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Using AMQ Streams on OpenShift Container Platform

Red Hat AMQ 7.2

For Use with AMQ Streams 1.1.0

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 makes it easy to run Apache Kafka on OpenShift. Apache Kafka is a popular platform for streaming data delivery and processing. For more information about Apache Kafka, see the Apache Kafka website.

AMQ Streams is based on Apache Kafka 2.0.1 and consists of three main components:

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

This guide describes how to install and use Red Hat AMQ Streams.

1.1. Kafka Key Features

  • Scalability and performance

    • Designed for horizontal scalability
  • Message ordering guarantee

    • At partition level
  • Message rewind/replay

    • "Long term" storage
    • Allows to reconstruct application state by replaying the messages
    • Combined with compacted topics allows to use Kafka as key-value store

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 on to local deployments intended for development. This guide expects that an OpenShift cluster is available and the oc command-line tools are installed and configured to connect to the running cluster.

AMQ Streams is based on Strimzi 0.11.1. This chapter describes the procedures to deploy AMQ Streams on OpenShift 3.9 and later.

Note

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

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-1.1.0-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. Cluster Operator

AMQ Streams uses the Cluster Operator to deploy and manage Kafka (including Zookeeper) and Kafka Connect clusters. The Cluster Operator is deployed inside of the OpenShift cluster. To deploy a Kafka cluster, a Kafka resource with the cluster configuration has to be created within the OpenShift cluster. Based on what is declared inside of the Kafka resource, the Cluster Operator deploys a corresponding Kafka cluster. For more information about the different configuration options supported by the Kafka resource, see Section 3.1, “Kafka cluster configuration”

Note

AMQ Streams contains example YAML files, which make deploying a Cluster Operator easier.

2.2.1. Overview of the Cluster Operator component

The Cluster Operator is in charge of deploying a Kafka cluster alongside a Zookeeper ensemble. As part of the Kafka cluster, it can also deploy the topic operator which provides operator-style topic management via KafkaTopic custom resources. The Cluster Operator is also able to deploy a Kafka Connect cluster which connects to an existing Kafka cluster. On OpenShift such a cluster can be deployed using the Source2Image feature, providing an easy way of including more connectors.

Figure 2.1. Example Architecture diagram of the Cluster Operator.

Cluster Operator

When the Cluster Operator is up, it starts to watch for certain OpenShift resources containing the desired Kafka, Kafka Connect, or Kafka Mirror Maker cluster configuration. By default, it watches only in the same namespace or project where it is installed. The Cluster Operator can be configured to watch for more OpenShift projects or Kubernetes namespaces. Cluster Operator watches the following resources:

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

When a new Kafka, KafkaConnect, KafkaConnectS2I, or Kafka Mirror Maker resource is created in the OpenShift cluster, the operator gets the cluster description from the desired resource and starts creating a new Kafka, Kafka Connect, or Kafka Mirror Maker cluster by creating the necessary other OpenShift resources, such as StatefulSets, Services, ConfigMaps, and so on.

Every time the desired resource is updated by the user, the operator performs corresponding updates on the OpenShift resources which make up the Kafka, Kafka Connect, or Kafka Mirror Maker cluster. Resources are either patched or deleted and then re-created in order to make the Kafka, Kafka Connect, or Kafka Mirror Maker cluster reflect the state of the desired cluster resource. This might cause a rolling update which might lead to service disruption.

Finally, when the desired resource is deleted, the operator starts to undeploy the cluster and delete all the related OpenShift resources.

2.2.2. Deploying the Cluster Operator to OpenShift

Prerequisites

  • A user with cluster-admin role needs to be used, for example, 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-project/' install/cluster-operator/*RoleBinding*.yaml

    On MacOS, use:

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

Procedure

  1. Deploy the Cluster Operator

    oc apply -f install/cluster-operator -n _my-project_
    oc apply -f examples/templates/cluster-operator -n _my-project_

2.2.3. Deploying the Cluster Operator to watch multiple namespaces

Prerequisites

  • Edit the installation files according to the OpenShift project or Kubernetes 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 OpenShift projects or Kubernetes namespaces where Cluster Operator should watch for resources. For example:

    apiVersion: extensions/v1beta1
    kind: Deployment
    spec:
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: strimzi/cluster-operator:latest
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: myproject,myproject2,myproject3
  2. For all namespaces or projects which should be watched by the Cluster Operator, install the RoleBindings. Replace the my-namespace or my-project with the OpenShift project or Kubernetes namespace used in the previous step.

    On OpenShift this can be done using oc apply:

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

    On OpenShift this can be done using oc apply:

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

2.2.4. Deploying the Cluster Operator to watch all namespaces

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

Prerequisites

  • 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: extensions/v1beta1
      kind: Deployment
      spec:
        template:
          spec:
            # ...
            serviceAccountName: strimzi-cluster-operator
            containers:
            - name: strimzi-cluster-operator
              image: strimzi/cluster-operator:latest
              imagePullPolicy: IfNotPresent
              env:
              - name: STRIMZI_NAMESPACE
                value: "*"
              # ...
  2. Create ClusterRoleBindings that grant cluster-wide access to all OpenShift projects or Kubernetes namespaces to the Cluster Operator.

    On OpenShift, use the oc adm policy command:

    oc adm policy add-cluster-role-to-user strimzi-cluster-operator-namespaced --serviceaccount strimzi-cluster-operator -n my-project
    oc adm policy add-cluster-role-to-user strimzi-entity-operator --serviceaccount strimzi-cluster-operator -n my-project
    oc adm policy add-cluster-role-to-user strimzi-topic-operator --serviceaccount strimzi-cluster-operator -n my-project

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

  3. Deploy the Cluster Operator to your OpenShift cluster.

    On OpenShift, use the oc apply command:

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

2.3. 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.

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/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
# ...

2.3.1. Deploying the Kafka cluster to OpenShift

The following procedure describes how to deploy an ephemeral or persistent Kafka cluster to OpenShift on the command line. You can also deploy clusters in the OpenShift console.

Prerequisites

  • The Cluster Operator is deployed.

Procedure

  1. If you plan to use the cluster for development or testing purposes, 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

2.4. 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 or Kafka Connect Source-2-Image (S2I) cluster to your OpenShift cluster.

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

2.4.1. Deploying Kafka Connect to your OpenShift cluster

You can deploy a Kafka Connect cluster to your OpenShift cluster by using the Cluster Operator. Kafka Connect is provided as an OpenShift template that you can deploy from the command line or the OpenShift console.

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

2.4.2. Extending Kafka Connect with 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 using one of the following methods:

  • Create a Docker image from the Kafka Connect base image.
  • Create a container image using OpenShift builds and Source-to-Image (S2I).

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

A container image for running Kafka Connect using AMQ Streams is available on Red Hat Container Catalog as registry.access.redhat.com/amq7/amq-streams-kafka-connect:1.1.0-kafka-2.1.1. You can use this 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.access.redhat.com/amq7/amq-streams-kafka-connect:1.1.0-kafka-2.1.1 as the base image:

    FROM registry.access.redhat.com/amq7/amq-streams-kafka-connect:1.1.0-kafka-2.1.1
    USER root:root
    COPY ./my-plugins/ /opt/kafka/plugins/
    USER kafka:kafka
  2. Build the container image.
  3. Push your custom image to your container registry.
  4. Edit the KafkaConnect.spec.image property of the KafkaConnect custom resource to point to the new container image. If set, this property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable referred to in the next step.

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-connect-cluster
    spec:
      #...
      image: my-new-container-image
  5. 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.

Additional resources

2.4.2.2. 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 by AMQ Streams on the Red Hat Container Catalog as registry.access.redhat.com/amq7/amq-streams-kafka-connect-s2i:1.1.0-kafka-2.1.1. 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.

2.5. 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.

2.5.1. Deploying Kafka Mirror Maker to OpenShift

On OpenShift, Kafka Mirror Maker is provided in the form of a template. It can be deployed from the template using the command-line or through the OpenShift console.

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

2.6. Deploying example clients

Prerequisites

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

Procedure

  1. Deploy the producer.

    On OpenShift, use oc run:

    oc run kafka-producer -ti --image=registry.access.redhat.com/amq7/amq-streams-kafka:1.1.0-kafka-2.1.1 --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.

    On OpenShift, use oc run:

    oc run kafka-consumer -ti --image=registry.access.redhat.com/amq7/amq-streams-kafka:1.1.0-kafka-2.1.1 --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.

2.7. Topic Operator

2.7.1. Overview of the Topic Operator component

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

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
  • if a KafkaTopic is deleted, the operator will delete the topic it describes
  • if a KafkaTopic is 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
  • if a topic is deleted from the Kafka cluster, the operator will delete the KafkaTopic describing it
  • if a topic in the Kafka cluster is changed, 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 be 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.

2.7.2. 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 4.2.5, “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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the Topic Operator using the fields described in Section B.42, “EntityTopicOperatorSpec schema reference”.
  3. Create or update the Kafka resource in OpenShift.

    On OpenShift, use oc apply:

    oc apply -f your-file

Additional resources

2.8. User Operator

The User Operator provides a way of managing Kafka users via OpenShift resources.

2.8.1. Overview of the User Operator component

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser OpenShift 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 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.

2.8.2. 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.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

2.9. 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.

2.9.1. Designating Strimzi Administrators

Prerequisites

  • AMQ Streams CustomResourceDefinitions are installed.

Procedure

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

    On 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.

    On OpenShift, use oc adm:

    oc adm policy add-cluster-role-to-user strimzi-admin user1 user2

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

3.1. Kafka cluster configuration

The full schema of the Kafka resource is described in the Section B.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 those resources to be labelled in whatever way the user requires.

3.1.1. 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 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.

3.1.1.1. 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.

3.1.1.2. 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.

3.1.2. Kafka and Zookeeper storage

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

3.1.2.1. 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/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: ephemeral
    # ...
  zookeeper:
    # ...
    storage:
      type: ephemeral
    # ...

3.1.2.2. 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

Resizing persistent storage for existing AMQ Streams clusters is not currently supported. You must decide the necessary storage size before deploying the cluster.

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
# ...

Persistent Volume Claim naming

When the persistent storage is used, it will create 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.

3.1.2.3. 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.

3.1.2.3.1. 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.

Note

Adding and removing volumes from a JBOD configuration is not currently supported.

3.1.2.3.2. 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.

Additional resources

3.1.3. 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.

3.1.3.1. 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 you should see Section 3.1.21, “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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        replicas: 3
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

If your cluster already has topics defined see Section 3.1.21, “Scaling clusters”.

3.1.4. Kafka broker configuration

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

The only options which cannot be configured are those 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.

3.1.4.1. Kafka broker configuration

Kafka broker can be configured using the config property in Kafka.spec.kafka.

This property should contain the Kafka broker configuration options as keys. The values could be in one of the following JSON types:

  • String
  • Number
  • Boolean

Users can specify and configure the options listed in Apache Kafka 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:

  • 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

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 Cluster Operator log file. All other options will be passed to Kafka.

Important

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

An example showing Kafka broker configuration

apiVersion: kafka.strimzi.io/v1alpha1
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
    # ...

3.1.4.2. Configuring Kafka brokers

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1alpha1
    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:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.5. Kafka broker listeners

AMQ Streams allows users to configure the listeners which will be enabled in Kafka brokers. Two types of listeners are supported:

  • Plain listener on port 9092 (without encryption)
  • TLS listener on port 9093 (with encryption)

3.1.5.1. Mutual TLS authentication for clients

3.1.5.1.1. Mutual TLS authentication

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

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.

3.1.5.1.2. 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.

3.1.5.2. 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 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 one each authentication exchange. This means that the exchange is resilient against replay attacks.
3.1.5.2.1. 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.

3.1.5.2.2. 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
  • When you want to have authentication for unencrypted communication

3.1.5.3. 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

When none of these properties are defined, the listener will be disabled.

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: {}
# ...

3.1.5.3.1. External listener

The external listener is used to connect to a Kafka cluster from outside of an OpenShift environment. AMQ Streams supports three types of external listeners:

  • route
  • loadbalancer
  • nodeport

Exposing Kafka using OpenShift Routes

An external listener of type route exposes Kafka by 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.

When exposing Kafka using OpenShift Routes, TLS encryption is always used.

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 3.1.5.5, “Accessing Kafka using OpenShift routes”.

Exposing Kafka using loadbalancers

External listeners of type loadbalancer expose Kafka by 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.

For more information on using loadbalancers to access Kafka, see Section 3.1.5.6, “Accessing Kafka using loadbalancers”.

Exposing Kafka using node ports

External listeners of type nodeport expose Kafka by using NodePort type Services. When exposing Kafka in this way, 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 3.1.5.7, “Accessing Kafka using node ports routes”.

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
# ...

3.1.5.3.2. Listener authentication

The listener sub-properties can also contain additional configuration. Both listeners support the authentication property. This 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 though that listener.

An example where the plain listener is configured for SCRAM-SHA authentication and the tls listener with mutual TLS authentication

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

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

3.1.5.3.3. 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 to only selected applications or namespaces, use the networkPolicyPeers field. Each listener can have a different networkPolicyPeers configuration.

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 above 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 the NetworkPolicy resource in Kubernetes. 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.

3.1.5.4. 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/v1alpha1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      plain: {}
    # ...
  zookeeper:
    # ...
  1. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.5.5. 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/v1alpha1
    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 extract secret/_cluster-name_-cluster-ca-cert --keys=ca.crt --to=- > 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

3.1.5.6. 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          external:
            type: loadbalancer
            tls: true
            # ...
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

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

    On OpenShift 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.

    On OpenShift 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.

    On OpenShift this can be done using oc extract:

    oc extract secret/cluster-name-cluster-ca-cert --keys=ca.crt --to=- > 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

3.1.5.7. Accessing Kafka using node ports routes

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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          external:
            type: nodeport
            tls: true
            # ...
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

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

    On OpenShift 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.

    On OpenShift 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.

    On OpenShift this can be done using oc extract:

    oc extract secret/cluster-name-cluster-ca-cert --keys=ca.crt --to=- > 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

3.1.5.8. 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          tls:
            networkPolicyPeers:
              - podSelector:
                  matchLabels:
                    app: kafka-client
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    On OpenShift use oc apply:

    oc apply -f your-file

Additional resources

3.1.6. 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.

3.1.6.1. Authentication

Authentication is configured as part of the listener configuration in the authentication property. When the authentication property is missing, no authentication will be enabled on given listener. The authentication mechanism which will be used is defined by the type field.

The supported authentication mechanisms are:

  • TLS client authentication
  • SASL SCRAM-SHA-512
3.1.6.1.1. TLS client authentication

TLS Client authentication can be 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
# ...

3.1.6.2. Configuring authentication in Kafka brokers

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the listeners property in the Kafka.spec.kafka resource. Add the authentication field to the listeners where you want to enable authentication. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          tls:
            authentication:
              type: tls
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.6.3. Authorization

Authorization can be configured using the authorization property in the Kafka.spec.kafka resource. When the authorization property is missing, no authorization will be enabled. When authorization is enabled it will be applied for all enabled listeners. The authorization method is defined by the type field.

Currently, the only supported authorization method is the Simple authorization.

