Deploying and Managing AMQ Streams on OpenShift

Red Hat AMQ Streams 2.4

Deploy AMQ Streams 2.4 on OpenShift Container Platform

Abstract

Deploy AMQ Streams to an OpenShift cluster using the OperatorHub or installation artifacts. Use the AMQ Streams operators to deploy and manage Kafka components. Upgrade AMQ Streams to take advantage of new features. As part of the upgrade, upgrade Kafka to the latest supported version.

Making open source more inclusive

Red Hat is committed to replacing problematic language in our code, documentation, and web properties. We are beginning with these four terms: master, slave, blacklist, and whitelist. Because of the enormity of this endeavor, these changes will be implemented gradually over several upcoming releases. For more details, see our CTO Chris Wright’s message.

Chapter 1. Deployment overview

AMQ Streams simplifies the process of running Apache Kafka in an OpenShift cluster.

This guide provides instructions on all the options available for deploying and upgrading AMQ Streams, describing what is deployed, and the order of deployment required to run Apache Kafka in an OpenShift cluster.

As well as describing the deployment steps, the guide also provides pre- and post-deployment instructions to prepare for and verify a deployment. The guide also describes additional deployment options for introducing metrics.

Upgrade instructions are provided for AMQ Streams and Kafka upgrades.

AMQ Streams is designed to work on all types of OpenShift cluster regardless of distribution, from public and private clouds to local deployments intended for development.

1.1. Configuring a deployment

The deployment procedures in this guide are designed to help you set up the initial structure of your deployment. After setting up the structure, you can use custom resources to configure the deployment to your precise needs. The deployment procedures use the example installation files provided with AMQ Streams. The procedures highlight any important configuration considerations, but they do not describe all the configuration options available.

You might want to review the configuration options available for Kafka components before you deploy AMQ Streams. For more information on the configuration options, see the Custom resource API reference.

1.1.1. Securing Kafka

On deployment, the Cluster Operator automatically sets up TLS certificates for data encryption and authentication within your cluster.

AMQ Streams provides additional configuration options for encryption, authentication and authorization:

1.1.2. Monitoring a deployment

AMQ Streams supports additional deployment options to monitor your deployment.

1.1.3. CPU and memory resource limits and requests

By default, the AMQ Streams Cluster Operator does not specify requests and limits for CPU and memory resources for any operands it deploys.

Having sufficient resources is important for applications like Kafka to be stable and deliver good performance.

The right amount of resources you should use depends on the specific requirements and use-cases.

You should consider configuring the CPU and memory resources. You can set resource requests and limits for each container in the AMQ Streams custom resources.

1.2. AMQ Streams custom resources

A deployment of Kafka components to an OpenShift cluster using AMQ Streams is highly configurable through the application of custom resources. Custom resources are created as instances of APIs added by Custom resource definitions (CRDs) to extend OpenShift resources.

CRDs act as configuration instructions to describe the custom resources in an OpenShift cluster, and are provided with AMQ Streams for each Kafka component used in a deployment, as well as users and topics. CRDs and custom resources are defined as YAML files. Example YAML files are provided with the AMQ Streams distribution.

CRDs also allow AMQ Streams resources to benefit from native OpenShift features like CLI accessibility and configuration validation.

1.2.1. AMQ Streams custom resource example

CRDs require a one-time installation in a cluster to define the schemas used to instantiate and manage AMQ Streams-specific resources.

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

Depending on the cluster setup, installation typically requires cluster admin privileges.

Note

Access to manage custom resources is limited to AMQ Streams administrators. For more information, see Section 4.6, “Designating AMQ Streams administrators”.

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

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

Warning

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

Each AMQ Streams-specific custom resource conforms to the schema defined by the CRD for the resource’s kind. The custom resources for AMQ Streams components have common configuration properties, which are defined under spec.

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

Kafka topic CRD

apiVersion: kafka.strimzi.io/v1beta2
kind: CustomResourceDefinition
metadata: 1
  name: kafkatopics.kafka.strimzi.io
  labels:
    app: strimzi
spec: 2
  group: kafka.strimzi.io
  versions:
    v1beta2
  scope: Namespaced
  names:
    # ...
    singular: kafkatopic
    plural: kafkatopics
    shortNames:
    - kt 3
  additionalPrinterColumns: 4
      # ...
  subresources:
    status: {} 5
  validation: 6
    openAPIV3Schema:
      properties:
        spec:
          type: object
          properties:
            partitions:
              type: integer
              minimum: 1
            replicas:
              type: integer
              minimum: 1
              maximum: 32767
      # ...

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

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

Here is a corresponding example of a KafkaTopic custom resource.

Kafka topic custom resource

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic 1
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster 2
spec: 3
  partitions: 1
  replicas: 1
  config:
    retention.ms: 7200000
    segment.bytes: 1073741824
status:
  conditions: 4
    lastTransitionTime: "2019-08-20T11:37:00.706Z"
    status: "True"
    type: Ready
  observedGeneration: 1
  / ...

1
The kind and apiVersion identify the CRD of which the custom resource is an instance.
2
A label, applicable only to KafkaTopic and KafkaUser resources, that defines the name of the Kafka cluster (which is same as the name of the Kafka resource) to which a topic or user belongs.
3
The spec shows the number of partitions and replicas for the topic as well as the configuration parameters for the topic itself. In this example, the retention period for a message to remain in the topic and the segment file size for the log are specified.
4
Status conditions for the KafkaTopic resource. The type condition changed to Ready at the lastTransitionTime.

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

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

1.3. Using the Kafka Bridge to connect with a Kafka cluster

You can use the AMQ Streams Kafka Bridge API to create and manage consumers and send and receive records over HTTP rather than the native Kafka protocol.

When you set up the Kafka Bridge you configure HTTP access to the Kafka cluster. You can then use the Kafka Bridge to produce and consume messages from the cluster, as well as performing other operations through its REST interface.

Additional resources

1.4. Document Conventions

User-replaced values

User-replaced values, also known as replaceables, are shown in italics with angle brackets (< >). Underscores ( _ ) are used for multi-word values. If the value refers to code or commands, monospace is also used.

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

1.5. Additional resources

Chapter 2. AMQ Streams installation methods

You can install AMQ Streams on OpenShift 4.10 to 4.13 in two ways.

Installation methodDescription

Installation artifacts (YAML files)

Download Red Hat AMQ Streams 2.4 OpenShift Installation and Example Files from the AMQ Streams software downloads page. Deploy the YAML installation artifacts to your OpenShift cluster using oc. You start by deploying the Cluster Operator from install/cluster-operator to a single namespace, multiple namespaces, or all namespaces.

You can also use the install/ artifacts to deploy the following:

  • AMQ Streams administrator roles (strimzi-admin)
  • A standalone Topic Operator (topic-operator)
  • A standalone User Operator (user-operator)
  • AMQ Streams Drain Cleaner (drain-cleaner)

OperatorHub

Use the AMQ Streams operator in the OperatorHub to deploy AMQ Streams to a single namespace or all namespaces.

For the greatest flexibility, choose the installation artifacts method. The OperatorHub method provides a standard configuration and allows you to take advantage of automatic updates.

Note

Installation of AMQ Streams using Helm is not supported.

Chapter 3. What is deployed with AMQ Streams

Apache Kafka components are provided for deployment to OpenShift with the AMQ Streams distribution. The Kafka components are generally run as clusters for availability.

A typical deployment incorporating Kafka components might include:

  • Kafka cluster of broker nodes
  • ZooKeeper cluster of replicated ZooKeeper instances
  • Kafka Connect cluster for external data connections
  • Kafka MirrorMaker cluster to mirror the Kafka cluster in a secondary cluster
  • Kafka Exporter to extract additional Kafka metrics data for monitoring
  • Kafka Bridge to make HTTP-based requests to the Kafka cluster

Not all of these components are mandatory, though you need Kafka and ZooKeeper as a minimum. Some components can be deployed without Kafka, such as MirrorMaker or Kafka Connect.

3.1. Order of deployment

The required order of deployment to an OpenShift cluster is as follows:

  1. Deploy the Cluster Operator to manage your Kafka cluster
  2. Deploy the Kafka cluster with the ZooKeeper cluster, and include the Topic Operator and User Operator in the deployment
  3. Optionally deploy:

    • The Topic Operator and User Operator standalone if you did not deploy them with the Kafka cluster
    • Kafka Connect
    • Kafka MirrorMaker
    • Kafka Bridge
    • Components for the monitoring of metrics

The Cluster Operator creates OpenShift resources for the components, such as Deployment, Service, and Pod resources. The names of the OpenShift resources are appended with the name specified for a component when it’s deployed. For example, a Kafka cluster named my-kafka-cluster has a service named my-kafka-cluster-kafka.

Chapter 4. Preparing for your AMQ Streams deployment

This section shows how you prepare for an AMQ Streams deployment, describing:

Note

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

4.1. Deployment prerequisites

To deploy AMQ Streams, you will need the following:

  • An OpenShift 4.10 to 4.13 cluster.

    AMQ Streams is based on Strimzi 0.34.x.

  • The oc command-line tool is installed and configured to connect to the running cluster.

4.2. Downloading AMQ Streams release artifacts

To use deployment files to install AMQ Streams, download and extract the files from the AMQ Streams software downloads page.

AMQ Streams release artifacts include sample YAML files to help you deploy the components of AMQ Streams to OpenShift, perform common operations, and configure your Kafka cluster.

Use oc to deploy the Cluster Operator from the install/cluster-operator folder of the downloaded ZIP file. For more information about deploying and configuring the Cluster Operator, see Section 6.2, “Deploying the Cluster Operator”.

In addition, if you want to use standalone installations of the Topic and User Operators with a Kafka cluster that is not managed by the AMQ Streams Cluster Operator, you can deploy them from the install/topic-operator and install/user-operator folders.

Note

Additionally, AMQ Streams container images are available through the Red Hat Ecosystem Catalog. However, we recommend that you use the YAML files provided to deploy AMQ Streams.

4.3. Example configuration and deployment files

Use the example configuration and deployment files provided with AMQ Streams to deploy Kafka components with different configurations and monitor your deployment. Example configuration files for custom resources contain important properties and values, which you can extend with additional supported configuration properties for your own deployment.

4.3.1. Example files location

The example files are provided with the downloadable release artifacts from the AMQ Streams software downloads page.

You can download and apply the examples using the oc command-line tool. The examples can serve as a starting point when building your own Kafka component configuration for deployment.

Note

If you installed AMQ Streams using the Operator, you can still download the example files and use them to upload configuration.

4.3.2. Example files provided with AMQ Streams

The release artifacts include an examples directory that contains the configuration examples.

Examples directory

examples
├── user 1
├── topic 2
├── security 3
│   ├── tls-auth
│   ├── scram-sha-512-auth
│   └── keycloak-authorization
├── mirror-maker 4
├── metrics 5
├── kafka 6
├── cruise-control 7
├── connect 8
└── bridge 9

1
KafkaUser custom resource configuration, which is managed by the User Operator.
2
KafkaTopic custom resource configuration, which is managed by Topic Operator.
3
Authentication and authorization configuration for Kafka components. Includes example configuration for TLS and SCRAM-SHA-512 authentication. The Red Hat Single Sign-On example includes Kafka custom resource configuration and a Red Hat Single Sign-On realm specification. You can use the example to try Red Hat Single Sign-On authorization services. There is also an example with enabled oauth authentication and keycloak authorization metrics.
4
Kafka custom resource configuration for a deployment of Mirror Maker. Includes example configuration for replication policy and synchronization frequency.
5
Metrics configuration, including Prometheus installation and Grafana dashboard files.
6
Kafka custom resource configuration for a deployment of Kafka. Includes example configuration for an ephemeral or persistent single or multi-node deployment.
7
Kafka custom resource with a deployment configuration for Cruise Control. Includes KafkaRebalance custom resources to generate optimizations proposals from Cruise Control, with example configurations to use the default or user optimization goals.
8
KafkaConnect and KafkaConnector custom resource configuration for a deployment of Kafka Connect. Includes example configuration for a single or multi-node deployment.
9
KafkaBridge custom resource configuration for a deployment of Kafka Bridge.

4.4. Pushing container images to your own registry

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

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

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

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

Container imageNamespace/RepositoryDescription

Kafka

  • registry.redhat.io/amq-streams/kafka-34-rhel8:2.4.0
  • registry.redhat.io/amq-streams/kafka-33-rhel8:2.4.0

AMQ Streams image for running Kafka, including:

  • Kafka Broker
  • Kafka Connect
  • Kafka MirrorMaker
  • ZooKeeper
  • TLS Sidecars

Operator

  • registry.redhat.io/amq-streams/strimzi-rhel8-operator:2.4.0

AMQ Streams image for running the operators:

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

Kafka Bridge

  • registry.redhat.io/amq-streams/bridge-rhel8:2.4.0

AMQ Streams image for running the AMQ Streams Kafka Bridge

AMQ Streams Drain Cleaner

  • registry.redhat.io/amq-streams/drain-cleaner-rhel8:2.4.0

AMQ Streams image for running the AMQ Streams Drain Cleaner

4.5. Creating a pull secret for authentication to the container image registry

The installation YAML files provided by AMQ Streams pull container images directly from the Red Hat Ecosystem Catalog. If an AMQ Streams deployment requires authentication, configure authentication credentials in a secret and add it to the installation YAML.

Note

Authentication is not usually required, but might be requested on certain platforms.

Prerequisites

  • You need your Red Hat username and password or the login details from your Red Hat registry service account.
Note

You can use your Red Hat subscription to create a registry service account from the Red Hat Customer Portal.

Procedure

  1. Create a pull secret containing your login details and the container registry where the AMQ Streams image is pulled from:

    oc create secret docker-registry <pull_secret_name> \
        --docker-server=registry.redhat.io \
        --docker-username=<user_name> \
        --docker-password=<password> \
        --docker-email=<email>

    Add your user name and password. The email address is optional.

  2. Edit the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml deployment file to specify the pull secret using the STRIMZI_IMAGE_PULL_SECRET environment variable:

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: strimzi-cluster-operator
    spec:
      # ...
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
            # ...
            env:
              - name: STRIMZI_IMAGE_PULL_SECRETS
                value: "<pull_secret_name>"
    # ...

    The secret applies to all pods created by the Cluster Operator.

4.6. Designating AMQ Streams administrators

AMQ Streams provides custom resources for configuration of your deployment. By default, permission to view, create, edit, and delete these resources is limited to OpenShift cluster administrators. AMQ Streams provides two cluster roles that you can use to assign these rights to other users:

  • strimzi-view allows users to view and list AMQ Streams resources.
  • strimzi-admin allows users to also create, edit or delete AMQ Streams resources.

When you install these roles, they will automatically aggregate (add) these rights to the default OpenShift cluster roles. strimzi-view aggregates to the view role, and strimzi-admin aggregates to the edit and admin roles. Because of the aggregation, you might not need to assign these roles to users who already have similar rights.

The following procedure shows how to assign a strimzi-admin role that allows non-cluster administrators to manage AMQ Streams resources.

A system administrator can designate AMQ Streams administrators after the Cluster Operator is deployed.

Prerequisites

Procedure

  1. Create the strimzi-view and strimzi-admin cluster roles in OpenShift.

    oc create -f install/strimzi-admin
  2. If needed, assign the roles that provide access rights to users that require them.

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

Chapter 5. Installing AMQ Streams from the OperatorHub using the web console

Install the AMQ Streams operator from the OperatorHub in the OpenShift Container Platform web console.

The procedures in this section show how to:

5.1. Installing the AMQ Streams operator from the OperatorHub

You can install and subscribe to the AMQ Streams operator using the OperatorHub in the OpenShift Container Platform web console.

This procedure describes how to create a project and install the AMQ Streams operator to that project. A project is a representation of a namespace. For manageability, it is a good practice to use namespaces to separate functions.

Warning

Make sure you use the appropriate update channel. If you are on a supported version of OpenShift, installing AMQ Streams from the default stable channel is generally safe. However, we do not recommend enabling automatic updates on the stable channel. An automatic upgrade will skip any necessary steps prior to upgrade. Use automatic upgrades only on version-specific channels.

Prerequisites

  • Access to an OpenShift Container Platform web console using an account with cluster-admin or strimzi-admin permissions.

Procedure

  1. Navigate in the OpenShift web console to the Home > Projects page and create a project (namespace) for the installation.

    We use a project named amq-streams-kafka in this example.

  2. Navigate to the Operators > OperatorHub page.
  3. Scroll or type a keyword into the Filter by keyword box to find the AMQ Streams operator.

    The operator is located in the Streaming & Messaging category.

  4. Click AMQ Streams to display the operator information.
  5. Read the information about the operator and click Install.
  6. On the Install Operator page, choose from the following installation and update options:

    • Update Channel: Choose the update channel for the operator.

      • The (default) stable channel contains all the latest updates and releases, including major, minor, and micro releases, which are assumed to be well tested and stable.
      • An amq-streams-X.x channel contains the minor and micro release updates for a major release, where X is the major release version number.
      • An amq-streams-X.Y.x channel contains the micro release updates for a minor release, where X is the major release version number and Y is the minor release version number.
    • Installation Mode: Choose the project you created to install the operator on a specific namespace.

      You can install the AMQ Streams operator to all namespaces in the cluster (the default option) or a specific namespace. We recommend that you dedicate a specific namespace to the Kafka cluster and other AMQ Streams components.

    • Update approval: By default, the AMQ Streams operator is automatically upgraded to the latest AMQ Streams version by the Operator Lifecycle Manager (OLM). Optionally, select Manual if you want to manually approve future upgrades. For more information, see the Operators guide in the OpenShift documentation.
  7. Click Install to install the operator to your selected namespace.

    The AMQ Streams operator deploys the Cluster Operator, CRDs, and role-based access control (RBAC) resources to the selected namespace.

  8. After the operator is ready for use, navigate to Operators > Installed Operators to verify that the operator has installed to the selected namespace.

    The status will show as Succeeded.

    You can now use the AMQ Streams operator to deploy Kafka components, starting with a Kafka cluster.

Note

If you navigate to Workloads > Deployments, you can see the deployment details for the Cluster Operator and Entity Operator. The name of the Cluster Operator includes a version number: amq-streams-cluster-operator-<version>. The name is different when deploying the Cluster Operator using the AMQ Streams installation artifacts. In this case, the name is strimzi-cluster-operator.

5.2. Deploying Kafka components using the AMQ Streams operator

When installed on Openshift, the AMQ Streams operator makes Kafka components available for installation from the user interface.

The following Kafka components are available for installation:

  • Kafka
  • Kafka Connect
  • Kafka MirrorMaker
  • Kafka MirrorMaker 2
  • Kafka Topic
  • Kafka User
  • Kafka Bridge
  • Kafka Connector
  • Kafka Rebalance

You select the component and create an instance. As a minimum, you create a Kafka instance. This procedure describes how to create a Kafka instance using the default settings. You can configure the default installation specification before you perform the installation.

The process is the same for creating instances of other Kafka components.

Prerequisites

Procedure

  1. Navigate in the web console to the Operators > Installed Operators page and click AMQ Streams to display the operator details.

    From Provided APIs, you can create instances of Kafka components.

  2. Click Create instance under Kafka to create a Kafka instance.

    By default, you’ll create a Kafka cluster called my-cluster with three Kafka broker nodes and three ZooKeeper nodes. The cluster uses ephemeral storage.

  3. Click Create to start the installation of Kafka.

    Wait until the status changes to Ready.

Chapter 6. Deploying AMQ Streams using installation artifacts

Having prepared your environment for a deployment of AMQ Streams, you can deploy AMQ Streams to an OpenShift cluster. Use the installation files provided with the release artifacts.

AMQ Streams is based on Strimzi 0.34.x. You can deploy AMQ Streams 2.4 on OpenShift 4.10 to 4.13.

The steps to deploy AMQ Streams using the installation files are as follows:

  1. Deploy the Cluster Operator
  2. Use the Cluster Operator to deploy the following:

  3. Optionally, deploy the following Kafka components according to your requirements:

Note

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

6.1. Basic deployment path

You can set up a deployment where AMQ Streams manages a single Kafka cluster in the same namespace. You might use this configuration for development or testing. Or you can use AMQ Streams in a production environment to manage a number of Kafka clusters in different namespaces.

The first step for any deployment of AMQ Streams is to install the Cluster Operator using the install/cluster-operator files.

A single command applies all the installation files in the cluster-operator folder: oc apply -f ./install/cluster-operator.

The command sets up everything you need to be able to create and manage a Kafka deployment, including the following:

  • Cluster Operator (Deployment, ConfigMap)
  • AMQ Streams CRDs (CustomResourceDefinition)
  • RBAC resources (ClusterRole, ClusterRoleBinding, RoleBinding)
  • Service account (ServiceAccount)

The basic deployment path is as follows:

  1. Download the release artifacts
  2. Create an OpenShift namespace in which to deploy the Cluster Operator
  3. Deploy the Cluster Operator

    1. Update the install/cluster-operator files to use the namespace created for the Cluster Operator
    2. Install the Cluster Operator to watch one, multiple, or all namespaces
  4. Create a Kafka cluster

After which, you can deploy other Kafka components and set up monitoring of your deployment.

6.2. Deploying the Cluster Operator

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

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

By default, a single replica of the Cluster Operator is deployed. You can add replicas with leader election so that additional Cluster Operators are on standby in case of disruption. For more information, see Section 13.2.5, “Running multiple Cluster Operator replicas with leader election”.

6.2.1. Specifying the namespaces the Cluster Operator watches

The Cluster Operator watches for updates in the namespaces where the Kafka resources are deployed. When you deploy the Cluster Operator, you specify which namespaces to watch. You can specify the following namespaces:

Note

The Cluster Operator can watch one, multiple, or all namespaces in an OpenShift cluster. The Topic Operator and User Operator watch for KafkaTopic and KafkaUser resources in a single namespace. For more information, see Section 13.1, “Watching namespaces with AMQ Streams operators”.

The Cluster Operator watches for changes to the following resources:

  • Kafka for the Kafka cluster.
  • KafkaConnect for the Kafka Connect cluster.
  • KafkaConnector for creating and managing connectors in a Kafka Connect cluster.
  • KafkaMirrorMaker for the Kafka MirrorMaker instance.
  • KafkaMirrorMaker2 for the Kafka MirrorMaker 2 instance.
  • KafkaBridge for the Kafka Bridge instance.
  • KafkaRebalance for the Cruise Control optimization requests.

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

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

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

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

6.2.2. Deploying the Cluster Operator to watch a single namespace

This procedure shows how to deploy the Cluster Operator to watch AMQ Streams resources in a single namespace in your OpenShift cluster.

Prerequisites

  • You need an account with permission to create and manage CustomResourceDefinition and RBAC (ClusterRole, and RoleBinding) resources.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    On Linux, use:

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

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
  2. Deploy the Cluster Operator:

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  3. Check the status of the deployment:

    oc get deployments -n my-cluster-operator-namespace

    Output shows the deployment name and readiness

    NAME                      READY  UP-TO-DATE  AVAILABLE
    strimzi-cluster-operator  1/1    1           1

    READY shows the number of replicas that are ready/expected. The deployment is successful when the AVAILABLE output shows 1.

6.2.3. Deploying the Cluster Operator to watch multiple namespaces

This procedure shows how to deploy the Cluster Operator to watch AMQ Streams resources across multiple namespaces in your OpenShift cluster.

Prerequisites

  • You need an account with permission to create and manage CustomResourceDefinition and RBAC (ClusterRole, and RoleBinding) resources.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    On Linux, use:

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

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
  2. Edit the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file to add a list of all the namespaces the Cluster Operator will watch to the STRIMZI_NAMESPACE environment variable.

    For example, in this procedure the Cluster Operator will watch the namespaces watched-namespace-1, watched-namespace-2, watched-namespace-3.

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq-streams/strimzi-rhel8-operator:2.4.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: watched-namespace-1,watched-namespace-2,watched-namespace-3
  3. For each namespace listed, install the RoleBindings.

    In this example, we replace watched-namespace in these commands with the namespaces listed in the previous step, repeating them for watched-namespace-1, watched-namespace-2, watched-namespace-3:

    oc create -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n <watched_namespace>
    oc create -f install/cluster-operator/023-RoleBinding-strimzi-cluster-operator.yaml -n <watched_namespace>
    oc create -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n <watched_namespace>
  4. Deploy the Cluster Operator:

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  5. Check the status of the deployment:

    oc get deployments -n my-cluster-operator-namespace

    Output shows the deployment name and readiness

    NAME                      READY  UP-TO-DATE  AVAILABLE
    strimzi-cluster-operator  1/1    1           1

    READY shows the number of replicas that are ready/expected. The deployment is successful when the AVAILABLE output shows 1.

6.2.4. Deploying the Cluster Operator to watch all namespaces

This procedure shows how to deploy the Cluster Operator to watch AMQ Streams resources across all namespaces in your OpenShift cluster.

When running in this mode, the Cluster Operator automatically manages clusters in any new namespaces that are created.

Prerequisites

  • You need an account with permission to create and manage CustomResourceDefinition and RBAC (ClusterRole, and RoleBinding) resources.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    On Linux, use:

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

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
  2. Edit the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file to set the value of the STRIMZI_NAMESPACE environment variable to *.

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          # ...
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq-streams/strimzi-rhel8-operator:2.4.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: "*"
            # ...
  3. Create ClusterRoleBindings that grant cluster-wide access for all namespaces to the Cluster Operator.

    oc create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
    oc create clusterrolebinding strimzi-cluster-operator-watched --clusterrole=strimzi-cluster-operator-watched --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
    oc create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
  4. Deploy the Cluster Operator to your OpenShift cluster.

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  5. Check the status of the deployment:

    oc get deployments -n my-cluster-operator-namespace

    Output shows the deployment name and readiness

    NAME                      READY  UP-TO-DATE  AVAILABLE
    strimzi-cluster-operator  1/1    1           1

    READY shows the number of replicas that are ready/expected. The deployment is successful when the AVAILABLE output shows 1.

6.3. Deploying Kafka

To be able to manage a Kafka cluster with the Cluster Operator, you must deploy it as a Kafka resource. AMQ Streams provides example deployment files to do this. You can use these files to deploy the Topic Operator and User Operator at the same time.

After you have deployed the Cluster Operator, use a Kafka resource to deploy the following components:

When installing Kafka, AMQ Streams also installs a ZooKeeper cluster and adds the necessary configuration to connect Kafka with ZooKeeper.

If you haven’t deployed a Kafka cluster as a Kafka resource, you can’t use the Cluster Operator to manage it. This applies, for example, to a Kafka cluster running outside of OpenShift. However, you can use the Topic Operator and User Operator with a Kafka cluster that is not managed by AMQ Streams, by deploying them as standalone components. You can also deploy and use other Kafka components with a Kafka cluster not managed by AMQ Streams.

6.3.1. Deploying the Kafka cluster

This procedure shows how to deploy a Kafka cluster to your OpenShift cluster using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a Kafka resource.

AMQ Streams provides the following example files you can use to create a Kafka cluster:

kafka-persistent.yaml
Deploys a persistent cluster with three ZooKeeper and three Kafka nodes.
kafka-jbod.yaml
Deploys a persistent cluster with three ZooKeeper and three Kafka nodes (each using multiple persistent volumes).
kafka-persistent-single.yaml
Deploys a persistent cluster with a single ZooKeeper node and a single Kafka node.
kafka-ephemeral.yaml
Deploys an ephemeral cluster with three ZooKeeper and three Kafka nodes.
kafka-ephemeral-single.yaml
Deploys an ephemeral cluster with three ZooKeeper nodes and a single Kafka node.

In this procedure, we use the examples for an ephemeral and persistent Kafka cluster deployment.

Ephemeral cluster
In general, an ephemeral (or 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 persistent volumes to store ZooKeeper and Kafka data. A PersistentVolume is acquired using a PersistentVolumeClaim to make it independent of the actual type of the PersistentVolume. The PersistentVolumeClaim can use a StorageClass to trigger automatic volume provisioning. When no StorageClass is specified, OpenShift will try to use the default StorageClass.

The following examples show some common types of persistent volumes:

  • If your OpenShift cluster runs on Amazon AWS, OpenShift can provision Amazon EBS volumes
  • If your OpenShift cluster runs on Microsoft Azure, OpenShift can provision Azure Disk Storage volumes
  • If your OpenShift cluster runs on Google Cloud, OpenShift can provision Persistent Disk volumes
  • If your OpenShift cluster runs on bare metal, OpenShift can provision local persistent volumes

The example YAML files specify the latest supported Kafka version, and configuration for its supported log message format version and inter-broker protocol version. The inter.broker.protocol.version property for the Kafka config must be the version supported by the specified Kafka version (spec.kafka.version). The property represents the version of Kafka protocol used in a Kafka cluster.

From Kafka 3.0.0, when the inter.broker.protocol.version is set to 3.0 or higher, the log.message.format.version option is ignored and doesn’t need to be set.

An update to the inter.broker.protocol.version is required when upgrading Kafka.

The example clusters are 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 Kafka resource in the relevant YAML file.

Default cluster name and specified Kafka versions

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    version: 3.4.0
    #...
    config:
      #...
      log.message.format.version: "3.4"
      inter.broker.protocol.version: "3.4"
  # ...

Procedure

  1. Create and deploy an ephemeral or persistent cluster.

    • To create and deploy an ephemeral cluster:

      oc apply -f examples/kafka/kafka-ephemeral.yaml
    • To create and deploy a persistent cluster:

      oc apply -f examples/kafka/kafka-persistent.yaml
  2. Check the status of the deployment:

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the pod names and readiness

    NAME                        READY   STATUS    RESTARTS
    my-cluster-entity-operator  3/3     Running   0
    my-cluster-kafka-0          1/1     Running   0
    my-cluster-kafka-1          1/1     Running   0
    my-cluster-kafka-2          1/1     Running   0
    my-cluster-zookeeper-0      1/1     Running   0
    my-cluster-zookeeper-1      1/1     Running   0
    my-cluster-zookeeper-2      1/1     Running   0

    my-cluster is the name of the Kafka cluster.

    A sequential index number starting with 0 identifies each Kafka and ZooKeeper pod created.

    With the default deployment, you create an Entity Operator cluster, 3 Kafka pods, and 3 ZooKeeper pods.

    READY shows the number of replicas that are ready/expected. The deployment is successful when the STATUS shows as Running.

Additional resources

Kafka cluster configuration

6.3.2. Deploying the Topic Operator using the Cluster Operator

This procedure describes how to deploy the Topic Operator using the Cluster Operator.

You configure the entityOperator property of the Kafka resource to include the topicOperator. By default, the Topic Operator watches for KafkaTopic resources in the namespace of the Kafka cluster deployed by the Cluster Operator. You can also specify a namespace using watchedNamespace in the Topic Operator spec. A single Topic Operator can watch a single namespace. One namespace should be watched by only one Topic Operator.

If you use AMQ Streams to deploy multiple Kafka clusters into the same namespace, enable the Topic Operator for only one Kafka cluster or use the watchedNamespace property to configure the Topic Operators to watch other namespaces.

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 about configuring the entityOperator and topicOperator properties, see Configuring the Entity Operator.

Procedure

  1. Edit the entityOperator properties of the Kafka resource to include topicOperator:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the Topic Operator spec using the properties described in the EntityTopicOperatorSpec schema reference.

    Use an empty object ({}) if you want all properties to use their default values.

  3. Create or update the resource:

    oc apply -f <kafka_configuration_file>
  4. Check the status of the deployment:

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the pod name and readiness

    NAME                        READY   STATUS    RESTARTS
    my-cluster-entity-operator  3/3     Running   0
    # ...

    my-cluster is the name of the Kafka cluster.

    READY shows the number of replicas that are ready/expected. The deployment is successful when the STATUS shows as Running.

6.3.3. Deploying the User Operator using the Cluster Operator

This procedure describes how to deploy the User Operator using the Cluster Operator.

You configure the entityOperator property of the Kafka resource to include the userOperator. By default, the User Operator watches for KafkaUser resources in the namespace of the Kafka cluster deployment. You can also specify a namespace using watchedNamespace in the User Operator spec. A single User Operator can watch a single namespace. One namespace should be watched by only one User Operator.

If you want to use the User Operator with a Kafka cluster that is not managed by AMQ Streams, you must deploy the User Operator as a standalone component.

For more information about configuring the entityOperator and userOperator properties, see Configuring the Entity Operator.

Procedure

  1. Edit the entityOperator properties of the Kafka resource to include userOperator:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the User Operator spec using the properties described in EntityUserOperatorSpec schema reference.

    Use an empty object ({}) if you want all properties to use their default values.

  3. Create or update the resource:

    oc apply -f <kafka_configuration_file>
  4. Check the status of the deployment:

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the pod name and readiness

    NAME                        READY   STATUS    RESTARTS
    my-cluster-entity-operator  3/3     Running   0
    # ...

    my-cluster is the name of the Kafka cluster.

    READY shows the number of replicas that are ready/expected. The deployment is successful when the STATUS shows as Running.

6.4. Deploying Kafka Connect

Kafka Connect is a tool for streaming data between Apache Kafka and other systems. For example, Kafka Connect might integrate Kafka with external databases or storage and messaging systems.

In AMQ Streams, Kafka Connect is deployed in distributed mode. Kafka Connect can also work in standalone mode, but this is not supported by AMQ Streams.

Using the concept of connectors, Kafka Connect provides a framework for moving large amounts of data into and out of your Kafka cluster while maintaining scalability and reliability.

The Cluster Operator manages Kafka Connect clusters deployed using the KafkaConnect resource and connectors created using the KafkaConnector resource.

In order to use Kafka Connect, you need to do the following.

Note

The term connector is used interchangeably to mean a connector instance running within a Kafka Connect cluster, or a connector class. In this guide, the term connector is used when the meaning is clear from the context.

6.4.1. Deploying Kafka Connect to your OpenShift cluster

This procedure shows how to deploy a Kafka Connect cluster to your OpenShift cluster using the Cluster Operator.

A Kafka Connect cluster deployment is implemented with a configurable number of nodes (also called workers) that distribute the workload of connectors as tasks so that the message flow is highly scalable and reliable.

The deployment uses a YAML file to provide the specification to create a KafkaConnect resource.

AMQ Streams provides example configuration files. In this procedure, we use the following example file:

  • examples/connect/kafka-connect.yaml

Procedure

  1. Deploy Kafka Connect to your OpenShift cluster. Use the examples/connect/kafka-connect.yaml file to deploy Kafka Connect.

    oc apply -f examples/connect/kafka-connect.yaml
  2. Check the status of the deployment:

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the deployment name and readiness

    NAME                                 READY  STATUS   RESTARTS
    my-connect-cluster-connect-<pod_id>  1/1    Running  0

    my-connect-cluster is the name of the Kafka Connect cluster.

    A pod ID identifies each pod created.

    With the default deployment, you create a single Kafka Connect pod.

    READY shows the number of replicas that are ready/expected. The deployment is successful when the STATUS shows as Running.

6.4.2. Configuring Kafka Connect for multiple instances

If you are running multiple instances of Kafka Connect, you have to change the default configuration of the following config properties:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: connect-cluster 1
    offset.storage.topic: connect-cluster-offsets 2
    config.storage.topic: connect-cluster-configs 3
    status.storage.topic: connect-cluster-status  4
    # ...
# ...
1
The Kafka Connect cluster ID within Kafka.
2
Kafka topic that stores connector offsets.
3
Kafka topic that stores connector and task status configurations.
4
Kafka topic that stores connector and task status updates.
Note

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

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

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

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

6.4.3. Adding connectors

Kafka Connect uses connectors to integrate with other systems to stream data. A connector is an instance of a Kafka Connector class, which can be one of the following type:

Source connector
A source connector is a runtime entity that fetches data from an external system and feeds it to Kafka as messages.
Sink connector
A sink connector is a runtime entity that fetches messages from Kafka topics and feeds them to an external system.

Kafka Connect uses a plugin architecture to provide the implementation artifacts for connectors. Plugins allow connections to other systems and provide additional configuration to manipulate data. Plugins include connectors and other components, such as data converters and transforms. A connector operates with a specific type of external system. Each connector defines a schema for its configuration. You supply the configuration to Kafka Connect to create a connector instance within Kafka Connect. Connector instances then define a set of tasks for moving data between systems.

Add connector plugins to Kafka Connect in one of the following ways:

After plugins have been added to the container image, you can start, stop, and manage connector instances in the following ways:

You can also create new connector instances using these options.

6.4.3.1. Building a new container image with connector plugins automatically

Configure Kafka Connect so that AMQ Streams automatically builds a new container image with additional connectors. You define the connector plugins using the .spec.build.plugins property of the KafkaConnect custom resource. AMQ Streams will automatically download and add the connector plugins into a new container image. The container is pushed into the container repository specified in .spec.build.output and automatically used in the Kafka Connect deployment.

Prerequisites

You need to provide your own container registry where images can be pushed to, stored, and pulled from. AMQ Streams supports private container registries as well as public registries such as Quay or Docker Hub.

Procedure

  1. Configure the KafkaConnect custom resource by specifying the container registry in .spec.build.output, and additional connectors in .spec.build.plugins:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: my-connect-cluster
    spec: 1
      #...
      build:
        output: 2
          type: docker
          image: my-registry.io/my-org/my-connect-cluster:latest
          pushSecret: my-registry-credentials
        plugins: 3
          - name: debezium-postgres-connector
            artifacts:
              - type: tgz
                url: https://repo1.maven.org/maven2/io/debezium/debezium-connector-postgres/2.1.3.Final/debezium-connector-postgres-2.1.3.Final-plugin.tar.gz
                sha512sum: c4ddc97846de561755dc0b021a62aba656098829c70eb3ade3b817ce06d852ca12ae50c0281cc791a5a131cb7fc21fb15f4b8ee76c6cae5dd07f9c11cb7c6e79
          - name: camel-telegram
            artifacts:
              - type: tgz
                url: https://repo.maven.apache.org/maven2/org/apache/camel/kafkaconnector/camel-telegram-kafka-connector/0.11.5/camel-telegram-kafka-connector-0.11.5-package.tar.gz
                sha512sum: d6d9f45e0d1dbfcc9f6d1c7ca2046168c764389c78bc4b867dab32d24f710bb74ccf2a007d7d7a8af2dfca09d9a52ccbc2831fc715c195a3634cca055185bd91
      #...
    1
    2
    (Required) Configuration of the container registry where new images are pushed.
    3
    (Required) List of connector plugins and their artifacts to add to the new container image. Each plugin must be configured with at least one artifact.
  2. Create or update the resource:

    $ oc apply -f <kafka_connect_configuration_file>
  3. Wait for the new container image to build, and for the Kafka Connect cluster to be deployed.
  4. Use the Kafka Connect REST API or KafkaConnector custom resources to use the connector plugins you added.

6.4.3.2. Building a new container image with connector plugins from the Kafka Connect base image

Create a custom Docker image with connector plugins from the Kafka Connect base image Add the custom image to the /opt/kafka/plugins directory.

You can use the Kafka container image on Red Hat Ecosystem Catalog as a base image for creating your own custom image with additional connector plugins.

At startup, the AMQ Streams version of Kafka Connect loads any third-party connector plugins contained in the /opt/kafka/plugins directory.

