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Deploying and Upgrading (0.39.0)

Table of Contents

1. Deployment overview

Strimzi simplifies the process of running Apache Kafka in a Kubernetes cluster.

This guide provides instructions for deploying and managing Strimzi. Deployment options and steps are covered using the example installation files included with Strimzi. While the guide highlights important configuration considerations, it does not cover all available options. For a deeper understanding of the Kafka component configuration options, refer to the Strimzi Custom Resource API Reference.

In addition to deployment instructions, the guide offers pre- and post-deployment guidance. It covers setting up and securing client access to your Kafka cluster. Furthermore, it explores additional deployment options such as metrics integration, distributed tracing, and cluster management tools like Cruise Control and the Strimzi Drain Cleaner. You’ll also find recommendations on managing Strimzi and fine-tuning Kafka configuration for optimal performance.

Upgrade instructions are provided for both Strimzi and Kafka, to help keep your deployment up to date.

Strimzi is designed to be compatible with all types of Kubernetes clusters, irrespective of their distribution. Whether your deployment involves public or private clouds, or if you are setting up a local development environment, the instructions in this guide are applicable in all cases.

1.1. Strimzi custom resources

Deployment of Kafka components to a Kubernetes cluster using Strimzi is highly configurable through the application of custom resources. These custom resources are created as instances of APIs added by Custom Resource Definitions (CRDs) to extend Kubernetes resources.

CRDs act as configuration instructions to describe the custom resources in a Kubernetes cluster, and are provided with Strimzi 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 Strimzi distribution.

CRDs also allow Strimzi resources to benefit from native Kubernetes features like CLI accessibility and configuration validation.

1.1.1. Strimzi custom resource example

CRDs require a one-time installation in a cluster to define the schemas used to instantiate and manage Strimzi-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 Strimzi administrators. For more information, see Designating Strimzi administrators.

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

Each Strimzi-specific custom resource conforms to the schema defined by the CRD for the resource’s kind. The custom resources for Strimzi 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, kubectl get kafkatopic my-topic or kubectl get kafkatopics.

  3. The shortname can be used in CLI commands. For example, kubectl get kt can be used as an abbreviation instead of kubectl 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 Strimzi 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 Strimzi.

Additional resources

1.2. Strimzi operators

Strimzi operators are purpose-built with specialist operational knowledge to effectively manage Kafka on Kubernetes. Each operator performs a distinct function.

Cluster Operator

The Cluster Operator handles the deployment and management of Apache Kafka clusters on Kubernetes. It automates the setup of Kafka brokers, and other Kafka components and resources.

Topic Operator

The Topic Operator manages the creation, configuration, and deletion of topics within Kafka clusters.

User Operator

The User Operator manages Kafka users that require access to Kafka brokers.

When you deploy Strimzi, you first deploy the Cluster Operator. The Cluster Operator is then ready to handle the deployment of Kafka. You can also deploy the Topic Operator and User Operator using the Cluster Operator (recommended) or as standalone operators. You would use a standalone operator with a Kafka cluster that is not managed by the Cluster Operator.

The Topic Operator and User Operator are part of the Entity Operator. The Cluster Operator can deploy one or both operators based on the Entity Operator configuration.

Important

To deploy the standalone operators, you need to set environment variables to connect to a Kafka cluster. These environment variables do not need to be set if you are deploying the operators using the Cluster Operator as they will be set by the Cluster Operator.

1.2.1. Watching Strimzi resources in Kubernetes namespaces

Operators watch and manage Strimzi resources in Kubernetes namespaces. The Cluster Operator can watch a single namespace, multiple namespaces, or all namespaces in a Kubernetes 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.

1.2.2. Managing RBAC resources

The Cluster Operator creates and manages role-based access control (RBAC) resources for Strimzi components that need access to Kubernetes resources.

For the Cluster Operator to function, it needs permission within the Kubernetes cluster to interact with Kafka resources, such as Kafka and KafkaConnect, as well as managed resources like ConfigMap, Pod, Deployment, and Service.

Permission is specified through the following Kubernetes RBAC resources:

  • ServiceAccount

  • Role and ClusterRole

  • RoleBinding and ClusterRoleBinding

Delegating privileges to Strimzi 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 Strimzi components. Role bindings associate the cluster roles with the service account.

