be set to a list of the Zookeeper nodes, given as a comma-separated list of hostname:‍port pairs. This should be the same Zookeeper cluster that your Kafka cluster is using.
    4. The STRIMZI_NAMESPACE environment variable in Deployment.spec.template.spec.containers[0].env should be set to the OpenShift namespace in which you want the operator to watch for KafkaUser resources.
  2. Deploy the User Operator.

    This can be done using oc apply:

    oc apply -f install/user-operator
  3. Verify that the User Operator has been deployed successfully. This can be done using oc describe:

    oc describe deployment strimzi-user-operator

    The User Operator is deployed once the Replicas: entry shows 1 available.

    Note

    This could take some time if you have a slow connection to the OpenShift and the images have not been downloaded before.

Additional resources

Chapter 5. Using the Topic Operator

5.1. Topic Operator usage recommendations

When working with topics, be consistent and always operate on either KafkaTopic resources or topics directly. Avoid routinely switching between both methods for a given topic.

Use topic names that reflect the nature of the topic, and remember that names cannot be changed later.

If creating a topic in Kafka, use a name that is a valid OpenShift resource name, otherwise the Topic Operator will need to create the corresponding KafkaTopic with a name that conforms to the OpenShift rules.

Note

Recommendations for identifiers and names in OpenShift are outlined in Identifiers and Names in OpenShift community article.

Kafka topic naming conventions

Kafka and OpenShift impose their own validation rules for the naming of topics in Kafka and KafkaTopic.metadata.name respectively. There are valid names for each which are invalid in the other.

Using the spec.topicName property, it is possible to create a valid topic in Kafka with a name that would be invalid for the KafkaTopic in OpenShift.

The spec.topicName property inherits Kafka naming validation rules:

  • The name must not be longer than 249 characters.
  • Valid characters for Kafka topics are ASCII alphanumerics, ., _, and -.
  • The name cannot be . or .., though . can be used in a name, such as exampleTopic. or .exampleTopic.

spec.topicName must not be changed.

For example:

kind: KafkaTopic
metadata:
  name: topic-name-1
spec:
  topicName: topicName-1 # Upper case is invalid in OpenShift
  # ...

cannot be changed to

kind: KafkaTopic
metadata:
  name: topic-name-1
spec:
  topicName: name-2
  # ...
Note

Some Kafka client applications, such as Kafka Streams, can create topics in Kafka programmatically. If those topics have names that are invalid OpenShift resource names, the Topic Operator gives them valid names based on the Kafka names. Invalid characters are replaced and a hash is appended to the name.

5.2. Creating a topic

This procedure describes how to create a Kafka topic using a KafkaTopic OpenShift resource.

Prerequisites

Procedure

  1. Prepare a file containing the KafkaTopic to be created

    An example KafkaTopic

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaTopic
    metadata:
      name: orders
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      partitions: 10
      replicas: 2

    Note

    It is recommended that the topic name given is a valid OpenShift resource name, as it is then not necessary to set the KafkaTopic.spec.topicName property. The KafkaTopic.spec.topicName cannot be changed after creation.

    Note

    The KafkaTopic.spec.partitions cannot be decreased.

  2. Create the KafkaTopic resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

5.3. Changing a topic

This procedure describes how to change the configuration of an existing Kafka topic by using a KafkaTopic OpenShift resource.

Prerequisites

Procedure

  1. Prepare a file containing the desired KafkaTopic

    An example KafkaTopic

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaTopic
    metadata:
      name: orders
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      partitions: 16
      replicas: 2

    Tip

    You can get the current version of the resource using oc get kafkatopic orders -o yaml.

    Note

    Changing topic names using the KafkaTopic.spec.topicName variable and decreasing partition size using the KafkaTopic.spec.partitions variable is not supported by Kafka.

    Caution

    Increasing spec.partitions for topics with keys will change how records are partitioned, which can be particularly problematic when the topic uses semantic partitioning.

  2. Update the KafkaTopic resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file

Additional resources

5.4. Deleting a topic

This procedure describes how to delete a Kafka topic using a KafkaTopic OpenShift resource.

Prerequisites

  • A running Kafka cluster.
  • A running Topic Operator (typically deployed with the Entity Operator).
  • An existing KafkaTopic to be deleted.
  • delete.topic.enable=true (default)
Note

The delete.topic.enable property must be set to true in Kafka.spec.kafka.config. Otherwise, the steps outlined here will delete the KafkaTopic resource, but the Kafka topic and its data will remain. After reconciliation by the Topic Operator, the custom resource is then recreated.

Procedure

  • Delete the KafkaTopic resource in OpenShift.

    This can be done using oc delete:

    oc delete kafkatopic your-topic-name

Additional resources

Chapter 6. Using the User Operator

The User Operator provides a way of managing Kafka users via OpenShift resources.