Procedure

  1. Create a new Dockerfile using registry.redhat.io/amq-streams/kafka-34-rhel8:2.4.0 as the base image:

    FROM registry.redhat.io/amq-streams/kafka-34-rhel8:2.4.0
    USER root:root
    COPY ./my-plugins/ /opt/kafka/plugins/
    USER 1001

    Example plugins file

    $ tree ./my-plugins/
    ./my-plugins/
    ├── debezium-connector-mongodb
    │   ├── bson-<version>.jar
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mongodb-<version>.jar
    │   ├── debezium-core-<version>.jar
    │   ├── LICENSE.txt
    │   ├── mongodb-driver-core-<version>.jar
    │   ├── README.md
    │   └── # ...
    ├── debezium-connector-mysql
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mysql-<version>.jar
    │   ├── debezium-core-<version>.jar
    │   ├── LICENSE.txt
    │   ├── mysql-binlog-connector-java-<version>.jar
    │   ├── mysql-connector-java-<version>.jar
    │   ├── README.md
    │   └── # ...
    └── debezium-connector-postgres
        ├── CHANGELOG.md
        ├── CONTRIBUTE.md
        ├── COPYRIGHT.txt
        ├── debezium-connector-postgres-<version>.jar
        ├── debezium-core-<version>.jar
        ├── LICENSE.txt
        ├── postgresql-<version>.jar
        ├── protobuf-java-<version>.jar
        ├── README.md
        └── # ...

    The COPY command points to the plugin files to copy to the container image.

    This example adds plugins for Debezium connectors (MongoDB, MySQL, and PostgreSQL), though not all files are listed for brevity. Debezium running in Kafka Connect looks the same as any other Kafka Connect task.

  2. Build the container image.
  3. Push your custom image to your container registry.
  4. Point to the new container image.

    You can point to the image in one of the following ways:

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

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

      apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
      spec: 1
        #...
        image: my-new-container-image 2
        config: 3
          #...
      1
      2
      The docker image for the pods.
      3
      Configuration of the Kafka Connect workers (not connectors).
    • Edit the STRIMZI_KAFKA_CONNECT_IMAGES environment variable in the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file to point to the new container image, and then reinstall the Cluster Operator.

6.4.3.3. Deploying KafkaConnector resources

Deploy KafkaConnector resources to manage connectors. The KafkaConnector custom resource offers an OpenShift-native approach to management of connectors by the Cluster Operator. You don’t need to send HTTP requests to manage connectors, as with the Kafka Connect REST API. You manage a running connector instance by updating its corresponding KafkaConnector resource, and then applying the updates. The Cluster Operator updates the configurations of the running connector instances. You remove a connector by deleting its corresponding KafkaConnector.

KafkaConnector resources must be deployed to the same namespace as the Kafka Connect cluster they link to.

In the configuration shown in this procedure, the autoRestart property is set to true. This enables automatic restarts of failed connectors and tasks. Up to seven restart attempts are made, after which restarts must be made manually. You annotate the KafkaConnector resource to restart a connector or restart a connector task manually.

Example connectors

You can use your own connectors or try the examples provided by AMQ Streams. Up until Apache Kafka 3.1.0, example file connector plugins were included with Apache Kafka. Starting from the 3.1.1 and 3.2.0 releases of Apache Kafka, the examples need to be added to the plugin path as any other connector.

AMQ Streams provides an example KafkaConnector configuration file (examples/connect/source-connector.yaml) for the example file connector plugins, which creates the following connector instances as KafkaConnector resources:

  • A FileStreamSourceConnector instance that reads each line from the Kafka license file (the source) and writes the data as messages to a single Kafka topic.
  • A FileStreamSinkConnector instance that reads messages from the Kafka topic and writes the messages to a temporary file (the sink).

We use the example file to create connectors in this procedure.

Note

The example connectors are not intended for use in a production environment.

Prerequisites

  • A Kafka Connect deployment
  • The Cluster Operator is running

Procedure

  1. Add the FileStreamSourceConnector and FileStreamSinkConnector plugins to Kafka Connect in one of the following ways:

  2. Set the strimzi.io/use-connector-resources annotation to true in the Kafka Connect configuration.

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: my-connect-cluster
      annotations:
        strimzi.io/use-connector-resources: "true"
    spec:
        # ...

    With the KafkaConnector resources enabled, the Cluster Operator watches for them.

  3. Edit the examples/connect/source-connector.yaml file:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-source-connector 1
      labels:
        strimzi.io/cluster: my-connect-cluster 2
    spec:
      class: org.apache.kafka.connect.file.FileStreamSourceConnector 3
      tasksMax: 2 4
      autoRestart: 5
        enabled: true
      config: 6
        file: "/opt/kafka/LICENSE" 7
        topic: my-topic 8
        # ...
    1
    Name of the KafkaConnector resource, which is used as the name of the connector. Use any name that is valid for an OpenShift resource.
    2
    Name of the Kafka Connect cluster to create the connector instance in. Connectors must be deployed to the same namespace as the Kafka Connect cluster they link to.
    3
    Full name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
    4
    Maximum number of Kafka Connect tasks that the connector can create.
    5
    Enables automatic restarts of failed connectors and tasks.
    6
    Connector configuration as key-value pairs.
    7
    This example source connector configuration reads data from the /opt/kafka/LICENSE file.
    8
    Kafka topic to publish the source data to.
  4. Create the source KafkaConnector in your OpenShift cluster:

    oc apply -f examples/connect/source-connector.yaml
  5. Create an examples/connect/sink-connector.yaml file:

    touch examples/connect/sink-connector.yaml
  6. Paste the following YAML into the sink-connector.yaml file:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-sink-connector
      labels:
        strimzi.io/cluster: my-connect
    spec:
      class: org.apache.kafka.connect.file.FileStreamSinkConnector 1
      tasksMax: 2
      config: 2
        file: "/tmp/my-file" 3
        topics: my-topic 4
    1
    Full name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
    2
    Connector configuration as key-value pairs.
    3
    Temporary file to publish the source data to.
    4
    Kafka topic to read the source data from.
  7. Create the sink KafkaConnector in your OpenShift cluster:

    oc apply -f examples/connect/sink-connector.yaml
  8. Check that the connector resources were created:

    oc get kctr --selector strimzi.io/cluster=<my_connect_cluster> -o name
    
    my-source-connector
    my-sink-connector

    Replace <my_connect_cluster> with the name of your Kafka Connect cluster.

  9. In the container, execute kafka-console-consumer.sh to read the messages that were written to the topic by the source connector:

    oc exec <my_kafka_cluster>-kafka-0 -i -t -- bin/kafka-console-consumer.sh --bootstrap-server <my_kafka_cluster>-kafka-bootstrap.NAMESPACE.svc:9092 --topic my-topic --from-beginning

    Replace <my_kafka_cluster> with the name of your Kafka cluster.

Source and sink connector configuration options

The connector configuration is defined in the spec.config property of the KafkaConnector resource.

The FileStreamSourceConnector and FileStreamSinkConnector classes support the same configuration options as the Kafka Connect REST API. Other connectors support different configuration options.

Table 6.1. Configuration options for the FileStreamSource connector class

NameTypeDefault valueDescription

file

String

Null

Source file to write messages to. If not specified, the standard input is used.

topic

List

Null

The Kafka topic to publish data to.

Table 6.2. Configuration options for FileStreamSinkConnector class

NameTypeDefault valueDescription

file

String

Null

Destination file to write messages to. If not specified, the standard output is used.

topics

List

Null

One or more Kafka topics to read data from.

topics.regex

String

Null

A regular expression matching one or more Kafka topics to read data from.

6.4.3.4. Manually restarting connectors

If you are using KafkaConnector resources to manage connectors, use the restart annotation to manually trigger a restart of a connector.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaConnector custom resource that controls the Kafka connector you want to restart:

    oc get KafkaConnector
  2. Restart the connector by annotating the KafkaConnector resource in OpenShift.

    oc annotate KafkaConnector <kafka_connector_name> strimzi.io/restart=true

    The restart annotation is set to true.

  3. Wait for the next reconciliation to occur (every two minutes by default).

    The Kafka connector is restarted, as long as the annotation was detected by the reconciliation process. When Kafka Connect accepts the restart request, the annotation is removed from the KafkaConnector custom resource.

6.4.3.5. Manually restarting Kafka connector tasks

If you are using KafkaConnector resources to manage connectors, use the restart-task annotation to manually trigger a restart of a connector task.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaConnector custom resource that controls the Kafka connector task you want to restart:

    oc get KafkaConnector
  2. Find the ID of the task to be restarted from the KafkaConnector custom resource. Task IDs are non-negative integers, starting from 0:

    oc describe KafkaConnector <kafka_connector_name>
  3. Use the ID to restart the connector task by annotating the KafkaConnector resource in OpenShift:

    oc annotate KafkaConnector <kafka_connector_name> strimzi.io/restart-task=0

    In this example, task 0 is restarted.

  4. Wait for the next reconciliation to occur (every two minutes by default).

    The Kafka connector task is restarted, as long as the annotation was detected by the reconciliation process. When Kafka Connect accepts the restart request, the annotation is removed from the KafkaConnector custom resource.

6.4.3.6. Exposing the Kafka Connect API

Use the Kafka Connect REST API as an alternative to using KafkaConnector resources to manage connectors. The Kafka Connect REST API is available as a service running on <connect_cluster_name>-connect-api:8083, where <connect_cluster_name> is the name of your Kafka Connect cluster. The service is created when you create a Kafka Connect instance.

The operations supported by the Kafka Connect REST API are described in the Apache Kafka Connect API documentation.

Note

The strimzi.io/use-connector-resources annotation enables KafkaConnectors. If you applied the annotation to your KafkaConnect resource configuration, you need to remove it to use the Kafka Connect API. Otherwise, manual changes made directly using the Kafka Connect REST API are reverted by the Cluster Operator.

You can add the connector configuration as a JSON object.

Example curl request to add connector configuration

curl -X POST \
  http://my-connect-cluster-connect-api:8083/connectors \
  -H 'Content-Type: application/json' \
  -d '{ "name": "my-source-connector",
    "config":
    {
      "connector.class":"org.apache.kafka.connect.file.FileStreamSourceConnector",
      "file": "/opt/kafka/LICENSE",
      "topic":"my-topic",
      "tasksMax": "4",
      "type": "source"
    }
}'

The API is only accessible within the OpenShift cluster. If you want to make the Kafka Connect API accessible to applications running outside of the OpenShift cluster, you can expose it manually by creating one of the following features:

  • LoadBalancer or NodePort type services
  • Ingress resources (Kubernetes only)
  • OpenShift routes (OpenShift only)
Note

The connection is insecure, so allow external access advisedly.

If you decide to create services, use the labels from the selector of the <connect_cluster_name>-connect-api service to configure the pods to which the service will route the traffic:

Selector configuration for the service

# ...
selector:
  strimzi.io/cluster: my-connect-cluster 1
  strimzi.io/kind: KafkaConnect
  strimzi.io/name: my-connect-cluster-connect 2
#...

1
Name of the Kafka Connect custom resource in your OpenShift cluster.
2
Name of the Kafka Connect deployment created by the Cluster Operator.

You must also create a NetworkPolicy that allows HTTP requests from external clients.

Example NetworkPolicy to allow requests to the Kafka Connect API

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: my-custom-connect-network-policy
spec:
  ingress:
  - from:
    - podSelector: 1
        matchLabels:
          app: my-connector-manager
    ports:
    - port: 8083
      protocol: TCP
  podSelector:
    matchLabels:
      strimzi.io/cluster: my-connect-cluster
      strimzi.io/kind: KafkaConnect
      strimzi.io/name: my-connect-cluster-connect
  policyTypes:
  - Ingress

1
The label of the pod that is allowed to connect to the API.

To add the connector configuration outside the cluster, use the URL of the resource that exposes the API in the curl command.

6.4.3.7. Limiting access to the Kafka Connect API

It is crucial to restrict access to the Kafka Connect API only to trusted users to prevent unauthorized actions and potential security issues. The Kafka Connect API provides extensive capabilities for altering connector configurations, which makes it all the more important to take security precautions. Someone with access to the Kafka Connect API could potentially obtain sensitive information that an administrator may assume is secure.

The Kafka Connect REST API can be accessed by anyone who has authenticated access to the OpenShift cluster and knows the endpoint URL, which includes the hostname/IP address and port number.

For example, suppose an organization uses a Kafka Connect cluster and connectors to stream sensitive data from a customer database to a central database. The administrator uses a configuration provider plugin to store sensitive information related to connecting to the customer database and the central database, such as database connection details and authentication credentials. The configuration provider protects this sensitive information from being exposed to unauthorized users. However, someone who has access to the Kafka Connect API can still obtain access to the customer database without the consent of the administrator. They can do this by setting up a fake database and configuring a connector to connect to it. They then modify the connector configuration to point to the customer database, but instead of sending the data to the central database, they send it to the fake database. By configuring the connector to connect to the fake database, the login details and credentials for connecting to the customer database are intercepted, even though they are stored securely in the configuration provider.

If you are using the KafkaConnector custom resources, then by default the OpenShift RBAC rules permit only OpenShift cluster administrators to make changes to connectors. You can also designate non-cluster administrators to manage AMQ Streams resources. With KafkaConnector resources enabled in your Kafka Connect configuration, changes made directly using the Kafka Connect REST API are reverted by the Cluster Operator. If you are not using the KafkaConnector resource, the default RBAC rules do not limit access to the Kafka Connect API. If you want to limit direct access to the Kafka Connect REST API using OpenShift RBAC, you need to enable and use the KafkaConnector resources.

For improved security, we recommend configuring the following properties for the Kafka Connect API:

org.apache.kafka.disallowed.login.modules

(Kafka 3.4 or later) Set the org.apache.kafka.disallowed.login.modules Java system property to prevent the use of insecure login modules. For example, specifying com.sun.security.auth.module.JndiLoginModule prevents the use of the Kafka JndiLoginModule.

Example configuration for disallowing login modules

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true"
spec:
  # ...
  jvmOptions:
    javaSystemProperties:
      - name: org.apache.kafka.disallowed.login.modules
        value: com.sun.security.auth.module.JndiLoginModule, org.apache.kafka.common.security.kerberos.KerberosLoginModule
# ...

Only allow trusted login modules and follow the latest advice from Kafka for the version you are using. As a best practice, you should explicitly disallow insecure login modules in your Kafka Connect configuration by using the org.apache.kafka.disallowed.login.modules system property.

connector.client.config.override.policy

Set the connector.client.config.override.policy property to None to prevent connector configurations from overriding the Kafka Connect configuration and the consumers and producers it uses.

Example configuration to specify connector override policy

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true"
spec:
  # ...
  config:
    connector.client.config.override.policy: None
# ...

6.4.3.8. Switching from using the Kafka Connect API to using KafkaConnector custom resources

You can switch from using the Kafka Connect API to using KafkaConnector custom resources to manage your connectors. To make the switch, do the following in the order shown:

  1. Deploy KafkaConnector resources with the configuration to create your connector instances.
  2. Enable KafkaConnector resources in your Kafka Connect configuration by setting the strimzi.io/use-connector-resources annotation to true.
Warning

If you enable KafkaConnector resources before creating them, you delete all connectors.

To switch from using KafkaConnector resources to using the Kafka Connect API, first remove the annotation that enables the KafkaConnector resources from your Kafka Connect configuration. Otherwise, manual changes made directly using the Kafka Connect REST API are reverted by the Cluster Operator.

When making the switch, check the status of the KafkaConnect resource. The value of metadata.generation (the current version of the deployment) must match status.observedGeneration (the latest reconciliation of the resource). When the Kafka Connect cluster is Ready, you can delete the KafkaConnector resources.

6.5. Deploying Kafka MirrorMaker

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

6.5.1. Deploying Kafka MirrorMaker to your OpenShift cluster

This procedure shows how to deploy a Kafka MirrorMaker cluster to your OpenShift cluster using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a KafkaMirrorMaker or KafkaMirrorMaker2 resource depending on the version of MirrorMaker deployed.

Important

Kafka MirrorMaker 1 (referred to as just MirrorMaker in the documentation) has been deprecated in Apache Kafka 3.0.0 and will be removed in Apache Kafka 4.0.0. As a result, the KafkaMirrorMaker custom resource which is used to deploy Kafka MirrorMaker 1 has been deprecated in AMQ Streams as well. The KafkaMirrorMaker resource will be removed from AMQ Streams when we adopt Apache Kafka 4.0.0. As a replacement, use the KafkaMirrorMaker2 custom resource with the IdentityReplicationPolicy.

AMQ Streams provides example configuration files. In this procedure, we use the following example files:

  • examples/mirror-maker/kafka-mirror-maker.yaml
  • examples/mirror-maker/kafka-mirror-maker-2.yaml

Procedure

  1. Deploy Kafka MirrorMaker to your OpenShift cluster:

    For MirrorMaker:

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

    For MirrorMaker 2:

    oc apply -f examples/mirror-maker/kafka-mirror-maker-2.yaml
  2. Check the status of the deployment:

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the deployment name and readiness

    NAME                                    READY  STATUS   RESTARTS
    my-mirror-maker-mirror-maker-<pod_id>   1/1    Running  1
    my-mm2-cluster-mirrormaker2-<pod_id>    1/1    Running  1

    my-mirror-maker is the name of the Kafka MirrorMaker cluster. my-mm2-cluster is the name of the Kafka MirrorMaker 2 cluster.

    A pod ID identifies each pod created.

    With the default deployment, you install a single MirrorMaker or MirrorMaker 2 pod.

    READY shows the number of replicas that are ready/expected. The deployment is successful when the STATUS shows as Running.

6.6. Deploying Kafka Bridge

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

6.6.1. Deploying Kafka Bridge to your OpenShift cluster

This procedure shows how to deploy a Kafka Bridge cluster to your OpenShift cluster using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a KafkaBridge resource.

AMQ Streams provides example configuration files. In this procedure, we use the following example file:

  • examples/bridge/kafka-bridge.yaml

Procedure

  1. Deploy Kafka Bridge to your OpenShift cluster:

    oc apply -f examples/bridge/kafka-bridge.yaml
  2. Check the status of the deployment:

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the deployment name and readiness

    NAME                       READY  STATUS   RESTARTS
    my-bridge-bridge-<pod_id>  1/1    Running  0

    my-bridge is the name of the Kafka Bridge cluster.

    A pod ID identifies each pod created.

    With the default deployment, you install a single Kafka Bridge pod.

    READY shows the number of replicas that are ready/expected. The deployment is successful when the STATUS shows as Running.

6.6.2. Exposing the Kafka Bridge service to your local machine

Use port forwarding to expose the AMQ Streams Kafka Bridge service to your local machine on http://localhost:8080.

Note

Port forwarding is only suitable for development and testing purposes.

Procedure

  1. List the names of the pods in your OpenShift cluster:

    oc get pods -o name
    
    pod/kafka-consumer
    # ...
    pod/my-bridge-bridge-<pod_id>
  2. Connect to the Kafka Bridge pod on port 8080:

    oc port-forward pod/my-bridge-bridge-<pod_id> 8080:8080 &
    Note

    If port 8080 on your local machine is already in use, use an alternative HTTP port, such as 8008.

API requests are now forwarded from port 8080 on your local machine to port 8080 in the Kafka Bridge pod.

6.6.3. Accessing the Kafka Bridge outside of OpenShift

After deployment, the AMQ Streams Kafka Bridge can only be accessed by applications running in the same OpenShift cluster. These applications use the <kafka_bridge_name>-bridge-service service to access the API.

If you want to make the Kafka Bridge accessible to applications running outside of the OpenShift cluster, you can expose it manually by creating one of the following features:

  • LoadBalancer or NodePort type services
  • Ingress resources (Kubernetes only)
  • OpenShift routes (OpenShift only)

If you decide to create Services, use the labels from the selector of the <kafka_bridge_name>-bridge-service service to configure the pods to which the service will route the traffic:

  # ...
  selector:
    strimzi.io/cluster: kafka-bridge-name 1
    strimzi.io/kind: KafkaBridge
  #...
1
Name of the Kafka Bridge custom resource in your OpenShift cluster.

6.7. Alternative standalone deployment options for AMQ Streams operators

You can perform a standalone deployment of the Topic Operator and User Operator. Consider a standalone deployment of these operators if you are using a Kafka cluster that is not managed by the Cluster Operator.

You deploy the operators to OpenShift. Kafka can be running outside of OpenShift. For example, you might be using a Kafka as a managed service. You adjust the deployment configuration for the standalone operator to match the address of your Kafka cluster.

6.7.1. Deploying the standalone Topic Operator

This procedure shows how to deploy the Topic Operator as a standalone component for topic management. You can use a standalone Topic Operator with a Kafka cluster that is not managed by the Cluster Operator.

A standalone deployment can operate with any Kafka cluster.

Standalone deployment files are provided with AMQ Streams. Use the 05-Deployment-strimzi-topic-operator.yaml deployment file to deploy the Topic Operator. Add or set the environment variables needed to make a connection to a Kafka cluster.

The Topic Operator watches for KafkaTopic resources in a single namespace. You specify the namespace to watch, and the connection to the Kafka cluster, in the Topic Operator configuration. A single Topic Operator can watch a single namespace. One namespace should be watched by only one Topic Operator. If you want to use more than one Topic Operator, configure each of them to watch different namespaces. In this way, you can use Topic Operators with multiple Kafka clusters.

Prerequisites

  • You are running a Kafka cluster for the Topic Operator to connect to.

    As long as the standalone Topic Operator is correctly configured for connection, the Kafka cluster can be running on a bare-metal environment, a virtual machine, or as a managed cloud application service.

Procedure

  1. Edit the env properties in the install/topic-operator/05-Deployment-strimzi-topic-operator.yaml standalone deployment file.

    Example standalone Topic Operator deployment configuration

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: strimzi-topic-operator
      labels:
        app: strimzi
    spec:
      # ...
      template:
        # ...
        spec:
          # ...
          containers:
            - name: strimzi-topic-operator
              # ...
              env:
                - name: STRIMZI_NAMESPACE 1
                  valueFrom:
                    fieldRef:
                      fieldPath: metadata.namespace
                - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS 2
                  value: my-kafka-bootstrap-address:9092
                - name: STRIMZI_RESOURCE_LABELS 3
                  value: "strimzi.io/cluster=my-cluster"
                - name: STRIMZI_ZOOKEEPER_CONNECT 4
                  value: my-cluster-zookeeper-client:2181
                - name: STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS 5
                  value: "18000"
                - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS 6
                  value: "120000"
                - name: STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS 7
                  value: "6"
                - name: STRIMZI_LOG_LEVEL 8
                  value: INFO
                - name: STRIMZI_TLS_ENABLED 9
                  value: "false"
                - name: STRIMZI_JAVA_OPTS 10
                  value: "-Xmx=512M -Xms=256M"
                - name: STRIMZI_JAVA_SYSTEM_PROPERTIES 11
                  value: "-Djavax.net.debug=verbose -DpropertyName=value"
                - name: STRIMZI_PUBLIC_CA 12
                  value: "false"
                - name: STRIMZI_TLS_AUTH_ENABLED 13
                  value: "false"
                - name: STRIMZI_SASL_ENABLED 14
                  value: "false"
                - name: STRIMZI_SASL_USERNAME 15
                  value: "admin"
                - name: STRIMZI_SASL_PASSWORD 16
                  value: "password"
                - name: STRIMZI_SASL_MECHANISM 17
                  value: "scram-sha-512"
                - name: STRIMZI_SECURITY_PROTOCOL 18
                  value: "SSL"

    1
    The OpenShift namespace for the Topic Operator to watch for KafkaTopic resources. Specify the namespace of the Kafka cluster.
    2
    The host and port pair of the bootstrap broker address to discover and connect to all brokers in the Kafka cluster. Use a comma-separated list to specify two or three broker addresses in case a server is down.
    3
    The label to identify the KafkaTopic resources managed by the Topic Operator. This does not have to be the name of the Kafka cluster. It can be the label assigned to the KafkaTopic resource. If you deploy more than one Topic Operator, the labels must be unique for each. That is, the operators cannot manage the same resources.
    4
    The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
    5
    The ZooKeeper session timeout, in milliseconds. The default is 18000 (18 seconds).
    6
    The interval between periodic reconciliations, in milliseconds. The default is 120000 (2 minutes).
    7
    The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential backoff. Consider increasing this value when topic creation takes more time due to the number of partitions or replicas. The default is 6 attempts.
    8
    The level for printing logging messages. You can set the level to ERROR, WARNING, INFO, DEBUG, or TRACE.
    9
    Enables TLS support for encrypted communication with the Kafka brokers.
    10
    (Optional) The Java options used by the JVM running the Topic Operator.
    11
    (Optional) The debugging (-D) options set for the Topic Operator.
    12
    (Optional) Skips the generation of trust store certificates if TLS is enabled through STRIMZI_TLS_ENABLED. If this environment variable is enabled, the brokers must use a public trusted certificate authority for their TLS certificates. The default is false.
    13
    (Optional) Generates key store certificates for mTLS authentication. Setting this to false disables client authentication with mTLS to the Kafka brokers. The default is true.
    14
    (Optional) Enables SASL support for client authentication when connecting to Kafka brokers. The default is false.
    15
    (Optional) The SASL username for client authentication. Mandatory only if SASL is enabled through STRIMZI_SASL_ENABLED.
    16
    (Optional) The SASL password for client authentication. Mandatory only if SASL is enabled through STRIMZI_SASL_ENABLED.
    17
    (Optional) The SASL mechanism for client authentication. Mandatory only if SASL is enabled through STRIMZI_SASL_ENABLED. You can set the value to plain, scram-sha-256, or scram-sha-512.
    18
    (Optional) The security protocol used for communication with Kafka brokers. The default value is "PLAINTEXT". You can set the value to PLAINTEXT, SSL, SASL_PLAINTEXT, or SASL_SSL.
  2. If you want to connect to Kafka brokers that are using certificates from a public certificate authority, set STRIMZI_PUBLIC_CA to true. Set this property to true, for example, if you are using Amazon AWS MSK service.
  3. If you enabled mTLS with the STRIMZI_TLS_ENABLED environment variable, specify the keystore and truststore used to authenticate connection to the Kafka cluster.

    Example mTLS configuration

    # ....
    env:
      - name: STRIMZI_TRUSTSTORE_LOCATION 1
        value: "/path/to/truststore.p12"
      - name: STRIMZI_TRUSTSTORE_PASSWORD 2
        value: "TRUSTSTORE-PASSWORD"
      - name: STRIMZI_KEYSTORE_LOCATION 3
        value: "/path/to/keystore.p12"
      - name: STRIMZI_KEYSTORE_PASSWORD 4
        value: "KEYSTORE-PASSWORD"
    # ...

    1
    The truststore contains the public keys of the Certificate Authorities used to sign the Kafka and ZooKeeper server certificates.
    2
    The password for accessing the truststore.
    3
    The keystore contains the private key for mTLS authentication.
    4
    The password for accessing the keystore.
  4. Deploy the Topic Operator.

    oc create -f install/topic-operator
  5. Check the status of the deployment:

    oc get deployments

    Output shows the deployment name and readiness

    NAME                    READY  UP-TO-DATE  AVAILABLE
    strimzi-topic-operator  1/1    1           1

    READY shows the number of replicas that are ready/expected. The deployment is successful when the AVAILABLE output shows 1.

6.7.2. Deploying the standalone User Operator

This procedure shows how to deploy the User Operator as a standalone component for user management. You can use a standalone User Operator with a Kafka cluster that is not managed by the Cluster Operator.

A standalone deployment can operate with any Kafka cluster.

Standalone deployment files are provided with AMQ Streams. Use the 05-Deployment-strimzi-user-operator.yaml deployment file to deploy the User Operator. Add or set the environment variables needed to make a connection to a Kafka cluster.

The User Operator watches for KafkaUser resources in a single namespace. You specify the namespace to watch, and the connection to the Kafka cluster, in the User Operator configuration. A single User Operator can watch a single namespace. One namespace should be watched by only one User Operator. If you want to use more than one User Operator, configure each of them to watch different namespaces. In this way, you can use the User Operator with multiple Kafka clusters.

Prerequisites

  • You are running a Kafka cluster for the User Operator to connect to.

    As long as the standalone User Operator is correctly configured for connection, the Kafka cluster can be running on a bare-metal environment, a virtual machine, or as a managed cloud application service.

Procedure

  1. Edit the following env properties in the install/user-operator/05-Deployment-strimzi-user-operator.yaml standalone deployment file.

    Example standalone User Operator deployment configuration

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: strimzi-user-operator
      labels:
        app: strimzi
    spec:
      # ...
      template:
        # ...
        spec:
          # ...
          containers:
            - name: strimzi-user-operator
              # ...
              env:
                - name: STRIMZI_NAMESPACE 1
                  valueFrom:
                    fieldRef:
                      fieldPath: metadata.namespace
                - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS 2
                  value: my-kafka-bootstrap-address:9092
                - name: STRIMZI_CA_CERT_NAME 3
                  value: my-cluster-clients-ca-cert
                - name: STRIMZI_CA_KEY_NAME 4
                  value: my-cluster-clients-ca
                - name: STRIMZI_LABELS 5
                  value: "strimzi.io/cluster=my-cluster"
                - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS 6
                  value: "120000"
                - name: STRIMZI_WORK_QUEUE_SIZE 7
                  value: 10000
                - name: STRIMZI_CONTROLLER_THREAD_POOL_SIZE 8
                  value: 10
                - name: STRIMZI_USER_OPERATIONS_THREAD_POOL_SIZE 9
                  value: 4
                - name: STRIMZI_LOG_LEVEL 10
                  value: INFO
                - name: STRIMZI_GC_LOG_ENABLED 11
                  value: "true"
                - name: STRIMZI_CA_VALIDITY 12
                  value: "365"
                - name: STRIMZI_CA_RENEWAL 13
                  value: "30"
                - name: STRIMZI_JAVA_OPTS 14
                  value: "-Xmx=512M -Xms=256M"
                - name: STRIMZI_JAVA_SYSTEM_PROPERTIES 15
                  value: "-Djavax.net.debug=verbose -DpropertyName=value"
                - name: STRIMZI_SECRET_PREFIX 16
                  value: "kafka-"
                - name: STRIMZI_ACLS_ADMIN_API_SUPPORTED 17
                  value: "true"
                - name: STRIMZI_MAINTENANCE_TIME_WINDOWS 18
                  value: '* * 8-10 * * ?;* * 14-15 * * ?'
                - name: STRIMZI_KAFKA_ADMIN_CLIENT_CONFIGURATION 19
                  value: |
                    default.api.timeout.ms=120000
                    request.timeout.ms=60000
                - name: STRIMZI_KRAFT_ENABLED 20
                  value: "false"

    1
    The OpenShift namespace for the User Operator to watch for KafkaUser resources. Only one namespace can be specified.
    2
    The host and port pair of the bootstrap broker address to discover and connect to all brokers in the Kafka cluster. Use a comma-separated list to specify two or three broker addresses in case a server is down.
    3
    The OpenShift Secret that contains the public key (ca.crt) value of the CA (certificate authority) that signs new user certificates for mTLS authentication.
    4
    The OpenShift Secret that contains the private key (ca.key) value of the CA that signs new user certificates for mTLS authentication.
    5
    The label to identify the KafkaUser resources managed by the User Operator. This does not have to be the name of the Kafka cluster. It can be the label assigned to the KafkaUser resource. If you deploy more than one User Operator, the labels must be unique for each. That is, the operators cannot manage the same resources.
    6
    The interval between periodic reconciliations, in milliseconds. The default is 120000 (2 minutes).
    7
    The size of the controller event queue. The size of the queue should be at least as big as the maximal amount of users you expect the User Operator to operate. The default is 1024.
    8
    The size of the worker pool for reconciling the users. Bigger pool might require more resources, but it will also handle more KafkaUser resources The default is 50.
    9
    The size of the worker pool for Kafka Admin API and OpenShift operations. Bigger pool might require more resources, but it will also handle more KafkaUser resources The default is 4.
    10
    The level for printing logging messages. You can set the level to ERROR, WARNING, INFO, DEBUG, or TRACE.
    11
    Enables garbage collection (GC) logging. The default is true.
    12
    The validity period for the CA. The default is 365 days.
    13
    The renewal period for the CA. The renewal period is measured backwards from the expiry date of the current certificate. The default is 30 days to initiate certificate renewal before the old certificates expire.
    14
    (Optional) The Java options used by the JVM running the User Operator
    15
    (Optional) The debugging (-D) options set for the User Operator
    16
    (Optional) Prefix for the names of OpenShift secrets created by the User Operator.
    17
    (Optional) Indicates whether the Kafka cluster supports management of authorization ACL rules using the Kafka Admin API. When set to false, the User Operator will reject all resources with simple authorization ACL rules. This helps to avoid unnecessary exceptions in the Kafka cluster logs. The default is true.
    18
    (Optional) Semi-colon separated list of Cron Expressions defining the maintenance time windows during which the expiring user certificates will be renewed.
    19
    (Optional) Configuration options for configuring the Kafka Admin client used by the User Operator in the properties format.
    20
    (Optional) Indicates whether the Kafka cluster the User Operator is connecting to is using KRaft instead of ZooKeeper. Set this variable to true if the Kafka cluster uses KRaft. The default is false. Note that some features are not available when running against KRaft clusters. For example, management of SCRAM-SHA-512 users is disabled because Apache Kafka currently does not support it.
  2. If you are using mTLS to connect to the Kafka cluster, specify the secrets used to authenticate connection. Otherwise, go to the next step.

    Example mTLS configuration

    # ....
    env:
      - name: STRIMZI_CLUSTER_CA_CERT_SECRET_NAME 1
        value: my-cluster-cluster-ca-cert
      - name: STRIMZI_EO_KEY_SECRET_NAME 2
        value: my-cluster-entity-operator-certs
    # ..."

    1
    The OpenShift Secret that contains the public key (ca.crt) value of the CA that signs Kafka broker certificates.
    2
    The OpenShift Secret that contains the certificate public key (entity-operator.crt) and private key (entity-operator.key) that is used for mTLS authentication against the Kafka cluster.
  3. Deploy the User Operator.

    oc create -f install/user-operator
  4. Check the status of the deployment:

    oc get deployments

    Output shows the deployment name and readiness

    NAME                   READY  UP-TO-DATE  AVAILABLE
    strimzi-user-operator  1/1    1           1

    READY shows the number of replicas that are ready/expected. The deployment is successful when the AVAILABLE output shows 1.

Chapter 7. Setting up client access to a Kafka cluster

After you have deployed AMQ Streams, you can set up client access to your Kafka cluster. To verify the deployment, you can deploy example producer and consumer clients. Otherwise, create listeners that provide client access within or outside the OpenShift cluster.

7.1. Deploying example clients

Deploy example producer and consumer clients to send and receive messages. You can use these clients to verify a deployment of AMQ Streams.

Prerequisites

  • The Kafka cluster is available for the clients.

Procedure

  1. Deploy a Kafka producer.

    oc run kafka-producer -ti --image=registry.redhat.io/amq-streams/kafka-34-rhel8:2.4.0 --rm=true --restart=Never -- bin/kafka-console-producer.sh --bootstrap-server cluster-name-kafka-bootstrap:9092 --topic my-topic
  2. Type a message into the console where the producer is running.
  3. Press Enter to send the message.
  4. Deploy a Kafka consumer.

    oc run kafka-consumer -ti --image=registry.redhat.io/amq-streams/kafka-34-rhel8:2.4.0 --rm=true --restart=Never -- bin/kafka-console-consumer.sh --bootstrap-server cluster-name-kafka-bootstrap:9092 --topic my-topic --from-beginning
  5. Confirm that you see the incoming messages in the consumer console.

7.2. Configuring listeners to connect to Kafka brokers

Use listeners for client connection to Kafka brokers. AMQ Streams provides a generic GenericKafkaListener schema with properties to configure listeners through the Kafka resource. The GenericKafkaListener provides a flexible approach to listener configuration. You can specify properties to configure internal listeners for connecting within the OpenShift cluster or external listeners for connecting outside the OpenShift cluster.

Specify a connection type to expose Kafka in the listener configuration. The type chosen depends on your requirements, and your environment and infrastructure. The following listener types are supported:

Internal listeners
  • internal to connect within the same OpenShift cluster
  • cluster-ip to expose Kafka using per-broker ClusterIP services
External listeners
  • nodeport to use ports on OpenShift nodes
  • loadbalancer to use loadbalancer services
  • ingress to use Kubernetes Ingress and the Ingress NGINX Controller for Kubernetes (Kubernetes only)
  • route to use OpenShift Route and the default HAProxy router (OpenShift only)
Important

Do not use ingress on OpenShift, use the route type instead. The Ingress NGINX Controller is only intended for use on Kubernetes. The route type is only supported on OpenShift.

An internal type listener configuration uses a headless service and the DNS names given to the broker pods. You might want to join your OpenShift network to an outside network. In which case, you can configure an internal type listener (using the useServiceDnsDomain property) so that the OpenShift service DNS domain (typically .cluster.local) is not used. You can also configure a cluster-ip type of listener that exposes a Kafka cluster based on per-broker ClusterIP services. This is a useful option when you can’t route through the headless service or you wish to incorporate a custom access mechanism. For example, you might use this listener when building your own type of external listener for a specific Ingress controller or the OpenShift Gateway API.

External listeners handle access to a Kafka cluster from networks that require different authentication mechanisms. You can configure external listeners for client access outside an OpenShift environment using a specified connection mechanism, such as a loadbalancer or route. For example, loadbalancers might not be suitable for certain infrastructure, such as bare metal, where node ports provide a better option.

Each listener is defined as an array in the Kafka resource.

Example listener configuration

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: false
        configuration:
          useServiceDnsDomain: true
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
      - name: external
        port: 9094
        type: route
        tls: true
        configuration:
          brokerCertChainAndKey:
            secretName: my-secret
            certificate: my-certificate.crt
            key: my-key.key
    # ...

You can configure as many listeners as required, as long as their names and ports are unique. You can also configure listeners for secure connection using authentication.

If you want to know more about the pros and cons of each connection type, refer to Accessing Apache Kafka in Strimzi.

Note

If you scale your Kafka cluster while using external listeners, it might trigger a rolling update of all Kafka brokers. This depends on the configuration.

7.3. Setting up client access to a Kafka cluster using listeners

Using the address of the Kafka cluster, you can provide access to a client in the same OpenShift cluster; or provide external access to a client on a different OpenShift namespace or outside OpenShift entirely. This procedure shows how to configure client access to a Kafka cluster from outside OpenShift or from another OpenShift cluster.

A Kafka listener provides access to the Kafka cluster. Client access is secured using the following configuration:

  1. An external listener is configured for the Kafka cluster, with TLS encryption and mTLS authentication, and Kafka simple authorization enabled.
  2. A KafkaUser is created for the client, with mTLS authentication, and Access Control Lists (ACLs) defined for simple authorization.

You can configure your listener to use mutual tls, scram-sha-512, or oauth authentication. mTLS always uses encryption, but encryption is also recommended when using SCRAM-SHA-512 and OAuth 2.0 authentication.

You can configure simple, oauth, opa, or custom authorization for Kafka brokers. When enabled, authorization is applied to all enabled listeners.

When you configure the KafkaUser authentication and authorization mechanisms, ensure they match the equivalent Kafka configuration:

  • KafkaUser.spec.authentication matches Kafka.spec.kafka.listeners[*].authentication
  • KafkaUser.spec.authorization matches Kafka.spec.kafka.authorization

You should have at least one listener supporting the authentication you want to use for the KafkaUser.

Note

Authentication between Kafka users and Kafka brokers depends on the authentication settings for each. For example, it is not possible to authenticate a user with mTLS if it is not also enabled in the Kafka configuration.