3.1.6.3.1. Simple authorization

Simple authorization is using the SimpleAclAuthorizer plugin. SimpleAclAuthorizer is the default authorization plugin which is part of Apache Kafka. To enable simple authorization, the type field should be set to simple.

An example of Simple authorization

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

3.1.6.4. Configuring authorization in Kafka brokers

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        authorization:
          type: simple
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.7. Zookeeper replicas

Zookeeper clusters or ensembles usually run with an odd number of nodes and always requires the majority of the nodes to be available in order to maintain a quorum. Maintaining a quorum is important because when the Zookeeper cluster loses a quorum, it will stop responding to clients. As a result, a Zookeeper cluster without a quorum will cause the Kafka brokers to stop working as well. This is why having a stable and highly available Zookeeper cluster is very important for AMQ Streams.

A Zookeeper cluster is usually deployed with three, five, or seven nodes.

Three nodes
Zookeeper cluster consisting of three nodes 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 nodes
Zookeeper cluster consisting of five nodes requires at least three nodes to be up and running in order to maintain the quorum. It can tolerate two nodes being unavailable.
Seven nodes
Zookeeper cluster consisting of seven nodes 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.

3.1.7.1. 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/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    replicas: 3
    # ...

3.1.7.2. Changing number of replicas

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        replicas: 3
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.8. 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 Zookeeper documentation.

The only 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.

3.1.8.1. Zookeeper configuration

Zookeeper nodes can be configured using the config property in Kafka.spec.zookeeper. This property should contain the Zookeeper configuration options as keys. The values could be in 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 will be ignored and a warning message will be printed to the Custer Operator log file. All other options will be 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/v1alpha1
kind: Kafka
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    config:
      autopurge.snapRetainCount: 3
      autopurge.purgeInterval: 1
    # ...

3.1.8.2. Configuring Zookeeper

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        config:
          autopurge.snapRetainCount: 3
          autopurge.purgeInterval: 1
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.9. Entity Operator

The Entity Operator is responsible for managing different entities in a running Kafka cluster. The currently supported entities are:

Kafka topics
managed by the Topic Operator.
Kafka users
managed by the User Operator

Both Topic and User Operators can be deployed on their own. But the easiest way to deploy them is together with the Kafka cluster as part of the Entity Operator. The Entity Operator can include either one or both of them depending on the configuration. They will be automatically configured to manage the topics and users of the Kafka cluster with which they are deployed.

For more information about Topic Operator, see Section 4.2, “Topic Operator”. For more information about how to use Topic Operator to create or delete topics, see Chapter 5, Using the Topic Operator.

3.1.9.1. Configuration

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

The entityOperator property supports several sub-properties:

  • tlsSidecar
  • affinity
  • tolerations
  • topicOperator
  • userOperator

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 3.1.17, “TLS sidecar”.

The affinity and tolerations properties can be used to configure how OpenShift schedules the Entity Operator pod. For more details about pod scheduling, see Section 3.1.18, “Configuring pod scheduling”.

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

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

Example of basic configuration enabling both operators

apiVersion: kafka.strimzi.io/v1alpha1
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.

3.1.9.1.1. Topic Operator

Topic Operator deployment can be configured using additional options inside the topicOperator object. 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 is 90.
zookeeperSessionTimeoutSeconds
The Zookeeper session timeout in seconds. Default is 20 seconds.
topicMetadataMaxAttempts
The number of attempts for getting topics metadata from Kafka. The time between each attempt is defined as an exponential back-off. You might want to increase this value when topic creation could take more time due to its many partitions or replicas. Default is 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 3.1.16, “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 3.1.10, “CPU and memory resources”.
logging
The logging property configures the logging of the Topic Operator For more details about logging configuration, see Section 3.1.11, “Logging”.

Example of Topic Operator configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
    # ...

3.1.9.1.2. User Operator

User Operator deployment can be configured using additional options inside the userOperator object. 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 is 120.
zookeeperSessionTimeoutSeconds
The Zookeeper session timeout in seconds. Default is 6 seconds.
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 3.1.16, “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 3.1.10, “CPU and memory resources”.
logging
The logging property configures the logging of the User Operator. For more details about logging configuration, see Section 3.1.11, “Logging”.

Example of Topic Operator configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60
    # ...

3.1.9.2. 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/v1alpha1
    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.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.10. CPU and memory resources

For every deployed container, AMQ Streams allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. AMQ Streams supports two types of resources:

  • Memory
  • CPU

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

3.1.10.1. Resource limits and requests

Resource limits and requests can be configured using the resources property in 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
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
3.1.10.1.1. Resource requests

Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.

Important

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

Resource requests can be specified in the request property. The resource requests currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource request configuration

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

It is also possible to specify a resource request just for one of the resources:

An example showing resource request configuration with memory request only

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

Or:

An example showing resource request configuration with CPU request only

# ...
resources:
  requests:
    cpu: 12
# ...

3.1.10.1.2. Resource limits

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

Resource limits can be specified in the limits property. The resource limits currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource limits configuration

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

It is also possible to specify the resource limit just for one of the resources:

An example showing resource limit configuration with memory request only

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

Or:

An example showing resource limits configuration with CPU request only

# ...
resources:
  requests:
    cpu: 12
# ...

3.1.10.1.3. 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.

An example of using different CPU units

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

Note

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

For more details about the CPU specification, see the Meaning of CPU website.

3.1.10.1.4. 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
# ...

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

3.1.10.1.5. Additional resources

3.1.10.2. 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: "8"
            memory: 64Gi
          limits:
            cpu: "12"
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

3.1.11. Logging

Logging enables you to diagnose error and performance issues of AMQ Streams. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.

This section provides information about different loggers and describes how to configure log levels.

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

3.1.11.1. Using inline logging setting

Procedure

  1. Edit the YAML file to specify the loggers and their level for the required components. For example:

    apiVersion: {KafkaApiVersion}
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: inline
          loggers:
           logger.name: "INFO"
        # ...

    In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see log4j manual.

  2. Create or update the Kafka resource in OpenShift.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.11.2. Using external ConfigMap for logging setting

Procedure

  1. Edit the YAML file to specify the name of the ConfigMap which should be used for the required components. For example:

    apiVersion: {KafkaApiVersion}
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: external
          name: customConfigMap
        # ...

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

  2. Create or update the Kafka resource in OpenShift.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.11.3. Loggers

AMQ Streams consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.

Components and their loggers are listed below.

  • Kafka

    • 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

    • zookeeper.root.logger
  • Kafka Connect and Kafka Connect with Source2Image support

    • connect.root.logger.level
    • log4j.logger.org.apache.zookeeper
    • log4j.logger.org.I0Itec.zkclient
    • log4j.logger.org.reflections
  • Kafka Mirror Maker

    • mirrormaker.root.logger
  • Topic Operator

    • rootLogger.level
  • User Operator

    • rootLogger.level

It is also possible to enable and disable garbage collector (GC) logging, for more information see Section 3.1.15.1, “JVM configuration”

3.1.12. 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.

3.1.12.1. 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 represent 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        rack:
          topologyKey: failure-domain.beta.kubernetes.io/zone
        # ...
  3. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional Resources

3.1.13. Healthchecks

Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift can assume that the application is not healthy and attempt 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

3.1.13.1. 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
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

Both livenessProbe and readinessProbe support two additional options:

  • initialDelaySeconds
  • timeoutSeconds

The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.

The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.

An example of liveness and readiness probe configuration

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

3.1.13.2. 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/v1alpha1
    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.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.14. 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.

3.1.14.1. Metrics configuration

Prometheus metrics can be 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/v1alpha1
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/v1alpha1
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:
    # ...

3.1.14.2. 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.15. JVM Options

Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. AMQ Streams allows configuring some of these options.

3.1.15.1. JVM configuration

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

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

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 -Xms.
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.

3.1.15.1.1. 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
# ...

3.1.15.2. Configuring JVM options

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.16. 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.

3.1.16.1. 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
3.1.16.1.1. 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.

3.1.16.1.2. 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. strimzi/kafka-stunnel:latest container image.
  • For Zookeeper nodes:

    1. Container image specified in the STRIMZI_DEFAULT_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/zookeeper:latest 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. strimzi/zookeeper-stunnel:latest container image.
  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/user-operator:latest 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. strimzi/entity-operator-stunnel:latest container image.
  • For Kafka Connect:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/kafka-connect:latest 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. strimzi/kafka-connect-s2i:latest 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/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

3.1.16.2. 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.17. 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

3.1.17.1. 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 3.1.16, “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 3.1.13.1, “Healthcheck configurations”.

Example of TLS sidecar configuration

apiVersion: kafka.strimzi.io/v1alpha1
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:
    # ...

3.1.17.2. 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/v1alpha1
    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.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.18. Configuring pod scheduling

Important

When two application 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.

3.1.18.1. Scheduling pods based on other applications

3.1.18.1.1. 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.

3.1.18.1.2. Affinity

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

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

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.

3.1.18.1.3. 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.18.2. Scheduling pods to specific nodes

3.1.18.2.1. 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.

3.1.18.2.2. Affinity

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

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

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.

3.1.18.2.3. 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.

    On OpenShift 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.18.3. Using dedicated nodes

3.1.18.3.1. 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.

3.1.18.3.2. Affinity

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

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

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.

3.1.18.3.3. Tolerations

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

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

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

3.1.18.3.4. 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

    On OpenShift 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.

    On OpenShift 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        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.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.1.19. 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 a StatefulSet resource in OpenShift.

    + On OpenShift, use 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. Once the rolling update of all the pods is complete, the annotation is removed from the StatefulSet.

Additional resources

3.1.20. 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 a StatefulSet resource in OpenShift.

    + On OpenShift, use 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. Once the rolling update of all the pods is complete, the annotation is removed from the StatefulSet.

Additional resources

3.1.21. Scaling clusters

3.1.21.1. Scaling Kafka clusters

3.1.21.1.1. 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.

3.1.21.1.2. 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.

3.1.21.2. 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.

3.1.21.2.1. 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.

3.1.21.3. 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:

    On OpenShift:

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

    On OpenShift:

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

3.1.21.4. Creating reassignment JSON files manually

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

3.1.21.5. 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.

3.1.21.6. 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:

    On OpenShift:

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

    For example:

    cat reassignment.json | \
      oc rsh -c kafka my-cluster-kafka-0 /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.

    On OpenShift:

    oc rsh -c kafka broker-pod \
      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:

    On OpenShift:

    oc rsh -c kafka my-cluster-kafka-0 \
      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:

    On OpenShift:

    oc rsh -c kafka my-cluster-kafka-0 \
      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.

    On OpenShift:

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

    For example, on {OpenShift},

    oc rsh -c kafka my-cluster-kafka-0 \
      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.

3.1.21.7. 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:

    On OpenShift:

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

    For example:

    cat reassignment.json | \
      oc rsh -c kafka my-cluster-kafka-0 /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.

    On OpenShift:

    oc rsh -c kafka broker-pod \
      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:

    On OpenShift:

    oc rsh -c kafka my-cluster-kafka-0 \
      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:

    On OpenShift:

    oc rsh -c kafka my-cluster-kafka-0 \
      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.

    On OpenShift:

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

    For example, on {OpenShift},

    oc rsh -c kafka my-cluster-kafka-0 \
      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 rsh <BrokerN> -c kafka /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).

3.1.22. 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.

  1. Annotate the Pod resource in OpenShift.

    + On OpenShift use oc annotate:

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

Additional resources

3.1.23. 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.

  1. Annotate the Pod resource in OpenShift.

    + On OpenShift use oc annotate:

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

Additional resources

3.1.24. 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.

3.1.24.1. 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.

3.1.24.2. 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

3.1.24.3. 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
      maintenanceTimeWindows:
        - "* * 8-10 * * ?"
        - "* * 14-15 * * ?"
  2. Create or update the resource.

    On OpenShift, use oc apply:

    oc apply -f your-file

Additional resources

3.1.25. 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 B.46, “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 those resources to be labelled in whatever way the user requires.

3.2.1. Replicas

Kafka Connect clusters can run with a different number of nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running 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. When the node where Kafka Connect is deployed to crashes, OpenShift will automatically take care of rescheduling 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 already up and running.

3.2.1.1. Configuring the number of nodes

Number of Kafka Connect nodes can be 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/v1alpha1
    kind: KafkaConnectS2I
    metadata:
      name: my-cluster
    spec:
      # ...
      replicas: 3
      # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.2.2. Bootstrap servers

Kafka Connect cluster always works together with a Kafka cluster. The Kafka cluster is specified in the form of a list of bootstrap servers. On OpenShift, the 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.

The list of bootstrap servers can be configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. The servers should be a comma-separated list containing 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 a given 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/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-cluster
    spec:
      # ...
      bootstrapServers: my-cluster-kafka-bootstrap:9092
      # ...
  2. Create or update the resource.

    On OpenShift 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 will try to connect to Kafka brokers using a plain text connection. If you would prefer to use TLS additional configuration will be necessary.

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 should be stored in X509 format.

An example showing TLS configuration with multiple certificates

apiVersion: kafka.strimzi.io/v1alpha1
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/v1alpha1
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

Procedure

  1. Find out the name of the secret with the certificate which should be used for TLS Server Authentication and the key under which the certificate is stored in the secret. If such secret does not exist yet, prepare the certificate in a file and create the secret.

    On OpenShift 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/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      tls:
        trustedCertificates:
          - secretName: my-cluster-cluster-cert
            certificate: ca.crt
      # ...
  3. Create or update the resource.

    On OpenShift 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 any authentication. Authentication can be enabled in the KafkaConnect and KafkaConnectS2I resources.

3.2.4.1. Authentication support in Kafka Connect

Authentication can be configured in 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 currently supported authentication types are:

  • TLS client authentication
  • SASL based authentication using SCRAM-SHA-512 mechanism
3.2.4.1.1. TLS Client Authentication

To use the TLS client authentication, set the type property to the value tls. TLS client authentication is using TLS certificate to authenticate. The certificate has to be specified in the certificateAndKey property. It is always loaded from an OpenShift secret. Inside the secret, it has to be stored in the X509 format under two different keys: for public and private keys.

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 showing TLS client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
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. SCRAM-SHA-512 authentication

To configure Kafka Connect to use 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 such a Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Warning

Do not specify the actual password in the password field.

An example showing SCRAM-SHA-512 client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
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
  # ...

3.2.4.2. Configuring TLS client authentication in Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Find out the name of the Secret with the public and private keys which should be used for TLS Client Authentication and the keys under which they are stored in the Secret. If such a Secret does not exist yet, prepare the keys in a file and create the Secret.

    On OpenShift 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/v1alpha1
    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.

    On OpenShift 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

Procedure

  1. Find out the name of the Secret with the password which should be used for authentication and the key under which the password is stored in the Secret. If such a Secret does not exist yet, prepare a file with the password and create the Secret.

    On OpenShift this can be done using oc create:

    echo -n '1f2d1e2e67df' > <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/v1alpha1
    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 most of the options listed in Apache Kafka documentation.

The only options which cannot be configured are those related to the following areas:

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

  • String
  • Number
  • Boolean

Users can specify and configure the options listed in the Apache Kafka 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:

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

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 Connect.