6.1. Overview of the User Operator component

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser resources that describe Kafka users and ensuring that they are configured properly in the Kafka cluster. For example:

  • if a KafkaUser is created, the User Operator will create the user it describes
  • if a KafkaUser is deleted, the User Operator will delete the user it describes
  • if a KafkaUser is changed, the User Operator will update the user it describes

Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the OpenShift resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.

The User Operator allows you to declare a KafkaUser as part of your application’s deployment. When the user is created, the user credentials will be created in a Secret. Your application needs to use the user and its credentials for authentication and to produce or consume messages.

In addition to managing credentials for authentication, the User Operator also manages authorization rules by including a description of the user’s rights in the KafkaUser declaration.

6.2. Mutual TLS authentication

Mutual TLS authentication is always used for the communication between Kafka brokers and Zookeeper pods.

Mutual authentication or two-way authentication is when both the server and the client present certificates. AMQ Streams can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. When you configure mutual authentication, the broker authenticates the client and the client authenticates the broker.

Note

TLS authentication is more commonly one-way, with one party authenticating the identity of another. For example, when HTTPS is used between a web browser and a web server, the server obtains proof of the identity of the browser.

6.2.1. When to use mutual TLS authentication for clients

Mutual TLS authentication is recommended for authenticating Kafka clients when:

  • The client supports authentication using mutual TLS authentication
  • It is necessary to use the TLS certificates rather than passwords
  • You can reconfigure and restart client applications periodically so that they do not use expired certificates.

6.3. Creating a Kafka user with mutual TLS authentication

Prerequisites

Procedure

  1. Prepare a YAML file containing the KafkaUser to be created.

    An example KafkaUser

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaUser
    metadata:
      name: my-user
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      authentication:
        type: tls
      authorization:
        type: simple
        acls:
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Read
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Describe
          - resource:
              type: group
              name: my-group
              patternType: literal
            operation: Read

  2. Create the KafkaUser resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file
  3. Use the credentials from the secret my-user in your application

Additional resources

6.4. SCRAM-SHA authentication

SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. AMQ Streams can configure Kafka to use SASL (Simple Authentication and Security Layer) SCRAM-SHA-512 to provide authentication on both unencrypted and TLS-encrypted client connections. TLS authentication is always used internally between Kafka brokers and Zookeeper nodes. When used with a TLS client connection, the TLS protocol provides encryption, but is not used for authentication.

The following properties of SCRAM make it safe to use SCRAM-SHA even on unencrypted connections:

  • The passwords are not sent in the clear over the communication channel. Instead the client and the server are each challenged by the other to offer proof that they know the password of the authenticating user.
  • The server and client each generate a new challenge for each authentication exchange. This means that the exchange is resilient against replay attacks.

6.4.1. Supported SCRAM credentials

AMQ Streams supports SCRAM-SHA-512 only. When a KafkaUser.spec.authentication.type is configured with scram-sha-512 the User Operator will generate a random 12 character password consisting of upper and lowercase ASCII letters and numbers.

6.4.2. When to use SCRAM-SHA authentication for clients

SCRAM-SHA is recommended for authenticating Kafka clients when:

  • The client supports authentication using SCRAM-SHA-512
  • It is necessary to use passwords rather than the TLS certificates
  • Authentication for unencrypted communication is required

6.5. Creating a Kafka user with SCRAM SHA authentication

Prerequisites

Procedure

  1. Prepare a YAML file containing the KafkaUser to be created.

    An example KafkaUser

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaUser
    metadata:
      name: my-user
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      authentication:
        type: scram-sha-512
      authorization:
        type: simple
        acls:
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Read
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Describe
          - resource:
              type: group
              name: my-group
              patternType: literal
            operation: Read

  2. Create the KafkaUser resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file
  3. Use the credentials from the secret my-user in your application

Additional resources

6.6. Editing a Kafka user

This procedure describes how to change the configuration of an existing Kafka user by using a KafkaUser OpenShift resource.

Prerequisites

Procedure

  1. Prepare a YAML file containing the desired KafkaUser.

    An example KafkaUser

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaUser
    metadata:
      name: my-user
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      authentication:
        type: tls
      authorization:
        type: simple
        acls:
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Read
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Describe
          - resource:
              type: group
              name: my-group
              patternType: literal
            operation: Read

  2. Update the KafkaUser resource in OpenShift.

    This can be done using oc apply:

    oc apply -f your-file
  3. Use the updated credentials from the my-user secret in your application.

Additional resources

6.7. Deleting a Kafka user

This procedure describes how to delete a Kafka user created with KafkaUser OpenShift resource.

Prerequisites

Procedure

  • Delete the KafkaUser resource in OpenShift.

    This can be done using oc delete:

    oc delete kafkauser your-user-name

Additional resources

  • For more information