AMQ Streams operators automate the configuration process and create the certificates required for authentication:

  • The Cluster Operator creates the listeners and sets up the cluster and client certificate authority (CA) certificates to enable authentication with the Kafka cluster.
  • The User Operator creates the user representing the client and the security credentials used for client authentication, based on the chosen authentication type.

You add the certificates to your client configuration.

In this procedure, the CA certificates generated by the Cluster Operator are used, but you can replace them by installing your own certificates. You can also configure your listener to use a Kafka listener certificate managed by an external CA (certificate authority).

Certificates are available in PEM (.crt) and PKCS #12 (.p12) formats. This procedure uses PEM certificates. Use PEM certificates with clients that use certificates in X.509 format.

Note

For internal clients in the same OpenShift cluster and namespace, you can mount the cluster CA certificate in the pod specification. For more information, see Configuring internal clients to trust the cluster CA.

Prerequisites

  • The Kafka cluster is available for connection by a client running outside the OpenShift cluster
  • The Cluster Operator and User Operator are running in the cluster

Procedure

  1. Configure the Kafka cluster with a Kafka listener.

    • Define the authentication required to access the Kafka broker through the listener.
    • Enable authorization on the Kafka broker.

      Example listener configuration

      apiVersion: kafka.strimzi.io/v1beta2
      kind: Kafka
      metadata:
        name: my-cluster
        namespace: myproject
      spec:
        kafka:
          # ...
          listeners: 1
          - name: external 2
            port: 9094 3
            type: <listener_type> 4
            tls: true 5
            authentication:
              type: tls 6
            configuration: 7
              #...
          authorization: 8
            type: simple
            superUsers:
              - super-user-name 9
        # ...

      1
      Configuration options for enabling external listeners are described in the Generic Kafka listener schema reference.
      2
      Name to identify the listener. Must be unique within the Kafka cluster.
      3
      Port number used by the listener inside Kafka. The port number has to be unique within a given Kafka cluster. Allowed port numbers are 9092 and higher with the exception of ports 9404 and 9999, which are already used for Prometheus and JMX. Depending on the listener type, the port number might not be the same as the port number that connects Kafka clients.
      4
      External listener type specified as route (OpenShift only), loadbalancer, nodeport or ingress (Kubernetes only). An internal listener is specified as internal or cluster-ip.
      5
      Required. TLS encryption on the listener. For route and ingress type listeners it must be set to true. For mTLS authentication, also use the authentication property.
      6
      Client authentication mechanism on the listener. For server and client authentication using mTLS, you specify tls: true and authentication.type: tls.
      7
      (Optional) Depending on the requirements of the listener type, you can specify additional listener configuration.
      8
      Authorization specified as simple, which uses the AclAuthorizer Kafka plugin.
      9
      (Optional) Super users can access all brokers regardless of any access restrictions defined in ACLs.
      Warning

      An OpenShift Route address comprises the name of the Kafka cluster, the name of the listener, and the name of the namespace it is created in. For example, my-cluster-kafka-listener1-bootstrap-myproject (CLUSTER-NAME-kafka-LISTENER-NAME-bootstrap-NAMESPACE). If you are using a route listener type, be careful that the whole length of the address does not exceed a maximum limit of 63 characters.

  2. Create or update the Kafka resource.

    oc apply -f <kafka_configuration_file>

    The Kafka cluster is configured with a Kafka broker listener using mTLS authentication.

    A service is created for each Kafka broker pod.

    A service is created to serve as the bootstrap address for connection to the Kafka cluster.

    A service is also created as the external bootstrap address for external connection to the Kafka cluster using nodeport listeners.

    The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret <cluster_name>-cluster-ca-cert.

    Note

    If you scale your Kafka cluster while using external listeners, it might trigger a rolling update of all Kafka brokers. This depends on the configuration.

  3. Retrieve the bootstrap address you can use to access the Kafka cluster from the status of the Kafka resource.

    oc get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'

    For example:

    oc get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}'

    Use the bootstrap address in your Kafka client to connect to the Kafka cluster.

  4. Create or modify a user representing the client that requires access to the Kafka cluster.

    • Specify the same authentication type as the Kafka listener.
    • Specify the authorization ACLs for simple authorization.

      Example user configuration

      apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaUser
      metadata:
        name: my-user
        labels:
          strimzi.io/cluster: my-cluster 1
      spec:
        authentication:
          type: tls 2
        authorization:
          type: simple
          acls: 3
            - resource:
                type: topic
                name: my-topic
                patternType: literal
              operations:
                - Describe
                - Read
            - resource:
                type: group
                name: my-group
                patternType: literal
              operations:
                - Read

      1
      The label must match the label of the Kafka cluster.
      2
      Authentication specified as mutual tls.
      3
      Simple authorization requires an accompanying list of ACL rules to apply to the user. The rules define the operations allowed on Kafka resources based on the username (my-user).
  5. Create or modify the KafkaUser resource.

    oc apply -f USER-CONFIG-FILE

    The user is created, as well as a secret with the same name as the KafkaUser resource. The secret contains a public and private key for mTLS authentication.

    Example secret

    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> # Public key of the clients CA
      user.crt: <user_certificate> # Public key of the user
      user.key: <user_private_key> # Private key of the user
      user.p12: <store> # PKCS #12 store for user certificates and keys
      user.password: <password_for_store> # Protects the PKCS #12 store

  6. Extract the cluster CA certificate from the <cluster_name>-cluster-ca-cert secret of the Kafka cluster.

    oc get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
  7. Extract the user CA certificate from the <user_name> secret.

    oc get secret <user_name> -o jsonpath='{.data.user\.crt}' | base64 -d > user.crt
  8. Extract the private key of the user from the <user_name> secret.

    oc get secret <user_name> -o jsonpath='{.data.user\.key}' | base64 -d > user.key
  9. Configure your client with the bootstrap address hostname and port for connecting to the Kafka cluster:

    props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "<hostname>:<port>");
  10. Configure your client with the truststore credentials to verify the identity of the Kafka cluster.

    Specify the public cluster CA certificate.

    Example truststore configuration

    props.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG, "SSL");
    props.put(SslConfigs.SSL_TRUSTSTORE_TYPE_CONFIG, "PEM");
    props.put(SslConfigs.SSL_TRUSTSTORE_CERTIFICATES_CONFIG, "<ca.crt_file_content>");

    SSL is the specified security protocol for mTLS authentication. Specify SASL_SSL for SCRAM-SHA-512 authentication over TLS. PEM is the file format of the truststore.

  11. Configure your client with the keystore credentials to verify the user when connecting to the Kafka cluster.

    Specify the public certificate and private key.

    Example keystore configuration

    props.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG, "SSL");
    props.put(SslConfigs.SSL_KEYSTORE_TYPE_CONFIG, "PEM");
    props.put(SslConfigs.SSL_KEYSTORE_CERTIFICATE_CHAIN_CONFIG, "<user.crt_file_content>");
    props.put(SslConfigs.SSL_KEYSTORE_KEY_CONFIG, "<user.key_file_content>");

    Add the keystore certificate and the private key directly to the configuration. Add as a single-line format. Between the BEGIN CERTIFICATE and END CERTIFICATE delimiters, start with a newline character (\n). End each line from the original certificate with \n too.

    Example keystore configuration

    props.put(SslConfigs.SSL_KEYSTORE_CERTIFICATE_CHAIN_CONFIG, "-----BEGIN CERTIFICATE----- \n<user_certificate_content_line_1>\n<user_certificate_content_line_n>\n-----END CERTIFICATE---");
    props.put(SslConfigs.SSL_KEYSTORE_KEY_CONFIG, "----BEGIN PRIVATE KEY-----\n<user_key_content_line_1>\n<user_key_content_line_n>\n-----END PRIVATE KEY-----");

7.4. Accessing Kafka using node ports

Use node ports to access an AMQ Streams Kafka cluster from an external client outside the OpenShift cluster.

To connect to a broker, you specify a hostname and port number for the Kafka bootstrap address, as well as the certificate used for TLS encryption.

The procedure shows basic nodeport listener configuration. You can use listener properties to enable TLS encryption (tls) and specify a client authentication mechanism (authentication). Add additional configuration using configuration properties. For example, you can use the following configuration properties with nodeport listeners:

preferredNodePortAddressType
Specifies the first address type that’s checked as the node address.
externalTrafficPolicy
Specifies whether the service routes external traffic to node-local or cluster-wide endpoints.
nodePort
Overrides the assigned node port numbers for the bootstrap and broker services.

For more information on listener configuration, see the GenericKafkaListener schema reference.

Prerequisites

  • A running Cluster Operator

In this procedure, the Kafka cluster name is my-cluster. The name of the listener is external.

Procedure

  1. Configure a Kafka resource with an external listener set to the nodeport type.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      labels:
        app: my-cluster
      name: my-cluster
      namespace: myproject
    spec:
      kafka:
        # ...
        listeners:
          - name: external
            port: 9094
            type: nodeport
            tls: true
            authentication:
              type: tls
            # ...
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    oc apply -f <kafka_configuration_file>

    A cluster CA certificate to verify the identity of the kafka brokers is created in the secret my-cluster-cluster-ca-cert.

    NodePort type services are created for each Kafka broker, as well as an external bootstrap service.

    Node port services created for the bootstrap and brokers

    NAME                                 TYPE      CLUSTER-IP      PORT(S)
    my-cluster-kafka-external-0          NodePort  172.30.55.13    9094:31789/TCP
    my-cluster-kafka-external-1          NodePort  172.30.250.248  9094:30028/TCP
    my-cluster-kafka-external-2          NodePort  172.30.115.81   9094:32650/TCP
    my-cluster-kafka-external-bootstrap  NodePort  172.30.30.23    9094:32650/TCP

    The bootstrap address used for client connection is propagated to the status of the Kafka resource.

    Example status for the bootstrap address

    status:
      clusterId: Y_RJQDGKRXmNF7fEcWldJQ
      conditions:
        - lastTransitionTime: '2023-01-31T14:59:37.113630Z'
          status: 'True'
          type: Ready
      listeners:
        # ...
        - addresses:
            - host: ip-10-0-224-199.us-west-2.compute.internal
              port: 32650
          bootstrapServers: 'ip-10-0-224-199.us-west-2.compute.internal:32650'
          certificates:
            - |
              -----BEGIN CERTIFICATE-----
    
              -----END CERTIFICATE-----
          name: external
          type: external
      observedGeneration: 2
     # ...

  3. Retrieve the bootstrap address you can use to access the Kafka cluster from the status of the Kafka resource.

    oc get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}'
    
    ip-10-0-224-199.us-west-2.compute.internal:32650
  4. Extract the cluster CA certificate.

    oc get secret my-cluster-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
  5. Configure your client to connect to the brokers.

    1. Specify the bootstrap host and port in your Kafka client as the bootstrap address to connect to the Kafka cluster. For example, ip-10-0-224-199.us-west-2.compute.internal:32650.
    2. Add the extracted certificate to the truststore of your Kafka client to configure a TLS connection.

      If you enabled a client authentication mechanism, you will also need to configure it in your client.

Note

If you are using your own listener certificates, check whether you need to add the CA certificate to the client’s truststore configuration. If it is a public (external) CA, you usually won’t need to add it.

7.5. Accessing Kafka using loadbalancers

Use loadbalancers to access an AMQ Streams Kafka cluster from an external client outside the OpenShift cluster.

To connect to a broker, you specify a hostname and port number for the Kafka bootstrap address, as well as the certificate used for TLS encryption.

The procedure shows basic loadbalancer listener configuration. You can use listener properties to enable TLS encryption (tls) and specify a client authentication mechanism (authentication). Add additional configuration using configuration properties. For example, you can use the following configuration properties with loadbalancer listeners:

loadBalancerSourceRanges
Restricts traffic to a specified list of CIDR (Classless Inter-Domain Routing) ranges.
externalTrafficPolicy
Specifies whether the service routes external traffic to node-local or cluster-wide endpoints.
loadBalancerIP
Requests a specific IP address when creating a loadbalancer.

For more information on listener configuration, see the GenericKafkaListener schema reference.

Prerequisites

  • A running Cluster Operator

In this procedure, the Kafka cluster name is my-cluster. The name of the listener is external.

Procedure

  1. Configure a Kafka resource with an external listener set to the loadbalancer type.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      labels:
        app: my-cluster
      name: my-cluster
      namespace: myproject
    spec:
      kafka:
        # ...
        listeners:
          - name: external
            port: 9095
            type: loadbalancer
            tls: true
            authentication:
              type: tls
            # ...
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    oc apply -f <kafka_configuration_file>

    A cluster CA certificate to verify the identity of the kafka brokers is also created in the secret my-cluster-cluster-ca-cert.

    loadbalancer type services and loadbalancers are created for each Kafka broker, as well as an external bootstrap service.

    Loadbalancer services and loadbalancers created for the bootstraps and brokers

    NAME                                  TYPE            CLUSTER-IP      PORT(S)
    my-cluster-kafka-external-0          LoadBalancer     172.30.204.234  9095:30011/TCP
    my-cluster-kafka-external-1          LoadBalancer     172.30.164.89   9095:32544/TCP
    my-cluster-kafka-external-2          LoadBalancer     172.30.73.151   9095:32504/TCP
    my-cluster-kafka-external-bootstrap  LoadBalancer     172.30.30.228   9095:30371/TCP
    
    NAME                                 EXTERNAL-IP (loadbalancer)
    my-cluster-kafka-external-0          a8a519e464b924000b6c0f0a05e19f0d-1132975133.us-west-2.elb.amazonaws.com
    my-cluster-kafka-external-1          ab6adc22b556343afb0db5ea05d07347-611832211.us-west-2.elb.amazonaws.com
    my-cluster-kafka-external-2          a9173e8ccb1914778aeb17eca98713c0-777597560.us-west-2.elb.amazonaws.com
    my-cluster-kafka-external-bootstrap  a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com

    The bootstrap address used for client connection is propagated to the status of the Kafka resource.

    Example status for the bootstrap address

    status:
      clusterId: Y_RJQDGKRXmNF7fEcWldJQ
      conditions:
        - lastTransitionTime: '2023-01-31T14:59:37.113630Z'
          status: 'True'
          type: Ready
      listeners:
        # ...
        - addresses:
            - host: >-
                a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com
              port: 9095
          bootstrapServers: >-
            a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com:9095
          certificates:
            - |
              -----BEGIN CERTIFICATE-----
    
              -----END CERTIFICATE-----
          name: external
          type: external
      observedGeneration: 2
     # ...

    The DNS addresses used for client connection are propagated to the status of each loadbalancer service.

    Example status for the bootstrap loadbalancer

    status:
      loadBalancer:
        ingress:
          - hostname: >-
              a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com
     # ...

  3. Retrieve the bootstrap address you can use to access the Kafka cluster from the status of the Kafka resource.

    oc get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}'
    
    a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com:9095
  4. Extract the cluster CA certificate.

    oc get secret my-cluster-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
  5. Configure your client to connect to the brokers.

    1. Specify the bootstrap host and port in your Kafka client as the bootstrap address to connect to the Kafka cluster. For example, a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com:9095.
    2. Add the extracted certificate to the truststore of your Kafka client to configure a TLS connection.

      If you enabled a client authentication mechanism, you will also need to configure it in your client.

Note

If you are using your own listener certificates, check whether you need to add the CA certificate to the client’s truststore configuration. If it is a public (external) CA, you usually won’t need to add it.

7.6. Accessing Kafka using OpenShift routes

Use OpenShift routes to access an AMQ Streams Kafka cluster from clients outside the OpenShift cluster.

To be able to use routes, add configuration for a route type listener in the Kafka custom resource. When applied, the configuration creates a dedicated route and service for an external bootstrap and each broker in the cluster. Clients connect to the bootstrap route, which routes them through the bootstrap service to connect to a broker. Per-broker connections are then established using DNS names, which route traffic from the client to the broker through the broker-specific routes and services.

To connect to a broker, you specify a hostname for the route bootstrap address, as well as the certificate used for TLS encryption. For access using routes, the port is always 443.

Warning

An OpenShift route address comprises the name of the Kafka cluster, the name of the listener, and the name of the project it is created in. For example, my-cluster-kafka-external-bootstrap-myproject (<cluster_name>-kafka-<listener_name>-bootstrap-<namespace>). Be careful that the whole length of the address does not exceed a maximum limit of 63 characters.

The procedure shows basic listener configuration. TLS encryption (tls) must be enabled. You can also specify a client authentication mechanism (authentication). Add additional configuration using configuration properties. For example, you can use the host configuration property with route listeners to specify the hostnames used by the bootstrap and per-broker services.

For more information on listener configuration, see the GenericKafkaListener schema reference.

TLS passthrough

TLS passthrough is enabled for routes created by AMQ Streams. Kafka uses a binary protocol over TCP, but routes are designed to work with a HTTP protocol. To be able to route TCP traffic through routes, AMQ Streams uses TLS passthrough with Server Name Indication (SNI).

SNI helps with identifying and passing connection to Kafka brokers. In passthrough mode, TLS encryption is always used. Because the connection passes to the brokers, the listeners use TLS certificates signed by the internal cluster CA and not the ingress certificates. To configure listeners to use your own listener certificates, use the brokerCertChainAndKey property.

Prerequisites

  • A running Cluster Operator

In this procedure, the Kafka cluster name is my-cluster. The name of the listener is external.

Procedure

  1. Configure a Kafka resource with an external listener set to the route type.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      labels:
        app: my-cluster
      name: my-cluster
      namespace: myproject
    spec:
      kafka:
        # ...
        listeners:
          - name: external
            port: 9094
            type: route
            tls: true 1
            authentication:
              type: tls
            # ...
        # ...
      zookeeper:
        # ...
    1
    For route type listeners, TLS encryption must be enabled (true).
  2. Create or update the resource.

    oc apply -f <kafka_configuration_file>

    A cluster CA certificate to verify the identity of the kafka brokers is created in the secret my-cluster-cluster-ca-cert.

    ClusterIP type services are created for each Kafka broker, as well as an external bootstrap service.

    A route is also created for each service, with a DNS address (host/port) to expose them using the default OpenShift HAProxy router.

    The routes are preconfigured with TLS passthrough.

    Routes created for the bootstraps and brokers

    NAME                                  HOST/PORT                                                   SERVICES                              PORT  TERMINATION
    my-cluster-kafka-external-0          my-cluster-kafka-external-0-my-project.router.com          my-cluster-kafka-external-0          9094  passthrough
    my-cluster-kafka-external-1          my-cluster-kafka-external-1-my-project.router.com          my-cluster-kafka-external-1          9094  passthrough
    my-cluster-kafka-external-2          my-cluster-kafka-external-2-my-project.router.com          my-cluster-kafka-external-2          9094  passthrough
    my-cluster-kafka-external-bootstrap  my-cluster-kafka-external-bootstrap-my-project.router.com  my-cluster-kafka-external-bootstrap  9094  passthrough

    The DNS addresses used for client connection are propagated to the status of each route.

    Example status for the bootstrap route

    status:
      ingress:
        - host: >-
            my-cluster-kafka-external-bootstrap-my-project.router.com
     # ...

  3. Use a target broker to check the client-server TLS connection on port 443 using the OpenSSL s_client.

    openssl s_client -connect my-cluster-kafka-external-0-my-project.router.com:443 -servername my-cluster-kafka-external-0-my-project.router.com -showcerts

    The server name is the SNI for passing the connection to the broker.

    If the connection is successful, the certificates for the broker are returned.

    Certificates for the broker

    Certificate chain
     0 s:O = io.strimzi, CN = my-cluster-kafka
       i:O = io.strimzi, CN = cluster-ca v0

  4. Retrieve the address of the bootstrap service from the status of the Kafka resource.

    oc get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}'
    
    my-cluster-kafka-external-bootstrap-my-project.router.com:443

    The address comprises the cluster name, the listener name, the project name and the domain of the router (router.com in this example).

  5. Extract the cluster CA certificate.

    oc get secret my-cluster-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
  6. Configure your client to connect to the brokers.

    1. Specify the address for the bootstrap service and port 443 in your Kafka client as the bootstrap address to connect to the Kafka cluster.
    2. Add the extracted certificate to the truststore of your Kafka client to configure a TLS connection.

      If you enabled a client authentication mechanism, you will also need to configure it in your client.

Note

If you are using your own listener certificates, check whether you need to add the CA certificate to the client’s truststore configuration. If it is a public (external) CA, you usually won’t need to add it.

Chapter 8. Managing secure access to Kafka

You can secure your Kafka cluster by managing the access each client has to the Kafka brokers.

A secure connection between Kafka brokers and clients can encompass:

  • Encryption for data exchange
  • Authentication to prove identity
  • Authorization to allow or decline actions executed by users

This chapter explains how to set up secure connections between Kafka brokers and clients, with sections describing:

  • Security options for Kafka clusters and clients
  • How to secure Kafka brokers
  • How to use an authorization server for OAuth 2.0 token-based authentication and authorization

8.1. Security options for Kafka

Use the Kafka resource to configure the mechanisms used for Kafka authentication and authorization.

8.1.1. Listener authentication

Configure client authentication for Kafka brokers when creating listeners. Specify the listener authentication type using the Kafka.spec.kafka.listeners.authentication property in the Kafka resource.

For clients inside the OpenShift cluster, you can create plain (without encryption) or tls internal listeners. The internal listener type use a headless service and the DNS names given to the broker pods. As an alternative to the headless service, you can also create a cluster-ip type of internal listener to expose Kafka using per-broker ClusterIP services. For clients outside the OpenShift cluster, you create external listeners and specify a connection mechanism, which can be nodeport, loadbalancer, ingress (Kubernetes only), or route (OpenShift only).

For more information on the configuration options for connecting an external client, see Chapter 7, Setting up client access to a Kafka cluster.

Supported authentication options:

  1. mTLS authentication (only on the listeners with TLS enabled encryption)
  2. SCRAM-SHA-512 authentication
  3. OAuth 2.0 token-based authentication
  4. Custom authentication

The authentication option you choose depends on how you wish to authenticate client access to Kafka brokers.

Note

Try exploring the standard authentication options before using custom authentication. Custom authentication allows for any type of kafka-supported authentication. It can provide more flexibility, but also adds complexity.

Figure 8.1. Kafka listener authentication options

options for listener authentication configuration

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

If no authentication property is specified then the listener does not authenticate clients which connect through that listener. The listener will accept all connections without authentication.

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

The following example shows:

  • A plain listener configured for SCRAM-SHA-512 authentication
  • A tls listener with mTLS authentication
  • An external listener with mTLS authentication

Each listener is configured with a unique name and port within a Kafka cluster.

Note

Listeners cannot be configured to use the ports reserved for inter-broker communication (9091 or 9090) and metrics (9404).

Example listener authentication configuration

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  namespace: myproject
spec:
  kafka:
    # ...
    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: true
        authentication:
          type: scram-sha-512
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
      - name: external
        port: 9094
        type: loadbalancer
        tls: true
        authentication:
          type: tls
# ...

8.1.1.1. mTLS authentication

mTLS authentication is always used for the communication between Kafka brokers and ZooKeeper pods.

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. For mutual, or two-way, authentication, both the server and the client present certificates. When you configure mTLS authentication, the broker authenticates the client (client authentication) and the client authenticates the broker (server authentication).

mTLS listener configuration in the Kafka resource requires the following:

  • tls: true to specify TLS encryption and server authentication
  • authentication.type: tls to specify the client authentication

When a Kafka cluster is created by the Cluster Operator, it creates a new secret with the name <cluster_name>-cluster-ca-cert. The secret contains a CA certificate. The CA certificate is in PEM and PKCS #12 format. To verify a Kafka cluster, add the CA certificate to the truststore in your client configuration. To verify a client, add a user certificate and key to the keystore in your client configuration. For more information on configuring a client for mTLS, see Section 8.2.2, “User authentication”.

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 browser obtains proof of the identity of the web server.

8.1.1.2. SCRAM-SHA-512 authentication

SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. AMQ Streams can configure Kafka to use SASL (Simple Authentication and Security Layer) SCRAM-SHA-512 to provide authentication on both unencrypted and encrypted client connections.

When SCRAM-SHA-512 authentication is used with a TLS connection, the TLS protocol provides the encryption, but is not used for authentication.

The following properties of SCRAM make it safe to use SCRAM-SHA-512 even on unencrypted connections:

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

When 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.

8.1.1.3. Network policies

By default, AMQ Streams automatically creates a NetworkPolicy resource for every listener that is enabled on a Kafka broker. This NetworkPolicy allows applications to connect to listeners in all namespaces. Use network policies as part of the listener configuration.

If you want to restrict access to a listener at the network level to only selected applications or namespaces, use the networkPolicyPeers property. Each listener can have a different networkPolicyPeers configuration. For more information on network policy peers, refer to the NetworkPolicyPeer API reference.

If you want to use custom network policies, you can set the STRIMZI_NETWORK_POLICY_GENERATION environment variable to false in the Cluster Operator configuration. For more information, see Section 13.2.3, “Configuring the Cluster Operator with environment variables”.

Note

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

8.1.1.4. Providing listener certificates

You can provide your own server certificates, called Kafka listener certificates, for TLS listeners or external listeners which have TLS encryption enabled. For more information, see Section 8.3.4, “Providing your own Kafka listener certificates for TLS encryption”.

8.1.2. Kafka authorization

Configure authorization for Kafka brokers using the Kafka.spec.kafka.authorization property in the Kafka resource. If the authorization property is missing, no authorization is enabled and clients have no restrictions. When enabled, authorization is applied to all enabled listeners. The authorization method is defined in the type field.

Supported authorization options:

Figure 8.2. Kafka cluster authorization options

options for kafka authorization configuration

8.1.2.1. Super users

Super users can access all resources in your Kafka cluster regardless of any access restrictions, and are supported by all authorization mechanisms.

To designate super users for a Kafka cluster, add a list of user principals to the superUsers property. If a user uses mTLS authentication, the username is the common name from the TLS certificate subject prefixed with CN=. If you are not using the User Operator and using your own certificates for mTLS, the username is the full certificate subject. A full certificate subject can have the following fields: CN=user,OU=my_ou,O=my_org,L=my_location,ST=my_state,C=my_country_code. Omit any fields that are not present.

An example configuration with super users

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  namespace: myproject
spec:
  kafka:
    # ...
    authorization:
      type: simple
      superUsers:
        - CN=client_1
        - user_2
        - CN=client_3
        - CN=client_4,OU=my_ou,O=my_org,L=my_location,ST=my_state,C=US
        - CN=client_5,OU=my_ou,O=my_org,C=GB
        - CN=client_6,O=my_org
    # ...

8.2. Security options for Kafka clients

Use the KafkaUser resource to configure the authentication mechanism, authorization mechanism, and access rights for Kafka clients. In terms of configuring security, clients are represented as users.

You can authenticate and authorize user access to Kafka brokers. Authentication permits access, and authorization constrains the access to permissible actions.

You can also create super users that have unconstrained access to Kafka brokers.

The authentication and authorization mechanisms must match the specification for the listener used to access the Kafka brokers.

For more information on configuring a KafkaUser resource to access Kafka brokers securely, see Section 7.3, “Setting up client access to a Kafka cluster using listeners”.

8.2.1. Identifying a Kafka cluster for user handling

A KafkaUser resource includes a label that defines the appropriate name of the Kafka cluster (derived from the name of the Kafka resource) to which it belongs.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster

The label is used by the User Operator to identify the KafkaUser resource and create a new user, and also in subsequent handling of the user.

If the label does not match the Kafka cluster, the User Operator cannot identify the KafkaUser and the user is not created.

If the status of the KafkaUser resource remains empty, check your label.

8.2.2. User authentication

Use the KafkaUser custom resource to configure authentication credentials for users (clients) that require access to a Kafka cluster. Configure the credentials using the authentication property in KafkaUser.spec. By specifying a type, you control what credentials are generated.

Supported authentication types:

  • tls for mTLS authentication
  • tls-external for mTLS authentication using external certificates
  • scram-sha-512 for SCRAM-SHA-512 authentication

If tls or scram-sha-512 is specified, the User Operator creates authentication credentials when it creates the user. If tls-external is specified, the user still uses mTLS, but no authentication credentials are created. Use this option when you’re providing your own certificates. When no authentication type is specified, the User Operator does not create the user or its credentials.

You can use tls-external to authenticate with mTLS using a certificate issued outside the User Operator. The User Operator does not generate a TLS certificate or a secret. You can still manage ACL rules and quotas through the User Operator in the same way as when you’re using the tls mechanism. This means that you use the CN=USER-NAME format when specifying ACL rules and quotas. USER-NAME is the common name given in a TLS certificate.

8.2.2.1. mTLS authentication

To use mTLS authentication, you set the type field in the KafkaUser resource to tls.

Example user with mTLS authentication enabled

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: tls
  # ...

The authentication type must match the equivalent configuration for the Kafka listener used to access the Kafka cluster.

When the user is created by the User Operator, it creates a new secret with the same name as the KafkaUser resource. The secret contains a private and public key for mTLS. The public key is contained in a user certificate, which is signed by a clients CA (certificate authority) when it is created. All keys are in X.509 format.

Note

If you are using the clients CA generated by the Cluster Operator, the user certificates generated by the User Operator are also renewed when the clients CA is renewed by the Cluster Operator.

The user secret provides keys and certificates in PEM and PKCS #12 formats.

Example 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> # Public key of the clients CA
  user.crt: <user_certificate> # Public key of the user
  user.key: <user_private_key> # Private key of the user
  user.p12: <store> # PKCS #12 store for user certificates and keys
  user.password: <password_for_store> # Protects the PKCS #12 store

When you configure a client, you specify the following:

  • Truststore properties for the public cluster CA certificate to verify the identity of the Kafka cluster
  • Keystore properties for the user authentication credentials to verify the client

The configuration depends on the file format (PEM or PKCS #12). This example uses PKCS #12 stores, and the passwords required to access the credentials in the stores.

Example client configuration using mTLS in PKCS #12 format

bootstrap.servers=<kafka_cluster_name>-kafka-bootstrap:9093 1
security.protocol=SSL 2
ssl.truststore.location=/tmp/ca.p12 3
ssl.truststore.password=<truststore_password> 4
ssl.keystore.location=/tmp/user.p12 5
ssl.keystore.password=<keystore_password> 6

1
The bootstrap server address to connect to the Kafka cluster.
2
The security protocol option when using TLS for encryption.
3
The truststore location contains the public key certificate (ca.p12) for the Kafka cluster. A cluster CA certificate and password is generated by the Cluster Operator in the <cluster_name>-cluster-ca-cert secret when the Kafka cluster is created.
4
The password (ca.password) for accessing the truststore.
5
The keystore location contains the public key certificate (user.p12) for the Kafka user.
6
The password (user.password) for accessing the keystore.

8.2.2.2. mTLS authentication using a certificate issued outside the User Operator

To use mTLS authentication using a certificate issued outside the User Operator, you set the type field in the KafkaUser resource to tls-external. A secret and credentials are not created for the user.

Example user with mTLS authentication that uses a certificate issued outside the User Operator

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: tls-external
  # ...

8.2.2.3. SCRAM-SHA-512 authentication

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

Example user with SCRAM-SHA-512 authentication enabled

apiVersion: kafka.strimzi.io/v1beta2
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, it creates a new secret with the same name as the KafkaUser resource. The secret contains the generated password in the password key, which is encoded with base64. In order to use the password, it must be decoded.

Example 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: Z2VuZXJhdGVkcGFzc3dvcmQ= 1
  sasl.jaas.config: b3JnLmFwYWNoZS5rYWZrYS5jb21tb24uc2VjdXJpdHkuc2NyYW0uU2NyYW1Mb2dpbk1vZHVsZSByZXF1aXJlZCB1c2VybmFtZT0ibXktdXNlciIgcGFzc3dvcmQ9ImdlbmVyYXRlZHBhc3N3b3JkIjsK 2

1
The generated password, base64 encoded.
2
The JAAS configuration string for SASL SCRAM-SHA-512 authentication, base64 encoded.

Decoding the generated password:

echo "Z2VuZXJhdGVkcGFzc3dvcmQ=" | base64 --decode
8.2.2.3.1. Custom password configuration

When a user is created, AMQ Streams generates a random password. You can use your own password instead of the one generated by AMQ Streams. To do so, create a secret with the password and reference it in the KafkaUser resource.

Example user with a password set for SCRAM-SHA-512 authentication

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: scram-sha-512
    password:
      valueFrom:
        secretKeyRef:
          name: my-secret 1
          key: my-password 2
  # ...

1
The name of the secret containing the predefined password.
2
The key for the password stored inside the secret.

8.2.3. User authorization

Use the KafkaUser custom resource to configure authorization rules for users (clients) that require access to a Kafka cluster. Configure the rules using the authorization property in KafkaUser.spec. By specifying a type, you control what rules are used.

To use simple authorization, you set the type property to simple in KafkaUser.spec.authorization. The simple authorization uses the Kafka Admin API to manage the ACL rules inside your Kafka cluster. Whether ACL management in the User Operator is enabled or not depends on your authorization configuration in the Kafka cluster.

  • For simple authorization, ACL management is always enabled.
  • For OPA authorization, ACL management is always disabled. Authorization rules are configured in the OPA server.
  • For Red Hat Single Sign-On authorization, you can manage the ACL rules directly in Red Hat Single Sign-On. You can also delegate authorization to the simple authorizer as a fallback option in the configuration. When delegation to the simple authorizer is enabled, the User Operator will enable management of ACL rules as well.
  • For custom authorization using a custom authorization plugin, use the supportsAdminApi property in the .spec.kafka.authorization configuration of the Kafka custom resource to enable or disable the support.

Authorization is cluster-wide. The authorization type must match the equivalent configuration in the Kafka custom resource.

If ACL management is not enabled, AMQ Streams rejects a resource if it contains any ACL rules.

If you’re using a standalone deployment of the User Operator, ACL management is enabled by default. You can disable it using the STRIMZI_ACLS_ADMIN_API_SUPPORTED environment variable.

If no authorization is specified, the User Operator does not provision any access rights for the user. Whether such a KafkaUser can still access resources depends on the authorizer being used. For example, for the AclAuthorizer this is determined by its allow.everyone.if.no.acl.found configuration.

8.2.3.1. ACL rules

AclAuthorizer uses ACL rules to manage access to Kafka brokers.

ACL rules grant access rights to the user, which you specify in the acls property.

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

8.2.3.2. Super user access to Kafka brokers

If a user is added to a list of super users in a Kafka broker configuration, the user is allowed unlimited access to the cluster regardless of any authorization constraints defined in ACLs in KafkaUser.

For more information on configuring super user access to brokers, see Kafka authorization.

8.2.3.3. User quotas

You can configure the spec for the KafkaUser resource to enforce quotas so that a user does not exceed a configured level of access to Kafka brokers. You can set size-based network usage and time-based CPU utilization thresholds. You can also add a partition mutation quota to control the rate at which requests to change partitions are accepted for user requests.

An example KafkaUser with user quotas

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  quotas:
    producerByteRate: 1048576 1
    consumerByteRate: 2097152 2
    requestPercentage: 55 3
    controllerMutationRate: 10 4

1
Byte-per-second quota on the amount of data the user can push to a Kafka broker
2
Byte-per-second quota on the amount of data the user can fetch from a Kafka broker
3
CPU utilization limit as a percentage of time for a client group
4
Number of concurrent partition creation and deletion operations (mutations) allowed per second

For more information on these properties, see the KafkaUserQuotas schema reference.

8.3. Securing access to Kafka brokers

To establish secure access to Kafka brokers, you configure and apply:

  • A Kafka resource to:

    • Create listeners with a specified authentication type
    • Configure authorization for the whole Kafka cluster
  • A KafkaUser resource to access the Kafka brokers securely through the listeners

Configure the Kafka resource to set up:

  • Listener authentication
  • Network policies that restrict access to Kafka listeners
  • Kafka authorization
  • Super users for unconstrained access to brokers

Authentication is configured independently for each listener. Authorization is always configured for the whole Kafka cluster.

The Cluster Operator creates the listeners and sets up the cluster and client certificate authority (CA) certificates to enable authentication within the Kafka cluster.

You can replace the certificates generated by the Cluster Operator by installing your own certificates.

You can also provide your own server certificates and private keys for any listener with TLS encryption enabled. These user-provided certificates are called Kafka listener certificates. Providing Kafka listener certificates allows you to leverage existing security infrastructure, such as your organization’s private CA or a public CA. Kafka clients will need to trust the CA which was used to sign the listener certificate. You must manually renew Kafka listener certificates when needed. Certificates are available in PKCS #12 format (.p12) and PEM (.crt) formats.

Use KafkaUser to enable the authentication and authorization mechanisms that a specific client uses to access Kafka.

Configure the KafkaUser resource to set up:

  • Authentication to match the enabled listener authentication
  • Authorization to match the enabled Kafka authorization
  • Quotas to control the use of resources by clients

The User Operator creates the user representing the client and the security credentials used for client authentication, based on the chosen authentication type.

Refer to the schema reference for more information on access configuration properties:

8.3.1. Securing Kafka brokers

This procedure shows the steps involved in securing Kafka brokers when running AMQ Streams.

The security implemented for Kafka brokers must be compatible with the security implemented for the clients requiring access.

  • Kafka.spec.kafka.listeners[*].authentication matches KafkaUser.spec.authentication
  • Kafka.spec.kafka.authorization matches KafkaUser.spec.authorization

The steps show the configuration for simple authorization and a listener using mTLS authentication. For more information on listener configuration, see the GenericKafkaListener schema reference.

Alternatively, you can use SCRAM-SHA or OAuth 2.0 for listener authentication, and OAuth 2.0 or OPA for Kafka authorization.

Procedure

  1. Configure the Kafka resource.

    1. Configure the authorization property for authorization.
    2. Configure the listeners property to create a listener with authentication.

      For example:

      apiVersion: kafka.strimzi.io/v1beta2
      kind: Kafka
      spec:
        kafka:
          # ...
          authorization: 1
            type: simple
            superUsers: 2
              - CN=client_1
              - user_2
              - CN=client_3
          listeners:
            - name: tls
              port: 9093
              type: internal
              tls: true
              authentication:
                type: tls 3
          # ...
        zookeeper:
          # ...
      1
      2
      List of user principals with unlimited access to Kafka. CN is the common name from the client certificate when mTLS authentication is used.
      3
      Listener authentication mechanisms may be configured for each listener, and specified as mTLS, SCRAM-SHA-512, or token-based OAuth 2.0.

      If you are configuring an external listener, the configuration is dependent on the chosen connection mechanism.

  2. Create or update the Kafka resource.

    oc apply -f <kafka_configuration_file>

    The Kafka cluster is configured with a Kafka broker listener using mTLS authentication.

    A service is created for each Kafka broker pod.

    A service is created to serve as the bootstrap address for connection to the Kafka cluster.

    The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret <cluster_name>-cluster-ca-cert.

8.3.2. Securing user access to Kafka

Create or modify a KafkaUser to represent a client that requires secure access to the Kafka cluster.

When you configure the KafkaUser authentication and authorization mechanisms, ensure they match the equivalent Kafka configuration:

  • KafkaUser.spec.authentication matches Kafka.spec.kafka.listeners[*].authentication
  • KafkaUser.spec.authorization matches Kafka.spec.kafka.authorization

This procedure shows how a user is created with mTLS authentication. You can also create a user with SCRAM-SHA authentication.

The authentication required depends on the type of authentication configured for the Kafka broker listener.

Note

Authentication between Kafka users and Kafka brokers depends on the authentication settings for each. For example, it is not possible to authenticate a user with mTLS if it is not also enabled in the Kafka configuration.