Important

The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In such cases, the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object should be fixed and the cluster operator will roll out the new configuration to all Kafka Connect nodes.

Selected 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 will be automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

An example showing Kafka Connect configuration

apiVersion: kafka.strimzi.io/v1alpha1
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. 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/v1alpha1
    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.

    On OpenShift 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 specify the resources which should be reserved for it and the maximum resources that can be consumed by it. AMQ Streams supports two types of resources:

  • Memory
  • CPU

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

3.2.6.1. Resource limits and requests

Resource limits and requests can be configured using the resources property in 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
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
3.2.6.1.1. Resource requests

Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.

Important

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

Resource requests can be specified in the request property. The resource requests currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource request configuration

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

It is also possible to specify a resource request just for one of the resources:

An example showing resource request configuration with memory request only

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

Or:

An example showing resource request configuration with CPU request only

# ...
resources:
  requests:
    cpu: 12
# ...

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 be always available. The container can use the resources up to the limit only when they are available. The resource limits should be always higher than the resource requests.

Resource limits can be specified in the limits property. The resource limits currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource limits configuration

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

It is also possible to specify the resource limit just for one of the resources:

An example showing resource limit configuration with memory request only

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

Or:

An example showing resource limits configuration with CPU request only

# ...
resources:
  requests:
    cpu: 12
# ...

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.

An example of using different CPU units

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

Note

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

For more details about the CPU specification, see the Meaning of CPU website.

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
# ...

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

3.2.6.1.5. Additional resources

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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: "8"
            memory: 64Gi
          limits:
            cpu: "12"
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

3.2.7. Logging

Logging enables you to diagnose error and performance issues of AMQ Streams. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.

This section provides information about different loggers and describes how to configure log levels.

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

3.2.7.1. Using inline logging setting

Procedure

  1. Edit the YAML file to specify the loggers and their level for the required components. For example:

    apiVersion: {KafkaApiVersion}
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: inline
          loggers:
           logger.name: "INFO"
        # ...

    In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see log4j manual.

  2. Create or update the Kafka resource in OpenShift.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.2.7.2. Using external ConfigMap for logging setting

Procedure

  1. Edit the YAML file to specify the name of the ConfigMap which should be used for the required components. For example:

    apiVersion: {KafkaApiVersion}
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: external
          name: customConfigMap
        # ...

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

  2. Create or update the Kafka resource in OpenShift.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.2.7.3. Loggers

AMQ Streams consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.

Components and their loggers are listed below.

  • Kafka

    • 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

    • zookeeper.root.logger
  • Kafka Connect and Kafka Connect with Source2Image support

    • connect.root.logger.level
    • log4j.logger.org.apache.zookeeper
    • log4j.logger.org.I0Itec.zkclient
    • log4j.logger.org.reflections
  • Kafka Mirror Maker

    • mirrormaker.root.logger
  • Topic Operator

    • rootLogger.level
  • User Operator

    • rootLogger.level

It is also possible to enable and disable garbage collector (GC) logging, for more information see Section 3.2.10.1, “JVM configuration”

3.2.8. Healthchecks

Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift can assume that the application is not healthy and attempt 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
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

Both livenessProbe and readinessProbe support two additional options:

  • initialDelaySeconds
  • timeoutSeconds

The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.

The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.

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/v1alpha1
    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.

    On OpenShift 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.

3.2.9.1. Metrics configuration

Prometheus metrics can be 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/v1alpha1
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/v1alpha1
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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.2.10. JVM Options

Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. AMQ Streams allows configuring 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

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 -Xms.
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 or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift 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
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. strimzi/kafka-stunnel:latest container image.
  • For Zookeeper nodes:

    1. Container image specified in the STRIMZI_DEFAULT_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/zookeeper:latest 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. strimzi/zookeeper-stunnel:latest container image.
  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/user-operator:latest 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. strimzi/entity-operator-stunnel:latest container image.
  • For Kafka Connect:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/kafka-connect:latest 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. strimzi/kafka-connect-s2i:latest 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/v1alpha1
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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.2.12. Configuring pod scheduling

Important

When two application 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
  • Kafka.spec.zookeeper
  • Kafka.spec.entityOperator
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift 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
  • Kafka.spec.zookeeper
  • Kafka.spec.entityOperator
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

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.

    On OpenShift 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    On OpenShift 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
  • Kafka.spec.zookeeper
  • Kafka.spec.entityOperator
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

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 ca be configured using the tolerations property in following resources:

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

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

    On OpenShift 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.

    On OpenShift 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        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.

    On OpenShift 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/v1alpha1
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/v1alpha1
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/v1alpha1
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/v1alpha1
    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.

    On OpenShift 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/v1alpha1
    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.

    On OpenShift 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 B.59, “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 those resources to be labelled in whatever way the user requires.

3.3.1. Replicas

Kafka Connect clusters can run with a different number of nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running 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. When the node where Kafka Connect is deployed to crashes, OpenShift will automatically take care of rescheduling 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 already up and running.

3.3.1.1. Configuring the number of nodes

Number of Kafka Connect nodes can be 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/v1alpha1
    kind: KafkaConnectS2I
    metadata:
      name: my-cluster
    spec:
      # ...
      replicas: 3
      # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.3.2. Bootstrap servers

Kafka Connect cluster always works together with a Kafka cluster. The Kafka cluster is specified in the form of a list of bootstrap servers. On OpenShift, the 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.

The list of bootstrap servers can be configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. The servers should be a comma-separated list containing 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 a given 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/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-cluster
    spec:
      # ...
      bootstrapServers: my-cluster-kafka-bootstrap:9092
      # ...
  2. Create or update the resource.

    On OpenShift 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 will try to connect to Kafka brokers using a plain text connection. If you would prefer to use TLS additional configuration will be necessary.

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 should be stored in X509 format.

An example showing TLS configuration with multiple certificates

apiVersion: kafka.strimzi.io/v1alpha1
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/v1alpha1
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

Procedure

  1. Find out the name of the secret with the certificate which should be used for TLS Server Authentication and the key under which the certificate is stored in the secret. If such secret does not exist yet, prepare the certificate in a file and create the secret.

    On OpenShift 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/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      tls:
        trustedCertificates:
          - secretName: my-cluster-cluster-cert
            certificate: ca.crt
      # ...
  3. Create or update the resource.

    On OpenShift 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 any authentication. Authentication can be enabled in the KafkaConnect and KafkaConnectS2I resources.

3.3.4.1. Authentication support in Kafka Connect

Authentication can be configured in 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 currently supported authentication types are:

  • TLS client authentication
  • SASL based authentication using SCRAM-SHA-512 mechanism
3.3.4.1.1. TLS Client Authentication

To use the TLS client authentication, set the type property to the value tls. TLS client authentication is using TLS certificate to authenticate. The certificate has to be specified in the certificateAndKey property. It is always loaded from an OpenShift secret. Inside the secret, it has to be stored in the X509 format under two different keys: for public and private keys.

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 showing TLS client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
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. SCRAM-SHA-512 authentication

To configure Kafka Connect to use 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 such a Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Warning

Do not specify the actual password in the password field.

An example showing SCRAM-SHA-512 client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
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
  # ...

3.3.4.2. Configuring TLS client authentication in Kafka Connect

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Find out the name of the Secret with the public and private keys which should be used for TLS Client Authentication and the keys under which they are stored in the Secret. If such a Secret does not exist yet, prepare the keys in a file and create the Secret.

    On OpenShift 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/v1alpha1
    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.

    On OpenShift 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

Procedure

  1. Find out the name of the Secret with the password which should be used for authentication and the key under which the password is stored in the Secret. If such a Secret does not exist yet, prepare a file with the password and create the Secret.

    On OpenShift this can be done using oc create:

    echo -n '1f2d1e2e67df' > <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/v1alpha1
    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 most of the options listed in Apache Kafka documentation.

The only options which cannot be configured are those related to the following areas:

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

  • String
  • Number
  • Boolean

Users can specify and configure the options listed in the Apache Kafka 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:

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

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 Connect.

Important

The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In such cases, the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object should be fixed and the cluster operator will roll out the new configuration to all Kafka Connect nodes.

Selected 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 will be automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

An example showing Kafka Connect configuration

apiVersion: kafka.strimzi.io/v1alpha1
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. 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/v1alpha1
    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.

    On OpenShift 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 specify the resources which should be reserved for it and the maximum resources that can be consumed by it. AMQ Streams supports two types of resources:

  • Memory
  • CPU

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

3.3.6.1. Resource limits and requests

Resource limits and requests can be configured using the resources property in 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
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
3.3.6.1.1. Resource requests

Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.

Important

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

Resource requests can be specified in the request property. The resource requests currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource request configuration

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

It is also possible to specify a resource request just for one of the resources:

An example showing resource request configuration with memory request only

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

Or:

An example showing resource request configuration with CPU request only

# ...
resources:
  requests:
    cpu: 12
# ...

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 be always available. The container can use the resources up to the limit only when they are available. The resource limits should be always higher than the resource requests.

Resource limits can be specified in the limits property. The resource limits currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource limits configuration

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

It is also possible to specify the resource limit just for one of the resources:

An example showing resource limit configuration with memory request only

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

Or:

An example showing resource limits configuration with CPU request only

# ...
resources:
  requests:
    cpu: 12
# ...

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.

An example of using different CPU units

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

Note

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

For more details about the CPU specification, see the Meaning of CPU website.

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
# ...

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

3.3.6.1.5. Additional resources

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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: "8"
            memory: 64Gi
          limits:
            cpu: "12"
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

3.3.7. Logging

Logging enables you to diagnose error and performance issues of AMQ Streams. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.

This section provides information about different loggers and describes how to configure log levels.

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

3.3.7.1. Using inline logging setting

Procedure

  1. Edit the YAML file to specify the loggers and their level for the required components. For example:

    apiVersion: {KafkaApiVersion}
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: inline
          loggers:
           logger.name: "INFO"
        # ...

    In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see log4j manual.

  2. Create or update the Kafka resource in OpenShift.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.3.7.2. Using external ConfigMap for logging setting

Procedure

  1. Edit the YAML file to specify the name of the ConfigMap which should be used for the required components. For example:

    apiVersion: {KafkaApiVersion}
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: external
          name: customConfigMap
        # ...

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

  2. Create or update the Kafka resource in OpenShift.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.3.7.3. Loggers

AMQ Streams consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.

Components and their loggers are listed below.

  • Kafka

    • 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

    • zookeeper.root.logger
  • Kafka Connect and Kafka Connect with Source2Image support

    • connect.root.logger.level
    • log4j.logger.org.apache.zookeeper
    • log4j.logger.org.I0Itec.zkclient
    • log4j.logger.org.reflections
  • Kafka Mirror Maker

    • mirrormaker.root.logger
  • Topic Operator

    • rootLogger.level
  • User Operator

    • rootLogger.level

It is also possible to enable and disable garbage collector (GC) logging, for more information see Section 3.3.10.1, “JVM configuration”

3.3.8. Healthchecks

Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift can assume that the application is not healthy and attempt 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
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

Both livenessProbe and readinessProbe support two additional options:

  • initialDelaySeconds
  • timeoutSeconds

The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.

The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.

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/v1alpha1
    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.

    On OpenShift 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.

3.3.9.1. Metrics configuration

Prometheus metrics can be 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/v1alpha1
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/v1alpha1
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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.3.10. JVM Options

Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. AMQ Streams allows configuring 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

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 -Xms.
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 or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift 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
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. strimzi/kafka-stunnel:latest container image.
  • For Zookeeper nodes:

    1. Container image specified in the STRIMZI_DEFAULT_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/zookeeper:latest 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. strimzi/zookeeper-stunnel:latest container image.
  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/user-operator:latest 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. strimzi/entity-operator-stunnel:latest container image.
  • For Kafka Connect:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/kafka-connect:latest 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. strimzi/kafka-connect-s2i:latest 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/v1alpha1
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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.3.12. Configuring pod scheduling

Important

When two application 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
  • Kafka.spec.zookeeper
  • Kafka.spec.entityOperator
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift 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
  • Kafka.spec.zookeeper
  • Kafka.spec.entityOperator
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

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.

    On OpenShift 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    On OpenShift 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
  • Kafka.spec.zookeeper
  • Kafka.spec.entityOperator
  • KafkaConnect.spec
  • KafkaConnectS2I.spec

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 ca be configured using the tolerations property in following resources:

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

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

    On OpenShift 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.

    On OpenShift 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        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.

    On OpenShift 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/v1alpha1
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/v1alpha1
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/v1alpha1
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/v1alpha1
    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.

    On OpenShift 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/v1alpha1
    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.

    On OpenShift 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 by AMQ Streams on the Red Hat Container Catalog as registry.access.redhat.com/amq7/amq-streams-kafka-connect-s2i:1.1.0-kafka-2.1.1. 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

The full schema of the KafkaMirrorMaker resource is described in the Section B.73, “KafkaMirrorMaker schema reference”. All labels that apply to the desired KafkaMirrorMaker resource will also be applied to the OpenShift resources making up Mirror Maker. This provides a convenient mechanism for those resources to be labelled in whatever way the user requires.

3.4.1. Replicas

It is possible to run multiple Mirror Maker replicas. The number of replicas is defined in the KafkaMirrorMaker resource. You can run multiple Mirror Maker replicas to provide better availability and scalability. However, 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.

3.4.1.1. Configuring the number of replicas

The number of Kafka Mirror Maker replicas can be configured using the replicas property in KafkaMirrorMaker.spec.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      replicas: 3
      # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.4.2. Bootstrap servers

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. The bootstrap server lists can refer to Kafka clusters which do not need to be deployed in the same OpenShift cluster. They can even refer to any Kafka cluster not deployed by AMQ Streams or even deployed by AMQ Streams but on a different OpenShift cluster and accessible from 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 way used for exposing the clusters (routes, nodeports or loadbalancers).

The list of bootstrap servers can be configured in the KafkaMirrorMaker.spec.consumer.bootstrapServers and KafkaMirrorMaker.spec.producer.bootstrapServers properties. The servers should be a comma-separated list containing one or more Kafka brokers or a Service pointing to Kafka brokers specified as a <hostname>:<port> pairs.

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.

3.4.2.1. Configuring bootstrap servers

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.bootstrapServers and KafkaMirrorMaker.spec.producer.bootstrapServers properties. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      consumer:
        bootstrapServers: my-source-cluster-kafka-bootstrap:9092
      # ...
      producer:
        bootstrapServers: my-target-cluster-kafka-bootstrap:9092
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.4.3. Whitelist

You specify the list topics that the Kafka Mirror Maker has to mirror from the source to the target Kafka cluster in the KafkaMirrorMaker resource using the whitelist option. It 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.

3.4.3.1. Configuring the topics whitelist

Specify the list topics that have to be mirrored by the Kafka Mirror Maker from source to target Kafka cluster using the whitelist property in KafkaMirrorMaker.spec.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the whitelist property in the KafkaMirrorMaker resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      whitelist: "my-topic|other-topic"
      # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.4.4. Consumer group identifier

The Kafka Mirror Maker uses Kafka consumer to consume messages and it behaves like any other Kafka consumer client. It is in charge to consume the messages from the source Kafka cluster which will be mirrored to the target Kafka cluster. The consumer needs to be part of a consumer group for being assigned partitions.