Kubernetes 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 sections describe the RBAC resources required by the Cluster Operator.

ClusterRole resources

The Cluster Operator uses ClusterRole resources to provide the necessary access to resources. Depending on the Kubernetes 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.

The RBAC 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.

All cluster roles are required by the Cluster Operator in order to delegate privileges.

Table 1. ClusterRole resources
Name Description

strimzi-cluster-operator-namespaced

Access rights for namespace-scoped resources used by the Cluster Operator to deploy and manage the operands.

strimzi-cluster-operator-global

Access rights for cluster-scoped resources used by the Cluster Operator to deploy and manage the operands.

strimzi-cluster-operator-leader-election

Access rights used by the Cluster Operator for leader election.

strimzi-cluster-operator-watched

Access rights used by the Cluster Operator to watch and manage the Strimzi custom resources.

strimzi-kafka-broker

Access rights to allow Kafka brokers to get the topology labels from Kubernetes worker nodes when rack-awareness is used.

strimzi-entity-operator

Access rights used by the Topic and User Operators to manage Kafka users and topics.

strimzi-kafka-client

Access rights to allow Kafka Connect, MirrorMaker (1 and 2), and Kafka Bridge to get the topology labels from Kubernetes worker nodes when rack-awareness is used.
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.

Table 2. ClusterRoleBinding resources
Name Description

strimzi-cluster-operator

Grants the Cluster Operator the rights from the strimzi-cluster-operator-global cluster role.

strimzi-cluster-operator-kafka-broker-delegation

Grants the Cluster Operator the rights from the strimzi-entity-operator cluster role.

strimzi-cluster-operator-kafka-client-delegation

Grants the Cluster Operator the rights from the strimzi-kafka-client cluster role.
Table 3. RoleBinding resources
Name Description

strimzi-cluster-operator

Grants the Cluster Operator the rights from the strimzi-cluster-operator-namespaced cluster role.

strimzi-cluster-operator-leader-election

Grants the Cluster Operator the rights from the strimzi-cluster-operator-leader-election cluster role.

strimzi-cluster-operator-watched

Grants the Cluster Operator the rights from the strimzi-cluster-operator-watched cluster role.

strimzi-cluster-operator-entity-operator-delegation

Grants the Cluster Operator the rights from the strimzi-cluster-operator-entity-operator-delegation cluster role.
ServiceAccount resources

The Cluster Operator runs using the strimzi-cluster-operator ServiceAccount. This service account grants it the privileges it requires to manage the operands. The Cluster Operator creates additional ClusterRoleBinding and RoleBinding resources to delegate some of these RBAC rights to the operands.

Each of the operands uses its own service account created by the Cluster Operator. This allows the Cluster Operator to follow the principle of least privilege and give the operands only the access rights that are really need.

Table 4. ServiceAccount resources
Name Used by

<cluster_name>-zookeeper

ZooKeeper pods

<cluster_name>-kafka

Kafka broker pods

<cluster_name>-entity-operator

Entity Operator

<cluster_name>-cruise-control

Cruise Control pods

<cluster_name>-kafka-exporter

Kafka Exporter pods

<cluster_name>-connect

Kafka Connect pods

<cluster_name>-mirror-maker

MirrorMaker pods

<cluster_name>-mirrormaker2

MirrorMaker 2 pods

<cluster_name>-bridge

Kafka Bridge pods

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

You can use the Strimzi 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. Seamless FIPS support

Federal Information Processing Standards (FIPS) are standards for computer security and interoperability. When running Strimzi on a FIPS-enabled Kubernetes cluster, the OpenJDK used in Strimzi container images automatically switches to FIPS mode. From version 0.33, Strimzi can run on FIPS-enabled Kubernetes clusters without any changes or special configuration. It uses only the FIPS-compliant security libraries from the OpenJDK.

Minimum password length

When running in the FIPS mode, SCRAM-SHA-512 passwords need to be at least 32 characters long. From Strimzi 0.33, the default password length in Strimzi User Operator is set to 32 characters as well. If you have a Kafka cluster with custom configuration that uses a password length that is less than 32 characters, you need to update your configuration. If you have any users with passwords shorter than 32 characters, you need to regenerate a password with the required length. You can do that, for example, by deleting the user secret and waiting for the User Operator to create a new password with the appropriate length.