Prerequisites

The authentication type in KafkaUser should match the authentication configured in Kafka brokers.

Procedure

  1. Configure the KafkaUser resource.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaUser
    metadata:
      name: my-user
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      authentication: 1
        type: tls
      authorization:
        type: simple 2
        acls:
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operations:
              - Describe
              - Read
          - resource:
              type: group
              name: my-group
              patternType: literal
            operations:
              - Read
    1
    User authentication mechanism, defined as mutual tls or scram-sha-512.
    2
    Simple authorization, which requires an accompanying list of ACL rules.
  2. Create or update the KafkaUser resource.

    oc apply -f <user_config_file>

    The user is created, as well as a Secret with the same name as the KafkaUser resource. The Secret contains a private and public key for mTLS authentication.

For information on configuring a Kafka client with properties for secure connection to Kafka brokers, see Section 7.3, “Setting up client access to a Kafka cluster using listeners”.

8.3.3. Restricting access to Kafka listeners using network policies

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

Prerequisites

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

Procedure

  1. Open the Kafka resource.
  2. In the networkPolicyPeers property, 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/v1beta2
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          - name: tls
            port: 9093
            type: internal
            tls: true
            authentication:
              type: tls
            networkPolicyPeers:
              - podSelector:
                  matchLabels:
                    app: kafka-client
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    Use oc apply:

    oc apply -f your-file

8.3.4. Providing your own Kafka listener certificates for TLS encryption

Listeners provide client access to Kafka brokers. Configure listeners in the Kafka resource, including the configuration required for client access using TLS.

By default, the listeners use certificates signed by the internal CA (certificate authority) certificates generated by AMQ Streams. A CA certificate is generated by the Cluster Operator when it creates a Kafka cluster. When you configure a client for TLS, you add the CA certificate to its truststore configuration to verify the Kafka cluster. You can also install and use your own CA certificates. Or you can configure a listener using brokerCertChainAndKey properties and use a custom server certificate.

The brokerCertChainAndKey properties allow you to access Kafka brokers using your own custom certificates at the listener-level. You create a secret with your own private key and server certificate, then specify the key and certificate in the listener’s brokerCertChainAndKey configuration. You can use a certificate signed by a public (external) CA or a private CA. If signed by a public CA, you usually won’t need to add it to a client’s truststore configuration. Custom certificates are not managed by AMQ Streams, so you need to renew them manually.

Note

Listener certificates are used for TLS encryption and server authentication only. They are not used for TLS client authentication. If you want to use your own certificate for TLS client authentication as well, you must install and use your own clients CA.

Prerequisites

  • The Cluster Operator is running.
  • Each listener requires the following:

If you are not using a self-signed certificate, you can provide a certificate that includes the whole CA chain in the certificate.

You can only use the brokerCertChainAndKey properties if TLS encryption (tls: true) is configured for the listener.

Note

AMQ Streams does not support the use of encrypted private keys for TLS. The private key stored in the secret must be unencrypted for this to work.

Procedure

  1. Create a Secret containing your private key and server certificate:

    oc create secret generic my-secret --from-file=my-listener-key.key --from-file=my-listener-certificate.crt
  2. Edit the Kafka resource for your cluster.

    Configure the listener to use your Secret, certificate file, and private key file in the configuration.brokerCertChainAndKey property.

    Example configuration for a loadbalancer external listener with TLS encryption enabled

    # ...
    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: false
      - name: external
        port: 9094
        type: loadbalancer
        tls: true
        configuration:
          brokerCertChainAndKey:
            secretName: my-secret
            certificate: my-listener-certificate.crt
            key: my-listener-key.key
    # ...

    Example configuration for a TLS listener

    # ...
    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: false
      - name: tls
        port: 9093
        type: internal
        tls: true
        configuration:
          brokerCertChainAndKey:
            secretName: my-secret
            certificate: my-listener-certificate.crt
            key: my-listener-key.key
    # ...

  3. Apply the new configuration to create or update the resource:

    oc apply -f kafka.yaml

    The Cluster Operator starts a rolling update of the Kafka cluster, which updates the configuration of the listeners.

    Note

    A rolling update is also started if you update a Kafka listener certificate in a Secret that is already used by a listener.

8.3.5. Alternative subjects in server certificates for Kafka listeners

In order to use TLS hostname verification with your own Kafka listener certificates, you must use the correct Subject Alternative Names (SANs) for each listener. The certificate SANs must specify hostnames for the following:

  • All of the Kafka brokers in your cluster
  • The Kafka cluster bootstrap service

You can use wildcard certificates if they are supported by your CA.

8.3.5.1. Examples of SANs for internal listeners

Use the following examples to help you specify hostnames of the SANs in your certificates for your internal listeners.

Replace <cluster-name> with the name of the Kafka cluster and <namespace> with the OpenShift namespace where the cluster is running.

Wildcards example for a type: internal listener

//Kafka brokers
*.<cluster-name>-kafka-brokers
*.<cluster-name>-kafka-brokers.<namespace>.svc

// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc

Non-wildcards example for a type: internal listener

// Kafka brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers.<namespace>.svc
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers.<namespace>.svc
# ...

// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc

Non-wildcards example for a type: cluster-ip listener

// Kafka brokers
<cluster-name>-kafka-<listener-name>-0
<cluster-name>-kafka-<listener-name>-0.<namespace>.svc
<cluster-name>-kafka-<listener-name>-1
<cluster-name>-kafka-<listener-name>-1.<namespace>.svc
# ...

// Bootstrap service
<cluster-name>-kafka-<listener-name>-bootstrap
<cluster-name>-kafka-<listener-name>-bootstrap.<namespace>.svc

8.3.5.2. Examples of SANs for external listeners

For external listeners which have TLS encryption enabled, the hostnames you need to specify in certificates depends on the external listener type.

Table 8.1. SANs for each type of external listener

External listener typeIn the SANs, specify…​

ingress

Addresses of all Kafka broker Ingress resources and the address of the bootstrap Ingress.

You can use a matching wildcard name.

route

Addresses of all Kafka broker Routes and the address of the bootstrap Route.

You can use a matching wildcard name.

loadbalancer

Addresses of all Kafka broker loadbalancers and the bootstrap loadbalancer address.

You can use a matching wildcard name.

nodeport

Addresses of all OpenShift worker nodes that the Kafka broker pods might be scheduled to.

You can use a matching wildcard name.

8.4. Using OAuth 2.0 token-based authentication

AMQ Streams supports the use of OAuth 2.0 authentication using the OAUTHBEARER and PLAIN mechanisms.

OAuth 2.0 enables standardized token-based authentication and authorization between applications, using a central authorization server to issue tokens that grant limited access to resources.

You can configure OAuth 2.0 authentication, then OAuth 2.0 authorization.

Kafka brokers and clients both need to be configured to use OAuth 2.0. OAuth 2.0 authentication can also be used in conjunction with simple or OPA-based Kafka authorization.

Using OAuth 2.0 token-based authentication, application clients can access resources on application servers (called resource servers) without exposing account credentials.

The application client passes an access token as a means of authenticating, which application servers can also use to determine the level of access to grant. The authorization server handles the granting of access and inquiries about access.

In the context of AMQ Streams:

  • Kafka brokers act as OAuth 2.0 resource servers
  • Kafka clients act as OAuth 2.0 application clients

Kafka clients authenticate to Kafka brokers. The brokers and clients communicate with the OAuth 2.0 authorization server, as necessary, to obtain or validate access tokens.

For a deployment of AMQ Streams, OAuth 2.0 integration provides:

  • Server-side OAuth 2.0 support for Kafka brokers
  • Client-side OAuth 2.0 support for Kafka MirrorMaker, Kafka Connect and the Kafka Bridge

8.4.1. OAuth 2.0 authentication mechanisms

AMQ Streams supports the OAUTHBEARER and PLAIN mechanisms for OAuth 2.0 authentication. Both mechanisms allow Kafka clients to establish authenticated sessions with Kafka brokers. The authentication flow between clients, the authorization server, and Kafka brokers is different for each mechanism.

We recommend that you configure clients to use OAUTHBEARER whenever possible. OAUTHBEARER provides a higher level of security than PLAIN because client credentials are never shared with Kafka brokers. Consider using PLAIN only with Kafka clients that do not support OAUTHBEARER.

You configure Kafka broker listeners to use OAuth 2.0 authentication for connecting clients. If necessary, you can use the OAUTHBEARER and PLAIN mechanisms on the same oauth listener. The properties to support each mechanism must be explicitly specified in the oauth listener configuration.

OAUTHBEARER overview

OAUTHBEARER is automatically enabled in the oauth listener configuration for the Kafka broker. You can set the enableOauthBearer property to true, though this is not required.

  # ...
  authentication:
    type: oauth
    # ...
    enableOauthBearer: true

Many Kafka client tools use libraries that provide basic support for OAUTHBEARER at the protocol level. To support application development, AMQ Streams provides an OAuth callback handler for the upstream Kafka Client Java libraries (but not for other libraries). Therefore, you do not need to write your own callback handlers. An application client can use the callback handler to provide the access token. Clients written in other languages, such as Go, must use custom code to connect to the authorization server and obtain the access token.

With OAUTHBEARER, the client initiates a session with the Kafka broker for credentials exchange, where credentials take the form of a bearer token provided by the callback handler. Using the callbacks, you can configure token provision in one of three ways:

  • Client ID and Secret (by using the OAuth 2.0 client credentials mechanism)
  • A long-lived access token, obtained manually at configuration time
  • A long-lived refresh token, obtained manually at configuration time
Note

OAUTHBEARER authentication can only be used by Kafka clients that support the OAUTHBEARER mechanism at the protocol level.

PLAIN overview

To use PLAIN, you must enable it in the oauth listener configuration for the Kafka broker.

In the following example, PLAIN is enabled in addition to OAUTHBEARER, which is enabled by default. If you want to use PLAIN only, you can disable OAUTHBEARER by setting enableOauthBearer to false.

  # ...
  authentication:
    type: oauth
    # ...
    enablePlain: true
    tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token

PLAIN is a simple authentication mechanism used by all Kafka client tools. To enable PLAIN to be used with OAuth 2.0 authentication, AMQ Streams provides OAuth 2.0 over PLAIN server-side callbacks.

With the AMQ Streams implementation of PLAIN, the client credentials are not stored in ZooKeeper. Instead, client credentials are handled centrally behind a compliant authorization server, similar to when OAUTHBEARER authentication is used.

When used with the OAuth 2.0 over PLAIN callbacks, Kafka clients authenticate with Kafka brokers using either of the following methods:

  • Client ID and secret (by using the OAuth 2.0 client credentials mechanism)
  • A long-lived access token, obtained manually at configuration time

For both methods, the client must provide the PLAIN username and password properties to pass credentials to the Kafka broker. The client uses these properties to pass a client ID and secret or username and access token.

Client IDs and secrets are used to obtain access tokens.

Access tokens are passed as password property values. You pass the access token with or without an $accessToken: prefix.

  • If you configure a token endpoint (tokenEndpointUri) in the listener configuration, you need the prefix.
  • If you don’t configure a token endpoint (tokenEndpointUri) in the listener configuration, you don’t need the prefix. The Kafka broker interprets the password as a raw access token.

If the password is set as the access token, the username must be set to the same principal name that the Kafka broker obtains from the access token. You can specify username extraction options in your listener using the userNameClaim, fallbackUserNameClaim, fallbackUsernamePrefix, and userInfoEndpointUri properties. The username extraction process also depends on your authorization server; in particular, how it maps client IDs to account names.

Note

OAuth over PLAIN does not support password grant mechanism. You can only 'proxy' through SASL PLAIN mechanism the client credentials (clientId + secret) or the access token as described above.

8.4.2. OAuth 2.0 Kafka broker configuration

Kafka broker configuration for OAuth 2.0 involves:

  • Creating the OAuth 2.0 client in the authorization server
  • Configuring OAuth 2.0 authentication in the Kafka custom resource
Note

In relation to the authorization server, Kafka brokers and Kafka clients are both regarded as OAuth 2.0 clients.

8.4.2.1. OAuth 2.0 client configuration on an authorization server

To configure a Kafka broker to validate the token received during session initiation, the recommended approach is to create an OAuth 2.0 client definition in an authorization server, configured as confidential, with the following client credentials enabled:

  • Client ID of kafka (for example)
  • Client ID and Secret as the authentication mechanism
Note

You only need to use a client ID and secret when using a non-public introspection endpoint of the authorization server. The credentials are not typically required when using public authorization server endpoints, as with fast local JWT token validation.

8.4.2.2. OAuth 2.0 authentication configuration in the Kafka cluster

To use OAuth 2.0 authentication in the Kafka cluster, you specify, for example, a tls listener configuration for your Kafka cluster custom resource with the authentication method oauth:

Assigining the authentication method type for OAuth 2.0

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
      #...

You can configure OAuth 2.0 authentication in your listeners. We recommend using OAuth 2.0 authentication together with TLS encryption (tls: true). Without encryption, the connection is vulnerable to network eavesdropping and unauthorized access through token theft.

You configure an external listener with type: oauth for a secure transport layer to communicate with the client.

Using OAuth 2.0 with an external listener

# ...
listeners:
  - name: external
    port: 9094
    type: loadbalancer
    tls: true
    authentication:
      type: oauth
    #...

The tls property is false by default, so it must be enabled.

When you have defined the type of authentication as OAuth 2.0, you add configuration based on the type of validation, either as fast local JWT validation or token validation using an introspection endpoint.

The procedure to configure OAuth 2.0 for listeners, with descriptions and examples, is described in Configuring OAuth 2.0 support for Kafka brokers.

8.4.2.3. Fast local JWT token validation configuration

Fast local JWT token validation checks a JWT token signature locally.

The local check ensures that a token:

  • Conforms to type by containing a (typ) claim value of Bearer for an access token
  • Is valid (not expired)
  • Has an issuer that matches a validIssuerURI

You specify a validIssuerURI attribute when you configure the listener, so that any tokens not issued by the authorization server are rejected.

The authorization server does not need to be contacted during fast local JWT token validation. You activate fast local JWT token validation by specifying a jwksEndpointUri attribute, the endpoint exposed by the OAuth 2.0 authorization server. The endpoint contains the public keys used to validate signed JWT tokens, which are sent as credentials by Kafka clients.

Note

All communication with the authorization server should be performed using TLS encryption.

You can configure a certificate truststore as an OpenShift Secret in your AMQ Streams project namespace, and use a tlsTrustedCertificates attribute to point to the OpenShift Secret containing the truststore file.

You might want to configure a userNameClaim to properly extract a username from the JWT token. If you want to use Kafka ACL authorization, you need to identify the user by their username during authentication. (The sub claim in JWT tokens is typically a unique ID, not a username.)

Example configuration for fast local JWT token validation

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    #...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
          validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
          jwksEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/certs>
          userNameClaim: preferred_username
          maxSecondsWithoutReauthentication: 3600
          tlsTrustedCertificates:
          - secretName: oauth-server-cert
            certificate: ca.crt
    #...

8.4.2.4. OAuth 2.0 introspection endpoint configuration

Token validation using an OAuth 2.0 introspection endpoint treats a received access token as opaque. The Kafka broker sends an access token to the introspection endpoint, which responds with the token information necessary for validation. Importantly, it returns up-to-date information if the specific access token is valid, and also information about when the token expires.

To configure OAuth 2.0 introspection-based validation, you specify an introspectionEndpointUri attribute rather than the jwksEndpointUri attribute specified for fast local JWT token validation. Depending on the authorization server, you typically have to specify a clientId and clientSecret, because the introspection endpoint is usually protected.

Example configuration for an introspection endpoint

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
          clientId: kafka-broker
          clientSecret:
            secretName: my-cluster-oauth
            key: clientSecret
          validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
          introspectionEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/token/introspect>
          userNameClaim: preferred_username
          maxSecondsWithoutReauthentication: 3600
          tlsTrustedCertificates:
          - secretName: oauth-server-cert
            certificate: ca.crt

8.4.3. Session re-authentication for Kafka brokers

You can configure oauth listeners to use Kafka session re-authentication for OAuth 2.0 sessions between Kafka clients and Kafka brokers. This mechanism enforces the expiry of an authenticated session between the client and the broker after a defined period of time. When a session expires, the client immediately starts a new session by reusing the existing connection rather than dropping it.

Session re-authentication is disabled by default. To enable it, you set a time value for maxSecondsWithoutReauthentication in the oauth listener configuration. The same property is used to configure session re-authentication for OAUTHBEARER and PLAIN authentication. For an example configuration, see Section 8.4.6.2, “Configuring OAuth 2.0 support for Kafka brokers”.

Session re-authentication must be supported by the Kafka client libraries used by the client.

Session re-authentication can be used with fast local JWT or introspection endpoint token validation.

Client re-authentication

When the broker’s authenticated session expires, the client must re-authenticate to the existing session by sending a new, valid access token to the broker, without dropping the connection.

If token validation is successful, a new client session is started using the existing connection. If the client fails to re-authenticate, the broker will close the connection if further attempts are made to send or receive messages. Java clients that use Kafka client library 2.2 or later automatically re-authenticate if the re-authentication mechanism is enabled on the broker.

Session re-authentication also applies to refresh tokens, if used. When the session expires, the client refreshes the access token by using its refresh token. The client then uses the new access token to re-authenticate to the existing session.

Session expiry for OAUTHBEARER and PLAIN

When session re-authentication is configured, session expiry works differently for OAUTHBEARER and PLAIN authentication.

For OAUTHBEARER and PLAIN, using the client ID and secret method:

  • The broker’s authenticated session will expire at the configured maxSecondsWithoutReauthentication.
  • The session will expire earlier if the access token expires before the configured time.

For PLAIN using the long-lived access token method:

  • The broker’s authenticated session will expire at the configured maxSecondsWithoutReauthentication.
  • Re-authentication will fail if the access token expires before the configured time. Although session re-authentication is attempted, PLAIN has no mechanism for refreshing tokens.

If maxSecondsWithoutReauthentication is not configured, OAUTHBEARER and PLAIN clients can remain connected to brokers indefinitely, without needing to re-authenticate. Authenticated sessions do not end with access token expiry. However, this can be considered when configuring authorization, for example, by using keycloak authorization or installing a custom authorizer.

8.4.4. OAuth 2.0 Kafka client configuration

A Kafka client is configured with either:

  • The credentials required to obtain a valid access token from an authorization server (client ID and Secret)
  • A valid long-lived access token or refresh token, obtained using tools provided by an authorization server

The only information ever sent to the Kafka broker is an access token. The credentials used to authenticate with the authorization server to obtain the access token are never sent to the broker.

When a client obtains an access token, no further communication with the authorization server is needed.

The simplest mechanism is authentication with a client ID and Secret. Using a long-lived access token, or a long-lived refresh token, adds more complexity because there is an additional dependency on authorization server tools.

Note

If you are using long-lived access tokens, you may need to configure the client in the authorization server to increase the maximum lifetime of the token.

If the Kafka client is not configured with an access token directly, the client exchanges credentials for an access token during Kafka session initiation by contacting the authorization server. The Kafka client exchanges either:

  • Client ID and Secret
  • Client ID, refresh token, and (optionally) a secret
  • Username and password, with client ID and (optionally) a secret

8.4.5. OAuth 2.0 client authentication flows

OAuth 2.0 authentication flows depend on the underlying Kafka client and Kafka broker configuration. The flows must also be supported by the authorization server used.

The Kafka broker listener configuration determines how clients authenticate using an access token. The client can pass a client ID and secret to request an access token.

If a listener is configured to use PLAIN authentication, the client can authenticate with a client ID and secret or username and access token. These values are passed as the username and password properties of the PLAIN mechanism.

Listener configuration supports the following token validation options:

  • You can use fast local token validation based on JWT signature checking and local token introspection, without contacting an authorization server. The authorization server provides a JWKS endpoint with public certificates that are used to validate signatures on the tokens.
  • You can use a call to a token introspection endpoint provided by an authorization server. Each time a new Kafka broker connection is established, the broker passes the access token received from the client to the authorization server. The Kafka broker checks the response to confirm whether or not the token is valid.
Note

An authorization server might only allow the use of opaque access tokens, which means that local token validation is not possible.

Kafka client credentials can also be configured for the following types of authentication:

  • Direct local access using a previously generated long-lived access token
  • Contact with the authorization server for a new access token to be issued (using a client ID and a secret, or a refresh token, or a username and a password)

8.4.5.1. Example client authentication flows using the SASL OAUTHBEARER mechanism

You can use the following communication flows for Kafka authentication using the SASL OAUTHBEARER mechanism.

Client using client ID and secret, with broker delegating validation to authorization server

Client using client ID and secret with broker delegating validation to authorization server

  1. The Kafka client requests an access token from the authorization server using a client ID and secret, and optionally a refresh token. Alternatively, the client may authenticate using a username and a password.
  2. The authorization server generates a new access token.
  3. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
  4. The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server using its own client ID and secret.
  5. A Kafka client session is established if the token is valid.

Client using client ID and secret, with broker performing fast local token validation

Client using client ID and secret with broker performing fast local token validation

  1. The Kafka client authenticates with the authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token. Alternatively, the client may authenticate using a username and a password.
  2. The authorization server generates a new access token.
  3. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
  4. The Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.

Client using long-lived access token, with broker delegating validation to authorization server

Client using long-lived access token with broker delegating validation to authorization server

  1. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server, using its own client ID and secret.
  3. A Kafka client session is established if the token is valid.

Client using long-lived access token, with broker performing fast local validation

Client using long-lived access token with broker performing fast local validation

  1. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. The Kafka broker validates the access token locally using a JWT token signature check and local token introspection.
Warning

Fast local JWT token signature validation is suitable only for short-lived tokens as there is no check with the authorization server if a token has been revoked. Token expiration is written into the token, but revocation can happen at any time, so cannot be accounted for without contacting the authorization server. Any issued token would be considered valid until it expires.

8.4.5.2. Example client authentication flows using the SASL PLAIN mechanism

You can use the following communication flows for Kafka authentication using the OAuth PLAIN mechanism.

Client using a client ID and secret, with the broker obtaining the access token for the client

Client using a client ID and secret with the broker obtaining the access token for the client

  1. The Kafka client passes a clientId as a username and a secret as a password.
  2. The Kafka broker uses a token endpoint to pass the clientId and secret to the authorization server.
  3. The authorization server returns a fresh access token or an error if the client credentials are not valid.
  4. The Kafka broker validates the token in one of the following ways:

    1. If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if the token validation is successful.
    2. If local token introspection is used, a request is not made to the authorization server. The Kafka broker validates the access token locally using a JWT token signature check.

Client using a long-lived access token without a client ID and secret

Client using a long-lived access token without a client ID and secret

  1. The Kafka client passes a username and password. The password provides the value of an access token that was obtained manually and configured before running the client.
  2. The password is passed with or without an $accessToken: string prefix depending on whether or not the Kafka broker listener is configured with a token endpoint for authentication.

    1. If the token endpoint is configured, the password should be prefixed by $accessToken: to let the broker know that the password parameter contains an access token rather than a client secret. The Kafka broker interprets the username as the account username.
    2. If the token endpoint is not configured on the Kafka broker listener (enforcing a no-client-credentials mode), the password should provide the access token without the prefix. The Kafka broker interprets the username as the account username. In this mode, the client doesn’t use a client ID and secret, and the password parameter is always interpreted as a raw access token.
  3. The Kafka broker validates the token in one of the following ways:

    1. If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if token validation is successful.
    2. If local token introspection is used, there is no request made to the authorization server. Kafka broker validates the access token locally using a JWT token signature check.

8.4.6. Configuring OAuth 2.0 authentication

OAuth 2.0 is used for interaction between Kafka clients and AMQ Streams components.

In order to use OAuth 2.0 for AMQ Streams, you must:

8.4.6.1. Configuring an OAuth 2.0 authorization server

This procedure describes in general what you need to do to configure an authorization server for integration with AMQ Streams.

These instructions are not product specific.

The steps are dependent on the chosen authorization server. Consult the product documentation for the authorization server for information on how to set up OAuth 2.0 access.

Note

If you already have an authorization server deployed, you can skip the deployment step and use your current deployment.

Procedure

  1. Deploy the authorization server to your cluster.
  2. Access the CLI or admin console for the authorization server to configure OAuth 2.0 for AMQ Streams.

    Now prepare the authorization server to work with AMQ Streams.

  3. Configure a kafka-broker client.
  4. Configure clients for each Kafka client component of your application.

What to do next

After deploying and configuring the authorization server, configure the Kafka brokers to use OAuth 2.0.

8.4.6.2. Configuring OAuth 2.0 support for Kafka brokers

This procedure describes how to configure Kafka brokers so that the broker listeners are enabled to use OAuth 2.0 authentication using an authorization server.

We advise use of OAuth 2.0 over an encrypted interface through through a listener with tls: true. Plain listeners are not recommended.

If the authorization server is using certificates signed by the trusted CA and matching the OAuth 2.0 server hostname, TLS connection works using the default settings. Otherwise, you may need to configure the truststore with proper certificates or disable the certificate hostname validation.

When configuring the Kafka broker you have two options for the mechanism used to validate the access token during OAuth 2.0 authentication of the newly connected Kafka client:

Before you start

For more information on the configuration of OAuth 2.0 authentication for Kafka broker listeners, see:

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed

Procedure

  1. Update the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    oc edit kafka my-cluster
  2. Configure the Kafka broker listeners configuration.

    The configuration for each type of listener does not have to be the same, as they are independent.

    The examples here show the configuration options as configured for external listeners.

    Example 1: Configuring fast local JWT token validation

    #...
    - name: external
      port: 9094
      type: loadbalancer
      tls: true
      authentication:
        type: oauth 1
        validIssuerUri: <https://<auth-server-address>/auth/realms/external> 2
        jwksEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/certs> 3
        userNameClaim: preferred_username 4
        maxSecondsWithoutReauthentication: 3600 5
        tlsTrustedCertificates: 6
        - secretName: oauth-server-cert
          certificate: ca.crt
        disableTlsHostnameVerification: true 7
        jwksExpirySeconds: 360 8
        jwksRefreshSeconds: 300 9
        jwksMinRefreshPauseSeconds: 1 10

    1
    Listener type set to oauth.
    2
    URI of the token issuer used for authentication.
    3
    URI of the JWKS certificate endpoint used for local JWT validation.
    4
    The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authentication flow and the authorization server used.
    5
    (Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
    6
    (Optional) Trusted certificates for TLS connection to the authorization server.
    7
    (Optional) Disable TLS hostname verification. Default is false.
    8
    The duration the JWKS certificates are considered valid before they expire. Default is 360 seconds. If you specify a longer time, consider the risk of allowing access to revoked certificates.
    9
    The period between refreshes of JWKS certificates. The interval must be at least 60 seconds shorter than the expiry interval. Default is 300 seconds.
    10
    The minimum pause in seconds between consecutive attempts to refresh JWKS public keys. When an unknown signing key is encountered, the JWKS keys refresh is scheduled outside the regular periodic schedule with at least the specified pause since the last refresh attempt. The refreshing of keys follows the rule of exponential backoff, retrying on unsuccessful refreshes with ever increasing pause, until it reaches jwksRefreshSeconds. The default value is 1.

    Example 2: Configuring token validation using an introspection endpoint

    - name: external
      port: 9094
      type: loadbalancer
      tls: true
      authentication:
        type: oauth
        validIssuerUri: <https://<auth-server-address>/auth/realms/external>
        introspectionEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token/introspect> 1
        clientId: kafka-broker 2
        clientSecret: 3
          secretName: my-cluster-oauth
          key: clientSecret
        userNameClaim: preferred_username 4
        maxSecondsWithoutReauthentication: 3600 5

    1
    URI of the token introspection endpoint.
    2
    Client ID to identify the client.
    3
    Client Secret and client ID is used for authentication.
    4
    The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authorization server used.
    5
    (Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.

    Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional (optional) configuration settings you can use:

      # ...
      authentication:
        type: oauth
        # ...
        checkIssuer: false 1
        checkAudience: true 2
        fallbackUserNameClaim: client_id 3
        fallbackUserNamePrefix: client-account- 4
        validTokenType: bearer 5
        userInfoEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/userinfo 6
        enableOauthBearer: false 7
        enablePlain: true 8
        tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token 9
        customClaimCheck: "@.custom == 'custom-value'" 10
        clientAudience: AUDIENCE 11
        clientScope: SCOPE 12
        connectTimeoutSeconds: 60 13
        readTimeoutSeconds: 60 14
        httpRetries: 2 15
        httpRetryPauseMs: 300 16
        groupsClaim: "$.groups" 17
        groupsClaimDelimiter: "," 18
    1
    If your authorization server does not provide an iss claim, it is not possible to perform an issuer check. In this situation, set checkIssuer to false and do not specify a validIssuerUri. Default is true.
    2
    If your authorization server provides an aud (audience) claim, and you want to enforce an audience check, set checkAudience to true. Audience checks identify the intended recipients of tokens. As a result, the Kafka broker will reject tokens that do not have its clientId in their aud claim. Default is false.
    3
    An authorization server may not provide a single attribute to identify both regular users and clients. When a client authenticates in its own name, the server might provide a client ID. When a user authenticates using a username and password, to obtain a refresh token or an access token, the server might provide a username attribute in addition to a client ID. Use this fallback option to specify the username claim (attribute) to use if a primary user ID attribute is not available.
    4
    In situations where fallbackUserNameClaim is applicable, it may also be necessary to prevent name collisions between the values of the username claim, and those of the fallback username claim. Consider a situation where a client called producer exists, but also a regular user called producer exists. In order to differentiate between the two, you can use this property to add a prefix to the user ID of the client.
    5
    (Only applicable when using introspectionEndpointUri) Depending on the authorization server you are using, the introspection endpoint may or may not return the token type attribute, or it may contain different values. You can specify a valid token type value that the response from the introspection endpoint has to contain.
    6
    (Only applicable when using introspectionEndpointUri) The authorization server may be configured or implemented in such a way to not provide any identifiable information in an Introspection Endpoint response. In order to obtain the user ID, you can configure the URI of the userinfo endpoint as a fallback. The userNameClaim, fallbackUserNameClaim, and fallbackUserNamePrefix settings are applied to the response of userinfo endpoint.
    7
    Set this to false to disable the OAUTHBEARER mechanism on the listener. At least one of PLAIN or OAUTHBEARER has to be enabled. Default is true.
    8
    Set to true to enable PLAIN authentication on the listener, which is supported for clients on all platforms.
    9
    Additional configuration for the PLAIN mechanism. If specified, clients can authenticate over PLAIN by passing an access token as the password using an $accessToken: prefix. For production, always use https:// urls.
    10
    Additional custom rules can be imposed on the JWT access token during validation by setting this to a JsonPath filter query. If the access token does not contain the necessary data, it is rejected. When using the introspectionEndpointUri, the custom check is applied to the introspection endpoint response JSON.
    11
    An audience parameter passed to the token endpoint. An audience is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId and secret. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.
    12
    A scope parameter passed to the token endpoint. A scope is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId and secret. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.
    13
    The connect timeout in seconds when connecting to the authorization server. The default value is 60.
    14
    The read timeout in seconds when connecting to the authorization server. The default value is 60.
    15
    The maximum number of times to retry a failed HTTP request to the authorization server. The default value is 0, meaning that no retries are performed. To use this option effectively, consider reducing the timeout times for the connectTimeoutSeconds and readTimeoutSeconds options. However, note that retries may prevent the current worker thread from being available to other requests, and if too many requests stall, it could make the Kafka broker unresponsive.
    16
    The time to wait before attempting another retry of a failed HTTP request to the authorization server. By default, this time is set to zero, meaning that no pause is applied. This is because many issues that cause failed requests are per-request network glitches or proxy issues that can be resolved quickly. However, if your authorization server is under stress or experiencing high traffic, you may want to set this option to a value of 100 ms or more to reduce the load on the server and increase the likelihood of successful retries.
    17
    A JsonPath query that is used to extract groups information from either the JWT token or the introspection endpoint response. This option is not set by default. By configuring this option, a custom authorizer can make authorization decisions based on user groups.
    18
    A delimiter used to parse groups information when it is returned as a single delimited string. The default value is ',' (comma).
  3. Save and exit the editor, then wait for rolling updates to complete.
  4. Check the update in the logs or by watching the pod state transitions:

    oc logs -f ${POD_NAME} -c ${CONTAINER_NAME}
    oc get pod -w

    The rolling update configures the brokers to use OAuth 2.0 authentication.

8.4.6.3. Configuring Kafka Java clients to use OAuth 2.0

Configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers. Add a callback plugin to your client pom.xml file, then configure your client for OAuth 2.0.

Specify the following in your client configuration:

  • A SASL (Simple Authentication and Security Layer) security protocol:

    • SASL_SSL for authentication over TLS encrypted connections
    • SASL_PLAINTEXT for authentication over unencrypted connections

      Use SASL_SSL for production and SASL_PLAINTEXT for local development only. When using SASL_SSL, additional ssl.truststore configuration is needed. The truststore configuration is required for secure connection (https://) to the OAuth 2.0 authorization server. To verify the OAuth 2.0 authorization server, add the CA certificate for the authorization server to the truststore in your client configuration. You can configure a truststore in PEM or PKCS #12 format.

  • A Kafka SASL mechanism:

    • OAUTHBEARER for credentials exchange using a bearer token
    • PLAIN to pass client credentials (clientId + secret) or an access token
  • A JAAS (Java Authentication and Authorization Service) module that implements the SASL mechanism:

    • org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule implements the OAUTHBEARER mechanism
    • org.apache.kafka.common.security.plain.PlainLoginModule implements the PLAIN mechanism
  • SASL authentication properties, which support the following authentication methods:

    • OAuth 2.0 client credentials
    • OAuth 2.0 password grant (deprecated)
    • Access token
    • Refresh token

Add the SASL authentication properties as JAAS configuration (sasl.jaas.config). How you configure the authentication properties depends on the authentication method you are using to access the OAuth 2.0 authorization server. In this procedure, the properties are specified in a properties file, then loaded into the client configuration.

Note

You can also specify authentication properties as environment variables, or as Java system properties. For Java system properties, you can set them using setProperty and pass them on the command line using the -D option.

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
  • Kafka brokers are configured for OAuth 2.0

Procedure

  1. Add the client library with OAuth 2.0 support to the pom.xml file for the Kafka client:

    <dependency>
     <groupId>io.strimzi</groupId>
     <artifactId>kafka-oauth-client</artifactId>
     <version>0.12.0.redhat-00006</version>
    </dependency>
  2. Configure the client properties by specifying the following configuration in a properties file:

    • The security protocol
    • The SASL mechanism
    • The JAAS module and authentication properties according to the method being used

      For example, we can add the following to a client.properties file:

      Client credentials mechanism properties

      security.protocol=SASL_SSL 1
      sasl.mechanism=OAUTHBEARER 2
      ssl.truststore.location=/tmp/truststore.p12 3
      ssl.truststore.password=$STOREPASS
      ssl.truststore.type=PKCS12
      sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        oauth.token.endpoint.uri="<token_endpoint_url>" \ 4
        oauth.client.id="<client_id>" \ 5
        oauth.client.secret="<client_secret>" \ 6
        oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \ 7
        oauth.ssl.truststore.password="$STOREPASS" \ 8
        oauth.ssl.truststore.type="PKCS12" \ 9
        oauth.scope="<scope>" \ 10
        oauth.audience="<audience>" ; 11

      1
      SASL_SSL security protocol for TLS-encrypted connections. Use SASL_PLAINTEXT over unencrypted connections for local development only.
      2
      The SASL mechanism specified as OAUTHBEARER or PLAIN.
      3
      The truststore configuration for secure access to the Kafka cluster.
      4
      URI of the authorization server token endpoint.
      5
      Client ID, which is the name used when creating the client in the authorization server.
      6
      Client secret created when creating the client in the authorization server.
      7
      The location contains the public key certificate (truststore.p12) for the authorization server.
      8
      The password for accessing the truststore.
      9
      The truststore type.
      10
      (Optional) The scope for requesting the token from the token endpoint. An authorization server may require a client to specify the scope.
      11
      (Optional) The audience for requesting the token from the token endpoint. An authorization server may require a client to specify the audience.

      Password grants mechanism properties

      security.protocol=SASL_SSL
      sasl.mechanism=OAUTHBEARER
      ssl.truststore.location=/tmp/truststore.p12
      ssl.truststore.password=$STOREPASS
      ssl.truststore.type=PKCS12
      sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        oauth.token.endpoint.uri="<token_endpoint_url>" \
        oauth.client.id="<client_id>" \ 1
        oauth.client.secret="<client_secret>" \ 2
        oauth.password.grant.username="<username>" \ 3
        oauth.password.grant.password="<password>" \ 4
        oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \
        oauth.ssl.truststore.password="$STOREPASS" \
        oauth.ssl.truststore.type="PKCS12" \
        oauth.scope="<scope>" \
        oauth.audience="<audience>" ;

      1
      Client ID, which is the name used when creating the client in the authorization server.
      2
      (Optional) Client secret created when creating the client in the authorization server.
      3
      Username for password grant authentication. OAuth password grant configuration (username and password) uses the OAuth 2.0 password grant method. To use password grants, create a user account for a client on your authorization server with limited permissions. The account should act like a service account. Use in environments where user accounts are required for authentication, but consider using a refresh token first.
      4
      Password for password grant authentication.
      Note

      SASL PLAIN does not support passing a username and password (password grants) using the OAuth 2.0 password grant method.

      Access token properties

      security.protocol=SASL_SSL
      sasl.mechanism=OAUTHBEARER
      ssl.truststore.location=/tmp/truststore.p12
      ssl.truststore.password=$STOREPASS
      ssl.truststore.type=PKCS12
      sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        oauth.token.endpoint.uri="<token_endpoint_url>" \
        oauth.access.token="<access_token>" ; 1
        oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \
        oauth.ssl.truststore.password="$STOREPASS" \
        oauth.ssl.truststore.type="PKCS12" \

      1
      Long-lived access token for Kafka clients.

      Refresh token properties

      security.protocol=SASL_SSL
      sasl.mechanism=OAUTHBEARER
      ssl.truststore.location=/tmp/truststore.p12
      ssl.truststore.password=$STOREPASS
      ssl.truststore.type=PKCS12
      sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        oauth.token.endpoint.uri="<token_endpoint_url>" \
        oauth.client.id="<client_id>" \ 1
        oauth.client.secret="<client_secret>" \ 2
        oauth.refresh.token="<refresh_token>" ; 3
        oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \
        oauth.ssl.truststore.password="$STOREPASS" \
        oauth.ssl.truststore.type="PKCS12" \

      1
      Client ID, which is the name used when creating the client in the authorization server.
      2
      (Optional) Client secret created when creating the client in the authorization server.
      3
      Long-lived refresh token for Kafka clients.
  3. Input the client properties for OAUTH 2.0 authentication into the Java client code.

    Example showing input of client properties

    Properties props = new Properties();
    try (FileReader reader = new FileReader("client.properties", StandardCharsets.UTF_8)) {
      props.load(reader);
    }

  4. Verify that the Kafka client can access the Kafka brokers.

8.4.6.4. Configuring OAuth 2.0 for Kafka components

This procedure describes how to configure Kafka components to use OAuth 2.0 authentication using an authorization server.

You can configure authentication for:

  • Kafka Connect
  • Kafka MirrorMaker
  • Kafka Bridge

In this scenario, the Kafka component and the authorization server are running in the same cluster.

Before you start

For more information on the configuration of OAuth 2.0 authentication for Kafka components, see the KafkaClientAuthenticationOAuth schema reference. The schema reference includes examples of configuration options.