3.4.4.1. Configuring the consumer group identifier

The consumer group identifier can be configured in the KafkaMirrorMaker.spec.consumer.groupId property.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.groupId property. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      consumer:
        groupId: "my-group"
      # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.4.5. Number of consumer streams

You can increase the throughput in mirroring topics by increase the number of consumer threads. More consumer threads will belong to the same configured consumer group. The topic partitions will be assigned across these consumer threads which will consume messages in parallel.

3.4.5.1. Configuring the number of consumer streams

The number of consumer streams can be configured using the KafkaMirrorMaker.spec.consumer.numStreams property.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.numStreams property. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      consumer:
        numStreams: 2
      # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.4.6. Connecting to Kafka brokers using TLS

By default, Kafka Mirror Maker will try to connect to Kafka brokers, in the source and target clusters, using a plain text connection. You must make additional configurations to use TLS.

3.4.6.1. TLS support in Kafka Mirror Maker

TLS support is configured in the tls sub-property of consumer and producer properties in KafkaMirrorMaker.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates should be stored in X.509 format.

An example showing TLS configuration with multiple certificates

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    tls:
      trustedCertificates:
        - secretName: my-source-secret
          certificate: ca.crt
        - secretName: my-other-source-secret
          certificate: certificate.crt
  # ...
  producer:
    tls:
      trustedCertificates:
        - secretName: my-target-secret
          certificate: ca.crt
        - secretName: my-other-target-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: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    tls:
      trustedCertificates:
        - secretName: my-source-secret
          certificate: ca.crt
        - secretName: my-source-secret
          certificate: ca2.crt
  # ...
  producer:
    tls:
      trustedCertificates:
        - secretName: my-target-secret
          certificate: ca.crt
        - secretName: my-target-secret
          certificate: ca2.crt
  # ...

3.4.6.2. Configuring TLS encryption in Kafka Mirror Maker

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure TLS for one or both the clusters. The following steps describe how to configure TLS on the consumer side for connecting to the source Kafka cluster:

  1. Find out the name of the secret with the certificate which should be used for TLS Server Authentication and the key under which the certificate is stored in the secret. If such secret does not exist yet, prepare the certificate in a file and create the secret.

    On OpenShift this can be done using oc create:

    oc create secret generic <my-secret> --from-file=<my-file.crt>
  2. Edit the KafkaMirrorMaker.spec.consumer.tls property. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      consumer:
        tls:
          trustedCertificates:
            - secretName: my-cluster-cluster-cert
              certificate: ca.crt
      # ...
  3. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

Repeat the above steps for configuring TLS on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.tls property.

3.4.7. Connecting to Kafka brokers with Authentication

By default, Kafka Mirror Maker will try to connect to Kafka brokers without any authentication. Authentication can be enabled in the KafkaMirrorMaker resource.

3.4.7.1. Authentication support in Kafka Mirror Maker

Authentication can be configured in the KafkaMirrorMaker.spec.consumer.authentication and KafkaMirrorMaker.spec.producer.authentication properties. The authentication property specifies the type of the authentication method which should be used and additional configuration details depending on the mechanism. The currently supported authentication types are:

  • TLS client authentication
  • SASL based authentication using SCRAM-SHA-512 mechanism
3.4.7.1.1. TLS Client Authentication

To use the TLS client authentication, set the type property to the value tls. The TLS client authentication uses TLS certificate to authenticate. The certificate has to be specified in the certificateAndKey property. It is always loaded from an OpenShift secret. Inside the secret, it has to be stored in the X.509 format separately as public and private keys.

Note

TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Mirror Maker see Section 3.4.6, “Connecting to Kafka brokers using TLS”.

An example showing TLS client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    authentication:
      type: tls
      certificateAndKey:
        secretName: my-source-secret
        certificate: public.crt
        key: private.key
  # ...
  producer:
    authentication:
      type: tls
      certificateAndKey:
        secretName: my-target-secret
        certificate: public.crt
        key: private.key
  # ...

3.4.7.1.2. SCRAM-SHA-512 authentication

To configure Kafka Mirror Maker to use SCRAM-SHA-512 authentication, set the type property to scram-sha-512. The broker listener to which clients are connecting must also be configured to use SCRAM-SHA-512 SASL authentication. 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 such a Secret and the password property contains the name of the key under which the password is stored inside the Secret.
Warning

Do not specify the actual password in the password field.

An example showing SCRAM-SHA-512 client authentication configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    authentication:
      type: scram-sha-512
      username: my-source-user
      passwordSecret:
        secretName: my-source-user
        password: my-source-password-key
  # ...
  producer:
    authentication:
      type: scram-sha-512
      username: my-producer-user
      passwordSecret:
        secretName: my-producer-user
        password: my-producer-password-key
  # ...

3.4.7.2. Configuring TLS client authentication in Kafka Mirror Maker

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator with a tls listener with tls authentication enabled

Procedure

As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure TLS client authentication for one or both the clusters. The following steps describe how to configure TLS client authentication on the consumer side for connecting to the source Kafka cluster:

  1. Find out the name of the Secret with the public and private keys which should be used for TLS Client Authentication and the keys under which they are stored in the Secret. If such a Secret does not exist yet, prepare the keys in a file and create the Secret.

    On OpenShift 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 KafkaMirrorMaker.spec.consumer.authentication property. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      consumer:
        authentication:
          type: tls
          certificateAndKey:
            secretName: my-secret
            certificate: my-public.crt
            key: my-private.key
      # ...
  3. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

Repeat the above steps for configuring TLS client authentication on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.authentication property.

3.4.7.3. Configuring SCRAM-SHA-512 authentication in Kafka Mirror Maker

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator with a listener configured for SCRAM-SHA-512 authentication
  • Username to be used for authentication

Procedure

As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure SCRAM-SHA-512 authentication for one or both the clusters. The following steps describe how to configure SCRAM-SHA-512 authentication on the consumer side for connecting to the source Kafka cluster:

  1. Find out the name of the Secret with the password which should be used for authentication and the key under which the password is stored in the Secret. If such a Secret does not exist yet, prepare a file with the password and create the Secret.

    On OpenShift this can be done using oc create:

    echo -n '1f2d1e2e67df' > <my-password.txt>
    oc create secret generic <my-secret> --from-file=<my-password.txt>
  2. Edit the KafkaMirrorMaker.spec.consumer.authentication property. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirrorMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      consumer:
        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>

Repeat the above steps for configuring SCRAM-SHA-512 authentication on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.authentication property.

3.4.8. Kafka Mirror Maker configuration

AMQ Streams allows you to customize the configuration of the Kafka Mirror Maker by editing most of the options for the related consumer and producer. Producer options are listed in Apache Kafka documentation. Consumer options are listed in Apache Kafka documentation.

The only options which cannot be configured are those related to the following areas:

  • Kafka cluster bootstrap address
  • Security (Encryption, Authentication, and Authorization)
  • Consumer group identifier

These options are automatically configured by AMQ Streams.

3.4.8.1. Kafka Mirror Maker configuration

Kafka Mirror Maker can be configured using the config sub-property in KafkaMirrorMaker.spec.consumer and KafkaMirrorMaker.spec.producer. This property should contain the Kafka Mirror Maker consumer and producer configuration options as keys. The values could be in one of the following JSON types:

  • String
  • Number
  • Boolean

Users can specify and configure the options listed in the Apache Kafka documentation and Apache Kafka 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:

  • 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 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.

An example showing Kafka Mirror Maker configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirroMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    config:
      max.poll.records: 100
      receive.buffer.bytes: 32768
  producer:
    config:
      compression.type: gzip
      batch.size: 8192
  # ...

3.4.8.2. Configuring Kafka Mirror Maker

Prerequisites

  • Two running Kafka clusters (source and target)
  • A running Cluster Operator

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.config and KafkaMirrorMaker.spec.producer.config properties. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaMirroMaker
    metadata:
      name: my-mirror-maker
    spec:
      # ...
      consumer:
        config:
          max.poll.records: 100
          receive.buffer.bytes: 32768
      producer:
        config:
          compression.type: gzip
          batch.size: 8192
      # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.4.9. CPU and memory resources

For every deployed container, AMQ Streams allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. AMQ Streams supports two types of resources:

  • Memory
  • CPU

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

3.4.9.1. Resource limits and requests

Resource limits and requests can be configured using the resources property in 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
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
3.4.9.1.1. Resource requests

Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.

Important

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

Resource requests can be specified in the request property. The resource requests currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource request configuration

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

It is also possible to specify a resource request just for one of the resources:

An example showing resource request configuration with memory request only

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

Or:

An example showing resource request configuration with CPU request only

# ...
resources:
  requests:
    cpu: 12
# ...

3.4.9.1.2. Resource limits

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

Resource limits can be specified in the limits property. The resource limits currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource limits configuration

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

It is also possible to specify the resource limit just for one of the resources:

An example showing resource limit configuration with memory request only

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

Or:

An example showing resource limits configuration with CPU request only

# ...
resources:
  requests:
    cpu: 12
# ...

3.4.9.1.3. 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.

An example of using different CPU units

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

Note

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

For more details about the CPU specification, see the Meaning of CPU website.

3.4.9.1.4. 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
# ...

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

3.4.9.1.5. Additional resources

3.4.9.2. 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: "8"
            memory: 64Gi
          limits:
            cpu: "12"
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

3.4.10. Logging

Logging enables you to diagnose error and performance issues of AMQ Streams. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.

This section provides information about different loggers and describes how to configure log levels.

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

3.4.10.1. Using inline logging setting

Procedure

  1. Edit the YAML file to specify the loggers and their level for the required components. For example:

    apiVersion: {KafkaApiVersion}
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: inline
          loggers:
           logger.name: "INFO"
        # ...

    In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see log4j manual.

  2. Create or update the Kafka resource in OpenShift.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.4.10.2. Using external ConfigMap for logging setting

Procedure

  1. Edit the YAML file to specify the name of the ConfigMap which should be used for the required components. For example:

    apiVersion: {KafkaApiVersion}
    kind: Kafka
    spec:
      kafka:
        # ...
        logging:
          type: external
          name: customConfigMap
        # ...

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

  2. Create or update the Kafka resource in OpenShift.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.4.10.3. Loggers

AMQ Streams consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.

Components and their loggers are listed below.

  • Kafka

    • 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

    • zookeeper.root.logger
  • Kafka Connect and Kafka Connect with Source2Image support

    • connect.root.logger.level
    • log4j.logger.org.apache.zookeeper
    • log4j.logger.org.I0Itec.zkclient
    • log4j.logger.org.reflections
  • Kafka Mirror Maker

    • mirrormaker.root.logger
  • Topic Operator

    • rootLogger.level
  • User Operator

    • rootLogger.level

It is also possible to enable and disable garbage collector (GC) logging, for more information see Section 3.4.12.1, “JVM configuration”

3.4.11. 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.

3.4.11.1. Metrics configuration

Prometheus metrics can be 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/v1alpha1
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/v1alpha1
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:
    # ...

3.4.11.2. 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.4.12. JVM Options

Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. AMQ Streams allows configuring some of these options.

3.4.12.1. JVM configuration

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

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

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 -Xms.
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.

3.4.12.1.1. 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
# ...

3.4.12.2. Configuring JVM options

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.4.13. 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.

3.4.13.1. 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
3.4.13.1.1. 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.

3.4.13.1.2. 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. strimzi/kafka-stunnel:latest container image.
  • For Zookeeper nodes:

    1. Container image specified in the STRIMZI_DEFAULT_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/zookeeper:latest 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. strimzi/zookeeper-stunnel:latest container image.
  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/user-operator:latest 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. strimzi/entity-operator-stunnel:latest container image.
  • For Kafka Connect:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE environment variable from the Cluster Operator configuration.
    2. strimzi/kafka-connect:latest 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. strimzi/kafka-connect-s2i:latest 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/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

3.4.13.2. 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.4.14. Configuring pod scheduling

Important

When two application 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.

3.4.14.1. Scheduling pods based on other applications

3.4.14.1.1. 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.

3.4.14.1.2. Affinity

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

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

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.

3.4.14.1.3. 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.4.14.2. Scheduling pods to specific nodes

3.4.14.2.1. 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.

3.4.14.2.2. Affinity

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

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

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.

3.4.14.2.3. 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.

    On OpenShift 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.4.14.3. Using dedicated nodes

3.4.14.3.1. 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.

3.4.14.3.2. Affinity

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

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

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.

3.4.14.3.3. Tolerations

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

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

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

3.4.14.3.4. 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

    On OpenShift 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.

    On OpenShift 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        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.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

3.4.15. List of resources created as part of Kafka Mirror Maker

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

<mirror-maker-name>-mirror-maker
Deployment which is in charge to create the Kafka Mirror Maker pods.
<mirror-maker-name>-config
ConfigMap which contains the Kafka Mirror Maker ancillary configuration 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. 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.

3.5.1. 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
  • KafkaConnect.spec
  • KafkaConnectS2I.spec
  • KafkaMirrorMakerSpec

In the following example, the template property is used to modify the labels in a Kafka broker’s StatefulSet:

apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
  labels:
    app: my-cluster
spec:
  kafka:
    # ...
    template:
      statefulset:
        metadata:
          labels:
            mylabel: myvalue
    # ...

Supported resources in Kafka cluster

When defined in a Kafka cluster, the template object can have the following fields:

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.

Supported resources in Zookeeper cluster

When defined in a Zookeeper cluster, the template object can have the following fields:

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.

Supported resources in Entity Operator

When defined in an Entity Operator , the template object can have the following fields:

deployment
Configures the Deployment used by the Entity Operator.
pod
Configures the Entity Operator Pod created by the Deployment.

Supported resources in Kafka Connect and Kafka Connect with Source2Image support

When used with Kafka Connect and Kafka Connect with Source2Image support , the template object can have the following fields:

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.

Supported resource in Kafka Mirror Maker

When used with Kafka Mirror Maker , the template object can have the following fields:

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.

3.5.2. Labels and Annotations

For every resource, you can configure additional Labels and Annotations. Labels and Annotations 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 by the user.

3.5.3. 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.

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.

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

3.5.4. 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

3.5.5. 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

3.5.6. 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
      labels:
        app: my-cluster
    spec:
      kafka:
        # ...
        template:
          statefulset:
            metadata:
              labels:
                mylabel: myvalue
        # ...
  2. Create or update the resource.

    On OpenShift, 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

The Cluster Operator is in charge of deploying a Kafka cluster alongside a Zookeeper ensemble. As part of the Kafka cluster, it can also deploy the topic operator which provides operator-style topic management via KafkaTopic custom resources. The Cluster Operator is also able to deploy a Kafka Connect cluster which connects to an existing Kafka cluster. On OpenShift such a cluster can be deployed using the Source2Image feature, providing an easy way of including more connectors.

Figure 4.1. Example Architecture diagram of the Cluster Operator.