Important
 If you are using FIPS-enabled Kubernetes clusters, you may experience higher memory consumption compared to regular Kubernetes clusters. To avoid any issues, we suggest increasing the memory request to at least 512Mi.
Additional resources

1.5. Document Conventions

User-replaced values

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

For example, the following code shows that <my_namespace> must be replaced by the correct namespace name:

sed -i 's/namespace: .*/namespace: <my_namespace>/' install/cluster-operator/*RoleBinding*.yaml

1.6. Additional resources

2. Strimzi installation methods

You can install Strimzi on Kubernetes 1.21 and later in three ways.

Installation method Description

Installation artifacts (YAML files)

Download the release artifacts from the GitHub releases page.

Download the strimzi-<version>.zip or strimzi-<version>.tar.gz archive file. The archive file contains installation artifacts and example configuration files.

Deploy the YAML installation artifacts to your Kubernetes cluster using kubectl. 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:

  • Strimi administrator roles (strimzi-admin)

  • A standalone Topic Operator (topic-operator)

  • A standalone User Operator (user-operator)

  • Strimzi Drain Cleaner (drain-cleaner)

OperatorHub.io

Use the Strimzi Kafka operator in the OperatorHub.io to deploy the Cluster Operator. You then deploy Strimzi components using custom resources.

Helm chart

Use a Helm chart to deploy the Cluster Operator. You then deploy Strimzi components using custom resources.

For the greatest flexibility, choose the installation artifacts method. The OperatorHub.io method provides a standard configuration and allows you to take advantage of automatic updates. Helm charts provide a convenient way to manage the installation of applications.

3. What is deployed with Strimzi

Apache Kafka components are provided for deployment to Kubernetes with the Strimzi 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

  • Cruise Control to rebalance topic partitions across broker nodes

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 a Kubernetes 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 Kubernetes resources for the components, such as Deployment, Service, and Pod resources. The names of the Kubernetes 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.

4. Preparing for your Strimzi deployment

Prepare for a deployment of Strimzi by completing any necessary pre-deployment tasks. Take the necessary preparatory steps according to your specific requirements, such as the following:

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 Strimzi, you will need the following:

  • A Kubernetes 1.21 and later cluster.

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

For more information on the tools available for running Kubernetes, see Install Tools in the Kubernetes documentation.

Note
 Strimzi supports some features that are specific to OpenShift, where such integration benefits OpenShift users and there is no equivalent implementation using standard Kubernetes.

oc and kubectl commands

The oc command functions as an alternative to kubectl. In almost all cases the example kubectl commands used in this guide can be done using oc simply by replacing the command name (options and arguments remain the same).

In other words, instead of using:

kubectl apply -f your-file

when using OpenShift you can use:

oc apply -f your-file
Note
 As an exception to this general rule, oc uses oc adm subcommands for cluster management functionality, whereas kubectl does not make this distinction. For example, the oc equivalent of kubectl taint is oc adm taint.

4.2. Operator deployment best practices

Potential issues can arise from installing more than one Strimzi operator in the same Kubernetes cluster, especially when using different versions. Each Strimzi operator manages a set of resources in a Kubernetes cluster. When you install multiple Strimzi operators, they may attempt to manage the same resources concurrently. This can lead to conflicts and unpredictable behavior within your cluster. Conflicts can still occur even if you deploy Strimzi operators in different namespaces within the same Kubernetes cluster. Although namespaces provide some degree of resource isolation, certain resources managed by the Strimzi operator, such as Custom Resource Definitions (CRDs) and roles, have a cluster-wide scope.

Additionally, installing multiple operators with different versions can result in compatibility issues between the operators and the Kafka clusters they manage. Different versions of Strimzi operators may introduce changes, bug fixes, or improvements that are not backward-compatible.

To avoid the issues associated with installing multiple Strimzi operators in a Kubernetes cluster, the following guidelines are recommended:

  • Install the Strimzi operator in a separate namespace from the Kafka cluster and other Kafka components it manages, to ensure clear separation of resources and configurations.

  • Use a single Strimzi operator to manage all your Kafka instances within a Kubernetes cluster.