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
  • Kafka brokers are configured for OAuth 2.0

Procedure

  1. Create a client secret and mount it to the component as an environment variable.

    For example, here we are creating a client Secret for the Kafka Bridge:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Secret
    metadata:
     name: my-bridge-oauth
    type: Opaque
    data:
     clientSecret: MGQ1OTRmMzYtZTllZS00MDY2LWI5OGEtMTM5MzM2NjdlZjQw 1
    1
    The clientSecret key must be in base64 format.
  2. Create or edit the resource for the Kafka component so that OAuth 2.0 authentication is configured for the authentication property.

    For OAuth 2.0 authentication, you can use the following options:

    • Client ID and secret
    • Client ID and refresh token
    • Access token
    • Username and password
    • TLS

    For example, here OAuth 2.0 is assigned to the Kafka Bridge client using a client ID and secret, and TLS:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaBridge
    metadata:
      name: my-bridge
    spec:
      # ...
      authentication:
        type: oauth 1
        tokenEndpointUri: https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token 2
        clientId: kafka-bridge
        clientSecret:
          secretName: my-bridge-oauth
          key: clientSecret
        tlsTrustedCertificates: 3
        - secretName: oauth-server-cert
          certificate: tls.crt
    1
    Authentication type set to oauth.
    2
    URI of the token endpoint for authentication.
    3
    Trusted certificates for TLS connection to the authorization server.

    Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional configuration options you can use:

    # ...
    spec:
      # ...
      authentication:
        # ...
        disableTlsHostnameVerification: true 1
        checkAccessTokenType: false 2
        accessTokenIsJwt: false 3
        scope: any 4
        audience: kafka 5
        connectTimeoutSeconds: 60 6
        readTimeoutSeconds: 60 7
        httpRetries: 2 8
        httpRetryPauseMs: 300 9
    1
    (Optional) Disable TLS hostname verification. Default is false.
    2
    If the authorization server does not return a typ (type) claim inside the JWT token, you can apply checkAccessTokenType: false to skip the token type check. Default is true.
    3
    If you are using opaque tokens, you can apply accessTokenIsJwt: false so that access tokens are not treated as JWT tokens.
    4
    (Optional) The scope for requesting the token from the token endpoint. An authorization server may require a client to specify the scope. In this case it is any.
    5
    (Optional) The audience for requesting the token from the token endpoint. An authorization server may require a client to specify the audience. In this case it is kafka.
    6
    (Optional) The connect timeout in seconds when connecting to the authorization server. The default value is 60.
    7
    (Optional) The read timeout in seconds when connecting to the authorization server. The default value is 60.
    8
    (Optional) The maximum number of times to retry a failed HTTP request to the authorization server. The default value is 0, meaning that no retries are performed. To use this option effectively, consider reducing the timeout times for the connectTimeoutSeconds and readTimeoutSeconds options. However, note that retries may prevent the current worker thread from being available to other requests, and if too many requests stall, it could make the Kafka broker unresponsive.
    9
    (Optional) The time to wait before attempting another retry of a failed HTTP request to the authorization server. By default, this time is set to zero, meaning that no pause is applied. This is because many issues that cause failed requests are per-request network glitches or proxy issues that can be resolved quickly. However, if your authorization server is under stress or experiencing high traffic, you may want to set this option to a value of 100 ms or more to reduce the load on the server and increase the likelihood of successful retries.
  3. Apply the changes to the deployment of your Kafka resource.

    oc apply -f your-file
  4. Check the update in the logs or by watching the pod state transitions:

    oc logs -f ${POD_NAME} -c ${CONTAINER_NAME}
    oc get pod -w

    The rolling updates configure the component for interaction with Kafka brokers using OAuth 2.0 authentication.

8.5. Using OAuth 2.0 token-based authorization

If you are using OAuth 2.0 with Red Hat Single Sign-On for token-based authentication, you can also use Red Hat Single Sign-On to configure authorization rules to constrain client access to Kafka brokers. Authentication establishes the identity of a user. Authorization decides the level of access for that user.

AMQ Streams supports the use of OAuth 2.0 token-based authorization through Red Hat Single Sign-On Authorization Services, which allows you to manage security policies and permissions centrally.

Security policies and permissions defined in Red Hat Single Sign-On are used to grant access to resources on Kafka brokers. Users and clients are matched against policies that permit access to perform specific actions on Kafka brokers.

Kafka allows all users full access to brokers by default, and also provides the AclAuthorizer plugin to configure authorization based on Access Control Lists (ACLs).

ZooKeeper stores ACL rules that grant or deny access to resources based on username. However, OAuth 2.0 token-based authorization with Red Hat Single Sign-On offers far greater flexibility on how you wish to implement access control to Kafka brokers. In addition, you can configure your Kafka brokers to use OAuth 2.0 authorization and ACLs.

8.5.1. OAuth 2.0 authorization mechanism

OAuth 2.0 authorization in AMQ Streams uses Red Hat Single Sign-On server Authorization Services REST endpoints to extend token-based authentication with Red Hat Single Sign-On by applying defined security policies on a particular user, and providing a list of permissions granted on different resources for that user. Policies use roles and groups to match permissions to users. OAuth 2.0 authorization enforces permissions locally based on the received list of grants for the user from Red Hat Single Sign-On Authorization Services.

8.5.1.1. Kafka broker custom authorizer

A Red Hat Single Sign-On authorizer (KeycloakRBACAuthorizer) is provided with AMQ Streams. To be able to use the Red Hat Single Sign-On REST endpoints for Authorization Services provided by Red Hat Single Sign-On, you configure a custom authorizer on the Kafka broker.

The authorizer fetches a list of granted permissions from the authorization server as needed, and enforces authorization locally on the Kafka Broker, making rapid authorization decisions for each client request.

8.5.2. Configuring OAuth 2.0 authorization support

This procedure describes how to configure Kafka brokers to use OAuth 2.0 authorization using Red Hat Single Sign-On Authorization Services.

Before you begin

Consider the access you require or want to limit for certain users. You can use a combination of Red Hat Single Sign-On groups, roles, clients, and users to configure access in Red Hat Single Sign-On.

Typically, groups are used to match users based on organizational departments or geographical locations. And roles are used to match users based on their function.

With Red Hat Single Sign-On, you can store users and groups in LDAP, whereas clients and roles cannot be stored this way. Storage and access to user data may be a factor in how you choose to configure authorization policies.

Note

Super users always have unconstrained access to a Kafka broker regardless of the authorization implemented on the Kafka broker.

Prerequisites

  • AMQ Streams must be configured to use OAuth 2.0 with Red Hat Single Sign-On for token-based authentication. You use the same Red Hat Single Sign-On server endpoint when you set up authorization.
  • OAuth 2.0 authentication must be configured with the maxSecondsWithoutReauthentication option to enable re-authentication.

Procedure

  1. Access the Red Hat Single Sign-On Admin Console or use the Red Hat Single Sign-On Admin CLI to enable Authorization Services for the Kafka broker client you created when setting up OAuth 2.0 authentication.
  2. Use Authorization Services to define resources, authorization scopes, policies, and permissions for the client.
  3. Bind the permissions to users and clients by assigning them roles and groups.
  4. Configure the Kafka brokers to use Red Hat Single Sign-On authorization by updating the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    oc edit kafka my-cluster
  5. Configure the Kafka broker kafka configuration to use keycloak authorization, and to be able to access the authorization server and Authorization Services.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        authorization:
          type: keycloak 1
          tokenEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token> 2
          clientId: kafka 3
          delegateToKafkaAcls: false 4
          disableTlsHostnameVerification: false 5
          superUsers: 6
          - CN=fred
          - sam
          - CN=edward
          tlsTrustedCertificates: 7
          - secretName: oauth-server-cert
            certificate: ca.crt
          grantsRefreshPeriodSeconds: 60 8
          grantsRefreshPoolSize: 5 9
          connectTimeoutSeconds: 60 10
          readTimeoutSeconds: 60 11
          httpRetries: 2 12
        #...
    1
    Type keycloak enables Red Hat Single Sign-On authorization.
    2
    URI of the Red Hat Single Sign-On token endpoint. For production, always use https:// urls. When you configure token-based oauth authentication, you specify a jwksEndpointUri as the URI for local JWT validation. The hostname for the tokenEndpointUri URI must be the same.
    3
    The client ID of the OAuth 2.0 client definition in Red Hat Single Sign-On that has Authorization Services enabled. Typically, kafka is used as the ID.
    4
    (Optional) Delegate authorization to Kafka AclAuthorizer if access is denied by Red Hat Single Sign-On Authorization Services policies. Default is false.
    5
    (Optional) Disable TLS hostname verification. Default is false.
    6
    (Optional) Designated super users.
    7
    (Optional) Trusted certificates for TLS connection to the authorization server.
    8
    (Optional) The time between two consecutive grants refresh runs. That is the maximum time for active sessions to detect any permissions changes for the user on Red Hat Single Sign-On. The default value is 60.
    9
    (Optional) The number of threads to use to refresh (in parallel) the grants for the active sessions. The default value is 5.
    10
    (Optional) The connect timeout in seconds when connecting to the Red Hat Single Sign-On token endpoint. The default value is 60.
    11
    (Optional) The read timeout in seconds when connecting to the Red Hat Single Sign-On token endpoint. The default value is 60.
    12
    (Optional) The maximum number of times to retry (without pausing) a failed HTTP request to the authorization server. The default value is 0, meaning that no retries are performed. To use this option effectively, consider reducing the timeout times for the connectTimeoutSeconds and readTimeoutSeconds options. However, note that retries may prevent the current worker thread from being available to other requests, and if too many requests stall, it could make the Kafka broker unresponsive.
  6. Save and exit the editor, then wait for rolling updates to complete.
  7. Check the update in the logs or by watching the pod state transitions:

    oc logs -f ${POD_NAME} -c kafka
    oc get pod -w

    The rolling update configures the brokers to use OAuth 2.0 authorization.

  8. Verify the configured permissions by accessing Kafka brokers as clients or users with specific roles, making sure they have the necessary access, or do not have the access they are not supposed to have.

8.5.3. Managing policies and permissions in Red Hat Single Sign-On Authorization Services

This section describes the authorization models used by Red Hat Single Sign-On Authorization Services and Kafka, and defines the important concepts in each model.

To grant permissions to access Kafka, you can map Red Hat Single Sign-On Authorization Services objects to Kafka resources by creating an OAuth client specification in Red Hat Single Sign-On. Kafka permissions are granted to user accounts or service accounts using Red Hat Single Sign-On Authorization Services rules.

Examples are shown of the different user permissions required for common Kafka operations, such as creating and listing topics.

8.5.3.1. Kafka and Red Hat Single Sign-On authorization models overview

Kafka and Red Hat Single Sign-On Authorization Services use different authorization models.

Kafka authorization model

Kafka’s authorization model uses resource types. When a Kafka client performs an action on a broker, the broker uses the configured KeycloakRBACAuthorizer to check the client’s permissions, based on the action and resource type.

Kafka uses five resource types to control access: Topic, Group, Cluster, TransactionalId, and DelegationToken. Each resource type has a set of available permissions.

Topic

  • Create
  • Write
  • Read
  • Delete
  • Describe
  • DescribeConfigs
  • Alter
  • AlterConfigs

Group

  • Read
  • Describe
  • Delete

Cluster

  • Create
  • Describe
  • Alter
  • DescribeConfigs
  • AlterConfigs
  • IdempotentWrite
  • ClusterAction

TransactionalId

  • Describe
  • Write

DelegationToken

  • Describe
Red Hat Single Sign-On Authorization Services model

The Red Hat Single Sign-On Authorization Services model has four concepts for defining and granting permissions: resources, authorization scopes, policies, and permissions.

Resources
A resource is a set of resource definitions that are used to match resources with permitted actions. A resource might be an individual topic, for example, or all topics with names starting with the same prefix. A resource definition is associated with a set of available authorization scopes, which represent a set of all actions available on the resource. Often, only a subset of these actions is actually permitted.
Authorization scopes
An authorization scope is a set of all the available actions on a specific resource definition. When you define a new resource, you add scopes from the set of all scopes.
Policies

A policy is an authorization rule that uses criteria to match against a list of accounts. Policies can match:

  • Service accounts based on client ID or roles
  • User accounts based on username, groups, or roles.
Permissions
A permission grants a subset of authorization scopes on a specific resource definition to a set of users.

Additional resources

8.5.3.2. Map Red Hat Single Sign-On Authorization Services to the Kafka authorization model

The Kafka authorization model is used as a basis for defining the Red Hat Single Sign-On roles and resources that will control access to Kafka.

To grant Kafka permissions to user accounts or service accounts, you first create an OAuth client specification in Red Hat Single Sign-On for the Kafka broker. You then specify Red Hat Single Sign-On Authorization Services rules on the client. Typically, the client id of the OAuth client that represents the broker is kafka. The example configuration files provided with AMQ Streams use kafka as the OAuth client id.

Note

If you have multiple Kafka clusters, you can use a single OAuth client (kafka) for all of them. This gives you a single, unified space in which to define and manage authorization rules. However, you can also use different OAuth client ids (for example, my-cluster-kafka or cluster-dev-kafka) and define authorization rules for each cluster within each client configuration.

The kafka client definition must have the Authorization Enabled option enabled in the Red Hat Single Sign-On Admin Console.

All permissions exist within the scope of the kafka client. If you have different Kafka clusters configured with different OAuth client IDs, they each need a separate set of permissions even though they’re part of the same Red Hat Single Sign-On realm.

When the Kafka client uses OAUTHBEARER authentication, the Red Hat Single Sign-On authorizer (KeycloakRBACAuthorizer) uses the access token of the current session to retrieve a list of grants from the Red Hat Single Sign-On server. To retrieve the grants, the authorizer evaluates the Red Hat Single Sign-On Authorization Services policies and permissions.

Authorization scopes for Kafka permissions

An initial Red Hat Single Sign-On configuration usually involves uploading authorization scopes to create a list of all possible actions that can be performed on each Kafka resource type. This step is performed once only, before defining any permissions. You can add authorization scopes manually instead of uploading them.

Authorization scopes must contain all the possible Kafka permissions regardless of the resource type:

  • Create
  • Write
  • Read
  • Delete
  • Describe
  • Alter
  • DescribeConfig
  • AlterConfig
  • ClusterAction
  • IdempotentWrite
Note

If you’re certain you won’t need a permission (for example, IdempotentWrite), you can omit it from the list of authorization scopes. However, that permission won’t be available to target on Kafka resources.

Resource patterns for permissions checks

Resource patterns are used for pattern matching against the targeted resources when performing permission checks. The general pattern format is RESOURCE-TYPE:PATTERN-NAME.

The resource types mirror the Kafka authorization model. The pattern allows for two matching options:

  • Exact matching (when the pattern does not end with *)
  • Prefix matching (when the pattern ends with *)

Example patterns for resources

Topic:my-topic
Topic:orders-*
Group:orders-*
Cluster:*

Additionally, the general pattern format can be prefixed by kafka-cluster:CLUSTER-NAME followed by a comma, where CLUSTER-NAME refers to the metadata.name in the Kafka custom resource.

Example patterns for resources with cluster prefix

kafka-cluster:my-cluster,Topic:*
kafka-cluster:*,Group:b_*

When the kafka-cluster prefix is missing, it is assumed to be kafka-cluster:*.

When defining a resource, you can associate it with a list of possible authorization scopes which are relevant to the resource. Set whatever actions make sense for the targeted resource type.

Though you may add any authorization scope to any resource, only the scopes supported by the resource type are considered for access control.

Policies for applying access permission

Policies are used to target permissions to one or more user accounts or service accounts. Targeting can refer to:

  • Specific user or service accounts
  • Realm roles or client roles
  • User groups
  • JavaScript rules to match a client IP address

A policy is given a unique name and can be reused to target multiple permissions to multiple resources.

Permissions to grant access

Use fine-grained permissions to pull together the policies, resources, and authorization scopes that grant access to users.

The name of each permission should clearly define which permissions it grants to which users. For example, Dev Team B can read from topics starting with x.

Additional resources

8.5.3.3. Example permissions required for Kafka operations

The following examples demonstrate the user permissions required for performing common operations on Kafka.

Create a topic

To create a topic, the Create permission is required for the specific topic, or for Cluster:kafka-cluster.

bin/kafka-topics.sh --create --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

List topics

If a user has the Describe permission on a specified topic, the topic is listed.

bin/kafka-topics.sh --list \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Display topic details

To display a topic’s details, Describe and DescribeConfigs permissions are required on the topic.

bin/kafka-topics.sh --describe --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Produce messages to a topic

To produce messages to a topic, Describe and Write permissions are required on the topic.

If the topic hasn’t been created yet, and topic auto-creation is enabled, the permissions to create a topic are required.

bin/kafka-console-producer.sh  --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties

Consume messages from a topic

To consume messages from a topic, Describe and Read permissions are required on the topic. Consuming from the topic normally relies on storing the consumer offsets in a consumer group, which requires additional Describe and Read permissions on the consumer group.

Two resources are needed for matching. For example:

Topic:my-topic
Group:my-group-*
bin/kafka-console-consumer.sh --topic my-topic --group my-group-1 --from-beginning \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --consumer.config /tmp/config.properties

Produce messages to a topic using an idempotent producer

As well as the permissions for producing to a topic, an additional IdempotentWrite permission is required on the Cluster:kafka-cluster resource.

Two resources are needed for matching. For example:

Topic:my-topic
Cluster:kafka-cluster
bin/kafka-console-producer.sh  --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties --producer-property enable.idempotence=true --request-required-acks -1

List consumer groups

When listing consumer groups, only the groups on which the user has the Describe permissions are returned. Alternatively, if the user has the Describe permission on the Cluster:kafka-cluster, all the consumer groups are returned.

bin/kafka-consumer-groups.sh --list \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Display consumer group details

To display a consumer group’s details, the Describe permission is required on the group and the topics associated with the group.

bin/kafka-consumer-groups.sh --describe --group my-group-1 \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Change topic configuration

To change a topic’s configuration, the Describe and Alter permissions are required on the topic.

bin/kafka-topics.sh --alter --topic my-topic --partitions 2 \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Display Kafka broker configuration

In order to use kafka-configs.sh to get a broker’s configuration, the DescribeConfigs permission is required on the Cluster:kafka-cluster.

bin/kafka-configs.sh --entity-type brokers --entity-name 0 --describe --all \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Change Kafka broker configuration

To change a Kafka broker’s configuration, DescribeConfigs and AlterConfigs permissions are required on Cluster:kafka-cluster.

bin/kafka-configs --entity-type brokers --entity-name 0 --alter --add-config log.cleaner.threads=2 \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Delete a topic

To delete a topic, the Describe and Delete permissions are required on the topic.

bin/kafka-topics.sh --delete --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Select a lead partition

To run leader selection for topic partitions, the Alter permission is required on the Cluster:kafka-cluster.

bin/kafka-leader-election.sh --topic my-topic --partition 0 --election-type PREFERRED  /
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --admin.config /tmp/config.properties

Reassign partitions

To generate a partition reassignment file, Describe permissions are required on the topics involved.

bin/kafka-reassign-partitions.sh --topics-to-move-json-file /tmp/topics-to-move.json --broker-list "0,1" --generate \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties > /tmp/partition-reassignment.json

To execute the partition reassignment, Describe and Alter permissions are required on Cluster:kafka-cluster. Also, Describe permissions are required on the topics involved.

bin/kafka-reassign-partitions.sh --reassignment-json-file /tmp/partition-reassignment.json --execute \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties

To verify partition reassignment, Describe, and AlterConfigs permissions are required on Cluster:kafka-cluster, and on each of the topics involved.

bin/kafka-reassign-partitions.sh --reassignment-json-file /tmp/partition-reassignment.json --verify \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties

8.5.4. Trying Red Hat Single Sign-On Authorization Services

This example explains how to use Red Hat Single Sign-On Authorization Services with keycloak authorization. Use Red Hat Single Sign-On Authorization Services to enforce access restrictions on Kafka clients. Red Hat Single Sign-On Authorization Services use authorization scopes, policies and permissions to define and apply access control to resources.

Red Hat Single Sign-On Authorization Services REST endpoints provide a list of granted permissions on resources for authenticated users. The list of grants (permissions) is fetched from the Red Hat Single Sign-On server as the first action after an authenticated session is established by the Kafka client. The list is refreshed in the background so that changes to the grants are detected. Grants are cached and enforced locally on the Kafka broker for each user session to provide fast authorization decisions.

AMQ Streams provides example configuration files. These include the following example files for setting up Red Hat Single Sign-On:

kafka-ephemeral-oauth-single-keycloak-authz.yaml
An example Kafka custom resource configured for OAuth 2.0 token-based authorization using Red Hat Single Sign-On. You can use the custom resource to deploy a Kafka cluster that uses keycloak authorization and token-based oauth authentication.
kafka-authz-realm.json
An example Red Hat Single Sign-On realm configured with sample groups, users, roles and clients. You can import the realm into a Red Hat Single Sign-On instance to set up fine-grained permissions to access Kafka.

If you want to try the example with Red Hat Single Sign-On, use these files to perform the tasks outlined in this section in the order shown.

Authentication

When you configure token-based oauth authentication, you specify a jwksEndpointUri as the URI for local JWT validation. When you configure keycloak authorization, you specify a tokenEndpointUri as the URI of the Red Hat Single Sign-On token endpoint. The hostname for both URIs must be the same.

Targeted permissions with group or role policies

In Red Hat Single Sign-On, confidential clients with service accounts enabled can authenticate to the server in their own name using a client ID and a secret. This is convenient for microservices that typically act in their own name, and not as agents of a particular user (like a web site). Service accounts can have roles assigned like regular users. They cannot, however, have groups assigned. As a consequence, if you want to target permissions to microservices using service accounts, you cannot use group policies, and should instead use role policies. Conversely, if you want to limit certain permissions only to regular user accounts where authentication with a username and password is required, you can achieve that as a side effect of using the group policies rather than the role policies. This is what is used in this example for permissions that start with ClusterManager. Performing cluster management is usually done interactively using CLI tools. It makes sense to require the user to log in before using the resulting access token to authenticate to the Kafka broker. In this case, the access token represents the specific user, rather than the client application.

8.5.4.1. Accessing the Red Hat Single Sign-On Admin Console

Set up Red Hat Single Sign-On, then connect to its Admin Console and add the preconfigured realm. Use the example kafka-authz-realm.json file to import the realm. You can check the authorization rules defined for the realm in the Admin Console. The rules grant access to the resources on the Kafka cluster configured to use the example Red Hat Single Sign-On realm.

Prerequisites

  • A running OpenShift cluster.
  • The AMQ Streams examples/security/keycloak-authorization/kafka-authz-realm.json file that contains the preconfigured realm.

Procedure

  1. Install the Red Hat Single Sign-On server using the Red Hat Single Sign-On Operator as described in Server Installation and Configuration in the Red Hat Single Sign-On documentation.
  2. Wait until the Red Hat Single Sign-On instance is running.
  3. Get the external hostname to be able to access the Admin Console.

    NS=sso
    oc get ingress keycloak -n $NS

    In this example, we assume the Red Hat Single Sign-On server is running in the sso namespace.

  4. Get the password for the admin user.

    oc get -n $NS pod keycloak-0 -o yaml | less

    The password is stored as a secret, so get the configuration YAML file for the Red Hat Single Sign-On instance to identify the name of the secret (secretKeyRef.name).

  5. Use the name of the secret to obtain the clear text password.

    SECRET_NAME=credential-keycloak
    oc get -n $NS secret $SECRET_NAME -o yaml | grep PASSWORD | awk '{print $2}' | base64 -D

    In this example, we assume the name of the secret is credential-keycloak.

  6. Log in to the Admin Console with the username admin and the password you obtained.

    Use https://HOSTNAME to access the Kubernetes Ingress.

    You can now upload the example realm to Red Hat Single Sign-On using the Admin Console.

  7. Click Add Realm to import the example realm.
  8. Add the examples/security/keycloak-authorization/kafka-authz-realm.json file, and then click Create.

    You now have kafka-authz as your current realm in the Admin Console.

    The default view displays the Master realm.

  9. In the Red Hat Single Sign-On Admin Console, go to Clients > kafka > Authorization > Settings and check that Decision Strategy is set to Affirmative.

    An affirmative policy means that at least one policy must be satisfied for a client to access the Kafka cluster.

  10. In the Red Hat Single Sign-On Admin Console, go to Groups, Users, Roles and Clients to view the realm configuration.

    Groups
    Groups are used to create user groups and set user permissions. Groups are sets of users with a name assigned. They are used to compartmentalize users into geographical, organizational or departmental units. Groups can be linked to an LDAP identity provider. You can make a user a member of a group through a custom LDAP server admin user interface, for example, to grant permissions on Kafka resources.
    Users
    Users are used to create users. For this example, alice and bob are defined. alice is a member of the ClusterManager group and bob is a member of ClusterManager-my-cluster group. Users can be stored in an LDAP identity provider.
    Roles
    Roles mark users or clients as having certain permissions. Roles are a concept analogous to groups. They are usually used to tag users with organizational roles and have the requisite permissions. Roles cannot be stored in an LDAP identity provider. If LDAP is a requirement, you can use groups instead, and add Red Hat Single Sign-On roles to the groups so that when users are assigned a group they also get a corresponding role.
    Clients

    Clients can have specific configurations. For this example, kafka, kafka-cli, team-a-client, and team-b-client clients are configured.

    • The kafka client is used by Kafka brokers to perform the necessary OAuth 2.0 communication for access token validation. This client also contains the authorization services resource definitions, policies, and authorization scopes used to perform authorization on the Kafka brokers. The authorization configuration is defined in the kafka client from the Authorization tab, which becomes visible when Authorization Enabled is switched on from the Settings tab.
    • The kafka-cli client is a public client that is used by the Kafka command line tools when authenticating with username and password to obtain an access token or a refresh token.
    • The team-a-client and team-b-client clients are confidential clients representing services with partial access to certain Kafka topics.
  11. In the Red Hat Single Sign-On Admin Console, go to Authorization > Permissions to see the granted permissions that use the resources and policies defined for the realm.

    For example, the kafka client has the following permissions:

    Dev Team A can write to topics that start with x_ on any cluster
    Dev Team B can read from topics that start with x_ on any cluster
    Dev Team B can update consumer group offsets that start with x_ on any cluster
    ClusterManager of my-cluster Group has full access to cluster config on my-cluster
    ClusterManager of my-cluster Group has full access to consumer groups on my-cluster
    ClusterManager of my-cluster Group has full access to topics on my-cluster
    Dev Team A
    The Dev Team A realm role can write to topics that start with x_ on any cluster. This combines a resource called Topic:x_*, Describe and Write scopes, and the Dev Team A policy. The Dev Team A policy matches all users that have a realm role called Dev Team A.
    Dev Team B
    The Dev Team B realm role can read from topics that start with x_ on any cluster. This combines Topic:x_*, Group:x_* resources, Describe and Read scopes, and the Dev Team B policy. The Dev Team B policy matches all users that have a realm role called Dev Team B. Matching users and clients have the ability to read from topics, and update the consumed offsets for topics and consumer groups that have names starting with x_.

8.5.4.2. Deploying a Kafka cluster with Red Hat Single Sign-On authorization

Deploy a Kafka cluster configured to connect to the Red Hat Single Sign-On server. Use the example kafka-ephemeral-oauth-single-keycloak-authz.yaml file to deploy the Kafka cluster as a Kafka custom resource. The example deploys a single-node Kafka cluster with keycloak authorization and oauth authentication.

Prerequisites

  • The Red Hat Single Sign-On authorization server is deployed to your OpenShift cluster and loaded with the example realm.
  • The Cluster Operator is deployed to your OpenShift cluster.
  • The AMQ Streams examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml custom resource.

Procedure

  1. Use the hostname of the Red Hat Single Sign-On instance you deployed to prepare a truststore certificate for Kafka brokers to communicate with the Red Hat Single Sign-On server.

    SSO_HOST=SSO-HOSTNAME
    SSO_HOST_PORT=$SSO_HOST:443
    STOREPASS=storepass
    
    echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.pem

    The certificate is required as Kubernetes Ingress is used to make a secure (HTTPS) connection.

    Usually there is not one single certificate, but a certificate chain. You only have to provide the top-most issuer CA, which is listed last in the /tmp/sso.pem file. You can extract it manually or using the following commands:

    Example command to extract the top CA certificate in a certificate chain

    split -p "-----BEGIN CERTIFICATE-----" sso.pem sso-
    for f in $(ls sso-*); do mv $f $f.pem; done
    cp $(ls sso-* | sort -r | head -n 1) sso-ca.crt

    Note

    A trusted CA certificate is normally obtained from a trusted source, and not by using the openssl command.

  2. Deploy the certificate to OpenShift as a secret.

    oc create secret generic oauth-server-cert --from-file=/tmp/sso-ca.crt -n $NS
  3. Set the hostname as an environment variable

    SSO_HOST=SSO-HOSTNAME
  4. Create and deploy the example Kafka cluster.

    cat examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml | sed -E 's#\${SSO_HOST}'"#$SSO_HOST#" | oc create -n $NS -f -

8.5.4.3. Preparing TLS connectivity for a CLI Kafka client session

Create a new pod for an interactive CLI session. Set up a truststore with a Red Hat Single Sign-On certificate for TLS connectivity. The truststore is to connect to Red Hat Single Sign-On and the Kafka broker.

Prerequisites

  • The Red Hat Single Sign-On authorization server is deployed to your OpenShift cluster and loaded with the example realm.

    In the Red Hat Single Sign-On Admin Console, check the roles assigned to the clients are displayed in Clients > Service Account Roles.

  • The Kafka cluster configured to connect with Red Hat Single Sign-On is deployed to your OpenShift cluster.

Procedure

  1. Run a new interactive pod container using the AMQ Streams Kafka image to connect to a running Kafka broker.

    NS=sso
    oc run -ti --restart=Never --image=registry.redhat.io/amq-streams/kafka-34-rhel8:2.4.0 kafka-cli -n $NS -- /bin/sh
    Note

    If oc times out waiting on the image download, subsequent attempts may result in an AlreadyExists error.

  2. Attach to the pod container.

    oc attach -ti kafka-cli -n $NS
  3. Use the hostname of the Red Hat Single Sign-On instance to prepare a certificate for client connection using TLS.

    SSO_HOST=SSO-HOSTNAME
    SSO_HOST_PORT=$SSO_HOST:443
    STOREPASS=storepass
    
    echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.pem

    Usually there is not one single certificate, but a certificate chain. You only have to provide the top-most issuer CA, which is listed last in the /tmp/sso.pem file. You can extract it manually or using the following command:

    Example command to extract the top CA certificate in a certificate chain

    split -p "-----BEGIN CERTIFICATE-----" sso.pem sso-
    for f in $(ls sso-*); do mv $f $f.pem; done
    cp $(ls sso-* | sort -r | head -n 1) sso-ca.crt

    Note

    A trusted CA certificate is normally obtained from a trusted source, and not by using the openssl command.

  4. Create a truststore for TLS connection to the Kafka brokers.

    keytool -keystore /tmp/truststore.p12 -storetype pkcs12 -alias sso -storepass $STOREPASS -import -file /tmp/sso-ca.crt -noprompt
  5. Use the Kafka bootstrap address as the hostname of the Kafka broker and the tls listener port (9093) to prepare a certificate for the Kafka broker.

    KAFKA_HOST_PORT=my-cluster-kafka-bootstrap:9093
    STOREPASS=storepass
    
    echo "Q" | openssl s_client -showcerts -connect $KAFKA_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/my-cluster-kafka.pem

    The obtained .pem file is usually not one single certificate, but a certificate chain. You only have to provide the top-most issuer CA, which is listed last in the /tmp/my-cluster-kafka.pem file. You can extract it manually or using the following command:

    Example command to extract the top CA certificate in a certificate chain

    split -p "-----BEGIN CERTIFICATE-----" /tmp/my-cluster-kafka.pem kafka-
    for f in $(ls kafka-*); do mv $f $f.pem; done
    cp $(ls kafka-* | sort -r | head -n 1) my-cluster-kafka-ca.crt

    Note

    A trusted CA certificate is normally obtained from a trusted source, and not by using the openssl command. For this example we assume the client is running in a pod in the same namespace where the Kafka cluster was deployed. If the client is accessing the Kafka cluster from outside the OpenShift cluster, you would have to first determine the bootstrap address. In that case you can also get the cluster certificate directly from the OpenShift secret, and there is no need for openssl. For more information, see Chapter 7, Setting up client access to a Kafka cluster.

  6. Add the certificate for the Kafka broker to the truststore.

    keytool -keystore /tmp/truststore.p12 -storetype pkcs12 -alias my-cluster-kafka -storepass $STOREPASS -import -file /tmp/my-cluster-kafka-ca.crt -noprompt

    Keep the session open to check authorized access.

8.5.4.4. Checking authorized access to Kafka using a CLI Kafka client session

Check the authorization rules applied through the Red Hat Single Sign-On realm using an interactive CLI session. Apply the checks using Kafka’s example producer and consumer clients to create topics with user and service accounts that have different levels of access.

Use the team-a-client and team-b-client clients to check the authorization rules. Use the alice admin user to perform additional administrative tasks on Kafka.

The AMQ Streams Kafka image used in this example contains Kafka producer and consumer binaries.

Prerequisites

Setting up client and admin user configuration

  1. Prepare a Kafka configuration file with authentication properties for the team-a-client client.

    SSO_HOST=SSO-HOSTNAME
    
    cat > /tmp/team-a-client.properties << EOF
    security.protocol=SASL_SSL
    ssl.truststore.location=/tmp/truststore.p12
    ssl.truststore.password=$STOREPASS
    ssl.truststore.type=PKCS12
    sasl.mechanism=OAUTHBEARER
    sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      oauth.client.id="team-a-client" \
      oauth.client.secret="team-a-client-secret" \
      oauth.ssl.truststore.location="/tmp/truststore.p12" \
      oauth.ssl.truststore.password="$STOREPASS" \
      oauth.ssl.truststore.type="PKCS12" \
      oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
    sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
    EOF

    The SASL OAUTHBEARER mechanism is used. This mechanism requires a client ID and client secret, which means the client first connects to the Red Hat Single Sign-On server to obtain an access token. The client then connects to the Kafka broker and uses the access token to authenticate.

  2. Prepare a Kafka configuration file with authentication properties for the team-b-client client.

    cat > /tmp/team-b-client.properties << EOF
    security.protocol=SASL_SSL
    ssl.truststore.location=/tmp/truststore.p12
    ssl.truststore.password=$STOREPASS
    ssl.truststore.type=PKCS12
    sasl.mechanism=OAUTHBEARER
    sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      oauth.client.id="team-b-client" \
      oauth.client.secret="team-b-client-secret" \
      oauth.ssl.truststore.location="/tmp/truststore.p12" \
      oauth.ssl.truststore.password="$STOREPASS" \
      oauth.ssl.truststore.type="PKCS12" \
      oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
    sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
    EOF
  3. Authenticate admin user alice by using curl and performing a password grant authentication to obtain a refresh token.

    USERNAME=alice
    PASSWORD=alice-password
    
    GRANT_RESPONSE=$(curl -X POST "https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" -H 'Content-Type: application/x-www-form-urlencoded' -d "grant_type=password&username=$USERNAME&password=$PASSWORD&client_id=kafka-cli&scope=offline_access" -s -k)
    
    REFRESH_TOKEN=$(echo $GRANT_RESPONSE | awk -F "refresh_token\":\"" '{printf $2}' | awk -F "\"" '{printf $1}')

    The refresh token is an offline token that is long-lived and does not expire.

  4. Prepare a Kafka configuration file with authentication properties for the admin user alice.

    cat > /tmp/alice.properties << EOF
    security.protocol=SASL_SSL
    ssl.truststore.location=/tmp/truststore.p12
    ssl.truststore.password=$STOREPASS
    ssl.truststore.type=PKCS12
    sasl.mechanism=OAUTHBEARER
    sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      oauth.refresh.token="$REFRESH_TOKEN" \
      oauth.client.id="kafka-cli" \
      oauth.ssl.truststore.location="/tmp/truststore.p12" \
      oauth.ssl.truststore.password="$STOREPASS" \
      oauth.ssl.truststore.type="PKCS12" \
      oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
    sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
    EOF

    The kafka-cli public client is used for the oauth.client.id in the sasl.jaas.config. Since it’s a public client it does not require a secret. The client authenticates with the refresh token that was authenticated in the previous step. The refresh token requests an access token behind the scenes, which is then sent to the Kafka broker for authentication.

Producing messages with authorized access

Use the team-a-client configuration to check that you can produce messages to topics that start with a_ or x_.

  1. Write to topic my-topic.

    bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic my-topic \
      --producer.config=/tmp/team-a-client.properties
    First message

    This request returns a Not authorized to access topics: [my-topic] error.

    team-a-client has a Dev Team A role that gives it permission to perform any supported actions on topics that start with a_, but can only write to topics that start with x_. The topic named my-topic matches neither of those rules.

  2. Write to topic a_messages.

    bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
      --producer.config /tmp/team-a-client.properties
    First message
    Second message

    Messages are produced to Kafka successfully.

  3. Press CTRL+C to exit the CLI application.
  4. Check the Kafka container log for a debug log of Authorization GRANTED for the request.

    oc logs my-cluster-kafka-0 -f -n $NS

Consuming messages with authorized access

Use the team-a-client configuration to consume messages from topic a_messages.

  1. Fetch messages from topic a_messages.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
      --from-beginning --consumer.config /tmp/team-a-client.properties

    The request returns an error because the Dev Team A role for team-a-client only has access to consumer groups that have names starting with a_.

  2. Update the team-a-client properties to specify the custom consumer group it is permitted to use.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
      --from-beginning --consumer.config /tmp/team-a-client.properties --group a_consumer_group_1

    The consumer receives all the messages from the a_messages topic.

Administering Kafka with authorized access

The team-a-client is an account without any cluster-level access, but it can be used with some administrative operations.

  1. List topics.

    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list

    The a_messages topic is returned.

  2. List consumer groups.

    bin/kafka-consumer-groups.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list

    The a_consumer_group_1 consumer group is returned.

    Fetch details on the cluster configuration.

    bin/kafka-configs.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties \
      --entity-type brokers --describe --entity-default

    The request returns an error because the operation requires cluster level permissions that team-a-client does not have.

Using clients with different permissions

Use the team-b-client configuration to produce messages to topics that start with b_.

  1. Write to topic a_messages.

    bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
      --producer.config /tmp/team-b-client.properties
    Message 1

    This request returns a Not authorized to access topics: [a_messages] error.

  2. Write to topic b_messages.

    bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic b_messages \
      --producer.config /tmp/team-b-client.properties
    Message 1
    Message 2
    Message 3

    Messages are produced to Kafka successfully.

  3. Write to topic x_messages.

    bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --producer.config /tmp/team-b-client.properties
    Message 1

    A Not authorized to access topics: [x_messages] error is returned, The team-b-client can only read from topic x_messages.

  4. Write to topic x_messages using team-a-client.

    bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --producer.config /tmp/team-a-client.properties
    Message 1

    This request returns a Not authorized to access topics: [x_messages] error. The team-a-client can write to the x_messages topic, but it does not have a permission to create a topic if it does not yet exist. Before team-a-client can write to the x_messages topic, an admin power user must create it with the correct configuration, such as the number of partitions and replicas.