Cluster Operator

When the Cluster Operator is up, it starts to watch for certain OpenShift resources containing the desired Kafka, Kafka Connect, or Kafka Mirror Maker cluster configuration. By default, it watches only in the same namespace or project where it is installed. The Cluster Operator can be configured to watch for more OpenShift projects or Kubernetes namespaces. Cluster Operator watches the following resources:

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

When a new Kafka, KafkaConnect, KafkaConnectS2I, or Kafka Mirror Maker resource is created in the OpenShift cluster, the operator gets the cluster description from the desired resource and starts creating a new Kafka, Kafka Connect, or Kafka Mirror Maker cluster by creating the necessary other OpenShift resources, such as StatefulSets, Services, ConfigMaps, and so on.

Every time the desired resource is updated by the user, the operator performs corresponding updates on the OpenShift resources which make up the Kafka, Kafka Connect, or Kafka Mirror Maker cluster. Resources are either patched or deleted and then re-created in order to make the Kafka, Kafka Connect, or Kafka Mirror Maker cluster reflect the state of the desired cluster resource. This might cause a rolling update which might lead to service disruption.

Finally, when the desired resource is deleted, the operator starts to undeploy the cluster and delete all the related OpenShift resources.

4.1.2. Deploying the Cluster Operator to OpenShift

Prerequisites

  • A user with cluster-admin role needs to be used, for example, 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-project/' install/cluster-operator/*RoleBinding*.yaml

    On MacOS, use:

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

Procedure

  1. Deploy the Cluster Operator

    oc apply -f install/cluster-operator -n _my-project_
    oc apply -f examples/templates/cluster-operator -n _my-project_

4.1.3. Deploying the Cluster Operator to watch multiple namespaces

Prerequisites

  • Edit the installation files according to the OpenShift project or Kubernetes 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 OpenShift projects or Kubernetes namespaces where Cluster Operator should watch for resources. For example:

    apiVersion: extensions/v1beta1
    kind: Deployment
    spec:
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: strimzi/cluster-operator:latest
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: myproject,myproject2,myproject3
  2. For all namespaces or projects which should be watched by the Cluster Operator, install the RoleBindings. Replace the my-namespace or my-project with the OpenShift project or Kubernetes namespace used in the previous step.

    On OpenShift this can be done using oc apply:

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

    On OpenShift this can be done using oc apply:

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

4.1.4. Deploying the Cluster Operator to watch all namespaces

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

Prerequisites

  • 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: extensions/v1beta1
      kind: Deployment
      spec:
        template:
          spec:
            # ...
            serviceAccountName: strimzi-cluster-operator
            containers:
            - name: strimzi-cluster-operator
              image: strimzi/cluster-operator:latest
              imagePullPolicy: IfNotPresent
              env:
              - name: STRIMZI_NAMESPACE
                value: "*"
              # ...
  2. Create ClusterRoleBindings that grant cluster-wide access to all OpenShift projects or Kubernetes namespaces to the Cluster Operator.

    On OpenShift, use the oc adm policy command:

    oc adm policy add-cluster-role-to-user strimzi-cluster-operator-namespaced --serviceaccount strimzi-cluster-operator -n my-project
    oc adm policy add-cluster-role-to-user strimzi-entity-operator --serviceaccount strimzi-cluster-operator -n my-project
    oc adm policy add-cluster-role-to-user strimzi-topic-operator --serviceaccount strimzi-cluster-operator -n my-project

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

  3. Deploy the Cluster Operator to your OpenShift cluster.

    On OpenShift, use the oc apply command:

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

4.1.5. 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.

4.1.6. Cluster Operator Configuration

The Cluster Operator can be configured through the following supported environment variables:

STRIMZI_NAMESPACE

A comma-separated list of OpenShift projects or Kubernetes namespaces that the operator should operate in. When not set, set to empty string, or to * the cluster operator will operate in all OpenShift projects or Kubernetes namespaces. The Cluster Operator deployment might use the Kubernetes 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: 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 2.0.0=strimzi/kafka:latest-kafka-2.0.0, 2.1.0=strimzi/kafka:latest-kafka-2.1.0. This is used when a Kafka.spec.kafka.version property is specified but not the Kafka.spec.kafka.image, as described in Section 3.1.16, “Container images”.
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
Optional, default strimzi/kafka-init:latest. 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 3.1.16, “Container images”.
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE
Optional, default strimzi/kafka-stunnel:latest. 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 3.1.16, “Container images”.
STRIMZI_DEFAULT_ZOOKEEPER_IMAGE
Optional, default strimzi/zookeeper:latest. 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 3.1.16, “Container images”.
STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE
Optional, default strimzi/zookeeper-stunnel:latest. 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 3.1.16, “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 2.0.0=strimzi/kafka:latest-kafka-connect-2.0.0, 2.1.0=strimzi/kafka-connect:latest-kafka-2.1.0. 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 2.0.0=strimzi/kafka:latest-kafka-connect-s2i-2.0.0, 2.1.0=strimzi/kafka-connect-s2i:latest-kafka-2.1.0. 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 2.0.0=strimzi/kafka-mirror-maker:latest-kafka-2.0.0, 2.1.0=strimzi/kafka-mirror-maker:latest-kafka-2.1.0. This is used when a KafkaMirrorMaker.spec.version property is specified but not the KafkaMirrorMaker.spec.image, as described in Section 3.4.13, “Container images”.
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
Optional, default strimzi/topic-operator:latest. 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 3.1.16, “Container images” of the Kafka resource.
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
Optional, default strimzi/user-operator:latest. 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 3.1.16, “Container images” of the Kafka resource.
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE
Optional, default strimzi/entity-operator-stunnel:latest. 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 3.1.16, “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.

4.1.7. Role-Based Access Control (RBAC)

4.1.7.1. 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.

4.1.7.2. 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.

4.1.7.3. 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: extensions/v1beta1
kind: Deployment
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
spec:
  replicas: 1
  template:
    metadata:
      labels:
        name: strimzi-cluster-operator
        strimzi.io/kind: cluster-operator
      # ...

Note line 12, where the the strimzi-cluster-operator ServiceAccount is specified as the serviceAccountName.

4.1.7.4. 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/v1beta1
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
  - kafkaconnects
  - kafkaconnects2is
  - kafkamirrormakers
  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

The second includes the permissions needed for cluster-scoped resources.

ClusterRole with cluster-scoped resources for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1beta1
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

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/v1beta1
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/v1beta1
kind: ClusterRole
metadata:
  name: strimzi-entity-operator
  labels:
    app: strimzi
rules:
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkatopics
  verbs:
  - get
  - list
  - watch
  - create
  - patch
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - events
  verbs:
  - create
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkausers
  verbs:
  - get
  - list
  - watch
  - create
  - patch
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - secrets
  verbs:
  - get
  - list
  - create
  - patch
  - update
  - delete

4.1.7.5. 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/v1beta1
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/v1beta1
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/v1beta1
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/v1beta1
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.

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
  • if a KafkaTopic is deleted, the operator will delete the topic it describes
  • if a KafkaTopic is 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
  • if a topic is deleted from the Kafka cluster, the operator will delete the KafkaTopic describing it
  • if a topic in the Kafka cluster is changed, 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 be 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. 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.

4.2.3. 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 4.2.5, “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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the Topic Operator using the fields described in Section B.42, “EntityTopicOperatorSpec schema reference”.
  3. Create or update the Kafka resource in OpenShift.

    On OpenShift, use oc apply:

    oc apply -f your-file

Additional resources

4.2.4. Configuring the Topic Operator with resource requests and limits

Prerequisites

  • A running Cluster Operator

Procedure

  1. Edit the Kafka resource specifying in the Kafka.spec.entityOperator.topicOperator.resources property the resource requests and limits you want the Topic Operator to have.

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      # kafka and zookeeper sections...
      topicOperator:
        resources:
          request:
            cpu: "1"
            memory: 500Mi
          limit:
            cpu: "1"
            memory: 500Mi
  2. Create or update the Kafka resource.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

4.2.5. 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.

    On OpenShift this can be done using oc apply:

    oc apply -f install/topic-operator
  3. Verify that the Topic Operator has been deployed successfully. On OpenShift 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

4.2.6. 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 for getting topics metadata from Kafka. The time between each attempt is defined as an exponential back-off. You might want to increase this value when topic creation could take more time due to its larger size (that is, many partitions/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 provides a way of managing Kafka users via OpenShift resources.

4.3.1. Overview of the User Operator component

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser OpenShift 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 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. 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.

    On OpenShift this can be done using oc apply:

    oc apply -f your-file

Additional resources

4.3.3. 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.

    On OpenShift this can be done using oc apply:

    oc apply -f install/user-operator
  3. Verify that the User Operator has been deployed successfully. On OpenShift 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

  • Be consistent and always operate on KafkaTopic resources or always operate on topics directly. Avoid routinely using both methods for a given topic.
  • When creating a KafkaTopic resource:

    • Remember that the name cannot be changed later.
    • Choose a name for the KafkaTopic resource that reflects the name of the topic it describes.
    • Ideally the KafkaTopic.metadata.name should be the same as its spec.topicName. To do this, the topic name will have to be a valid Kubernetes resource name.
  • When creating a topic:

    • Remember that the name cannot be changed later.
    • It is best to use a name that is a valid Kubernetes resource name, otherwise the operator will have to modify the name when creating the corresponding KafkaTopic.

5.2. Creating a topic

This procedure describes how to create a Kafka topic using a KafkaTopic OpenShift resource.

Prerequisites

  • A running Kafka cluster.
  • A running Topic Operator.

Procedure

  1. Prepare a file containing the KafkaTopic to be created

    An example KafkaTopic

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaTopic
    metadata:
      name: orders
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      partitions: 10
      replicas: 2

    Note

    It is recommended to use a topic name that is a valid OpenShift resource name. Doing this means that it is not necessary to set the KafkaTopic.spec.topicName property. In any case the KafkaTopic.spec.topicName cannot be changed after creation.

    Note

    The KafkaTopic.spec.partitions cannot be decreased.

  2. Create the KafkaTopic resource in OpenShift.

    On 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

  • A running Kafka cluster.
  • A running Topic Operator.
  • An existing KafkaTopic to be changed.

Procedure

  1. Prepare a file containing the desired KafkaTopic

    An example KafkaTopic

    apiVersion: kafka.strimzi.io/v1alpha1
    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.

    On 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.
  • An existing KafkaTopic to be deleted.

Procedure

  1. Delete the KafkaTopic resource in OpenShift.

    On OpenShift this can be done using oc:

    oc delete kafkatopic your-topic-name
    Note

    Whether the topic can actually be deleted depends on the value of the delete.topic.enable Kafka broker configuration, specified in the Kafka.spec.kafka.config property.

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 OpenShift 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 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 for clients

6.2.1. Mutual TLS authentication

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

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.

6.2.2. 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

  • A running Kafka cluster configured with a listener using TLS authentication.
  • A running User Operator.

Procedure

  1. Prepare a YAML file containing the KafkaUser to be created.

    An example KafkaUser

    apiVersion: kafka.strimzi.io/v1alpha1
    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.

    On 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 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 one 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
  • When you want to have authentication for unencrypted communication

6.5. Creating a Kafka user with SCRAM SHA authentication

Prerequisites

  • A running Kafka cluster configured with a listener using SCRAM SHA authentication.
  • A running User Operator.

Procedure

  1. Prepare a YAML file containing the KafkaUser to be created.

    An example KafkaUser

    apiVersion: kafka.strimzi.io/v1alpha1
    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.

    On 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

  • A running Kafka cluster.
  • A running User Operator.
  • An existing KafkaUser to be changed

Procedure

  1. Prepare a YAML file containing the desired KafkaUser.

    apiVersion: kafka.strimzi.io/v1alpha1
    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.

    + On 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

  • A running Kafka cluster.
  • A running User Operator.
  • An existing KafkaUser to be deleted.

Procedure

  1. Delete the KafkaUser resource in OpenShift.

    On OpenShift this can be done using oc:

    oc delete kafkauser your-user-name

6.8. Kafka User resource

The KafkaUser resource is used to declare a user with its authentication mechanism, authorization mechanism, and access rights.

6.8.1. Authentication

Authentication is configured using the authentication property in KafkaUser.spec. The authentication mechanism enabled for this user will be specified using the type field. Currently, the only supported authentication mechanisms are the TLS Client Authentication mechanism and the SCRAM-SHA-512 mechanism.

When no authentication mechanism is specified, User Operator will not create the user or its credentials.

6.8.1.1. TLS Client Authentication

To use TLS client authentication, set the type field to tls.

An example of KafkaUser with enabled TLS Client Authentication

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: tls
  # ...

When the user is created by the User Operator, it will create a new secret with the same name as the KafkaUser resource. The secret will contain a public and private key which should be used for the TLS Client Authentication. Bundled with them will be the public key of the client certification authority which was used to sign the user certificate. All keys will be in X509 format.

An example of the Secret with user credentials

apiVersion: v1
kind: Secret
metadata:
  name: my-user
  labels:
    strimzi.io/kind: KafkaUser
    strimzi.io/cluster: my-cluster
type: Opaque
data:
  ca.crt: # Public key of the Clients CA
  user.crt: # Public key of the user
  user.key: # Private key of the user

6.8.1.2. SCRAM-SHA-512 Authentication

To use SCRAM-SHA-512 authentication mechanism, set the type field to scram-sha-512.

An example of KafkaUser with enabled SCRAM-SHA-512 authentication

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: scram-sha-512
  # ...

When the user is created by the User Operator, the User Operator will create a new secret with the same name as the KafkaUser resource. The secret will contain the generated password in the password key.

An example of the Secret with user credentials

apiVersion: v1
kind: Secret
metadata:
  name: my-user
  labels:
    strimzi.io/kind: KafkaUser
    strimzi.io/cluster: my-cluster
type: Opaque
data:
  password: # Generated password

6.8.2. Authorization

Authorization is configured using the authorization property in KafkaUser.spec. The authorization type enabled for this user will be specified using the type field. Currently, the only supported authorization type is the Simple authorization.

When no authorization is specified, the User Operator will not provision any access rights for the user.

6.8.2.1. Simple Authorization

To use Simple Authorization, set the type property to simple. Simple authorization is using the SimpleAclAuthorizer plugin. SimpleAclAuthorizer is the default authorization plugin which is part of Apache Kafka. Simple Authorization allows you to specify list of ACL rules in the acls property.

The acls property should contain a list of AclRule objects. AclRule specifies the access rights whcih will be granted to the user. The AclRule object contains following properties:

type
Specifies the type of the ACL rule. The type can be either allow or deny. The type field is optional and when not specified, the ACL rule will be treated as allow rule.
operation

Specifies the operation which will be allowed or denied. Following operations are supported:

  • Read
  • Write
  • Delete
  • Alter
  • Describe
  • All
  • IdempotentWrite
  • ClusterAction
  • Create
  • AlterConfigs
  • DescribeConfigs

    Note

    Not every operation can be combined with every resource.

host
Specifies a remote host from which is the rule allowed or denied. Use * to allow or deny the operation from all hosts. The host field is optional and when not specified, the value * will be used as default.
resource

Specifies the resource for which the rule applies. Simple Authorization supports four different resource types:

  • Topics
  • Consumer Groups
  • Clusters
  • Transactional IDs

    The resource type can be specified in the type property. Use topic for Topics, group for Consumer Groups, cluster for clusters, and transactionalId for Transactional IDs.