Managing Kafka with an authorized admin user

Use admin user alice to manage Kafka. alice has full access to manage everything on any Kafka cluster.

  1. Create the x_messages topic as alice.

    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties \
      --topic x_messages --create --replication-factor 1 --partitions 1

    The topic is created successfully.

  2. List all topics as alice.

    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties --list
    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list
    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-b-client.properties --list

    Admin user alice can list all the topics, whereas team-a-client and team-b-client can only list the topics they have access to.

    The Dev Team A and Dev Team B roles both have Describe permission on topics that start with x_, but they cannot see the other team’s topics because they do not have Describe permissions on them.

  3. Use the team-a-client to produce messages to the x_messages topic:

    bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --producer.config /tmp/team-a-client.properties
    Message 1
    Message 2
    Message 3

    As alice created the x_messages topic, messages are produced to Kafka successfully.

  4. Use the team-b-client to produce messages to the x_messages topic.

    bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --producer.config /tmp/team-b-client.properties
    Message 4
    Message 5

    This request returns a Not authorized to access topics: [x_messages] error.

  5. Use the team-b-client to consume messages from the x_messages topic:

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --from-beginning --consumer.config /tmp/team-b-client.properties --group x_consumer_group_b

    The consumer receives all the messages from the x_messages topic.

  6. Use the team-a-client to consume messages from the x_messages topic.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --from-beginning --consumer.config /tmp/team-a-client.properties --group x_consumer_group_a

    This request returns a Not authorized to access topics: [x_messages] error.

  7. Use the team-a-client to consume messages from a consumer group that begins with a_.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --from-beginning --consumer.config /tmp/team-a-client.properties --group a_consumer_group_a

    This request returns a Not authorized to access topics: [x_messages] error.

    Dev Team A has no Read access on topics that start with a x_.

  8. Use alice to produce messages to the x_messages topic.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --from-beginning --consumer.config /tmp/alice.properties

    Messages are produced to Kafka successfully.

    alice can read from or write to any topic.

  9. Use alice to read the cluster configuration.

    bin/kafka-configs.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties \
      --entity-type brokers --describe --entity-default

    The cluster configuration for this example is empty.

Chapter 9. Managing TLS certificates

AMQ Streams supports TLS for encrypted communication between Kafka and AMQ Streams components.

Communication is always encrypted between the following components:

  • Communication between Kafka and ZooKeeper
  • Interbroker communication between Kafka brokers
  • Internodal communication between ZooKeeper nodes
  • AMQ Streams operator communication with Kafka brokers and ZooKeeper nodes

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

CA (certificate authority) certificates are generated by the Cluster Operator to verify the identities of components and clients. If you don’t want to use the CAs generated by the Cluster Operator, you can install your own cluster and clients CA certificates.

You can also provide Kafka listener certificates for TLS listeners or external listeners that have TLS encryption enabled. Use Kafka listener certificates to incorporate the security infrastructure you already have in place.

Note

Any certificates you provide are not renewed by the Cluster Operator.

Figure 9.1. Example architecture of the communication secured by TLS

Secure Communication

9.1. Internal cluster CA and clients CA

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

Similarly, each Kafka client application connecting to AMQ Streams using mTLS needs to use private keys and certificates. A second internal CA, named the clients CA, is used to sign certificates for the Kafka clients.

Both the cluster CA and clients CA have a self-signed public key certificate.

Kafka brokers are configured to trust certificates signed by either the cluster CA or clients CA. Components that clients do not need to connect to, such as ZooKeeper, only trust certificates signed by the cluster CA. Unless TLS encryption for external listeners is disabled, client applications must trust certificates signed by the cluster CA. This is also true for client applications that perform mTLS authentication.

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

You can replace the CA certificates for the cluster CA or clients CA with your own. For more information, see Section 9.7.1, “Installing your own CA certificates and private keys”. If you provide your own CA certificates, you must renew them before they expire.

9.2. Secrets generated by the operators

Secrets are created when custom resources are deployed, such as Kafka and KafkaUser. AMQ Streams uses these secrets to store private and public key certificates for Kafka clusters, clients, and users. The secrets are used for establishing TLS encrypted connections between Kafka brokers, and between brokers and clients. They are also used for mTLS authentication.

Cluster and clients secrets are always pairs: one contains the public key and one contains the private key.

Cluster secret
A cluster secret contains the cluster CA to sign Kafka broker certificates. Connecting clients use the certificate to establish a TLS encrypted connection with a Kafka cluster. The certificate verifies broker identity.
Client secret
A client secret contains the clients CA for a user to sign its own client certificate. This allows mutual authentication against the Kafka cluster. The broker validates a client’s identity through the certificate.
User secret
A user secret contains a private key and certificate. The secret is created and signed by the clients CA when a new user is created. The key and certificate are used to authenticate and authorize the user when accessing the cluster.

9.2.1. TLS authentication using keys and certificates in PEM or PKCS #12 format

The secrets created by AMQ Streams provide private keys and certificates in PEM (Privacy Enhanced Mail) and PKCS #12 (Public-Key Cryptography Standards) formats. PEM and PKCS #12 are OpenSSL-generated key formats for TLS communications using the SSL protocol.

You can configure mutual TLS (mTLS) authentication that uses the credentials contained in the secrets generated for a Kafka cluster and user.

To set up mTLS, you must first do the following:

When you deploy a Kafka cluster, a <cluster_name>-cluster-ca-cert secret is created with public key to verify the cluster. You use the public key to configure a truststore for the client.

When you create a KafkaUser, a <kafka_user_name> secret is created with the keys and certificates to verify the user (client). Use these credentials to configure a keystore for the client.

With the Kafka cluster and client set up to use mTLS, you extract credentials from the secrets and add them to your client configuration.

PEM keys and certificates

For PEM, you add the following to your client configuration:

Truststore
  • ca.crt from the <cluster_name>-cluster-ca-cert secret, which is the CA certificate for the cluster.
Keystore
  • user.crt from the <kafka_user_name> secret, which is the public certificate of the user.
  • user.key from the <kafka_user_name> secret, which is the private key of the user.
PKCS #12 keys and certificates

For PKCS #12, you add the following to your client configuration:

Truststore
  • ca.p12 from the <cluster_name>-cluster-ca-cert secret, which is the CA certificate for the cluster.
  • ca.password from the <cluster_name>-cluster-ca-cert secret, which is the password to access the public cluster CA certificate.
Keystore
  • user.p12 from the <kafka_user_name> secret, which is the public key certificate of the user.
  • user.password from the <kafka_user_name> secret, which is the password to access the public key certificate of the Kafka user.

PKCS #12 is supported by Java, so you can add the values of the certificates directly to your Java client configuration. You can also reference the certificates from a secure storage location. With PEM files, you must add the certificates directly to the client configuration in single-line format. Choose a format that’s suitable for establishing TLS connections between your Kafka cluster and client. Use PKCS #12 if you are unfamiliar with PEM.

Note

All keys are 2048 bits in size and, by default, are valid for 365 days from the initial generation. You can change the validity period.

9.2.2. Secrets generated by the Cluster Operator

The Cluster Operator generates the following certificates, which are saved as secrets in the OpenShift cluster. AMQ Streams uses these secrets by default.

The cluster CA and clients CA have separate secrets for the private key and public key.

<cluster_name>-cluster-ca
Contains the private key of the cluster CA. AMQ Streams and Kafka components use the private key to sign server certificates.
<cluster_name>-cluster-ca-cert
Contains the public key of the cluster CA. Kafka clients use the public key to verify the identity of the Kafka brokers they are connecting to with TLS server authentication.
<cluster_name>-clients-ca
Contains the private key of the clients CA. Kafka clients use the private key to sign new user certificates for mTLS authentication when connecting to Kafka brokers.
<cluster_name>-clients-ca-cert
Contains the public key of the clients CA. Kafka brokers use the public key to verify the identity of clients accessing the Kafka brokers when mTLS authentication is used.

Secrets for communication between AMQ Streams components contain a private key and a public key certificate signed by the cluster CA.

<cluster_name>-kafka-brokers
Contains the private and public keys for Kafka brokers.
<cluster_name>-zookeeper-nodes
Contains the private and public keys for ZooKeeper nodes.
<cluster_name>-cluster-operator-certs
Contains the private and public keys for encrypting communication between the Cluster Operator and Kafka or ZooKeeper.
<cluster_name>-entity-topic-operator-certs
Contains the private and public keys for encrypting communication between the Topic Operator and Kafka or ZooKeeper.
<cluster_name>-entity-user-operator-certs
Contains the private and public keys for encrypting communication between the User Operator and Kafka or ZooKeeper.
<cluster_name>-cruise-control-certs
Contains the private and public keys for encrypting communication between Cruise Control and Kafka or ZooKeeper.
<cluster_name>-kafka-exporter-certs
Contains the private and public keys for encrypting communication between Kafka Exporter and Kafka or ZooKeeper.
Note

You can provide your own server certificates and private keys to connect to Kafka brokers using Kafka listener certificates rather than certificates signed by the cluster CA.

9.2.3. Cluster CA secrets

Cluster CA secrets are managed by the Cluster Operator in a Kafka cluster.

Only the <cluster_name>-cluster-ca-cert secret is required by clients. All other cluster secrets are accessed by AMQ Streams components. You can enforce this using OpenShift role-based access controls, if necessary.

Note

The CA certificates in <cluster_name>-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.

Table 9.1. Fields in the <cluster_name>-cluster-ca secret

FieldDescription

ca.key

The current private key for the cluster CA.

Table 9.2. Fields in the <cluster_name>-cluster-ca-cert secret

FieldDescription

ca.p12

PKCS #12 store for storing certificates and keys.

ca.password

Password for protecting the PKCS #12 store.

ca.crt

The current certificate for the cluster CA.

Table 9.3. Fields in the <cluster_name>-kafka-brokers secret

FieldDescription

<cluster_name>-kafka-<num>.p12

PKCS #12 store for storing certificates and keys.

<cluster_name>-kafka-<num>.password

Password for protecting the PKCS #12 store.

<cluster_name>-kafka-<num>.crt

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

<cluster_name>-kafka-<num>.key

Private key for a Kafka broker pod <num>.

Table 9.4. Fields in the <cluster_name>-zookeeper-nodes secret

FieldDescription

<cluster_name>-zookeeper-<num>.p12

PKCS #12 store for storing certificates and keys.

<cluster_name>-zookeeper-<num>.password

Password for protecting the PKCS #12 store.

<cluster_name>-zookeeper-<num>.crt

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

<cluster_name>-zookeeper-<num>.key

Private key for ZooKeeper pod <num>.

Table 9.5. Fields in the <cluster_name>-cluster-operator-certs secret

FieldDescription

cluster-operator.p12

PKCS #12 store for storing certificates and keys.

cluster-operator.password

Password for protecting the PKCS #12 store.

cluster-operator.crt

Certificate for mTLS communication between the Cluster Operator and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

cluster-operator.key

Private key for mTLS communication between the Cluster Operator and Kafka or ZooKeeper.

Table 9.6. Fields in the <cluster_name>-entity-topic-operator-certs secret

FieldDescription

entity-operator.p12

PKCS #12 store for storing certificates and keys.

entity-operator.password

Password for protecting the PKCS #12 store.

entity-operator.crt

Certificate for mTLS communication between the Topic Operator and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

entity-operator.key

Private key for mTLS communication between the Topic Operator and Kafka or ZooKeeper.

Table 9.7. Fields in the <cluster_name>-entity-user-operator-certs secret

FieldDescription

entity-operator.p12

PKCS #12 store for storing certificates and keys.

entity-operator.password

Password for protecting the PKCS #12 store.

entity-operator.crt

Certificate for mTLS communication between the User Operator and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

entity-operator.key

Private key for mTLS communication between the User Operator and Kafka or ZooKeeper.

Table 9.8. Fields in the <cluster_name>-cruise-control-certs secret

FieldDescription

cruise-control.p12

PKCS #12 store for storing certificates and keys.

cruise-control.password

Password for protecting the PKCS #12 store.

cruise-control.crt

Certificate for mTLS communication between Cruise Control and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

cruise-control.key

Private key for mTLS communication between the Cruise Control and Kafka or ZooKeeper.

Table 9.9. Fields in the <cluster_name>-kafka-exporter-certs secret

FieldDescription

kafka-exporter.p12

PKCS #12 store for storing certificates and keys.

kafka-exporter.password

Password for protecting the PKCS #12 store.

kafka-exporter.crt

Certificate for mTLS communication between Kafka Exporter and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

kafka-exporter.key

Private key for mTLS communication between the Kafka Exporter and Kafka or ZooKeeper.

9.2.4. Clients CA secrets

Clients CA secrets are managed by the Cluster Operator in a Kafka cluster.

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

The <cluster_name>-clients-ca secret is used to sign the certificates of client applications. This secret must 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.

Table 9.10. Fields in the <cluster_name>-clients-ca secret

FieldDescription

ca.key

The current private key for the clients CA.

Table 9.11. Fields in the <cluster_name>-clients-ca-cert secret

FieldDescription

ca.p12

PKCS #12 store for storing certificates and keys.

ca.password

Password for protecting the PKCS #12 store.

ca.crt

The current certificate for the clients CA.

9.2.5. User secrets generated by the User Operator

User secrets are managed by the User Operator.

When a user is created using the User Operator, a secret is generated using the name of the user.

Table 9.12. Fields in the user_name secret

Secret nameField within secretDescription

<user_name>

user.p12

PKCS #12 store for storing certificates and keys.

user.password

Password for protecting the PKCS #12 store.

user.crt

Certificate for the user, signed by the clients CA

user.key

Private key for the user

9.2.6. Adding labels and annotations to cluster CA secrets

By configuring the clusterCaCert template property in the Kafka custom resource, you can add custom labels and annotations to the Cluster CA secrets created by the Cluster Operator. Labels and annotations are useful for identifying objects and adding contextual information. You configure template properties in AMQ Streams custom resources.

Example template customization to add labels and annotations to secrets

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    template:
      clusterCaCert:
        metadata:
          labels:
            label1: value1
            label2: value2
          annotations:
            annotation1: value1
            annotation2: value2
    # ...

9.2.7. Disabling ownerReference in the CA secrets

By default, the cluster and clients CA secrets are created with an ownerReference property that is set to the Kafka custom resource. This means that, when the Kafka custom resource is deleted, the CA secrets are also deleted (garbage collected) by OpenShift.

If you want to reuse the CA for a new cluster, you can disable the ownerReference by setting the generateSecretOwnerReference property for the cluster and clients CA secrets to false in the Kafka configuration. When the ownerReference is disabled, CA secrets are not deleted by OpenShift when the corresponding Kafka custom resource is deleted.

Example Kafka configuration with disabled ownerReference for cluster and clients CAs

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
# ...
  clusterCa:
    generateSecretOwnerReference: false
  clientsCa:
    generateSecretOwnerReference: false
# ...

9.3. Certificate renewal and validity periods

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 CA certificates automatically created by the Cluster Operator, you can configure the validity period of:

  • Cluster CA certificates in Kafka.spec.clusterCa.validityDays
  • Clients CA certificates in Kafka.spec.clientsCa.validityDays

The default validity period for both certificates is 365 days. Manually-installed CA certificates should have their own validity periods defined.

When a CA certificate expires, components and clients that still trust that certificate will not accept connections from peers whose certificates 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 initiates certificate renewal before the old CA certificates expire.

You can configure the renewal period of the certificates created by the Cluster Operator:

  • Cluster CA certificates in Kafka.spec.clusterCa.renewalDays
  • Clients CA certificates in Kafka.spec.clientsCa.renewalDays

The default renewal period for both certificates is 30 days.

The renewal period is measured backwards, from the expiry date of the current certificate.

Validity period against renewal period

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

To make a change to the validity and renewal periods after creating the Kafka cluster, you configure and apply the Kafka custom resource, and manually renew the CA certificates. If you do not manually renew the certificates, the new periods will be used the next time the certificate is renewed automatically.

Example Kafka configuration for certificate validity and renewal periods

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
# ...
  clusterCa:
    renewalDays: 30
    validityDays: 365
    generateCertificateAuthority: true
  clientsCa:
    renewalDays: 30
    validityDays: 365
    generateCertificateAuthority: true
# ...

The behavior of the Cluster Operator during the renewal period depends on the settings for the generateCertificateAuthority certificate generation properties for the cluster CA and clients CA.

true
If the properties are set to true, a CA certificate is generated automatically by the Cluster Operator, and renewed automatically within the renewal period.
false
If the properties are set to false, a CA certificate is not generated by the Cluster Operator. Use this option if you are installing your own certificates.

9.3.1. Renewal process with automatically generated CA certificates

The Cluster Operator performs the following processes in this order when renewing CA certificates:

  1. Generates a new CA certificate, but retains the existing key.

    The new certificate replaces the old one with the name ca.crt within the corresponding Secret.

  2. Generates 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. Restarts ZooKeeper nodes so that they will trust the new CA certificate and use the new client certificates.
  4. Restarts Kafka brokers so that they will trust the new CA certificate and use the new client certificates.
  5. Restarts the Topic and User Operators so that they will trust the new CA certificate and use the new client certificates.

    User certificates are signed by the clients CA. User certificates generated by the User Operator are renewed when the clients CA is renewed.

9.3.2. Client certificate renewal

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

When connecting to the cluster, and to ensure they operate correctly, client applications must:

  • Trust the cluster CA certificate published in the <cluster>-cluster-ca-cert Secret.
  • Use the credentials published in their <user-name> Secret to connect to the cluster.

    The User Secret provides credentials in PEM and PKCS #12 format, or it can provide a password when using SCRAM-SHA authentication. The User Operator creates the user credentials when a user is created.

You must ensure clients continue to work after certificate renewal. The renewal process depends on how the clients are configured.

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 to the cluster.

Note

For workloads running inside the same OpenShift cluster and namespace, Secrets can be mounted as a volume so the client Pods construct their keystores and truststores from the current state of the Secrets. For more details on this procedure, see Configuring internal clients to trust the cluster CA.

9.3.3. Manually renewing the CA certificates generated by the Cluster Operator

Cluster and clients CA certificates generated by the Cluster Operator auto-renew at the start of their respective certificate renewal periods. However, you can use the strimzi.io/force-renew annotation to manually renew one or both of these certificates before the certificate renewal period starts. You might do this for security reasons, or if you have changed the renewal or validity periods for the certificates.

A renewed certificate uses the same private key as the old certificate.

Note

If you are using your own CA certificates, the force-renew annotation cannot be used. Instead, follow the procedure for renewing your own CA certificates.

Prerequisites

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

Procedure

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

    Table 9.13. Annotation for the Secret that forces renewal of certificates

    CertificateSecretAnnotate command

    Cluster CA

    KAFKA-CLUSTER-NAME-cluster-ca-cert

    oc annotate secret KAFKA-CLUSTER-NAME-cluster-ca-cert strimzi.io/force-renew=true

    Clients CA

    KAFKA-CLUSTER-NAME-clients-ca-cert

    oc annotate secret KAFKA-CLUSTER-NAME-clients-ca-cert strimzi.io/force-renew=true

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

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

  2. Check the period the CA certificate is valid:

    For example, using an openssl command:

    oc get secret CA-CERTIFICATE-SECRET -o 'jsonpath={.data.CA-CERTIFICATE}' | base64 -d | openssl x509 -subject -issuer -startdate -enddate -noout

    CA-CERTIFICATE-SECRET is the name of the Secret, which is KAFKA-CLUSTER-NAME-cluster-ca-cert for the cluster CA certificate and KAFKA-CLUSTER-NAME-clients-ca-cert for the clients CA certificate.

    CA-CERTIFICATE is the name of the CA certificate, such as jsonpath={.data.ca\.crt}.

    The command returns a notBefore and notAfter date, which is the validity period for the CA certificate.

    For example, for a cluster CA certificate:

    subject=O = io.strimzi, CN = cluster-ca v0
    issuer=O = io.strimzi, CN = cluster-ca v0
    notBefore=Jun 30 09:43:54 2020 GMT
    notAfter=Jun 30 09:43:54 2021 GMT
  3. Delete old certificates from the Secret.

    When components are using the new certificates, older certificates might still be active. Delete the old certificates to remove any potential security risk.

9.3.4. Replacing private keys used by the CA certificates generated by the Cluster Operator

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

Note

If you are using your own CA certificates, the force-replace annotation cannot be used. Instead, follow the procedure for renewing your own CA certificates.

Prerequisites

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

Procedure

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

    Table 9.14. Commands for replacing private keys

    Private key forSecretAnnotate command

    Cluster CA

    CLUSTER-NAME-cluster-ca

    oc annotate secret CLUSTER-NAME-cluster-ca strimzi.io/force-replace=true

    Clients CA

    CLUSTER-NAME-clients-ca

    oc annotate secret CLUSTER-NAME-clients-ca strimzi.io/force-replace=true

At the next reconciliation the Cluster Operator will:

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

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

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

9.4. TLS connections

9.4.1. ZooKeeper communication

Communication between the ZooKeeper nodes on all ports, as well as between clients and ZooKeeper, is encrypted using TLS.

Communication between Kafka brokers and ZooKeeper nodes is also encrypted.

9.4.2. Kafka inter-broker communication

Communication between Kafka brokers is always encrypted using TLS. The connections between the Kafka controller and brokers use an internal control plane listener on port 9090. Replication of data between brokers, as well as internal connections from AMQ Streams operators, Cruise Control, or the Kafka Exporter use the replication listener on port 9091. These internal listeners are not available to Kafka clients.

9.4.3. Topic and User Operators

All Operators use encryption for communication with both Kafka and ZooKeeper. In Topic and User Operators, a TLS sidecar is used when communicating with ZooKeeper.

9.4.4. Cruise Control

Cruise Control uses encryption for communication with both Kafka and ZooKeeper. A TLS sidecar is used when communicating with ZooKeeper.

9.4.5. Kafka Client connections

Encrypted or unencrypted communication between Kafka brokers and clients is configured using the tls property for spec.kafka.listeners.

9.5. 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 a TLS listener — 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.

Follow the steps to configure trust certificates that are signed by the cluster CA for Java-based Kafka Producer, Consumer, and Streams APIs.

Choose the steps to follow according to the certificate format of the cluster CA: PKCS #12 (.p12) or PEM (.crt).

The steps describe how to mount the Cluster Secret that verifies the identity of the Kafka cluster to the client pod.

Prerequisites

  • The Cluster Operator must be running.
  • There needs to be a Kafka resource within the OpenShift cluster.
  • You need a Kafka client application inside the OpenShift cluster that will connect using TLS, and needs to trust the cluster CA certificate.
  • The client application must be running in the same namespace as the Kafka resource.

Using PKCS #12 format (.p12)

  1. Mount the cluster Secret as a volume when defining the client pod.

    For example:

    kind: Pod
    apiVersion: v1
    metadata:
      name: client-pod
    spec:
      containers:
      - name: client-name
        image: client-name
        volumeMounts:
        - name: secret-volume
          mountPath: /data/p12
        env:
        - name: SECRET_PASSWORD
          valueFrom:
            secretKeyRef:
              name: my-secret
              key: my-password
      volumes:
      - name: secret-volume
        secret:
          secretName: my-cluster-cluster-ca-cert

    Here we’re mounting the following:

    • The PKCS #12 file into an exact path, which can be configured
    • The password into an environment variable, where it can be used for Java configuration
  2. Configure the Kafka client with the following properties:

    • A security protocol option:

      • security.protocol: SSL when using TLS for encryption (with or without mTLS authentication).
      • security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.
    • ssl.truststore.location with the truststore location where the certificates were imported.
    • ssl.truststore.password with the password for accessing the truststore.
    • ssl.truststore.type=PKCS12 to identify the truststore type.

Using PEM format (.crt)

  1. Mount the cluster Secret as a volume when defining the client pod.

    For example:

    kind: Pod
    apiVersion: v1
    metadata:
      name: client-pod
    spec:
      containers:
      - name: client-name
        image: client-name
        volumeMounts:
        - name: secret-volume
          mountPath: /data/crt
      volumes:
      - name: secret-volume
        secret:
          secretName: my-cluster-cluster-ca-cert
  2. Use the extracted certificate to configure a TLS connection in clients that use certificates in X.509 format.

9.6. 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 an external listener – to trust the cluster CA certificate. Follow this procedure when setting up the client and during the renewal period, when the old clients CA certificate is replaced.

Follow the steps to configure trust certificates that are signed by the cluster CA for Java-based Kafka Producer, Consumer, and Streams APIs.

Choose the steps to follow according to the certificate format of the cluster CA: PKCS #12 (.p12) or PEM (.crt).

The steps describe how to obtain the certificate from the Cluster Secret that verifies the identity of the Kafka cluster.

Important

The <cluster_name>-cluster-ca-cert secret contains more than one CA certificate during the CA certificate renewal period. Clients must add all of them to their truststores.

Prerequisites

  • The Cluster Operator must be running.
  • There needs to be a Kafka resource within the OpenShift cluster.
  • You need a Kafka client application outside the OpenShift cluster that will connect using TLS, and needs to trust the cluster CA certificate.

Using PKCS #12 format (.p12)

  1. Extract the cluster CA certificate and password from the <cluster_name>-cluster-ca-cert Secret of the Kafka cluster.

    oc get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
    oc get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.password}' | base64 -d > ca.password

    Replace <cluster_name> with the name of the Kafka cluster.

  2. Configure the Kafka client with the following properties:

    • A security protocol option:

      • security.protocol: SSL when using TLS.
      • security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.
    • ssl.truststore.location with the truststore location where the certificates were imported.
    • ssl.truststore.password with the password for accessing the truststore. This property can be omitted if it is not needed by the truststore.
    • ssl.truststore.type=PKCS12 to identify the truststore type.

Using PEM format (.crt)

  1. Extract the cluster CA certificate from the <cluster_name>-cluster-ca-cert secret of the Kafka cluster.

    oc get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
  2. Use the extracted certificate to configure a TLS connection in clients that use certificates in X.509 format.

9.7. Using your own CA certificates and private keys

Install and use your own CA certificates and private keys instead of using the defaults generated by the Cluster Operator. You can replace the cluster and clients CA certificates and private keys.

You can switch to using your own CA certificates and private keys in the following ways:

  • Install your own CA certificates and private keys before deploying your Kafka cluster
  • Replace the default CA certificates and private keys with your own after deploying a Kafka cluster

The steps to replace the default CA certificates and private keys after deploying a Kafka cluster are the same as those used to renew your own CA certificates and private keys.

If you use your own certificates, they won’t be renewed automatically. You need to renew the CA certificates and private keys before they expire.

Renewal options:

  • Renew the CA certificates only
  • Renew CA certificates and private keys (or replace the defaults)

9.7.1. Installing your own CA certificates and private keys

Install your own CA certificates and private keys instead of using the cluster and clients CA certificates and private keys generated by the Cluster Operator.

By default, AMQ Streams uses the following cluster CA and clients CA secrets, which are renewed automatically.

  • Cluster CA secrets

    • <cluster_name>-cluster-ca
    • <cluster_name>-cluster-ca-cert
  • Clients CA secrets

    • <cluster_name>-clients-ca
    • <cluster_name>-clients-ca-cert

To install your own certificates, use the same names.

Prerequisites

  • The Cluster Operator is running.
  • A Kafka cluster is not yet deployed.

    If you have already deployed a Kafka cluster, you can replace the default CA certificates with your own.

  • 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 using the X509v3 Basic Constraints extension. Basic Constraints limit the path length of a certificate chain.
  • The OpenSSL TLS management tool for converting certificates.

Before you begin

The Cluster Operator generates keys and certificates in PEM (Privacy Enhanced Mail) and PKCS #12 (Public-Key Cryptography Standards) formats. You can add your own certificates in either format.

Some applications cannot use PEM certificates and support only PKCS #12 certificates. If you don’t have a cluster certificate in PKCS #12 format, use the OpenSSL TLS management tool to generate one from your ca.crt file.

Example certificate generation command

openssl pkcs12 -export -in ca.crt -nokeys -out ca.p12 -password pass:<P12_password> -caname ca.crt

Replace <P12_password> with your own password.

Procedure

  1. Create a new secret that contains the CA certificate.

    Client secret creation with a certificate in PEM format only

    oc create secret generic <cluster_name>-clients-ca-cert --from-file=ca.crt=ca.crt

    Cluster secret creation with certificates in PEM and PKCS #12 format

    oc create secret generic <cluster_name>-cluster-ca-cert \
      --from-file=ca.crt=ca.crt \
      --from-file=ca.p12=ca.p12 \
      --from-literal=ca.password=P12-PASSWORD

    Replace <cluster_name> with the name of your Kafka cluster.

  2. Create a new secret that contains the private key.

    oc create secret generic CA-KEY-SECRET --from-file=ca.key=ca.key
  3. Label the secrets.

    oc label secret CA-CERTIFICATE-SECRET strimzi.io/kind=Kafka strimzi.io/cluster=<cluster_name>
    oc label secret CA-KEY-SECRET strimzi.io/kind=Kafka strimzi.io/cluster=<cluster_name>
    • Label strimzi.io/kind=Kafka identifies the Kafka custom resource.
    • Label strimzi.io/cluster=<cluster_name> identifies the Kafka cluster.
  4. Annotate the secrets

    oc annotate secret CA-CERTIFICATE-SECRET strimzi.io/ca-cert-generation=CA-CERTIFICATE-GENERATION
    oc annotate secret CA-KEY-SECRET strimzi.io/ca-key-generation=CA-KEY-GENERATION
    • Annotation strimzi.io/ca-cert-generation=CA-CERTIFICATE-GENERATION defines the generation of a new CA certificate.
    • Annotation strimzi.io/ca-key-generation=CA-KEY-GENERATION defines the generation of a new CA key.

      Start from 0 (zero) as the incremental value (strimzi.io/ca-cert-generation=0) for your own CA certificate. Set a higher incremental value when you renew the certificates.

  5. 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: kafka.strimzi.io/v1beta2
    spec:
      # ...
      clusterCa:
        generateCertificateAuthority: false

9.7.2. Renewing your own CA certificates

If you are using your own CA certificates, you need to renew them manually. The Cluster Operator will not renew them automatically. Renew the CA certificates in the renewal period before they expire.

Perform the steps in this procedure when you are renewing CA certificates and continuing with the same private key. If you are renewing your own CA certificates and private keys, see Section 9.7.3, “Renewing or replacing CA certificates and private keys with your own”.

The procedure describes the renewal of CA certificates in PEM format.

Prerequisites

  • The Cluster Operator is running.
  • You have new cluster or clients X.509 certificates in PEM format.

Procedure

  1. Update the Secret for the CA certificate.

    Edit the existing secret to add the new CA certificate and update the certificate generation annotation value.

    oc edit secret <ca_certificate_secret_name>

    <ca_certificate_secret_name> is the name of the Secret, which is <kafka_cluster_name>-cluster-ca-cert for the cluster CA certificate and <kafka_cluster_name>-clients-ca-cert for the clients CA certificate.

    The following example shows a secret for a cluster CA certificate that’s associated with a Kafka cluster named my-cluster.

    Example secret configuration for a cluster CA certificate

    apiVersion: v1
    kind: Secret
    data:
      ca.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... 1
    metadata:
      annotations:
        strimzi.io/ca-cert-generation: "0" 2
      labels:
        strimzi.io/cluster: my-cluster
        strimzi.io/kind: Kafka
      name: my-cluster-cluster-ca-cert
      #...
    type: Opaque

    1
    Current base64-encoded CA certificate
    2
    Current CA certificate generation annotation value
  2. Encode your new CA certificate into base64.

    cat <path_to_new_certificate> | base64
  3. Update the CA certificate.

    Copy the base64-encoded CA certificate from the previous step as the value for the ca.crt property under data.

  4. Increase the value of the CA certificate generation annotation.

    Update the strimzi.io/ca-cert-generation annotation with a higher incremental value. For example, change strimzi.io/ca-cert-generation=0 to strimzi.io/ca-cert-generation=1. If the Secret is missing the annotation, the value is treated as 0, so add the annotation with a value of 1.

    When AMQ Streams generates certificates, the certificate generation annotation is automatically incremented by the Cluster Operator. For your own CA certificates, set the annotations with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates. The strimzi.io/ca-cert-generation has to be incremented on each CA certificate renewal.

  5. Save the secret with the new CA certificate and certificate generation annotation value.

    Example secret configuration updated with a new CA certificate

    apiVersion: v1
    kind: Secret
    data:
      ca.crt: GCa6LS3RTHeKFiFDGBOUDYFAZ0F... 1
    metadata:
      annotations:
        strimzi.io/ca-cert-generation: "1" 2
      labels:
        strimzi.io/cluster: my-cluster
        strimzi.io/kind: Kafka
      name: my-cluster-cluster-ca-cert
      #...
    type: Opaque

    1
    New base64-encoded CA certificate
    2
    New CA certificate generation annotation value

On the next reconciliation, the Cluster Operator performs a rolling update of ZooKeeper, Kafka, and other components to trust the new CA certificate.

If maintenance time windows are configured, the Cluster Operator will roll the pods at the first reconciliation within the next maintenance time window.

9.7.3. Renewing or replacing CA certificates and private keys with your own

If you are using your own CA certificates and private keys, you need to renew them manually. The Cluster Operator will not renew them automatically. Renew the CA certificates in the renewal period before they expire. You can also use the same procedure to replace the CA certificates and private keys generated by the AMQ Streams operators with your own.

Perform the steps in this procedure when you are renewing or replacing CA certificates and private keys. If you are only renewing your own CA certificates, see Section 9.7.2, “Renewing your own CA certificates”.

The procedure describes the renewal of CA certificates and private keys in PEM format.

Before going through the following steps, make sure that the CN (Common Name) of the new CA certificate is different from the current one. For example, when the Cluster Operator renews certificates automatically it adds a v<version_number> suffix to identify a version. Do the same with your own CA certificate by adding a different suffix on each renewal. By using a different key to generate a new CA certificate, you retain the current CA certificate stored in the Secret.

Prerequisites

  • The Cluster Operator is running.
  • You have new cluster or clients X.509 certificates and keys in PEM format.

Procedure

  1. Pause the reconciliation of the Kafka custom resource.

    1. Annotate the custom resource in OpenShift, setting the pause-reconciliation annotation to true:

      oc annotate Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation="true"

      For example, for a Kafka custom resource named my-cluster:

      oc annotate Kafka my-cluster strimzi.io/pause-reconciliation="true"
    2. Check that the status conditions of the custom resource show a change to ReconciliationPaused:

      oc describe Kafka <name_of_custom_resource>

      The type condition changes to ReconciliationPaused at the lastTransitionTime.

  2. Update the Secret for the CA certificate.

    1. Edit the existing secret to add the new CA certificate and update the certificate generation annotation value.

      oc edit secret <ca_certificate_secret_name>

      <ca_certificate_secret_name> is the name of the Secret, which is KAFKA-CLUSTER-NAME-cluster-ca-cert for the cluster CA certificate and KAFKA-CLUSTER-NAME-clients-ca-cert for the clients CA certificate.

      The following example shows a secret for a cluster CA certificate that’s associated with a Kafka cluster named my-cluster.

      Example secret configuration for a cluster CA certificate

      apiVersion: v1
      kind: Secret
      data:
        ca.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... 1
      metadata:
        annotations:
          strimzi.io/ca-cert-generation: "0" 2
        labels:
          strimzi.io/cluster: my-cluster
          strimzi.io/kind: Kafka
        name: my-cluster-cluster-ca-cert
        #...
      type: Opaque

      1
      Current base64-encoded CA certificate
      2
      Current CA certificate generation annotation value
    2. Rename the current CA certificate to retain it.

      Rename the current ca.crt property under data as ca-<date>.crt, where <date> is the certificate expiry date in the format YEAR-MONTH-DAYTHOUR-MINUTE-SECONDZ. For example ca-2022-01-26T17-32-00Z.crt:. Leave the value for the property as it is to retain the current CA certificate.

    3. Encode your new CA certificate into base64.

      cat <path_to_new_certificate> | base64
    4. Update the CA certificate.

      Create a new ca.crt property under data and copy the base64-encoded CA certificate from the previous step as the value for ca.crt property.

    5. Increase the value of the CA certificate generation annotation.

      Update the strimzi.io/ca-cert-generation annotation with a higher incremental value. For example, change strimzi.io/ca-cert-generation=0 to strimzi.io/ca-cert-generation=1. If the Secret is missing the annotation, the value is treated as 0, so add the annotation with a value of 1.

      When AMQ Streams generates certificates, the certificate generation annotation is automatically incremented by the Cluster Operator. For your own CA certificates, set the annotations with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates. The strimzi.io/ca-cert-generation has to be incremented on each CA certificate renewal.

    6. Save the secret with the new CA certificate and certificate generation annotation value.

      Example secret configuration updated with a new CA certificate

      apiVersion: v1
      kind: Secret
      data:
        ca.crt: GCa6LS3RTHeKFiFDGBOUDYFAZ0F... 1
        ca-2022-01-26T17-32-00Z.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... 2
      metadata:
        annotations:
          strimzi.io/ca-cert-generation: "1" 3
        labels:
          strimzi.io/cluster: my-cluster
          strimzi.io/kind: Kafka
        name: my-cluster-cluster-ca-cert
        #...
      type: Opaque

      1
      New base64-encoded CA certificate
      2
      Old base64-encoded CA certificate
      3
      New CA certificate generation annotation value
  3. Update the Secret for the CA key used to sign your new CA certificate.

    1. Edit the existing secret to add the new CA key and update the key generation annotation value.

      oc edit secret <ca_key_name>

      <ca_key_name> is the name of CA key, which is <kafka_cluster_name>-cluster-ca for the cluster CA key and <kafka_cluster_name>-clients-ca for the clients CA key.

      The following example shows a secret for a cluster CA key that’s associated with a Kafka cluster named my-cluster.

      Example secret configuration for a cluster CA key

      apiVersion: v1
      kind: Secret
      data:
        ca.key: SA1cKF1GFDzOIiPOIUQBHDNFGDFS... 1
      metadata:
        annotations:
          strimzi.io/ca-key-generation: "0" 2
        labels:
          strimzi.io/cluster: my-cluster
          strimzi.io/kind: Kafka
        name: my-cluster-cluster-ca
        #...
      type: Opaque

      1
      Current base64-encoded CA key
      2
      Current CA key generation annotation value
    2. Encode the CA key into base64.

      cat <path_to_new_key> | base64
    3. Update the CA key.

      Copy the base64-encoded CA key from the previous step as the value for the ca.key property under data.

    4. Increase the value of the CA key generation annotation.

      Update the strimzi.io/ca-key-generation annotation with a higher incremental value. For example, change strimzi.io/ca-key-generation=0 to strimzi.io/ca-key-generation=1. If the Secret is missing the annotation, it is treated as 0, so add the annotation with a value of 1.

      When AMQ Streams generates certificates, the key generation annotation is automatically incremented by the Cluster Operator. For your own CA certificates together with a new CA key, set the annotation with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates and keys. The strimzi.io/ca-key-generation has to be incremented on each CA certificate renewal.

  4. Save the secret with the new CA key and key generation annotation value.

    Example secret configuration updated with a new CA key

    apiVersion: v1
    kind: Secret
    data:
      ca.key: AB0cKF1GFDzOIiPOIUQWERZJQ0F... 1
    metadata:
      annotations:
        strimzi.io/ca-key-generation: "1" 2
      labels:
        strimzi.io/cluster: my-cluster
        strimzi.io/kind: Kafka
      name: my-cluster-cluster-ca
      #...
    type: Opaque

    1
    New base64-encoded CA key
    2
    New CA key generation annotation value
  5. Resume from the pause.

    To resume the Kafka custom resource reconciliation, set the pause-reconciliation annotation to false.

    oc annotate --overwrite Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation="false"

    You can also do the same by removing the pause-reconciliation annotation.

    oc annotate Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation-

On the next reconciliation, the Cluster Operator performs a rolling update of ZooKeeper, Kafka, and other components to trust the new CA certificate. When the rolling update is complete, the Cluster Operator will start a new one to generate new server certificates signed by the new CA key.