    Additionally, Topic, Group, and Transactional ID resources allow you to specify the name of the resource for which the rule applies. The name can be specified in the name property. The name can be either specified as literal or as a prefix. To specify the name as literal, set the patternType property to the value literal. Literal names will be taken exactly as they are specified in the name field. To specify the name as a prefix, set the patternType property to the value prefix. Prefix type names will use the value from the name only a prefix and will apply the rule to all resources with names starting with the value. The cluster type resources have no name.

For more details about SimpleAclAuthorizer, its ACL rules and the allowed combinations of resources and operations, see Authorization and ACLs.

For more information about the AclRule object, see AclRule schema reference.

An example KafkaUser

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  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: prefix
        operation: Read

6.8.3. Additional resources

Chapter 7. Security

AMQ Streams supports encrypted communication between the Kafka and AMQ Streams components using the TLS protocol. Communication between Kafka brokers (interbroker communication), between Zookeeper nodes (internodal communication), and between these and the AMQ Streams operators is always encrypted. Communication between Kafka clients and Kafka brokers is encrypted according to how the cluster is configured. For the Kafka and AMQ Streams components, TLS certificates are also used for authentication.

The Cluster Operator automatically sets up TLS certificates to enable encryption and authentication within your cluster. It also sets up other TLS certificates if you want to enable encryption or TLS authentication between Kafka brokers and clients.

7.1. Certificate Authorities

To support encryption, each AMQ Streams component needs its own private keys and public key certificates. All component certificates are signed by a Certificate Authority (CA) called the cluster CA.

Similarly, each Kafka client application connecting using TLS client authentication needs private keys and certificates. The clients CA is used to sign the certificates for the Kafka clients.

7.1.1. CA certificates

Each CA has a self-signed public key certificate.

Kafka brokers are configured to trust certificates signed by either the clients CA or the cluster CA. Components to which clients do not need to connect, such as Zookeeper, only trust certificates signed by the cluster CA. Client applications that perform mutual TLS authentication have to trust the certificates signed by the cluster CA.

By default, AMQ Streams generates and renews CA certificates automatically. You can configure the management of CA certificates in the Kafka.spec.clusterCa and Kafka.spec.clientsCa objects.

7.2. Certificates and Secrets

AMQ Streams stores CA, component and Kafka client private keys and certificates in Secrets. All keys are 2048 bits in size.

CA certificate validity periods, expressed as a number of days after certificate generation, can be configured in Kafka.spec.clusterCa.validityDays and Kafka.spec.clusterCa.validityDays.

7.2.1. Cluster CA Secrets

Table 7.1. Cluster CA Secrets managed by the Cluster Operator in <cluster>

Secret nameField within SecretDescription

<cluster>-cluster-ca

ca.key

The current private key for the cluster CA.

<cluster>-cluster-ca-cert

ca.crt

The current certificate for the cluster CA.

<cluster>-kafka-brokers

<cluster>-kafka-<num>.crt

Certificate for Kafka broker pod <num>. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

<cluster>-kafka-<num>.key

Private key for Kafka broker pod <num>.

<cluster>-zookeeper-nodes

<cluster>-zookeeper-<num>.crt

Certificate for Zookeeper node <num>. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

<cluster>-zookeeper-<num>.key

Private key for Zookeeper pod <num>.

<cluster>-entity-operator-certs

entity-operator_.crt

Certificate for TLS communication between the Entity Operator and Kafka or Zookeeper. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

entity-operator.key

Private key for TLS communication between the Entity Operator and Kafka or Zookeeper

The CA certificates in <cluster>-cluster-ca-cert must be trusted by Kafka client applications so that they validate the Kafka broker certificates when connecting to Kafka brokers over TLS.

Note

Only <cluster>-cluster-ca-cert needs to be used by clients. All other Secrets in the table above only need to be accessed by the AMQ Streams components. You can enforce this using OpenShift role-based access controls if necessary.

7.2.2. Client CA Secrets

Table 7.2. Clients CA Secrets managed by the Cluster Operator in <cluster>

Secret nameField within SecretDescription

<cluster>-clients-ca

ca.key

The current private key for the clients CA.

<cluster>-clients-ca-cert

ca.crt

The current certificate for the clients CA.

The certificates in <cluster>-clients-ca-cert are those which the Kafka brokers trust.

Note

<cluster>-cluster-ca is used to sign certificates of client applications. It needs to be accessible to the AMQ Streams components and for administrative access if you are intending to issue application certificates without using the User Operator. You can enforce this using OpenShift role-based access controls if necessary.

7.2.3. User Secrets

Table 7.3. Secrets managed by the User Operator

Secret nameField within SecretDescription

<user>

user.crt

Certificate for the user, signed by the clients CA

user.key

Private key for the user

7.3. Installing your own CA certificates

This procedure describes how to install your own CA certificates and private keys instead of using CA certificates and private keys generated by the Cluster Operator.

Prerequisites

  • The Cluster Operator is running.
  • A Kafka cluster is not yet deployed.
  • Your own X.509 certificates and keys in PEM format for the cluster CA or clients CA.

    • If you want to use a cluster or clients CA which is not a Root CA, you have to include the whole chain in the certificate file. The chain should be in the following order:

      1. The cluster or clients CA
      2. One or more intermediate CAs
      3. The root CA
    • All CAs in the chain should be configured as a CA in the X509v3 Basic Constraints.

Procedure

  1. Put your CA certificate in the corresponding Secret (<cluster>-cluster-ca-cert for the cluster CA or <cluster>-clients-ca-cert for the clients CA):

    On OpenShift, run the following commands:

    # Delete any existing secret (ignore "Not Exists" errors)
    oc delete secret <ca-cert-secret>
    # Create the new one
    oc create secret generic <ca-cert-secret> --from-file=ca.crt=<ca-cert-file>
  2. Put your CA key in the corresponding Secret (<cluster>-cluster-ca for the cluster CA or <cluster>-clients-ca for the clients CA)

    On OpenShift, run the following commands:

    # Delete the existing secret
    oc delete secret <ca-key-secret>
    # Create the new one
    oc create secret generic <ca-key-secret> --from-file=ca.key=<ca-key-file>
  3. Label both Secrets with labels strimzi.io/kind=Kafka and strimzi.io/cluster=<my-cluster>:

    On OpenShift, run the following commands:

    oc label secret <ca-cert-secret> strimzi.io/kind=Kafka strimzi.io/cluster=<my-cluster>
    oc label secret <ca-key-secret> strimzi.io/kind=Kafka strimzi.io/cluster=<my-cluster>
  4. Create the Kafka resource for your cluster, configuring either the Kafka.spec.clusterCa or the Kafka.spec.clientsCa object to not use generated CAs:

    Example fragment Kafka resource configuring the cluster CA to use certificates you supply for yourself

    kind: Kafka
    version: v1alpha1
    spec:
      # ...
      clusterCa:
        generateCertificateAuthority: false

7.4. Certificate renewal

The cluster CA and clients CA certificates are only valid for a limited time period, known as the validity period. This is usually defined as a number of days since the certificate was generated. For auto-generated CA certificates, you can configure the validity period in Kafka.spec.clusterCa.validityDays and Kafka.spec.clientsCa.validityDays. The default validity period for both certificates is 365 days. Manually-installed CA certificates should have their own validity period defined.

When a CA certificate expires, components and clients which still trust that certificate will not accept TLS connections from peers whose certificate were signed by the CA private key. The components and clients need to trust the new CA certificate instead.

To allow the renewal of CA certificates without a loss of service, the Cluster Operator will initiate certificate renewal before the old CA certificates expire. You can configure the renewal period in Kafka.spec.clusterCa.renewalDays and Kafka.spec.clientsCa.renewalDays (both default to 30 days). The renewal period is measured backwards, from the expiry date of the current certificate.

Not Before                                     Not After
    |                                              |
    |<--------------- validityDays --------------->|
                              <--- renewalDays --->|

The behavior of the Cluster Operator during the renewal period depends on whether the relevant setting is enabled, in either Kafka.spec.clusterCa.generateCertificateAuthority or Kafka.spec.clientsCa.generateCertificateAuthority.

7.4.1. Renewal process with generated CAs

The Cluster Operator performs the following process to renew CA certificates:

  1. Generate a new CA certificate, but retaining the existing key. The new certificate replaces the old one with the name ca.crt within the corresponding Secret.
  2. Generate new client certificates (for Zookeeper nodes, Kafka brokers, and the Entity Operator). This is not strictly necessary because the signing key has not changed, but it keeps the validity period of the client certificate in sync with the CA certificate.
  3. Restart Zookeeper nodes so that they will trust the new CA certificate and use the new client certificates.
  4. Restart Kafka brokers so that they will trust the new CA certificate and use the new client certificates.
  5. Restart the Topic and User Operators so that they will trust the new CA certificate and use the new client certificates.

7.4.2. Client applications

The Cluster Operator is not aware of all the client applications using the Kafka cluster.

Important

Depending on how your applications are configured, you might need take action to ensure they continue working after certificate renewal.

Consider the following important points to ensure that client applications continue working.

  • When they connect to the cluster, client applications must trust the cluster CA certificate published in <cluster>-cluster-ca-cert.
  • When using the User Operator to provision client certificates, client applications must use the current user.crt and user.key published in their <user> Secret when they connect to the cluster. For workloads running inside the same OpenShift cluster this can be achieved by mounting the secrets as a volume and having the client Pods construct their key- and truststores from the current state of the Secrets. For more details on this procedure, see Section 7.6, “Configuring internal clients to trust the cluster CA”.
  • When renewing client certificates, if you are provisioning client certificates and keys manually, you must generate new client certificates and ensure the new certificates are used by clients within the renewal period. Failure to do this by the end of the renewal period could result in client applications being unable to connect.

7.5. TLS connections

7.5.1. Zookeeper communication

Zookeeper does not support TLS itself. By deploying an stunnel sidecar within every Zookeeper pod, the Cluster Operator is able to provide data encryption and authentication between Zookeeper nodes in a cluster. Zookeeper communicates only with the stunnel sidecar over the loopback interface. The stunnel sidecar then proxies all Zookeeper traffic, TLS decrypting data upon entry into a Zookeeper pod and TLS encrypting data upon departure from a Zookeeper pod.

This TLS encrypting stunnel proxy is instantiated from the spec.zookeeper.stunnelImage specified in the Kafka resource.

7.5.2. Kafka interbroker communication

Communication between Kafka brokers is done through the REPLICATION listener on port 9091, which is encrypted by default.

Communication between Kafka brokers and Zookeeper nodes uses an stunnel sidecar, as described above.

7.5.3. Topic and User Operators

Like the Cluster Operator, the Topic and User Operators each use an `stunnel` sidecar when communicating with Zookeeper.
The Topic Operator connects to Kafka brokers on port 9091.

7.5.4. Kafka Client connections

Encrypted communication between Kafka brokers and clients running within the same OpenShift cluster is provided through the CLIENTTLS listener on port 9093.

Encrypted communication between Kafka brokers and clients running outside the same OpenShift cluster is provided through the EXTERNAL listener on port 9094.

Note

You can use the CLIENT listener on port 9092 for unencrypted communication with brokers.

7.6. Configuring internal clients to trust the cluster CA

This procedure describes how to configure a Kafka client that resides inside the OpenShift cluster — connecting to the tls listener on port 9093 — to trust the cluster CA certificate.

The easiest way to achieve this for an internal client is to use a volume mount to access the Secrets containing the necessary certificates and keys.

Prerequisites

  • The Cluster Operator is running.
  • A Kafka resource within the OpenShift cluster.
  • A Kafka client application inside the OpenShift cluster which will connect using TLS and needs to trust the cluster CA certificate.

Procedure

  1. When defining the client Pod
  2. The Kafka client has to be configured to trust certificates signed by this CA. For the Java-based Kafka Producer, Consumer, and Streams APIs, you can do this by importing the CA certificate into the JVM’s truststore using the following keytool command:

    keytool -keystore client.truststore.jks -alias CARoot -import -file ca.crt
  3. To configure the Kafka client, specify the following properties:

    • security.protocol: SSL when using TLS for encryption (with or without TLS authentication), or security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.
    • ssl.truststore.location: the truststore location where the certificates were imported.
    • ssl.truststore.password: the password for accessing the truststore. This property can be omitted if it is not needed by the truststore.

Additional resources

7.7. Configuring external clients to trust the cluster CA

This procedure describes how to configure a Kafka client that resides outside the OpenShift cluster – connecting to the external listener on port 9094 – to trust the cluster CA certificate.

You can use the same procedure to configure clients inside OpenShift, which connect to the tls listener on port 9093, but it is usually more convenient to access the Secrets using a volume mount in the client Pod.

Follow this procedure when setting up the client and during the renewal period, when the old clients CA certificate is replaced.

Important

The <cluster-name>-cluster-ca-cert Secret will contain more than one CA certificate during CA certificate renewal. Clients must add all of them to their truststores.

Prerequisites

  • The Cluster Operator is running.
  • A Kafka resource within the OpenShift cluster.
  • A Kafka client application outside the OpenShift cluster which will connect using TLS and needs to trust the cluster CA certificate.

Procedure

  1. Extract the cluster CA certificate from the generated <cluster-name>-cluster-ca-cert Secret.

    On OpenShift, run the following command to extract the certificates:

    oc extract secret/<cluster-name>-cluster-ca-cert --keys ca.crt
  2. The Kafka client has to be configured to trust certificates signed by this CA. For the Java-based Kafka Producer, Consumer, and Streams APIs, you can do this by importing the CA certificates into the JVM’s truststore using the following keytool command:

    keytool -keystore client.truststore.jks -alias CARoot -import -file ca.crt
  3. To configure the Kafka client, specify the following properties:

    • security.protocol: SSL when using TLS for encryption (with or without TLS authentication), or security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.
    • ssl.truststore.location: the truststore location where the certificates were imported.
    • ssl.truststore.password: the password for accessing the truststore. This property can be omitted if it is not needed by the truststore.

Additional resources

Chapter 8. AMQ Streams and Kafka upgrades

Each version of AMQ Streams supports a range of versions of Apache Kafka. You can upgrade to a higher Kafka version as long as that version is supported by your version of AMQ Streams. In some cases, you can also downgrade to a lower supported Kafka version.

When a newer version of AMQ Streams is available, it may provide support for newer versions of Kafka. Therefore, you will need to upgrade to the new version of AMQ Streams before you can upgrade to a higher supported Kafka version. Upgrading the version of AMQ Streams is done by upgrading the Cluster Operator deployment to the new version.

8.1. Upgrading the Cluster Operator from 1.0.0 to 1.1.0

This procedure will describe how to upgrade a Cluster Operator deployment from version 1.0.0 to version 1.1.0.

The availability of Kafka clusters managed by the Cluster Operator is not affected by the upgrade operation.

Prerequisites

  • An existing version 1.0.0 Cluster Operator deployment to be upgraded.

Procedure

  1. Update your existing Kafka, KafkaConnect, KafkaConnectS2I, and KafkaMirrorMaker resources, as follows:

    • Add Kafka.spec.kafka.version with the value 2.0.0
    • Add KafkaConnect.spec.version with the value 2.0.0
    • Add KafkaConnectS2I.spec.version with the value 2.0.0
    • Add KafkaMirrorMaker.spec.version with the value 2.0.0

      Wait for the associated rolling updates to complete.

  2. Backup the existing Cluster Operator resources.

    On OpenShift use oc get:

    oc get all -l app=strimzi -o yaml > strimzi-backup.yaml
  3. Update the Cluster Operator. You will need to modify the installation files according to the namespace the Cluster Operator is running 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

    If you modified one or more environment variables in your existing Cluster Operator Deployment, edit install/cluster-operator/050-Deployment-cluster-operator.yaml to reflect the changes that you made.

  4. When you have an updated configuration you can deploy it along with the rest of the install resources.

    On OpenShift use oc apply:

    oc apply -f install/cluster-operator

    Wait for the associated rolling updates to complete.

  5. Update existing resources to cope with deprecated custom resource properties.

    • If you have Kafka resources that specify Kafka.spec.topicOperator, rewrite them to use Kafka.spec.entityOperator.topicOperator instead.

8.2. Upgrading and downgrading Kafka versions

8.2.1. Versions and images overview

The Cluster Operator embeds knowledge of different Kafka versions, but not of the corresponding images in which those versions are provided. Using the STRIMZI_KAFKA_IMAGES environment variable, the Cluster Operator is configured with a mapping between the Kafka version and the image to be used when that version is requested in a given Kafka resource.

Each Kafka resource can be configured with a Kafka.spec.kafka.version. If Kafka.spec.kafka.image is not configured then the default image for the given version will be used. If Kafka.spec.kafka.image is given, this overrides the default.

Warning

The Cluster Operator cannot validate that an image actually contains a Kafka broker of the expected version. Take care to ensure that the given image corresponds to the given Kafka version.

8.2.2. Kafka upgrades using the Cluster Operator

How the Cluster Operator will perform an upgrade depends on the differences between:

  • The interbroker protocol version of the two Kafka versions
  • The log message format version of the two Kafka versions
  • The version of Zookeeper used by the two Kafka versions

When each of these versions is the same, as is typically the case for a patch level upgrade, then the Cluster Operator can perform the upgrade using a single rolling update of the Kafka brokers. When one or more of these versions differ, the Cluster Operator will require two or three rolling updates of the Kafka brokers to perform the upgrade.

8.2.3. Upgrading brokers to a newer Kafka version

This procedure describes how to upgrade a AMQ Streams Kafka cluster from one version of Kafka to a higher version; for example, from 2.0.0 to 2.1.1.

Prerequisites

  • The Cluster Operator, which supports both versions of Kafka, is up and running.
  • A Kafka resource to be upgraded.
  • You have checked that your Kafka.spec.kafka.config contains no options that are not supported in the version of Kafka that you are upgrading to.
  • Determine whether the new Kafka version has a different log message format version than the previous version. See the following table for help.
Kafka versionInterbroker protocol versionLog message format versionZookeeper version

2.0.0

2.0

2.0

3.4.13

2.1.1

2.1

2.1

3.4.13

Procedure

  1. If the log message format version of the previous Kafka version is the same as that of the new Kafka version, proceed to the next step. Otherwise, ensure that the Kafka.spec.kafka.config has the log.message.format.version configured to the default for the previous version.

    For example, if upgrading from Kafka 2.0.0:

    apiVersion: v1alpha1
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.0.0
        config:
          log.message.format.version: "2.0"
          # ...
    Note

    You must format the value of log.message.format.version as a string to prevent it from being interpreted as a number.

    If the log.message.format.version is unset, set it and then wait for the resulting rolling restart of the Kafka cluster to complete.

  2. Change the Kafka.spec.kafka.version to specify the new version, but leave the log.message.format.version as the previous version. If the image to be used is different from the image for the given version of Kafka configured in the Cluster Operator’s STRIMZI_KAFKA_IMAGES then configure the Kafka.spec.kafka.image as well.

    For example, if upgrading from Kafka 2.0.0 to 2.1.1:

    apiVersion: v1alpha1
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.1.1 1
        config:
          log.message.format.version: "2.0" 2
          # ...
    1
    This is changed to the new version
    2
    This remains at the previous version
  3. Wait for the Cluster Operator to upgrade the cluster. If the old and new versions of Kafka have different interbroker protocol versions, look in the Cluster Operator logs for an INFO level message in the following format:

    Reconciliation #<num>(watch) Kafka(<namespace>/<name>): Kafka version upgrade from <from-version> to <to-version>, phase 2 of 2 completed

    Alternatively, if the old and new versions of Kafka have the same interbroker protocol version, look in the Cluster Operator logs for an INFO level message in the following format:

    Reconciliation #<num>(watch) Kafka(<namespace>/<name>): Kafka version upgrade from <from-version> to <to-version>, phase 1 of 1 completed

    For example, using grep:

    oc logs -f <cluster-operator-pod-name> | grep -E "Kafka version upgrade from [0-9.]+ to [0-9.]+, phase ([0-9]+) of \1 completed"
  4. Upgrade all your client applications to use the new version of the client libraries.

    Warning

    You cannot downgrade after completing this step. If, for whatever reason, you need to revert the update at this point, follow the procedure Section 8.2.5, “Downgrading brokers to an older Kafka version”.

  5. If the log message format versions, as identified in step 1, are the same proceed to the next step. Otherwise change the log.message.format.version in Kafka.spec.kafka.config to the default version for the new version of Kafka now being used. For example, if upgrading to 2.1.1:

    apiVersion: v1alpha1
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.1.1
        config:
          log.message.format.version: "2.1"
          # ...
  6. Wait for the Cluster Operator to update the cluster.

Additional resources

8.2.4. Kafka downgrades using the Cluster Operator

Whether and how the Cluster Operator will perform a downgrade depends on the differences between:

  • The interbroker protocol version of the two Kafka versions
  • The log message format version of the two Kafka versions
  • The version of Zookeeper used by the two Kafka versions

If the downgraded version of Kafka has the same log message format version then downgrading is always possible. In this case the Cluster Operator will be able to downgrade by performing a single rolling restart of the brokers.

If the downgraded version of Kafka has a different log message format version, then downgrading is only possible if the running cluster has always had log.message.format.version set to the version used by the downgraded version. This is typically only the case if the upgrade procedure was aborted before the log.message.format.version was changed. In this case the downgrade will require two rolling restarts of the brokers if the interbroker protocol of the two versions is different, or a single rolling restart if they are the same.

8.2.5. Downgrading brokers to an older Kafka version

Note

The previous version is the Kafka version that you are downgrading to (such as 2.0.0). The new version is the version that you are downgrading from (such as 2.1.1).

You can downgrade a AMQ Streams Kafka cluster from one version to a lower version; for example, from 2.1.1 to 2.0.0. Downgrading is not possible if the new version has ever used a log.message.format.version that is not supported by the previous version (including where the default value for log.message.format.version is used).

For example:

apiVersion: v1alpha1
kind: Kafka
spec:
  # ...
  kafka:
    version: 2.1.1
    config:
      log.message.format.version: "2.0"
      # ...

This resource can be downgraded to Kafka version 2.0.0 because the log.message.format.version has not been changed. If the log.message.format.version were absent (so that the parameter took its default value for a 2.1.0 broker of 2.1), or was "2.1" then downgrade would not be possible.

Prerequisites

  • The Cluster Operator is up and running.
  • A Kafka resource to be downgraded.
  • The Kafka.spec.kafka.config has a log.message.format.version that is supported by the version being downgraded to.
  • You have checked that your Kafka.spec.kafka.config contains no options which are not supported in the version of Kafka being downgraded to.

Procedure

  1. Change the Kafka.spec.kafka.version to specify the previous version. If the image to be used is different from the image for the given version of Kafka configured in the Cluster Operator’s STRIMZI_KAFKA_IMAGES then configure the Kafka.spec.kafka.image as well.

    For example, if downgrading from Kafka 2.1.1 to 2.0.0:

    apiVersion: v1alpha1
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.0.0 1
        config:
          log.message.format.version: "2.0" 2
          # ...
    1
    This is changed to the previous version
    2
    This is unchanged
    Note

    It is necessary to format the value of log.message.format.version as a string to prevent it from being interpreted as a number.

  2. Wait for the Cluster Operator to downgrade the cluster. If both the previous and new versions of Kafka have a different interbroker protocol version, check the Cluster Operator logs for an INFO level message in the following format:

    Reconciliation #<num>(watch) Kafka(<namespace>/<name>): Kafka version downgrade from <from-version> to <to-version>, phase 2 of 2 completed

    Alternatively, if both the previous and new versions of Kafka have the same interbroker protocol version look in the Cluster Operator logs for an INFO level message in the following format:

    Reconciliation #<num>(watch) Kafka(<namespace>/<name>): Kafka version downgrade from <from-version> to <to-version>, phase 1 of 1 completed

    For example, using grep:

    oc logs -f <cluster-operator-pod-name> | grep -E "Kafka version downgrade from [0-9.]+ to [0-9.]+, phase ([0-9]+) of \1 completed"
  3. Downgrade each client application to use the previous version of the client libraries.

Appendix A. Frequently Asked Questions

A.1. Cluster Operator

A.1.1. Why do I need cluster admin privileges to install AMQ Streams?

To install AMQ Streams, you must have the ability to create Custom Resource Definitions (CRDs). CRDs instruct OpenShift about resources that are specific to AMQ Streams, such as Kafka, KafkaConnect, and so on. Because CRDs are a cluster-scoped resource rather than being scoped to a particular OpenShift namespace, they typically require cluster admin privileges to install.

In addition, you must also have the ability to create ClusterRoles and ClusterRoleBindings. Like CRDs, these are cluster-scoped resources that typically require cluster admin privileges.

The cluster administrator can inspect all the resources being installed (in the /install/ directory) to assure themselves that the ClusterRoles do not grant unnecessary privileges. For more information about why the Cluster Operator installation resources grant the ability to create ClusterRoleBindings see the following question.

After installation, the Cluster Operator will run as a regular Deployment; any non-admin user with privileges to access the Deployment can configure it.

By default, normal users will not have the privileges necessary to manipulate the custom resources, such as Kafka, KafkaConnect and so on, which the Cluster Operator deals with. These privileges can be granted using normal RBAC resources by the cluster administrator. See this procedure for more details of how to do this.

A.1.2. Why does the Cluster Operator require the ability to create ClusterRoleBindings? Is that not a security risk?

OpenShift has built-in privilege escalation prevention. That means that the Cluster Operator cannot grant privileges it does not have itself. Which in turn means that the Cluster Operator needs to have the privileges necessary for all the components it orchestrates.

In the context of this question there are two places where the Cluster Operator needs to create bindings to ClusterRoleBindings to ServiceAccounts:

  1. The Topic Operator and User Operator need to be able to manipulate KafkaTopics and KafkaUsers, respectively. The Cluster Operator therefore needs to be able to grant them this access, which it does by creating a Role and RoleBinding. For this reason the Cluster Operator itself needs to be able to create Roles and RoleBindings in the namespace that those operators will run in. However, because of the privilege escalation prevention, the Cluster Operator cannot grant privileges it does not have itself (in particular it cannot grant such privileges in namespace it cannot access).
  2. When using rack-aware partition assignment, AMQ Streams needs to be able to discover the failure domain (for example, the Availability Zone in AWS) of the node on which a broker pod is assigned. To do this the broker pod needs to be able to get information about the Node it is running on. A Node is a cluster-scoped resource, so access to it can only be granted via a ClusterRoleBinding (not a namespace-scoped RoleBinding). Therefore the Cluster Operator needs to be able to create ClusterRoleBindings. But again, because of privilege escalation prevention, the Cluster Operator cannot grant privileges it does not have itself (so it cannot, for example, create a ClusterRoleBinding to a ClusterRole to grant privileges that the Cluster Operator does not not already have).

A.1.3. Why can standard OpenShift users not create the custom resource (Kafka, KafkaTopic, and so on)?

Because, when they installed AMQ Streams, the OpenShift cluster administrator did not grant the necessary privileges to standard users.

See this FAQ answer for more details.

A.1.4. Log contains warnings about failing to acquire lock

For each cluster, the Cluster Operator always executes only one operation at a time. The Cluster Operator uses locks to make sure that there are never two parallel operations running for the same cluster. In case an operation requires more time to complete, other operations will wait until it is completed and the lock is released.

INFO
Examples of cluster operations are cluster creation, rolling update, scale down or scale up and so on.

If the wait for the lock takes too long, the operation times out and the following warning message will be printed to the log:

2018-03-04 17:09:24 WARNING AbstractClusterOperations:290 - Failed to acquire lock for kafka cluster lock::kafka::myproject::my-cluster

Depending on the exact configuration of STRIMZI_FULL_RECONCILIATION_INTERVAL_MS and STRIMZI_OPERATION_TIMEOUT_MS, this warning message may appear regularly without indicating any problems. The operations which time out will be picked up by the next periodic reconciliation. It will try to acquire the lock again and execute.

Should this message appear periodically even in situations when there should be no other operations running for a given cluster, it might indicate that due to some error the lock was not properly released. In such cases it is recommended to restart the cluster operator.

A.1.5. Hostname verification fails when connecting to NodePorts using TLS

Currently, off-cluster access using NodePorts with TLS encryption enabled does not support TLS hostname verification. As a result, the clients that verify the hostname will fail to connect. For example, the Java client will fail with the following exception:

Caused by: java.security.cert.CertificateException: No subject alternative names matching IP address 168.72.15.231 found
    at sun.security.util.HostnameChecker.matchIP(HostnameChecker.java:168)
    at sun.security.util.HostnameChecker.match(HostnameChecker.java:94)
    at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:455)
    at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:436)
    at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:252)
    at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:136)
    at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1501)
    ... 17 more

To connect, you must disable hostname verification. In the Java client, you can do this by setting the configuration option ssl.endpoint.identification.algorithm to an empty string.

When configuring the client using a properties file, you can do it this way:

ssl.endpoint.identification.algorithm=

When configuring the client directly in Java, set the configuration option to an empty string:

props.put("ssl.endpoint.identification.algorithm", "");

Appendix B. Custom Resource API Reference

B.1. Kafka schema reference

FieldDescription

spec

The specification of the Kafka and Zookeeper clusters, and Topic Operator.

KafkaSpec

B.2. KafkaSpec schema reference

Used in: Kafka

FieldDescription

kafka

Configuration of the Kafka cluster.

KafkaClusterSpec

zookeeper

Configuration of the Zookeeper cluster.

ZookeeperClusterSpec

topicOperator

The property topicOperator has been deprecated. This feature should now be configured at path spec.entityOerator.topicOperator. Configuration of the Topic Operator.

TopicOperatorSpec

entityOperator

Configuration of the Entity Operator.

EntityOperatorSpec

clusterCa

Configuration of the cluster certificate authority.

CertificateAuthority

clientsCa

Configuration of the clients certificate authority.