If maintenance time windows are configured, the Cluster Operator will roll the pods at the first reconciliation within the next maintenance time window.

Chapter 10. Scaling clusters by adding or removing brokers

Scaling Kafka clusters by adding brokers can increase the performance and reliability of the cluster. Adding more brokers increases available resources, allowing the cluster to handle larger workloads and process more messages. It can also improve fault tolerance by providing more replicas and backups. Conversely, removing underutilized brokers can reduce resource consumption and improve efficiency. Scaling must be done carefully to avoid disruption or data loss. By redistributing partitions across all brokers in the cluster, the resource utilization of each broker is reduced, which can increase the overall throughput of the cluster.

Note

To increase the throughput of a Kafka topic, you can increase the number of partitions for that topic. This allows the load of the topic to be shared between different brokers in the cluster. However, if every broker is constrained by a specific resource (such as I/O), adding more partitions will not increase the throughput. In this case, you need to add more brokers to the cluster.

Adjusting the Kafka.spec.kafka.replicas configuration affects the number of brokers in the cluster that act as replicas. The actual replication factor for topics is determined by settings for the default.replication.factor and min.insync.replicas, and the number of available brokers. For example, a replication factor of 3 means that each partition of a topic is replicated across three brokers, ensuring fault tolerance in the event of a broker failure.

Example replica configuration

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    replicas: 3
    # ...
  config: 1
      # ...
      default.replication.factor: 3
      min.insync.replicas: 2
 # ...

When you add add or remove brokers, Kafka does not automatically reassign partitions. The best way to do this is using Cruise Control. You can use Cruise Control’s add-brokers and remove-brokers modes when scaling a cluster up or down.

  • Use the add-brokers mode after scaling up a Kafka cluster to move partition replicas from existing brokers to the newly added brokers.
  • Use the remove-brokers mode before scaling down a Kafka cluster to move partition replicas off the brokers that are going to be removed.
Note

When scaling down brokers, you cannot specify which specific pod to remove from the cluster. Instead, the broker removal process starts from the highest numbered pod.

Chapter 11. Rebalancing clusters using Cruise Control

Cruise Control is an open source system that supports the following Kafka operations:

  • Monitoring cluster workload
  • Rebalancing a cluster based on predefined constraints

The operations help with running a more balanced Kafka cluster that uses broker pods more efficiently.

A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might not be evenly distributed across the available brokers. To rebalance the cluster, administrators must monitor the load on brokers and manually reassign busy partitions to brokers with spare capacity.

Cruise Control automates the cluster rebalancing process. It constructs a workload model of resource utilization for the cluster—​based on CPU, disk, and network load—​and generates optimization proposals (that you can approve or reject) for more balanced partition assignments. A set of configurable optimization goals is used to calculate these proposals.

You can generate optimization proposals in specific modes. The default full mode rebalances partitions across all brokers. You can also use the add-brokers and remove-brokers modes to accommodate changes when scaling a cluster up or down.

When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster. You configure and generate optimization proposals using a KafkaRebalance resource. You can configure the resource using an annotation so that optimization proposals are approved automatically or manually.

11.1. Cruise Control components and features

Cruise Control consists of four main components—​the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor—​and a REST API for client interactions. AMQ Streams utilizes the REST API to support the following Cruise Control features:

  • Generating optimization proposals from optimization goals.
  • Rebalancing a Kafka cluster based on an optimization proposal.
Optimization goals

An optimization goal describes a specific objective to achieve from a rebalance. For example, a goal might be to distribute topic replicas across brokers more evenly. You can change what goals to include through configuration. A goal is defined as a hard goal or soft goal. You can add hard goals through Cruise Control deployment configuration. You also have main, default, and user-provided goals that fit into each of these categories.

  • Hard goals are preset and must be satisfied for an optimization proposal to be successful.
  • Soft goals do not need to be satisfied for an optimization proposal to be successful. They can be set aside if it means that all hard goals are met.
  • Main goals are inherited from Cruise Control. Some are preset as hard goals. Main goals are used in optimization proposals by default.
  • Default goals are the same as the main goals by default. You can specify your own set of default goals.
  • User-provided goals are a subset of default goals that are configured for generating a specific optimization proposal.
Optimization proposals

Optimization proposals comprise the goals you want to achieve from a rebalance. You generate an optimization proposal to create a summary of proposed changes and the results that are possible with the rebalance. The goals are assessed in a specific order of priority. You can then choose to approve or reject the proposal. You can reject the proposal to run it again with an adjusted set of goals.

You can generate an optimization proposal in one of three modes.

  • full is the default mode and runs a full rebalance.
  • add-brokers is the mode you use after adding brokers when scaling up a Kafka cluster.
  • remove-brokers is the mode you use before removing brokers when scaling down a Kafka cluster.

Other Cruise Control features are not currently supported, including self healing, notifications, write-your-own goals, and changing the topic replication factor.

Additional resources

11.2. Optimization goals overview

Optimization goals are constraints on workload redistribution and resource utilization across a Kafka cluster. To rebalance a Kafka cluster, Cruise Control uses optimization goals to generate optimization proposals, which you can approve or reject.

11.2.1. Goals order of priority

AMQ Streams supports most of the optimization goals developed in the Cruise Control project. The supported goals, in the default descending order of priority, are as follows:

  1. Rack-awareness
  2. Minimum number of leader replicas per broker for a set of topics
  3. Replica capacity
  4. Capacity goals

    • Disk capacity
    • Network inbound capacity
    • Network outbound capacity
    • CPU capacity
  5. Replica distribution
  6. Potential network output
  7. Resource distribution goals

    • Disk utilization distribution
    • Network inbound utilization distribution
    • Network outbound utilization distribution
    • CPU utilization distribution
  8. Leader bytes-in rate distribution
  9. Topic replica distribution
  10. Leader replica distribution
  11. Preferred leader election
  12. Intra-broker disk capacity
  13. Intra-broker disk usage distribution

For more information on each optimization goal, see Goals in the Cruise Control Wiki.

Note

"Write your own" goals and Kafka assigner goals are not yet supported.

11.2.2. Goals configuration in AMQ Streams custom resources

You configure optimization goals in Kafka and KafkaRebalance custom resources. Cruise Control has configurations for hard optimization goals that must be satisfied, as well as main, default, and user-provided optimization goals.

You can specify optimization goals in the following configuration:

  • Main goals — Kafka.spec.cruiseControl.config.goals
  • Hard goals — Kafka.spec.cruiseControl.config.hard.goals
  • Default goals — Kafka.spec.cruiseControl.config.default.goals
  • User-provided goals — KafkaRebalance.spec.goals
Note

Resource distribution goals are subject to capacity limits on broker resources.

11.2.3. Hard and soft optimization goals

Hard goals are goals that must be satisfied in optimization proposals. Goals that are not configured as hard goals are known as soft goals. You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations. An optimization proposal that violates one or more soft goals, but satisfies all hard goals, is valid.

Cruise Control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by Cruise Control and not sent to the user for approval.

Note

For example, you might have a soft goal to distribute a topic’s replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met.

In Cruise Control, the following main optimization goals are preset as hard goals:

RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal

You configure hard goals in the Cruise Control deployment configuration, by editing the hard.goals property in Kafka.spec.cruiseControl.config.

  • To inherit the preset hard goals from Cruise Control, do not specify the hard.goals property in Kafka.spec.cruiseControl.config
  • To change the preset hard goals, specify the desired goals in the hard.goals property, using their fully-qualified domain names.

Example Kafka configuration for hard optimization goals

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl:
    brokerCapacity:
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    config:
      # Note that `default.goals` (superset) must also include all `hard.goals` (subset)
      default.goals: >
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal
      hard.goals: >
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal
      # ...

Increasing the number of configured hard goals will reduce the likelihood of Cruise Control generating valid optimization proposals.

If skipHardGoalCheck: true is specified in the KafkaRebalance custom resource, Cruise Control does not check that the list of user-provided optimization goals (in KafkaRebalance.spec.goals) contains all the configured hard goals (hard.goals). Therefore, if some, but not all, of the user-provided optimization goals are in the hard.goals list, Cruise Control will still treat them as hard goals even if skipHardGoalCheck: true is specified.

11.2.4. Main optimization goals

The main optimization goals are available to all users. Goals that are not listed in the main optimization goals are not available for use in Cruise Control operations.

Unless you change the Cruise Control deployment configuration, AMQ Streams will inherit the following main optimization goals from Cruise Control, in descending priority order:

RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal

Some of these goals are preset as hard goals.

To reduce complexity, we recommend that you use the inherited main optimization goals, unless you need to completely exclude one or more goals from use in KafkaRebalance resources. The priority order of the main optimization goals can be modified, if desired, in the configuration for default optimization goals.

You configure main optimization goals, if necessary, in the Cruise Control deployment configuration: Kafka.spec.cruiseControl.config.goals

  • To accept the inherited main optimization goals, do not specify the goals property in Kafka.spec.cruiseControl.config.
  • If you need to modify the inherited main optimization goals, specify a list of goals, in descending priority order, in the goals configuration option.
Note

To avoid errors when generating optimization proposals, make sure that any changes you make to the goals or default.goals in Kafka.spec.cruiseControl.config include all of the hard goals specified for the hard.goals property. To clarify, the hard goals must also be specified (as a subset) for the main optimization goals and default goals.

11.2.5. Default optimization goals

Cruise Control uses the default optimization goals to generate the cached optimization proposal. For more information about the cached optimization proposal, see Section 11.3, “Optimization proposals overview”.

You can override the default optimization goals by setting user-provided optimization goals in a KafkaRebalance custom resource.

Unless you specify default.goals in the Cruise Control deployment configuration, the main optimization goals are used as the default optimization goals. In this case, the cached optimization proposal is generated using the main optimization goals.

  • To use the main optimization goals as the default goals, do not specify the default.goals property in Kafka.spec.cruiseControl.config.
  • To modify the default optimization goals, edit the default.goals property in Kafka.spec.cruiseControl.config. You must use a subset of the main optimization goals.

Example Kafka configuration for default optimization goals

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl:
    brokerCapacity:
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    config:
      # Note that `default.goals` (superset) must also include all `hard.goals` (subset)
      default.goals: >
        com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
        com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,
        com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal
      hard.goals: >
        com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal
      # ...

If no default optimization goals are specified, the cached proposal is generated using the main optimization goals.

11.2.6. User-provided optimization goals

User-provided optimization goals narrow down the configured default goals for a particular optimization proposal. You can set them, as required, in spec.goals in a KafkaRebalance custom resource:

KafkaRebalance.spec.goals

User-provided optimization goals can generate optimization proposals for different scenarios. For example, you might want to optimize leader replica distribution across the Kafka cluster without considering disk capacity or disk utilization. So, you create a KafkaRebalance custom resource containing a single user-provided goal for leader replica distribution.

User-provided optimization goals must:

  • Include all configured hard goals, or an error occurs
  • Be a subset of the main optimization goals

To ignore the configured hard goals when generating an optimization proposal, add the skipHardGoalCheck: true property to the KafkaRebalance custom resource. See Section 11.6, “Generating optimization proposals”.

Additional resources

11.3. Optimization proposals overview

Configure a KafkaRebalance resource to generate optimization proposals and apply the suggested changes. An optimization proposal is a summary of proposed changes that would produce a more balanced Kafka cluster, with partition workloads distributed more evenly among the brokers.

Each optimization proposal is based on the set of optimization goals that was used to generate it, subject to any configured capacity limits on broker resources.

All optimization proposals are estimates of the impact of a proposed rebalance. You can approve or reject a proposal. You cannot approve a cluster rebalance without first generating the optimization proposal.

You can run optimization proposals in one of the following rebalancing modes:

  • full
  • add-brokers
  • remove-brokers

11.3.1. Rebalancing modes

You specify a rebalancing mode using the spec.mode property of the KafkaRebalance custom resource.

full
The full mode runs a full rebalance by moving replicas across all the brokers in the cluster. This is the default mode if the spec.mode property is not defined in the KafkaRebalance custom resource.
add-brokers
The add-brokers mode is used after scaling up a Kafka cluster by adding one or more brokers. Normally, after scaling up a Kafka cluster, new brokers are used to host only the partitions of newly created topics. If no new topics are created, the newly added brokers are not used and the existing brokers remain under the same load. By using the add-brokers mode immediately after adding brokers to the cluster, the rebalancing operation moves replicas from existing brokers to the newly added brokers. You specify the new brokers as a list using the spec.brokers property of the KafkaRebalance custom resource.
remove-brokers
The remove-brokers mode is used before scaling down a Kafka cluster by removing one or more brokers. If you scale down a Kafka cluster, brokers are shut down even if they host replicas. This can lead to under-replicated partitions and possibly result in some partitions being under their minimum ISR (in-sync replicas). To avoid this potential problem, the remove-brokers mode moves replicas off the brokers that are going to be removed. When these brokers are not hosting replicas anymore, you can safely run the scaling down operation. You specify the brokers you’re removing as a list in the spec.brokers property in the KafkaRebalance custom resource.

In general, use the full rebalance mode to rebalance a Kafka cluster by spreading the load across brokers. Use the add-brokers and remove-brokers modes only if you want to scale your cluster up or down and rebalance the replicas accordingly.

The procedure to run a rebalance is actually the same across the three different modes. The only difference is with specifying a mode through the spec.mode property and, if needed, listing brokers that have been added or will be removed through the spec.brokers property.

11.3.2. The results of an optimization proposal

When an optimization proposal is generated, a summary and broker load is returned.

Summary
The summary is contained in the KafkaRebalance resource. The summary provides an overview of the proposed cluster rebalance and indicates the scale of the changes involved. A summary of a successfully generated optimization proposal is contained in the Status.OptimizationResult property of the KafkaRebalance resource. The information provided is a summary of the full optimization proposal.
Broker load
The broker load is stored in a ConfigMap that contains data as a JSON string. The broker load shows before and after values for the proposed rebalance, so you can see the impact on each of the brokers in the cluster.

11.3.3. Manually approving or rejecting an optimization proposal

An optimization proposal summary shows the proposed scope of changes.

You can use the name of the KafkaRebalance resource to return a summary from the command line.

Returning an optimization proposal summary

oc describe kafkarebalance <kafka_rebalance_resource_name> -n <namespace>

You can also use the jq command line JSON parser tool.

Returning an optimization proposal summary using jq

oc get kafkarebalance -o json | jq <jq_query>.

Use the summary to decide whether to approve or reject an optimization proposal.

Approving an optimization proposal
You approve the optimization proposal by setting the strimzi.io/rebalance annotation of the KafkaRebalance resource to approve. Cruise Control applies the proposal to the Kafka cluster and starts a cluster rebalance operation.
Rejecting an optimization proposal
If you choose not to approve an optimization proposal, you can change the optimization goals or update any of the rebalance performance tuning options, and then generate another proposal. You can use the strimzi.io/refresh annotation to generate a new optimization proposal for a KafkaRebalance resource.

Use optimization proposals to assess the movements required for a rebalance. For example, a summary describes inter-broker and intra-broker movements. Inter-broker rebalancing moves data between separate brokers. Intra-broker rebalancing moves data between disks on the same broker when you are using a JBOD storage configuration. Such information can be useful even if you don’t go ahead and approve the proposal.

You might reject an optimization proposal, or delay its approval, because of the additional load on a Kafka cluster when rebalancing.

In the following example, the proposal suggests the rebalancing of data between separate brokers. The rebalance involves the movement of 55 partition replicas, totaling 12MB of data, across the brokers. Though the inter-broker movement of partition replicas has a high impact on performance, the total amount of data is not large. If the total data was much larger, you could reject the proposal, or time when to approve the rebalance to limit the impact on the performance of the Kafka cluster.

Rebalance performance tuning options can help reduce the impact of data movement. If you can extend the rebalance period, you can divide the rebalance into smaller batches. Fewer data movements at a single time reduces the load on the cluster.

Example optimization proposal summary

Name:         my-rebalance
Namespace:    myproject
Labels:       strimzi.io/cluster=my-cluster
Annotations:  API Version:  kafka.strimzi.io/v1alpha1
Kind:         KafkaRebalance
Metadata:
# ...
Status:
  Conditions:
    Last Transition Time:  2022-04-05T14:36:11.900Z
    Status:                ProposalReady
    Type:                  State
  Observed Generation:     1
  Optimization Result:
    Data To Move MB:  0
    Excluded Brokers For Leadership:
    Excluded Brokers For Replica Move:
    Excluded Topics:
    Intra Broker Data To Move MB:         12
    Monitored Partitions Percentage:      100
    Num Intra Broker Replica Movements:   0
    Num Leader Movements:                 24
    Num Replica Movements:                55
    On Demand Balancedness Score After:   82.91290759174306
    On Demand Balancedness Score Before:  78.01176356230222
    Recent Windows:                       5
  Session Id:                             a4f833bd-2055-4213-bfdd-ad21f95bf184

The proposal will also move 24 partition leaders to different brokers. This requires a change to the ZooKeeper configuration, which has a low impact on performance.

The balancedness scores are measurements of the overall balance of the Kafka cluster before and after the optimization proposal is approved. A balancedness score is based on optimization goals. If all goals are satisfied, the score is 100. The score is reduced for each goal that will not be met. Compare the balancedness scores to see whether the Kafka cluster is less balanced than it could be following a rebalance.

11.3.4. Automatically approving an optimization proposal

To save time, you can automate the process of approving optimization proposals. With automation, when you generate an optimization proposal it goes straight into a cluster rebalance.

To enable the optimization proposal auto-approval mechanism, create the KafkaRebalance resource with the strimzi.io/rebalance-auto-approval annotation set to true. If the annotation is not set or set to false, the optimization proposal requires manual approval.

Example rebalance request with auto-approval mechanism enabled

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
  annotations:
    strimzi.io/rebalance-auto-approval: "true"
spec:
  mode: # any mode
  # ...

You can still check the status when automatically approving an optimization proposal. The status of the KafkaRebalance resource moves to Ready when the rebalance is complete.

11.3.5. Optimization proposal summary properties

The following table explains the properties contained in the optimization proposal’s summary section.

Table 11.1. Properties contained in an optimization proposal summary

JSON propertyDescription

numIntraBrokerReplicaMovements

The total number of partition replicas that will be transferred between the disks of the cluster’s brokers.

Performance impact during rebalance operation: Relatively high, but lower than numReplicaMovements.

excludedBrokersForLeadership

Not yet supported. An empty list is returned.

numReplicaMovements

The number of partition replicas that will be moved between separate brokers.

Performance impact during rebalance operation: Relatively high.

onDemandBalancednessScoreBefore, onDemandBalancednessScoreAfter

A measurement of the overall balancedness of a Kafka Cluster, before and after the optimization proposal was generated.

The score is calculated by subtracting the sum of the BalancednessScore of each violated soft goal from 100. Cruise Control assigns a BalancednessScore to every optimization goal based on several factors, including priority—​the goal’s position in the list of default.goals or user-provided goals.

The Before score is based on the current configuration of the Kafka cluster. The After score is based on the generated optimization proposal.

intraBrokerDataToMoveMB

The sum of the size of each partition replica that will be moved between disks on the same broker (see also numIntraBrokerReplicaMovements).

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see dataToMoveMB).

recentWindows

The number of metrics windows upon which the optimization proposal is based.

dataToMoveMB

The sum of the size of each partition replica that will be moved to a separate broker (see also numReplicaMovements).

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete.

monitoredPartitionsPercentage

The percentage of partitions in the Kafka cluster covered by the optimization proposal. Affected by the number of excludedTopics.

excludedTopics

If you specified a regular expression in the spec.excludedTopicsRegex property in the KafkaRebalance resource, all topic names matching that expression are listed here. These topics are excluded from the calculation of partition replica/leader movements in the optimization proposal.

numLeaderMovements

The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration.

Performance impact during rebalance operation: Relatively low.

excludedBrokersForReplicaMove

Not yet supported. An empty list is returned.

11.3.6. Broker load properties

The broker load is stored in a ConfigMap (with the same name as the KafkaRebalance custom resource) as a JSON formatted string. This JSON string consists of a JSON object with keys for each broker IDs linking to a number of metrics for each broker. Each metric consist of three values. The first is the metric value before the optimization proposal is applied, the second is the expected value of the metric after the proposal is applied, and the third is the difference between the first two values (after minus before).

Note

The ConfigMap appears when the KafkaRebalance resource is in the ProposalReady state and remains after the rebalance is complete.

You can use the name of the ConfigMap to view its data from the command line.

Returning ConfigMap data

oc describe configmaps <my_rebalance_configmap_name> -n <namespace>

You can also use the jq command line JSON parser tool to extract the JSON string from the ConfigMap.

Extracting the JSON string from the ConfigMap using jq

oc get configmaps <my_rebalance_configmap_name> -o json | jq '.["data"]["brokerLoad.json"]|fromjson|.'

The following table explains the properties contained in the optimization proposal’s broker load ConfigMap:

JSON propertyDescription

leaders

The number of replicas on this broker that are partition leaders.

replicas

The number of replicas on this broker.

cpuPercentage

The CPU utilization as a percentage of the defined capacity.

diskUsedPercentage

The disk utilization as a percentage of the defined capacity.

diskUsedMB

The absolute disk usage in MB.

networkOutRate

The total network output rate for the broker.

leaderNetworkInRate

The network input rate for all partition leader replicas on this broker.

followerNetworkInRate

The network input rate for all follower replicas on this broker.

potentialMaxNetworkOutRate

The hypothetical maximum network output rate that would be realized if this broker became the leader of all the replicas it currently hosts.

11.3.7. Cached optimization proposal

Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster. If you generate an optimization proposal using the default optimization goals, Cruise Control returns the most recent cached proposal.

To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms setting in the Cruise Control deployment configuration. Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server.

11.4. Rebalance performance tuning overview

You can adjust several performance tuning options for cluster rebalances. These options control how partition replica and leadership movements in a rebalance are executed, as well as the bandwidth that is allocated to a rebalance operation.

11.4.1. Partition reassignment commands

Optimization proposals are comprised of separate partition reassignment commands. When you approve a proposal, the Cruise Control server applies these commands to the Kafka cluster.

A partition reassignment command consists of either of the following types of operations:

  • Partition movement: Involves transferring the partition replica and its data to a new location. Partition movements can take one of two forms:

    • Inter-broker movement: The partition replica is moved to a log directory on a different broker.
    • Intra-broker movement: The partition replica is moved to a different log directory on the same broker.
  • Leadership movement: This involves switching the leader of the partition’s replicas.

Cruise Control issues partition reassignment commands to the Kafka cluster in batches. The performance of the cluster during the rebalance is affected by the number of each type of movement contained in each batch.

11.4.2. Replica movement strategies

Cluster rebalance performance is also influenced by the replica movement strategy that is applied to the batches of partition reassignment commands. By default, Cruise Control uses the BaseReplicaMovementStrategy, which simply applies the commands in the order they were generated. However, if there are some very large partition reassignments early in the proposal, this strategy can slow down the application of the other reassignments.

Cruise Control provides four alternative replica movement strategies that can be applied to optimization proposals:

  • PrioritizeSmallReplicaMovementStrategy: Order reassignments in order of ascending size.
  • PrioritizeLargeReplicaMovementStrategy: Order reassignments in order of descending size.
  • PostponeUrpReplicaMovementStrategy: Prioritize reassignments for replicas of partitions which have no out-of-sync replicas.
  • PrioritizeMinIsrWithOfflineReplicasStrategy: Prioritize reassignments with (At/Under)MinISR partitions with offline replicas. This strategy will only work if cruiseControl.config.concurrency.adjuster.min.isr.check.enabled is set to true in the Kafka custom resource’s spec.

These strategies can be configured as a sequence. The first strategy attempts to compare two partition reassignments using its internal logic. If the reassignments are equivalent, then it passes them to the next strategy in the sequence to decide the order, and so on.

11.4.3. Intra-broker disk balancing

Moving a large amount of data between disks on the same broker has less impact than between separate brokers. If you are running a Kafka deployment that uses JBOD storage with multiple disks on the same broker, Cruise Control can balance partitions between the disks.

Note

If you are using JBOD storage with a single disk, intra-broker disk balancing will result in a proposal with 0 partition movements since there are no disks to balance between.

To perform an intra-broker disk balance, set rebalanceDisk to true under the KafkaRebalance.spec. When setting rebalanceDisk to true, do not set a goals field in the KafkaRebalance.spec, as Cruise Control will automatically set the intra-broker goals and ignore the inter-broker goals. Cruise Control does not perform inter-broker and intra-broker balancing at the same time.

11.4.4. Rebalance tuning options

Cruise Control provides several configuration options for tuning the rebalance parameters discussed above. You can set these tuning options when configuring and deploying Cruise Control with Kafka or optimization proposal levels:

  • The Cruise Control server setting can be set in the Kafka custom resource under Kafka.spec.cruiseControl.config.
  • The individual rebalance performance configurations can be set under KafkaRebalance.spec.

The relevant configurations are summarized in the following table.

Table 11.2. Rebalance performance tuning configuration

Cruise Control propertiesKafkaRebalance propertiesDefaultDescription

num.concurrent.partition.movements.per.broker

concurrentPartitionMovementsPerBroker

5

The maximum number of inter-broker partition movements in each partition reassignment batch

num.concurrent.intra.broker.partition.movements

concurrentIntraBrokerPartitionMovements

2

The maximum number of intra-broker partition movements in each partition reassignment batch

num.concurrent.leader.movements

concurrentLeaderMovements

1000

The maximum number of partition leadership changes in each partition reassignment batch

default.replication.throttle

replicationThrottle

Null (no limit)

The bandwidth (in bytes per second) to assign to partition reassignment

default.replica.movement.strategies

replicaMovementStrategies

BaseReplicaMovementStrategy

The list of strategies (in priority order) used to determine the order in which partition reassignment commands are executed for generated proposals. For the server setting, use a comma separated string with the fully qualified names of the strategy class (add com.linkedin.kafka.cruisecontrol.executor.strategy. to the start of each class name). For the KafkaRebalance resource setting use a YAML array of strategy class names.

-

rebalanceDisk

false

Enables intra-broker disk balancing, which balances disk space utilization between disks on the same broker. Only applies to Kafka deployments that use JBOD storage with multiple disks.

Changing the default settings affects the length of time that the rebalance takes to complete, as well as the load placed on the Kafka cluster during the rebalance. Using lower values reduces the load but increases the amount of time taken, and vice versa.

11.5. Configuring and deploying Cruise Control with Kafka

Configure a Kafka resource to deploy Cruise Control alongside a Kafka cluster. You can use the cruiseControl properties of the Kafka resource to configure the deployment. Deploy one instance of Cruise Control per Kafka cluster.

Use goals configuration in the Cruise Control config to specify optimization goals for generating optimization proposals. You can use brokerCapacity to change the default capacity limits for goals related to resource distribution. If brokers are running on nodes with heterogeneous network resources, you can use overrides to set network capacity limits for each broker.

If an empty object ({}) is used for the cruiseControl configuration, all properties use their default values.

For more information on the configuration options for Cruise Control, see the Custom resource API reference.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the cruiseControl property for the Kafka resource.

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

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      # ...
      cruiseControl:
        brokerCapacity: 1
          inboundNetwork: 10000KB/s
          outboundNetwork: 10000KB/s
          overrides: 2
          - brokers: [0]
            inboundNetwork: 20000KiB/s
            outboundNetwork: 20000KiB/s
          - brokers: [1, 2]
            inboundNetwork: 30000KiB/s
            outboundNetwork: 30000KiB/s
          # ...
        config: 3
          # Note that `default.goals` (superset) must also include all `hard.goals` (subset)
          default.goals: > 4
            com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
            com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,
            com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal
            # ...
          hard.goals: >
            com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal
            # ...
          cpu.balance.threshold: 1.1
          metadata.max.age.ms: 300000
          send.buffer.bytes: 131072
          webserver.http.cors.enabled: true 5
          webserver.http.cors.origin: "*"
          webserver.http.cors.exposeheaders: "User-Task-ID,Content-Type"
          # ...
        resources: 6
          requests:
            cpu: 1
            memory: 512Mi
          limits:
            cpu: 2
            memory: 2Gi
        logging: 7
            type: inline
            loggers:
              rootLogger.level: "INFO"
        template: 8
          pod:
            metadata:
              labels:
                label1: value1
            securityContext:
              runAsUser: 1000001
              fsGroup: 0
            terminationGracePeriodSeconds: 120
        readinessProbe: 9
          initialDelaySeconds: 15
          timeoutSeconds: 5
        livenessProbe:
          initialDelaySeconds: 15
          timeoutSeconds: 5
        metricsConfig: 10
          type: jmxPrometheusExporter
          valueFrom:
            configMapKeyRef:
              name: cruise-control-metrics
              key: metrics-config.yml
    # ...
    1
    Capacity limits for broker resources.
    2
    Overrides set network capacity limits for specific brokers when running on nodes with heterogeneous network resources.
    3
    Cruise Control configuration. Standard Cruise Control configuration may be provided, restricted to those properties not managed directly by AMQ Streams.
    4
    Optimization goals configuration, which can include configuration for default optimization goals (default.goals), main optimization goals (goals), and hard goals (hard.goals).
    5
    CORS enabled and configured for read-only access to the Cruise Control API.
    6
    Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
    7
    Cruise Control loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties key. Cruise Control has a single logger named rootLogger.level. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
    8
    Template customization. Here a pod is scheduled with additional security attributes.
    9
    Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
    10
    Prometheus metrics enabled. In this example, metrics are configured for the Prometheus JMX Exporter (the default metrics exporter).
  2. Create or update the resource:

    oc apply -f <kafka_configuration_file>
  3. Check the status of the deployment:

    oc get deployments -n <my_cluster_operator_namespace>

    Output shows the deployment name and readiness

    NAME                      READY  UP-TO-DATE  AVAILABLE
    my-cluster-cruise-control 1/1    1           1

    my-cluster is the name of the Kafka cluster.

    READY shows the number of replicas that are ready/expected. The deployment is successful when the AVAILABLE output shows 1.

Auto-created topics

The following table shows the three topics that are automatically created when Cruise Control is deployed. These topics are required for Cruise Control to work properly and must not be deleted or changed. You can change the name of the topic using the specified configuration option.

Table 11.3. Auto-created topics

Auto-created topic configurationDefault topic nameCreated byFunction

metric.reporter.topic

strimzi.cruisecontrol.metrics

AMQ Streams Metrics Reporter

Stores the raw metrics from the Metrics Reporter in each Kafka broker.

partition.metric.sample.store.topic

strimzi.cruisecontrol.partitionmetricsamples

Cruise Control

Stores the derived metrics for each partition. These are created by the Metric Sample Aggregator.

broker.metric.sample.store.topic

strimzi.cruisecontrol.modeltrainingsamples

Cruise Control

Stores the metrics samples used to create the Cluster Workload Model.

To prevent the removal of records that are needed by Cruise Control, log compaction is disabled in the auto-created topics.

Note

If the names of the auto-created topics are changed in a Kafka cluster that already has Cruise Control enabled, the old topics will not be deleted and should be manually removed.

What to do next

After configuring and deploying Cruise Control, you can generate optimization proposals.

Additional resources

11.6. Generating optimization proposals

When you create or update a KafkaRebalance resource, Cruise Control generates an optimization proposal for the Kafka cluster based on the configured optimization goals. Analyze the information in the optimization proposal and decide whether to approve it. You can use the results of the optimization proposal to rebalance your Kafka cluster.

You can run the optimization proposal in one of the following modes:

  • full (default)
  • add-brokers
  • remove-brokers

The mode you use depends on whether you are rebalancing across all the brokers already running in the Kafka cluster; or you want to rebalance after scaling up or before scaling down your Kafka cluster. For more information, see Rebalancing modes with broker scaling.

Prerequisites

  • You have deployed Cruise Control to your AMQ Streams cluster.
  • You have configured optimization goals and, optionally, capacity limits on broker resources.

For more information on configuring Cruise Control, see Section 11.5, “Configuring and deploying Cruise Control with Kafka”.

Procedure

  1. Create a KafkaRebalance resource and specify the appropriate mode.

    full mode (default)

    To use the default optimization goals defined in the Kafka resource, leave the spec property empty. Cruise Control rebalances a Kafka cluster in full mode by default.

    Example configuration with full rebalancing by default

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaRebalance
    metadata:
      name: my-rebalance
      labels:
        strimzi.io/cluster: my-cluster
    spec: {}

    You can also run a full rebalance by specifying the full mode through the spec.mode property.

    Example configuration specifying full mode

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaRebalance
    metadata:
      name: my-rebalance
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      mode: full

    add-brokers mode

    If you want to rebalance a Kafka cluster after scaling up, specify the add-brokers mode.

    In this mode, existing replicas are moved to the newly added brokers. You need to specify the brokers as a list.

    Example configuration specifying add-brokers mode

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaRebalance
    metadata:
      name: my-rebalance
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      mode: add-brokers
      brokers: [3, 4] 1

    1
    List of newly added brokers added by the scale up operation. This property is mandatory.
    remove-brokers mode

    If you want to rebalance a Kafka cluster before scaling down, specify the remove-brokers mode.

    In this mode, replicas are moved off the brokers that are going to be removed. You need to specify the brokers that are being removed as a list.

    Example configuration specifying remove-brokers mode

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaRebalance
    metadata:
      name: my-rebalance
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      mode: remove-brokers
      brokers: [3, 4] 1

    1
    List of brokers to be removed by the scale down operation. This property is mandatory.
    Note

    The following steps and the steps to approve or stop a rebalance are the same regardless of the rebalance mode you are using.

  2. To configure user-provided optimization goals instead of using the default goals, add the goals property and enter one or more goals.

    In the following example, rack awareness and replica capacity are configured as user-provided optimization goals:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaRebalance
    metadata:
      name: my-rebalance
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      goals:
        - RackAwareGoal
        - ReplicaCapacityGoal
  3. To ignore the configured hard goals, add the skipHardGoalCheck: true property:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaRebalance
    metadata:
      name: my-rebalance
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      goals:
        - RackAwareGoal
        - ReplicaCapacityGoal
      skipHardGoalCheck: true
  4. (Optional) To approve the optimization proposal automatically, set the strimzi.io/rebalance-auto-approval annotation to true:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaRebalance
    metadata:
      name: my-rebalance
      labels:
        strimzi.io/cluster: my-cluster
      annotations:
        strimzi.io/rebalance-auto-approval: "true"
    spec:
      goals:
        - RackAwareGoal
        - ReplicaCapacityGoal
      skipHardGoalCheck: true
  5. Create or update the resource:

    oc apply -f <kafka_rebalance_configuration_file>

    The Cluster Operator requests the optimization proposal from Cruise Control. This might take a few minutes depending on the size of the Kafka cluster.

  6. If you used the automatic approval mechanism, wait for the status of the optimization proposal to change to Ready. If you haven’t enabled the automatic approval mechanism, wait for the status of the optimization proposal to change to ProposalReady:

    oc get kafkarebalance -o wide -w -n <namespace>
    PendingProposal
    A PendingProposal status means the rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready.
    ProposalReady
    A ProposalReady status means the optimization proposal is ready for review and approval.

    When the status changes to ProposalReady, the optimization proposal is ready to approve.

  7. Review the optimization proposal.

    The optimization proposal is contained in the Status.Optimization Result property of the KafkaRebalance resource.

    oc describe kafkarebalance <kafka_rebalance_resource_name>

    Example optimization proposal

    Status:
      Conditions:
        Last Transition Time:  2020-05-19T13:50:12.533Z
        Status:                ProposalReady
        Type:                  State
      Observed Generation:     1
      Optimization Result:
        Data To Move MB:  0
        Excluded Brokers For Leadership:
        Excluded Brokers For Replica Move:
        Excluded Topics:
        Intra Broker Data To Move MB:         0
        Monitored Partitions Percentage:      100
        Num Intra Broker Replica Movements:   0
        Num Leader Movements:                 0
        Num Replica Movements:                26
        On Demand Balancedness Score After:   81.8666802863978
        On Demand Balancedness Score Before:  78.01176356230222
        Recent Windows:                       1
      Session Id:                             05539377-ca7b-45ef-b359-e13564f1458c

    The properties in the Optimization Result section describe the pending cluster rebalance operation. For descriptions of each property, see Contents of optimization proposals.

Insufficient CPU capacity

If a Kafka cluster is overloaded in terms of CPU utilization, you might see an insufficient CPU capacity error in the KafkaRebalance status. It’s worth noting that this utilization value is unaffected by the excludedTopics configuration. Although optimization proposals will not reassign replicas of excluded topics, their load is still considered in the utilization calculation.

Example CPU utilization error

com.linkedin.kafka.cruisecontrol.exception.OptimizationFailureException:
        [CpuCapacityGoal] Insufficient capacity for cpu (Utilization 615.21,
        Allowed Capacity 420.00, Threshold: 0.70). Add at least 3 brokers with
        the same cpu capacity (100.00) as broker-0. Add at least 3 brokers with
        the same cpu capacity (100.00) as broker-0.

Note

The error shows CPU capacity as a percentage rather than the number of CPU cores. For this reason, it does not directly map to the number of CPUs configured in the Kafka custom resource. It is like having a single virtual CPU per broker, which has the cycles of the CPUs configured in Kafka.spec.kafka.resources.limits.cpu. This has no effect on the rebalance behavior, since the ratio between CPU utilization and capacity remains the same.

11.7. Approving an optimization proposal

You can approve an optimization proposal generated by Cruise Control, if its status is ProposalReady. Cruise Control will then apply the optimization proposal to the Kafka cluster, reassigning partitions to brokers and changing partition leadership.

Caution

This is not a dry run. Before you approve an optimization proposal, you must:

Prerequisites

Procedure

Perform these steps for the optimization proposal that you want to approve.

  1. Unless the optimization proposal is newly generated, check that it is based on current information about the state of the Kafka cluster. To do so, refresh the optimization proposal to make sure it uses the latest cluster metrics:

    1. Annotate the KafkaRebalance resource in OpenShift with strimzi.io/rebalance=refresh:

      oc annotate kafkarebalance <kafka_rebalance_resource_name> strimzi.io/rebalance=refresh
  2. Wait for the status of the optimization proposal to change to ProposalReady:

    oc get kafkarebalance -o wide -w -n <namespace>
    PendingProposal
    A PendingProposal status means the rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready.
    ProposalReady
    A ProposalReady status means the optimization proposal is ready for review and approval.

    When the status changes to ProposalReady, the optimization proposal is ready to approve.

  3. Approve the optimization proposal that you want Cruise Control to apply.

    Annotate the KafkaRebalance resource in OpenShift with strimzi.io/rebalance=approve:

    oc annotate kafkarebalance <kafka_rebalance_resource_name> strimzi.io/rebalance=approve
  4. The Cluster Operator detects the annotated resource and instructs Cruise Control to rebalance the Kafka cluster.
  5. Wait for the status of the optimization proposal to change to Ready:

    oc get kafkarebalance -o wide -w -n <namespace>
    Rebalancing
    A Rebalancing status means the rebalancing is in progress.
    Ready
    A Ready status means the rebalance is complete.
    NotReady
    A NotReady status means an error occurred—​see Fixing problems with a KafkaRebalance resource.

    When the status changes to Ready, the rebalance is complete.

    To use the same KafkaRebalance custom resource to generate another optimization proposal, apply the refresh annotation to the custom resource. This moves the custom resource to the PendingProposal or ProposalReady state. You can then review the optimization proposal and approve it, if desired.

11.8. Stopping a cluster rebalance

Once started, a cluster rebalance operation might take some time to complete and affect the overall performance of the Kafka cluster.

If you want to stop a cluster rebalance operation that is in progress, apply the stop annotation to the KafkaRebalance custom resource. This instructs Cruise Control to finish the current batch of partition reassignments and then stop the rebalance. When the rebalance has stopped, completed partition reassignments have already been applied; therefore, the state of the Kafka cluster is different when compared to prior to the start of the rebalance operation. If further rebalancing is required, you should generate a new optimization proposal.

Note

The performance of the Kafka cluster in the intermediate (stopped) state might be worse than in the initial state.

Prerequisites

  • You have approved the optimization proposal by annotating the KafkaRebalance custom resource with approve.
  • The status of the KafkaRebalance custom resource is Rebalancing.

Procedure

  1. Annotate the KafkaRebalance resource in OpenShift:

    oc annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=stop
  2. Check the status of the KafkaRebalance resource:

    oc describe kafkarebalance rebalance-cr-name
  3. Wait until the status changes to Stopped.

11.9. Fixing problems with a KafkaRebalance resource

If an issue occurs when creating a KafkaRebalance resource or interacting with Cruise Control, the error is reported in the resource status, along with details of how to fix it. The resource also moves to the NotReady state.

To continue with the cluster rebalance operation, you must fix the problem in the KafkaRebalance resource itself or with the overall Cruise Control deployment. Problems might include the following:

  • A misconfigured parameter in the KafkaRebalance resource.
  • The strimzi.io/cluster label for specifying the Kafka cluster in the KafkaRebalance resource is missing.
  • The Cruise Control server is not deployed as the cruiseControl property in the Kafka resource is missing.
  • The Cruise Control server is not reachable.

After fixing the issue, you need to add the refresh annotation to the KafkaRebalance resource. During a “refresh”, a new optimization proposal is requested from the Cruise Control server.

Prerequisites

Procedure

  1. Get information about the error from the KafkaRebalance status:

    oc describe kafkarebalance rebalance-cr-name
  2. Attempt to resolve the issue in the KafkaRebalance resource.
  3. Annotate the KafkaRebalance resource in OpenShift:

    oc annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh
  4. Check the status of the KafkaRebalance resource:

    oc describe kafkarebalance rebalance-cr-name
  5. Wait until the status changes to PendingProposal, or directly to ProposalReady.

Chapter 12. Using the partition reassignment tool

When scaling a Kafka cluster, you may need to add or remove brokers and update the distribution of partitions or the replication factor of topics. To update partitions and topics, you can use the kafka-reassign-partitions.sh tool.

Neither the AMQ Streams Cruise Control integration nor the Topic Operator support changing the replication factor of a topic. However, you can change the replication factor of a topic using the kafka-reassign-partitions.sh tool.

The tool can also be used to reassign partitions and balance the distribution of partitions across brokers to improve performance. However, it is recommended to use Cruise Control for automated partition reassignments and cluster rebalancing. Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions.

It is recommended to run the kafka-reassign-partitions.sh tool as a separate interactive pod rather than within the broker container. Running the Kafka bin/ scripts within the broker container may cause a JVM to start with the same settings as the Kafka broker, which can potentially cause disruptions. By running the kafka-reassign-partitions.sh tool in a separate pod, you can avoid this issue. Running a pod with the -ti option creates an interactive pod with a terminal for running shell commands inside the pod.

Running an interactive pod with a terminal

oc run helper-pod -ti --image=registry.redhat.io/amq-streams/kafka-34-rhel8:2.4.0 --rm=true --restart=Never -- bash

12.1. Partition reassignment tool overview

The partition reassignment tool provides the following capabilities for managing Kafka partitions and brokers:

Redistributing partition replicas
Scale your cluster up and down by adding or removing brokers, and move Kafka partitions from heavily loaded brokers to under-utilized brokers. To do this, you must create a partition reassignment plan that identifies which topics and partitions to move and where to move them. Cruise Control is recommended for this type of operation as it automates the cluster rebalancing process.
Scaling topic replication factor up and down
Increase or decrease the replication factor of your Kafka topics. To do this, you must create a partition reassignment plan that identifies the existing replication assignment across partitions and an updated assignment with the replication factor changes.
Changing the preferred leader
Change the preferred leader of a Kafka partition. This can be useful if the current preferred leader is unavailable or if you want to redistribute load across the brokers in the cluster. To do this, you must create a partition reassignment plan that specifies the new preferred leader for each partition by changing the order of replicas.
Changing the log directories to use a specific JBOD volume
Change the log directories of your Kafka brokers to use a specific JBOD volume. This can be useful if you want to move your Kafka data to a different disk or storage device. To do this, you must create a partition reassignment plan that specifies the new log directory for each topic.

12.1.1. Generating a partition reassignment plan

The partition reassignment tool (kafka-reassign-partitions.sh) works by generating a partition assignment plan that specifies which partitions should be moved from their current broker to a new broker.

If you are satisfied with the plan, you can execute it. The tool then does the following:

  • Migrates the partition data to the new broker
  • Updates the metadata on the Kafka brokers to reflect the new partition assignments
  • Triggers a rolling restart of the Kafka brokers to ensure that the new assignments take effect

The partition reassignment tool 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 only want to reassign some 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 the partitions in the file have been moved to their intended brokers. If the reassignment is complete, --verify also removes any traffic throttles (--throttle) 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 must 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.

12.1.2. Specifying topics in a partition reassignment JSON file

The tool uses a reassignment JSON file that specifies the topics to reassign. You can generate a reassignment JSON file or create a file manually if you want to move specific partitions.

The reassignment JSON file has the following structure:

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

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

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

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

Example partition reassignment file

{
  "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.

12.1.3. Reassigning partitions between JBOD volumes

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

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

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

Example partition reassignment file specifying log directories

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

12.1.4. Throttling partition reassignment

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. Use the --throttle parameter with the kafka-reassign-partitions.sh tool to throttle a reassignment. You specify a maximum threshold in bytes per second for the movement of partitions between brokers. For example, --throttle 5000000 sets a maximum threshold for moving partitions of 50 MBps.

Throttling might cause the reassignment to take longer to complete.

  • If the throttle is too low, 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, clients will be impacted.

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

12.2. Generating a reassignment JSON file to reassign partitions

Generate a reassignment JSON file with the kafka-reassign-partitions.sh tool to reassign partitions after scaling a Kafka cluster. Adding or removing brokers does not automatically redistribute the existing partitions. To balance the partition distribution and take full advantage of the new brokers, you can reassign the partitions using the kafka-reassign-partitions.sh tool.

You run the tool from an interactive pod container connected to the Kafka cluster.

The following procedure describes a secure reassignment process that uses mTLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.

You’ll need the following to establish a connection:

  • The cluster CA certificate and password generated by the Cluster Operator when the Kafka cluster is created
  • The user CA certificate and password generated by the User Operator when a user is created for client access to the Kafka cluster

In this procedure, the CA certificates and corresponding passwords are extracted from the cluster and user secrets that contain them in PKCS #12 (.p12 and .password) format. The passwords allow access to the .p12 stores that contain the certificates. You use the .p12 stores to specify a truststore and keystore to authenticate connection to the Kafka cluster.

Prerequisites

  • You have a running Cluster Operator.
  • You have a running Kafka cluster based on a Kafka resource configured with internal TLS encryption and mTLS authentication.

    Kafka configuration with TLS encryption and mTLS authentication

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        listeners:
          # ...
          - name: tls
            port: 9093
            type: internal
            tls: true 1
            authentication:
              type: tls 2
        # ...

    1
    Enables TLS encryption for the internal listener.
    2
    Listener authentication mechanism specified as mutual tls.
  • The running Kafka cluster contains a set of topics and partitions to reassign.

    Example topic configuration for my-topic

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaTopic
    metadata:
      name: my-topic
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      partitions: 10
      replicas: 3
      config:
        retention.ms: 7200000
        segment.bytes: 1073741824
        # ...

  • You have a KafkaUser configured with ACL rules that specify permission to produce and consume topics from the Kafka brokers.

    Example Kafka user configuration with ACL rules to allow operations on my-topic and my-cluster

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaUser
    metadata:
      name: my-user
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      authentication: 1
        type: tls
      authorization:
        type: simple 2
        acls:
          # access to the topic
          - resource:
              type: topic
              name: my-topic
            operations:
              - Create
              - Describe
              - Read
              - AlterConfigs
            host: "*"
          # access to the cluster
          - resource:
              type: cluster
            operations:
              - Alter
              - AlterConfigs
            host: "*"
          # ...
      # ...

    1
    User authentication mechanism defined as mutual tls.
    2
    Simple authorization and accompanying list of ACL rules.

Procedure

  1. Extract the cluster CA certificate and password from the <cluster_name>-cluster-ca-cert secret of the Kafka cluster.

    oc get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
    oc get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.password}' | base64 -d > ca.password

    Replace <cluster_name> with the name of the Kafka cluster. When you deploy Kafka using the Kafka resource, a secret with the cluster CA certificate is created with the Kafka cluster name (<cluster_name>-cluster-ca-cert). For example, my-cluster-cluster-ca-cert.

  2. Run a new interactive pod container using the AMQ Streams Kafka image to connect to a running Kafka broker.

    oc run --restart=Never --image=registry.redhat.io/amq-streams/kafka-34-rhel8:2.4.0 <interactive_pod_name> -- /bin/sh -c "sleep 3600"

    Replace <interactive_pod_name> with the name of the pod.

  3. Copy the cluster CA certificate to the interactive pod container.

    oc cp ca.p12 <interactive_pod_name>:/tmp
  4. Extract the user CA certificate and password from the secret of the Kafka user that has permission to access the Kafka brokers.

    oc get secret <kafka_user> -o jsonpath='{.data.user\.p12}' | base64 -d > user.p12
    oc get secret <kafka_user> -o jsonpath='{.data.user\.password}' | base64 -d > user.password

    Replace <kafka_user> with the name of the Kafka user. When you create a Kafka user using the KafkaUser resource, a secret with the user CA certificate is created with the Kafka user name. For example, my-user.

  5. Copy the user CA certificate to the interactive pod container.

    oc cp user.p12 <interactive_pod_name>:/tmp

    The CA certificates allow the interactive pod container to connect to the Kafka broker using TLS.

  6. Create a config.properties file to specify the truststore and keystore used to authenticate connection to the Kafka cluster.

    Use the certificates and passwords you extracted in the previous steps.

    bootstrap.servers=<kafka_cluster_name>-kafka-bootstrap:9093 1
    security.protocol=SSL 2
    ssl.truststore.location=/tmp/ca.p12 3
    ssl.truststore.password=<truststore_password> 4
    ssl.keystore.location=/tmp/user.p12 5
    ssl.keystore.password=<keystore_password> 6
    1
    The bootstrap server address to connect to the Kafka cluster. Use your own Kafka cluster name to replace <kafka_cluster_name>.
    2
    The security protocol option when using TLS for encryption.
    3
    The truststore location contains the public key certificate (ca.p12) for the Kafka cluster.
    4
    The password (ca.password) for accessing the truststore.
    5
    The keystore location contains the public key certificate (user.p12) for the Kafka user.
    6
    The password (user.password) for accessing the keystore.
  7. Copy the config.properties file to the interactive pod container.

    oc cp config.properties <interactive_pod_name>:/tmp/config.properties
  8. Prepare a JSON file named topics.json that specifies the topics to move.

    Specify topic names as a comma-separated list.

    Example JSON file to reassign all the partitions of my-topic

    {
      "version": 1,
      "topics": [
        { "topic": "my-topic"}
      ]
    }

    You can also use this file to change the replication factor of a topic.

  9. Copy the topics.json file to the interactive pod container.

    oc cp topics.json <interactive_pod_name>:/tmp/topics.json
  10. Start a shell process in the interactive pod container.

    oc exec -n <namespace> -ti <interactive_pod_name> /bin/bash

    Replace <namespace> with the OpenShift namespace where the pod is running.

  11. Use the kafka-reassign-partitions.sh command to generate the reassignment JSON.

    Example command to move the partitions of my-topic to specified brokers

    bin/kafka-reassign-partitions.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --topics-to-move-json-file /tmp/topics.json \
      --broker-list 0,1,2,3,4 \
      --generate

12.3. Reassigning partitions after adding brokers

Use a reassignment file generated by the kafka-reassign-partitions.sh tool to reassign partitions after increasing the number of brokers in a Kafka cluster. The reassignment file should describe how partitions are reassigned to brokers in the enlarged Kafka cluster. You apply the reassignment specified in the file to the brokers and then verify the new partition assignments.

This procedure describes a secure scaling process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.

Note

Though you can use the kafka-reassign-partitions.sh tool, Cruise Control is recommended for automated partition reassignments and cluster rebalancing. Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions.

Prerequisites

  • You have a running Kafka cluster based on a Kafka resource configured with internal TLS encryption and mTLS authentication.
  • You have generated a reassignment JSON file named reassignment.json.
  • You are running an interactive pod container that is connected to the running Kafka broker.
  • You are connected as a KafkaUser configured with ACL rules that specify permission to manage the Kafka cluster and its topics.

See Generating reassignment JSON files.

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. If you haven’t done so, run an interactive pod container to generate a reassignment JSON file named reassignment.json.
  4. Copy the reassignment.json file to the interactive pod container.

    oc cp reassignment.json <interactive_pod_name>:/tmp/reassignment.json

    Replace <interactive_pod_name> with the name of the pod.

  5. Start a shell process in the interactive pod container.

    oc exec -n <namespace> -ti <interactive_pod_name> /bin/bash

    Replace <namespace> with the OpenShift namespace where the pod is running.

  6. Run the partition reassignment using the kafka-reassign-partitions.sh script from the interactive pod container.

    bin/kafka-reassign-partitions.sh --bootstrap-server
     <cluster_name>-kafka-bootstrap:9093 \
     --command-config /tmp/config.properties \
     --reassignment-json-file /tmp/reassignment.json \
     --execute

    Replace <cluster_name> with the name of your Kafka cluster. For example, my-cluster-kafka-bootstrap:9093

    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:

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --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.

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

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 10000000 \
      --execute
  7. Verify that 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.

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --verify

    The reassignment has finished when the --verify command reports that each of the partitions being moved has completed successfully. This final --verify will also have the effect of removing any reassignment throttles.

  8. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.

12.4. Reassigning partitions before removing brokers

Use a reassignment file generated by the kafka-reassign-partitions.sh tool to reassign partitions before decreasing the number of brokers in a Kafka cluster. The reassignment file must describe how partitions are reassigned to the remaining brokers in the Kafka cluster. You apply the reassignment specified in the file to the brokers and then verify the new partition assignments. Brokers in the highest numbered pods are removed first.

This procedure describes a secure scaling process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.

Note

Though you can use the kafka-reassign-partitions.sh tool, Cruise Control is recommended for automated partition reassignments and cluster rebalancing. Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions.

Prerequisites

  • You have a running Kafka cluster based on a Kafka resource configured with internal TLS encryption and mTLS authentication.
  • You have generated a reassignment JSON file named reassignment.json.
  • You are running an interactive pod container that is connected to the running Kafka broker.
  • You are connected as a KafkaUser configured with ACL rules that specify permission to manage the Kafka cluster and its topics.

See Generating reassignment JSON files.

Procedure

  1. If you haven’t done so, run an interactive pod container to generate a reassignment JSON file named reassignment.json.
  2. Copy the reassignment.json file to the interactive pod container.

    oc cp reassignment.json <interactive_pod_name>:/tmp/reassignment.json

    Replace <interactive_pod_name> with the name of the pod.

  3. Start a shell process in the interactive pod container.

    oc exec -n <namespace> -ti <interactive_pod_name> /bin/bash

    Replace <namespace> with the OpenShift namespace where the pod is running.

  4. Run the partition reassignment using the kafka-reassign-partitions.sh script from the interactive pod container.

    bin/kafka-reassign-partitions.sh --bootstrap-server
     <cluster_name>-kafka-bootstrap:9093 \
     --command-config /tmp/config.properties \
     --reassignment-json-file /tmp/reassignment.json \
     --execute

    Replace <cluster_name> with the name of your Kafka cluster. For example, my-cluster-kafka-bootstrap:9093

    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:

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --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.

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

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 10000000 \
      --execute
  5. Verify that 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.

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --verify

    The reassignment has finished when the --verify command reports that each of the partitions being moved has completed successfully. This final --verify will also have the effect of removing any reassignment throttles.

  6. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.
  7. When all the partition reassignments have finished, the brokers 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$, the broker still has live partitions and should not be stopped.

    You can check this by executing the command:

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

    where n is the number of the pods 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.

  8. When you have confirmed that the broker has no live partitions, you can edit the Kafka.spec.kafka.replicas property of your Kafka resource to reduce the number of brokers.

12.5. Changing the replication factor of topics

To change the replication factor of topics in a Kafka cluster, use the kafka-reassign-partitions.sh tool. This can be done by running the tool from an interactive pod container that is connected to the Kafka cluster, and using a reassignment file to describe how the topic replicas should be changed.

This procedure describes a secure process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.

Prerequisites

  • You have a running Kafka cluster based on a Kafka resource configured with internal TLS encryption and mTLS authentication.
  • You are running an interactive pod container that is connected to the running Kafka broker.
  • You have generated a reassignment JSON file named reassignment.json.
  • You are connected as a KafkaUser configured with ACL rules that specify permission to manage the Kafka cluster and its topics.

See Generating reassignment JSON files.

In this procedure, a topic called my-topic has 4 replicas and we want to reduce it to 3. A JSON file named topics.json specifies the topic, and was used to generate the reassignment.json file.

Example JSON file specifies my-topic

{
  "version": 1,
  "topics": [
    { "topic": "my-topic"}
  ]
}

Procedure

  1. If you haven’t done so, run an interactive pod container to generate a reassignment JSON file named reassignment.json.

    Example reassignment JSON file showing the current and proposed replica assignment

    Current partition replica assignment
    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[3,4,2,0],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[0,2,3,1],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[1,3,0,4],"log_dirs":["any","any","any","any"]}]}
    
    Proposed partition reassignment configuration
    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2,3],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3,4],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,4,0],"log_dirs":["any","any","any","any"]}]}

    Save a copy of this file locally in case you need to revert the changes later on.

  2. Edit the reassignment.json to remove a replica from each partition.

    For example use jq to remove the last replica in the list for each partition of the topic:

    Removing the last topic replica for each partition

    jq '.partitions[].replicas |= del(.[-1])' reassignment.json > reassignment.json

    Example reassignment file showing the updated replicas

    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,4],"log_dirs":["any","any","any","any"]}]}

  3. Copy the reassignment.json file to the interactive pod container.

    oc cp reassignment.json <interactive_pod_name>:/tmp/reassignment.json

    Replace <interactive_pod_name> with the name of the pod.

  4. Start a shell process in the interactive pod container.

    oc exec -n <namespace> -ti <interactive_pod_name> /bin/bash

    Replace <namespace> with the OpenShift namespace where the pod is running.

  5. Make the topic replica change using the kafka-reassign-partitions.sh script from the interactive pod container.

    bin/kafka-reassign-partitions.sh --bootstrap-server
     <cluster_name>-kafka-bootstrap:9093 \
     --command-config /tmp/config.properties \
     --reassignment-json-file /tmp/reassignment.json \
     --execute
    Note

    Removing replicas from a broker does not require any inter-broker data movement, so there is no need to throttle replication. If you are adding replicas, then you may want to change the throttle rate.

  6. Verify that the change to the topic replicas 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.

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --verify

    The reassignment has finished when the --verify command reports that each of the partitions being moved has completed successfully. This final --verify will also have the effect of removing any reassignment throttles.

  7. Run the bin/kafka-topics.sh command with the --describe option to see the results of the change to the topics.

    bin/kafka-topics.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --describe

    Results of reducing the number of replicas for a topic

    my-topic  Partition: 0  Leader: 0  Replicas: 0,1,2 Isr: 0,1,2
    my-topic  Partition: 1  Leader: 2  Replicas: 1,2,3 Isr: 1,2,3
    my-topic  Partition: 2  Leader: 3  Replicas: 2,3,4 Isr: 2,3,4

Chapter 13. Using AMQ Streams Operators

Use the AMQ Streams operators to manage your Kafka cluster, and Kafka topics and users.

13.1. Watching namespaces with AMQ Streams operators

Operators watch and manage AMQ Streams resources in namespaces. The Cluster Operator can watch a single namespace, multiple namespaces, or all namespaces in an OpenShift cluster. The Topic Operator and User Operator can watch a single namespace.

  • The Cluster Operator watches for Kafka resources
  • The Topic Operator watches for KafkaTopic resources
  • The User Operator watches for KafkaUser resources

The Topic Operator and the User Operator can only watch a single Kafka cluster in a namespace. And they can only be connected to a single Kafka cluster.

If multiple Topic Operators watch the same namespace, name collisions and topic deletion can occur. This is because each Kafka cluster uses Kafka topics that have the same name (such as __consumer_offsets). Make sure that only one Topic Operator watches a given namespace.

When using multiple User Operators with a single namespace, a user with a given username can exist in more than one Kafka cluster.

If you deploy the Topic Operator and User Operator using the Cluster Operator, they watch the Kafka cluster deployed by the Cluster Operator by default. You can also specify a namespace using watchedNamespace in the operator configuration.

For a standalone deployment of each operator, you specify a namespace and connection to the Kafka cluster to watch in the configuration.

13.2. Using the Cluster Operator

Use the Cluster Operator to deploy a Kafka cluster and other Kafka components.

13.2.1. Role-Based Access Control (RBAC) resources

The Cluster Operator creates and manages RBAC resources for AMQ Streams components that need access to OpenShift resources.

For the Cluster Operator to function, it needs permission within the OpenShift cluster to interact with Kafka resources, such as Kafka and KafkaConnect, as well as managed resources like ConfigMap, Pod, Deployment, StatefulSet, and Service.

Permission is specified through OpenShift role-based access control (RBAC) resources:

  • ServiceAccount
  • Role and ClusterRole
  • RoleBinding and ClusterRoleBinding

13.2.1.1. Delegating privileges to AMQ Streams components

The Cluster Operator runs under a service account called strimzi-cluster-operator. It is assigned cluster roles that give it permission to create the RBAC resources for AMQ Streams components. Role bindings associate the cluster roles with the service account.

OpenShift prevents components operating under one ServiceAccount from granting another ServiceAccount privileges that the granting ServiceAccount does not have. Because the Cluster Operator creates the RoleBinding and ClusterRoleBinding RBAC resources needed by the resources it manages, it requires a role that gives it the same privileges.

The following tables describe the RBAC resources created by the Cluster Operator.

Table 13.1. ServiceAccount resources

NameUsed by

<cluster_name>-kafka

Kafka broker pods

<cluster_name>-zookeeper

ZooKeeper pods

<cluster_name>-cluster-connect

Kafka Connect pods

<cluster_name>-mirror-maker

MirrorMaker pods

<cluster_name>-mirrormaker2

MirrorMaker 2 pods

<cluster_name>-bridge

Kafka Bridge pods

<cluster_name>-entity-operator

Entity Operator

Table 13.2. ClusterRole resources

NameUsed by

strimzi-cluster-operator-namespaced

Cluster Operator

strimzi-cluster-operator-global

Cluster Operator

strimzi-cluster-operator-leader-election

Cluster Operator

strimzi-kafka-broker

Cluster Operator, rack feature (when used)

strimzi-entity-operator

Cluster Operator, Topic Operator, User Operator

strimzi-kafka-client

Cluster Operator, Kafka clients for rack awareness

Table 13.3. ClusterRoleBinding resources

NameUsed by

strimzi-cluster-operator

Cluster Operator

strimzi-cluster-operator-kafka-broker-delegation

Cluster Operator, Kafka brokers for rack awareness

strimzi-cluster-operator-kafka-client-delegation

Cluster Operator, Kafka clients for rack awareness

Table 13.4. RoleBinding resources

NameUsed by

strimzi-cluster-operator

Cluster Operator

strimzi-cluster-operator-kafka-broker-delegation

Cluster Operator, Kafka brokers for rack awareness

13.2.1.2. Running the Cluster Operator using a ServiceAccount

The Cluster Operator is best run using a ServiceAccount:

Example ServiceAccount for the Cluster Operator

apiVersion: v1
kind: ServiceAccount
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi

The Deployment of the operator then needs to specify this in its spec.template.spec.serviceAccountName:

Partial example of Deployment for the Cluster Operator

apiVersion: apps/v1
kind: Deployment
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
spec:
  replicas: 1
  selector:
    matchLabels:
      name: strimzi-cluster-operator
      strimzi.io/kind: cluster-operator
  template:
      # ...

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

13.2.1.3. ClusterRole resources

The Cluster Operator uses ClusterRole resources to provide the necessary access to resources. Depending on the OpenShift cluster setup, a cluster administrator might be needed to create the cluster roles.

Note

Cluster administrator rights are only needed for the creation of ClusterRole resources. The Cluster Operator will not run under a cluster admin account.

ClusterRole resources follow the principle of least privilege and contain only those privileges needed by the Cluster Operator to operate the cluster of the Kafka component. The first set of assigned privileges allow the Cluster Operator to manage OpenShift resources such as StatefulSet, Deployment, Pod, and ConfigMap.

All cluster roles are required by the Cluster Operator in order to delegate privileges.

The Cluster Operator uses the strimzi-cluster-operator-namespaced and strimzi-cluster-operator-global cluster roles to grant permission at the namespace-scoped resources level and cluster-scoped resources level.

ClusterRole with namespaced resources for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-namespaced
  labels:
    app: strimzi
rules:
  # Resources in this role are used by the operator based on an operand being deployed in some namespace. When needed, you
  # can deploy the operator as a cluster-wide operator. But grant the rights listed in this role only on the namespaces
  # where the operands will be deployed. That way, you can limit the access the operator has to other namespaces where it
  # does not manage any clusters.
  - apiGroups:
      - "rbac.authorization.k8s.io"
    resources:
      # The cluster operator needs to access and manage rolebindings to grant Strimzi components cluster permissions
      - rolebindings
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - "rbac.authorization.k8s.io"
    resources:
      # The cluster operator needs to access and manage roles to grant the entity operator permissions
      - roles
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - ""
    resources:
      # The cluster operator needs to access and delete pods, this is to allow it to monitor pod health and coordinate rolling updates
      - pods
      # The cluster operator needs to access and manage service accounts to grant Strimzi components cluster permissions
      - serviceaccounts
      # The cluster operator needs to access and manage config maps for Strimzi components configuration
      - configmaps
      # The cluster operator needs to access and manage services and endpoints to expose Strimzi components to network traffic
      - services
      - endpoints
      # The cluster operator needs to access and manage secrets to handle credentials
      - secrets
      # The cluster operator needs to access and manage persistent volume claims to bind them to Strimzi components for persistent data
      - persistentvolumeclaims
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - "apps"
    resources:
      # The cluster operator needs to access and manage deployments to run deployment based Strimzi components
      - deployments
      - deployments/scale
      - deployments/status
      # The cluster operator needs to access and manage stateful sets to run stateful sets based Strimzi components
      - statefulsets
      # The cluster operator needs to access replica-sets to manage Strimzi components and to determine error states
      - replicasets
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - "" # legacy core events api, used by topic operator
      - "events.k8s.io" # new events api, used by cluster operator
    resources:
      # The cluster operator needs to be able to create events and delegate permissions to do so
      - events
    verbs:
      - create
  - apiGroups:
      # Kafka Connect Build on OpenShift requirement
      - build.openshift.io
    resources:
      - buildconfigs
      - buildconfigs/instantiate
      - builds
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - networking.k8s.io
    resources:
      # The cluster operator needs to access and manage network policies to lock down communication between Strimzi components
      - networkpolicies
      # The cluster operator needs to access and manage ingresses which allow external access to the services in a cluster
      - ingresses
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - route.openshift.io
    resources:
      # The cluster operator needs to access and manage routes to expose Strimzi components for external access
      - routes
      - routes/custom-host
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - image.openshift.io
    resources:
      # The cluster operator needs to verify the image stream when used for Kafka Connect image build
      - imagestreams
    verbs:
      - get
  - apiGroups:
      - policy
    resources:
      # The cluster operator needs to access and manage pod disruption budgets this limits the number of concurrent disruptions
      # that a Strimzi component experiences, allowing for higher availability
      - poddisruptionbudgets
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update

ClusterRole with cluster-scoped resources for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-global
  labels:
    app: strimzi
rules:
  - apiGroups:
      - "rbac.authorization.k8s.io"
    resources:
      # The cluster operator needs to create and manage cluster role bindings in the case of an install where a user
      # has specified they want their cluster role bindings generated
      - clusterrolebindings
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - storage.k8s.io
    resources:
      # The cluster operator requires "get" permissions to view storage class details
      # This is because only a persistent volume of a supported storage class type can be resized
      - storageclasses
    verbs:
      - get
  - apiGroups:
      - ""
    resources:
      # The cluster operator requires "list" permissions to view all nodes in a cluster
      # The listing is used to determine the node addresses when NodePort access is configured
      # These addresses are then exposed in the custom resource states
      - nodes
    verbs:
      - list

The strimzi-cluster-operator-leader-election cluster role represents the permissions needed for the leader election.

ClusterRole with leader election permissions

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-leader-election
  labels:
    app: strimzi
rules:
  - apiGroups:
      - coordination.k8s.io
    resources:
      # The cluster operator needs to access and manage leases for leader election
      # The "create" verb cannot be used with "resourceNames"
      - leases
    verbs:
      - create
  - apiGroups:
      - coordination.k8s.io
    resources:
      # The cluster operator needs to access and manage leases for leader election
      - leases
    resourceNames:
      # The default RBAC files give the operator only access to the Lease resource names strimzi-cluster-operator
      # If you want to use another resource name or resource namespace, you have to configure the RBAC resources accordingly
      - strimzi-cluster-operator
    verbs:
      - get
      - list
      - watch
      - delete
      - patch
      - update

The strimzi-kafka-broker cluster role represents the access needed by the init container in Kafka pods that use rack awareness.

A role binding named strimzi-<cluster_name>-kafka-init grants the <cluster_name>-kafka service account access to nodes within a cluster using the strimzi-kafka-broker role. If the rack feature is not used and the cluster is not exposed through nodeport, no binding is created.

ClusterRole for the Cluster Operator allowing it to delegate access to OpenShift nodes to the Kafka broker pods

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-kafka-broker
  labels:
    app: strimzi
rules:
  - apiGroups:
      - ""
    resources:
      # The Kafka Brokers require "get" permissions to view the node they are on
      # This information is used to generate a Rack ID that is used for High Availability configurations
      - nodes
    verbs:
      - get

The strimzi-entity-operator cluster role represents the access needed by the Topic Operator and User Operator.

The Topic Operator produces OpenShift events with status information, so the <cluster_name>-entity-operator service account is bound to the strimzi-entity-operator role, which grants this access via the strimzi-entity-operator role binding.

ClusterRole for the Cluster Operator allowing it to delegate access to events to the Topic and User Operators

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-entity-operator
  labels:
    app: strimzi
rules:
  - apiGroups:
      - "kafka.strimzi.io"
    resources:
      # The entity operator runs the KafkaTopic assembly operator, which needs to access and manage KafkaTopic resources
      - kafkatopics
      - kafkatopics/status
      # The entity operator runs the KafkaUser assembly operator, which needs to access and manage KafkaUser resources
      - kafkausers
      - kafkausers/status
    verbs:
      - get
      - list
      - watch
      - create
      - patch
      - update
      - delete
  - apiGroups:
      - ""
    resources:
      - events
    verbs:
      # The entity operator needs to be able to create events
      - create
  - apiGroups:
      - ""
    resources:
      # The entity operator user-operator needs to access and manage secrets to store generated credentials
      - secrets
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update

The strimzi-kafka-client cluster role represents the access needed by Kafka clients that use rack awareness.

ClusterRole for the Cluster Operator allowing it to delegate access to OpenShift nodes to the Kafka client-based pods

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-kafka-client
  labels:
    app: strimzi
rules:
  - apiGroups:
      - ""
    resources:
      # The Kafka clients (Connect, Mirror Maker, etc.) require "get" permissions to view the node they are on
      # This information is used to generate a Rack ID (client.rack option) that is used for consuming from the closest
      # replicas when enabled
      - nodes
    verbs:
      - get

13.2.1.4. ClusterRoleBinding resources

The Cluster Operator uses ClusterRoleBinding and RoleBinding resources to associate its ClusterRole with its ServiceAccount: Cluster role bindings are required by cluster roles containing cluster-scoped resources.

Example ClusterRoleBinding for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-cluster-operator-global
  apiGroup: rbac.authorization.k8s.io

Cluster role bindings are also needed for the cluster roles used in delegating privileges:

Example ClusterRoleBinding for the Cluster Operator and Kafka broker rack awareness

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator-kafka-broker-delegation
  labels:
    app: strimzi
# The Kafka broker cluster role must be bound to the cluster operator service account so that it can delegate the cluster role to the Kafka brokers.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-kafka-broker
  apiGroup: rbac.authorization.k8s.io

Example ClusterRoleBinding for the Cluster Operator and Kafka client rack awareness

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator-kafka-client-delegation
  labels:
    app: strimzi
# The Kafka clients cluster role must be bound to the cluster operator service account so that it can delegate the
# cluster role to the Kafka clients using it for consuming from closest replica.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-kafka-client
  apiGroup: rbac.authorization.k8s.io

Cluster roles containing only namespaced resources are bound using role bindings only.

Example RoleBinding for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-cluster-operator-namespaced
  apiGroup: rbac.authorization.k8s.io

Example RoleBinding for the Cluster Operator and Kafka broker rack awareness

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator-entity-operator-delegation
  labels:
    app: strimzi
# The Entity Operator cluster role must be bound to the cluster operator service account so that it can delegate the cluster role to the Entity Operator.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-entity-operator
  apiGroup: rbac.authorization.k8s.io

13.2.2. ConfigMap for Cluster Operator logging

Cluster Operator logging is configured through a ConfigMap named strimzi-cluster-operator.

A ConfigMap containing logging configuration is created when installing the Cluster Operator. This ConfigMap is described in the file install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml. You configure Cluster Operator logging by changing the data field log4j2.properties in this ConfigMap.

To update the logging configuration, you can edit the 050-ConfigMap-strimzi-cluster-operator.yaml file and then run the following command:

oc create -f install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml

Alternatively, edit the ConfigMap directly:

oc edit configmap strimzi-cluster-operator

To change the frequency of the reload interval, set a time in seconds in the monitorInterval option in the created ConfigMap.

If the ConfigMap is missing when the Cluster Operator is deployed, the default logging values are used.

If the ConfigMap is accidentally deleted after the Cluster Operator is deployed, the most recently loaded logging configuration is used. Create a new ConfigMap to load a new logging configuration.

Note

Do not remove the monitorInterval option from the ConfigMap.

13.2.3. Configuring the Cluster Operator with environment variables

You can configure the Cluster Operator using environment variables. The supported environment variables are listed here.

Note

The environment variables are specified for the container image of the Cluster Operator in its Deployment configuration file. (install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml)

STRIMZI_NAMESPACE

A comma-separated list of namespaces that the operator operates in. When not set, set to empty string, or set to *, the Cluster Operator operates in all namespaces.

The Cluster Operator deployment might use the downward API to set this automatically to the namespace the Cluster Operator is deployed in.

Example configuration for Cluster Operator namespaces

env:
  - name: STRIMZI_NAMESPACE
    valueFrom:
      fieldRef:
        fieldPath: metadata.namespace

STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
Optional, default is 120000 ms. The interval between periodic reconciliations, in milliseconds.
STRIMZI_OPERATION_TIMEOUT_MS
Optional, default 300000 ms. The timeout for internal operations, in milliseconds. Increase this value when using AMQ Streams on clusters where regular OpenShift operations take longer than usual (because of slow downloading of Docker images, for example).
STRIMZI_ZOOKEEPER_ADMIN_SESSION_TIMEOUT_MS
Optional, default 10000 ms. The session timeout for the Cluster Operator’s ZooKeeper admin client, in milliseconds. Increase the value if ZooKeeper requests from the Cluster Operator are regularly failing due to timeout issues. There is a maximum allowed session time set on the ZooKeeper server side via the maxSessionTimeout config. By default, the maximum session timeout value is 20 times the default tickTime (whose default is 2000) at 40000 ms. If you require a higher timeout, change the maxSessionTimeout ZooKeeper server configuration value.
STRIMZI_OPERATIONS_THREAD_POOL_SIZE
Optional, default 10. The worker thread pool size, which is used for various asynchronous and blocking operations that are run by the Cluster Operator.
STRIMZI_OPERATOR_NAME
Optional, defaults to the pod’s hostname. The operator name identifies the AMQ Streams instance when emitting OpenShift events.
STRIMZI_OPERATOR_NAMESPACE

The name of the namespace where the Cluster Operator is running. Do not configure this variable manually. Use the downward API.

env:
  - name: STRIMZI_OPERATOR_NAMESPACE
    valueFrom:
      fieldRef:
        fieldPath: metadata.namespace
STRIMZI_OPERATOR_NAMESPACE_LABELS

Optional. The labels of the namespace where the AMQ Streams Cluster Operator is running. Use namespace labels to configure the namespace selector in network policies. Network policies allow the AMQ Streams Cluster Operator access only to the operands from the namespace with these labels. When not set, the namespace selector in network policies is configured to allow access to the Cluster Operator from any namespace in the OpenShift cluster.

env:
  - name: STRIMZI_OPERATOR_NAMESPACE_LABELS
    value: label1=value1,label2=value2
STRIMZI_LABELS_EXCLUSION_PATTERN

Optional, default regex pattern is ^app.kubernetes.io/(?!part-of).*. The regex exclusion pattern used to filter labels propagation from the main custom resource to its subresources. The labels exclusion filter is not applied to labels in template sections such as spec.kafka.template.pod.metadata.labels.

env:
  - name: STRIMZI_LABELS_EXCLUSION_PATTERN
    value: "^key1.*"
STRIMZI_CUSTOM_{COMPONENT_NAME}_LABELS

Optional. One or more custom labels to apply to all the pods created by the {COMPONENT_NAME} custom resource. The Cluster Operator labels the pods when the custom resource is created or is next reconciled.

Labels can be applied to the following components:

  • KAFKA
  • KAFKA_CONNECT
  • KAFKA_CONNECT_BUILD
  • ZOOKEEPER
  • ENTITY_OPERATOR
  • KAFKA_MIRROR_MAKER2
  • KAFKA_MIRROR_MAKER
  • CRUISE_CONTROL
  • KAFKA_BRIDGE
  • KAFKA_EXPORTER
STRIMZI_CUSTOM_RESOURCE_SELECTOR

Optional. The label selector to filter the custom resources handled by the Cluster Operator. The operator will operate only on those custom resources that have the specified labels set. Resources without these labels will not be seen by the operator. The label selector applies to Kafka, KafkaConnect, KafkaBridge, KafkaMirrorMaker, and KafkaMirrorMaker2 resources. KafkaRebalance and KafkaConnector resources are operated only when their corresponding Kafka and Kafka Connect clusters have the