CertificateAuthority

maintenanceTimeWindows

A list of time windows for the maintenance tasks (that is, certificates renewal). Each time window is defined by a cron expression.

string array

B.3. KafkaClusterSpec schema reference

Used in: KafkaSpec

FieldDescription

replicas

The number of pods in the cluster.

integer

image

The docker image for the pods. The default value depends on the configured Kafka.spec.kafka.version.

string

storage

Storage configuration (disk). Cannot be updated. The type depends on the value of the storage.type property within the given object, which must be one of [ephemeral, persistent-claim, jbod].

EphemeralStorage, PersistentClaimStorage, JbodStorage

listeners

Configures listeners of Kafka brokers.

KafkaListeners

authorization

Authorization configuration for Kafka brokers. The type depends on the value of the authorization.type property within the given object, which must be one of [simple].

KafkaAuthorizationSimple

config

The kafka broker config. Properties with the following prefixes cannot be set: 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.

map

rack

Configuration of the broker.rack broker config.

Rack

brokerRackInitImage

The image of the init container used for initializing the broker.rack.

string

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

tolerations

Pod’s tolerations.See external documentation of core/v1 tolerations.

Toleration array

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

resources

Resource constraints (limits and requests).

ResourceRequirements

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

logging

Logging configuration for Kafka. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

tlsSidecar

TLS sidecar configuration.

TlsSidecar

template

Template for Kafka cluster resources. The template allows users to specify how are the StatefulSet, Pods and Services generated.

KafkaClusterTemplate

version

The kafka broker version. Defaults to 2.1.1. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

B.4. EphemeralStorage schema reference

Used in: JbodStorage, KafkaClusterSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type EphemeralStorage from PersistentClaimStorage. It must have the value ephemeral for the type EphemeralStorage.

FieldDescription

id

Storage identification number. It is mandatory only for storage volumes defined in a storage of type 'jbod'.

integer

type

Must be ephemeral.

string

B.5. PersistentClaimStorage schema reference

Used in: JbodStorage, KafkaClusterSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type PersistentClaimStorage from EphemeralStorage. It must have the value persistent-claim for the type PersistentClaimStorage.

FieldDescription

type

Must be persistent-claim.

string

size

When type=persistent-claim, defines the size of the persistent volume claim (i.e 1Gi). Mandatory when type=persistent-claim.

string

selector

Specifies a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume.

map

deleteClaim

Specifies if the persistent volume claim has to be deleted when the cluster is un-deployed.

boolean

class

The storage class to use for dynamic volume allocation.

string

id

Storage identification number. It is mandatory only for storage volumes defined in a storage of type 'jbod'.

integer

B.6. JbodStorage schema reference

Used in: KafkaClusterSpec

The type property is a discriminator that distinguishes the use of the type JbodStorage from EphemeralStorage, PersistentClaimStorage. It must have the value jbod for the type JbodStorage.

FieldDescription

type

Must be jbod.

string

volumes

List of volumes as Storage objects representing the JBOD disks array.

EphemeralStorage, PersistentClaimStorage array

B.7. KafkaListeners schema reference

Used in: KafkaClusterSpec

FieldDescription

plain

Configures plain listener on port 9092.

KafkaListenerPlain

tls

Configures TLS listener on port 9093.

KafkaListenerTls

external

Configures external listener on port 9094. The type depends on the value of the external.type property within the given object, which must be one of [route, loadbalancer, nodeport].

KafkaListenerExternalRoute, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort

B.8. KafkaListenerPlain schema reference

Used in: KafkaListeners

FieldDescription

authentication

Authentication configuration for this listener. Since this listener does not use TLS transport you cannot configure an authentication with type: tls. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list.See external documentation of networking/v1 networkpolicypeer.

NetworkPolicyPeer array

B.9. KafkaListenerAuthenticationTls schema reference

Used in: KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalRoute, KafkaListenerPlain, KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationTls from KafkaListenerAuthenticationScramSha512. It must have the value tls for the type KafkaListenerAuthenticationTls.

FieldDescription

type

Must be tls.

string

B.10. KafkaListenerAuthenticationScramSha512 schema reference

Used in: KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalRoute, KafkaListenerPlain, KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationScramSha512 from KafkaListenerAuthenticationTls. It must have the value scram-sha-512 for the type KafkaListenerAuthenticationScramSha512.

FieldDescription

type

Must be scram-sha-512.

string

B.11. KafkaListenerTls schema reference

Used in: KafkaListeners

FieldDescription

authentication

Authentication configuration for this listener. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list.See external documentation of networking/v1 networkpolicypeer.

NetworkPolicyPeer array

B.12. KafkaListenerExternalRoute schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalRoute from KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort. It must have the value route for the type KafkaListenerExternalRoute.

FieldDescription

type

Must be route.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list.See external documentation of networking/v1 networkpolicypeer.

NetworkPolicyPeer array

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

RouteListenerOverride

B.13. RouteListenerOverride schema reference

Used in: KafkaListenerExternalRoute

FieldDescription

bootstrap

External bootstrap service configuration.

RouteListenerBootstrapOverride

brokers

External broker services configuration.

RouteListenerBrokerOverride array

B.14. RouteListenerBootstrapOverride schema reference

Used in: RouteListenerOverride

FieldDescription

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

host

Host for the bootstrap route. This field will be used in the spec.host field of the OpenShift Route.

string

B.15. RouteListenerBrokerOverride schema reference

Used in: RouteListenerOverride

FieldDescription

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

host

Host for the broker route. This field will be used in the spec.host field of the OpenShift Route.

string

B.16. KafkaListenerExternalLoadBalancer schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalLoadBalancer from KafkaListenerExternalRoute, KafkaListenerExternalNodePort. It must have the value loadbalancer for the type KafkaListenerExternalLoadBalancer.

FieldDescription

type

Must be loadbalancer.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list.See external documentation of networking/v1 networkpolicypeer.

NetworkPolicyPeer array

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

LoadBalancerListenerOverride

tls

Enables TLS encryption on the listener. By default set to true for enabled TLS encryption.

boolean

B.17. LoadBalancerListenerOverride schema reference

Used in: KafkaListenerExternalLoadBalancer

FieldDescription

bootstrap

External bootstrap service configuration.

LoadBalancerListenerBootstrapOverride

brokers

External broker services configuration.

LoadBalancerListenerBrokerOverride array

B.18. LoadBalancerListenerBootstrapOverride schema reference

Used in: LoadBalancerListenerOverride

FieldDescription

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

B.19. LoadBalancerListenerBrokerOverride schema reference

Used in: LoadBalancerListenerOverride

FieldDescription

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

B.20. KafkaListenerExternalNodePort schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalNodePort from KafkaListenerExternalRoute, KafkaListenerExternalLoadBalancer. It must have the value nodeport for the type KafkaListenerExternalNodePort.

FieldDescription

type

Must be nodeport.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list.See external documentation of networking/v1 networkpolicypeer.

NetworkPolicyPeer array

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

NodePortListenerOverride

tls

Enables TLS encryption on the listener. By default set to true for enabled TLS encryption.

boolean

B.21. NodePortListenerOverride schema reference

Used in: KafkaListenerExternalNodePort

FieldDescription

bootstrap

External bootstrap service configuration.

NodePortListenerBootstrapOverride

brokers

External broker services configuration.

NodePortListenerBrokerOverride array

B.22. NodePortListenerBootstrapOverride schema reference

Used in: NodePortListenerOverride

FieldDescription

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

nodePort

Node port for the bootstrap service.

integer

B.23. NodePortListenerBrokerOverride schema reference

Used in: NodePortListenerOverride

FieldDescription

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

nodePort

Node port for the broker service.

integer

B.24. KafkaAuthorizationSimple schema reference

Used in: KafkaClusterSpec

The type property is a discriminator that distinguishes the use of the type KafkaAuthorizationSimple from other subtypes which may be added in the future. It must have the value simple for the type KafkaAuthorizationSimple.

FieldDescription

type

Must be simple.

string

superUsers

List of super users. Should contain list of user principals which should get unlimited access rights.

string array

B.25. Rack schema reference

Used in: KafkaClusterSpec

FieldDescription

topologyKey

A key that matches labels assigned to the OpenShift or Kubernetes cluster nodes. The value of the label is used to set the broker’s broker.rack config.

string

B.26. Probe schema reference

Used in: KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, TlsSidecar, ZookeeperClusterSpec

FieldDescription

initialDelaySeconds

The initial delay before first the health is first checked.

integer

timeoutSeconds

The timeout for each attempted health check.

integer

B.27. JvmOptions schema reference

Used in: KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMakerSpec, ZookeeperClusterSpec

FieldDescription

-XX

A map of -XX options to the JVM.

map

-Xms

-Xms option to to the JVM.

string

-Xmx

-Xmx option to to the JVM.

string

gcLoggingEnabled

Specifies whether the Garbage Collection logging is enabled. The default is true.

boolean

B.28. ResourceRequirements schema reference

Used in: EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMakerSpec, TlsSidecar, TopicOperatorSpec, ZookeeperClusterSpec

FieldDescription

limits

 

map

requests

 

map

B.29. InlineLogging schema reference

Used in: EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMakerSpec, TopicOperatorSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type InlineLogging from ExternalLogging. It must have the value inline for the type InlineLogging.

FieldDescription

type

Must be inline.

string

loggers

A Map from logger name to logger level.

map

B.30. ExternalLogging schema reference

Used in: EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMakerSpec, TopicOperatorSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type ExternalLogging from InlineLogging. It must have the value external for the type ExternalLogging.

FieldDescription

type

Must be external.

string

name

The name of the ConfigMap from which to get the logging configuration.

string

B.31. TlsSidecar schema reference

Used in: EntityOperatorSpec, KafkaClusterSpec, TopicOperatorSpec, ZookeeperClusterSpec

FieldDescription

image

The docker image for the container.

string

livenessProbe

Pod liveness checking.

Probe

logLevel

The log level for the TLS sidecar. Default value is notice.

string (one of [emerg, debug, crit, err, alert, warning, notice, info])

readinessProbe

Pod readiness checking.

Probe

resources

Resource constraints (limits and requests).

ResourceRequirements

B.32. KafkaClusterTemplate schema reference

Used in: KafkaClusterSpec

FieldDescription

statefulset

Template for Kafka StatefulSet.

ResourceTemplate

pod

Template for Kafka Pods.

PodTemplate

bootstrapService

Template for Kafka bootstrap Service.

ResourceTemplate

brokersService

Template for Kafka broker Service.

ResourceTemplate

externalBootstrapRoute

Template for Kafka external bootstrap Route.

ResourceTemplate

externalBootstrapService

Template for Kafka external bootstrap Service.

ResourceTemplate

perPodRoute

Template for Kafka per-pod Routes used for access from outside of OpenShift.

ResourceTemplate

perPodService

Template for Kafka per-pod Services used for access from outside of Kubernetes.

ResourceTemplate

podDisruptionBudget

Template for Kafka PodDisruptionBudget.

PodDisruptionBudgetTemplate

B.33. ResourceTemplate schema reference

Used in: EntityOperatorTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

FieldDescription

metadata

Metadata which should be applied to the resource.

MetadataTemplate

B.34. MetadataTemplate schema reference

Used in: PodDisruptionBudgetTemplate, PodTemplate, ResourceTemplate

FieldDescription

labels

Labels which should be added to the resource template. Can be applied to different resources such as StatefulSets, Deployments, Pods, and Services.

map

annotations

Annotations which should be added to the resource template. Can be applied to different resources such as StatefulSets, Deployments, Pods, and Services.

map

B.35. PodTemplate schema reference

Used in: EntityOperatorTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

FieldDescription

metadata

Metadata which should be applied to the resource.

MetadataTemplate

imagePullSecrets

List of references to secrets in the same namespace to use for pulling any of the images used by this Pod.See external documentation of core/v1 localobjectreference.

LocalObjectReference array

securityContext

Configures pod-level security attributes and common container settings.See external documentation of core/v1 podsecuritycontext.

PodSecurityContext

terminationGracePeriodSeconds

The grace period is the duration in seconds after the processes running in the pod are sent a termination signal and the time when the processes are forcibly halted with a kill signal. Set this value longer than the expected cleanup time for your process.Value must be non-negative integer. The value zero indicates delete immediately. Defaults to 30 seconds.

integer

B.36. PodDisruptionBudgetTemplate schema reference

Used in: KafkaClusterTemplate, KafkaConnectTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

FieldDescription

metadata

Metadata which should be applied to the PodDistruptionBugetTemplate resource.

MetadataTemplate

maxUnavailable

Maximum number of unavailable pods to allow voluntary Pod eviction. A Pod eviction will only be allowed when "maxUnavailable" or fewer pods are unavailable after the eviction. Setting this value to 0 will prevent all voluntary evictions and the pods will need to be evicted manually. Defaults to 1.

integer

B.37. ZookeeperClusterSpec schema reference

Used in: KafkaSpec

FieldDescription

replicas

The number of pods in the cluster.

integer

image

The docker image for the pods.

string

storage

Storage configuration (disk). Cannot be updated. The type depends on the value of the storage.type property within the given object, which must be one of [ephemeral, persistent-claim].

EphemeralStorage, PersistentClaimStorage

config

The zookeeper broker config. Properties with the following prefixes cannot be set: server., dataDir, dataLogDir, clientPort, authProvider, quorum.auth, requireClientAuthScheme.

map

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

tolerations

Pod’s tolerations.See external documentation of core/v1 tolerations.

Toleration array

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

resources

Resource constraints (limits and requests).

ResourceRequirements

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

logging

Logging configuration for Zookeeper. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

tlsSidecar

TLS sidecar configuration.

TlsSidecar

template

Template for Zookeeper cluster resources. The template allows users to specify how are the StatefulSet, Pods and Services generated.

ZookeeperClusterTemplate

B.38. ZookeeperClusterTemplate schema reference

Used in: ZookeeperClusterSpec

FieldDescription

statefulset

Template for Zookeeper StatefulSet.

ResourceTemplate

pod

Template for Zookeeper Pods.

PodTemplate

clientService

Template for Zookeeper client Service.

ResourceTemplate

nodesService

Template for Zookeeper nodes Service.

ResourceTemplate

podDisruptionBudget

Template for Zookeeper PodDisruptionBudget.

PodDisruptionBudgetTemplate

B.39. TopicOperatorSpec schema reference

Used in: KafkaSpec

FieldDescription

watchedNamespace

The namespace the Topic Operator should watch.

string

image

The image to use for the Topic Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the Zookeeper session.

integer

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

resources

Resource constraints (limits and requests).

ResourceRequirements

topicMetadataMaxAttempts

The number of attempts at getting topic metadata.

integer

tlsSidecar

TLS sidecar configuration.

TlsSidecar

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

jvmOptions

JVM Options for pods.

EntityOperatorJvmOptions

B.40. EntityOperatorJvmOptions schema reference

Used in: EntityTopicOperatorSpec, EntityUserOperatorSpec, TopicOperatorSpec

FieldDescription

gcLoggingEnabled

Specifies whether the Garbage Collection logging is enabled. The default is true.

boolean

B.41. EntityOperatorSpec schema reference

Used in: KafkaSpec

FieldDescription

topicOperator

Configuration of the Topic Operator.

EntityTopicOperatorSpec

userOperator

Configuration of the User Operator.

EntityUserOperatorSpec

affinity

Pod affinity rules.See external documentation of