  • Update the Strimzi operator and the supported Kafka version as often as possible to reflect the latest features and enhancements.

By following these best practices and ensuring consistent updates for a single Strimzi operator, you can enhance the stability of managing Kafka instances in a Kubernetes cluster. This approach also enables you to make the most of Strimzi’s latest features and capabilities.

4.3. Downloading Strimzi release artifacts

To use deployment files to install Strimzi, download and extract the files from the GitHub releases page.

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

Use kubectl 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 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 Strimzi Cluster Operator, you can deploy them from the install/topic-operator and install/user-operator folders.

Note
 Strimzi container images are also available through the Container Registry. However, we recommend that you use the YAML files provided to deploy Strimzi.

4.4. Pushing container images to your own registry

Container images for Strimzi are available in the Container Registry. The installation YAML files provided by Strimzi will pull the images directly from the Container Registry.

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

  1. Pull all container images listed here

  2. Push them into your own registry

  3. Update the image names in the YAML files used in deployment

Note
 Each Kafka version supported for the release has a separate image.
Container image Namespace/Repository Description

Kafka

  • quay.io/strimzi/kafka:0.39.0-kafka-3.5.0

  • quay.io/strimzi/kafka:0.39.0-kafka-3.5.1

  • quay.io/strimzi/kafka:0.39.0-kafka-3.5.2

  • quay.io/strimzi/kafka:0.39.0-kafka-3.6.0

  • quay.io/strimzi/kafka:0.39.0-kafka-3.6.1

Strimzi image for running Kafka, including:

  • Kafka Broker

  • Kafka Connect

  • Kafka MirrorMaker

  • ZooKeeper

  • TLS Sidecars

  • Cruise Control

Operator

  • quay.io/strimzi/operator:0.39.0

Strimzi image for running the operators:

  • Cluster Operator

  • Topic Operator

  • User Operator

  • Kafka Initializer

Kafka Bridge

  • quay.io/strimzi/kafka-bridge:0.27.0

Strimzi image for running the Strimzi kafka Bridge

Strimzi Drain Cleaner

  • quay.io/strimzi/drain-cleaner:1.0.1

Strimzi image for running the Strimzi Drain Cleaner

4.5. Designating Strimzi administrators

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

  • strimzi-view allows users to view and list Strimzi resources.

  • strimzi-admin allows users to also create, edit or delete Strimzi resources.

When you install these roles, they will automatically aggregate (add) these rights to the default Kubernetes 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 Strimzi resources.

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

Prerequisites
Procedure

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

    kubectl create -f install/strimzi-admin

  2. If needed, assign the roles that provide access rights to users that require them.

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

5. Deploying Strimzi using installation artifacts

Having prepared your environment for a deployment of Strimzi, you can deploy Strimzi to a Kubernetes cluster. Use the installation files provided with the release artifacts.

You can deploy Strimzi 0.39.0 on Kubernetes 1.21 and later.

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

  1. Deploy the Cluster Operator

  2. Use the Cluster Operator to deploy the following:

    1. Kafka cluster

    2. Topic Operator

    3. User Operator

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

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

5.1. Basic deployment path

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

The first step for any deployment of Strimzi 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: kubectl 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)

  • Strimzi CRDs (CustomResourceDefinition)

  • RBAC resources (ClusterRole, ClusterRoleBinding, RoleBinding)

  • Service account (ServiceAccount)

The basic deployment path is as follows:

  1. Download the release artifacts

  2. Create a Kubernetes 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.

5.2. Deploying the Cluster Operator

The Cluster Operator is responsible for deploying and managing Kafka clusters within a Kubernetes 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 Running multiple Cluster Operator replicas with leader election.

5.2.1. 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 in the Kubernetes cluster. You can specify the following namespaces:

Watching multiple selected namespaces has the most impact on performance due to increased processing overhead. To optimize performance for namespace monitoring, it is generally recommended to either watch a single namespace or monitor the entire cluster. Watching a single namespace allows for focused monitoring of namespace-specific resources, while monitoring all namespaces provides a comprehensive view of the cluster’s resources across all namespaces.

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 Kubernetes cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary Kubernetes resources, such as Deployments, Pods, Services and ConfigMaps.

Each time a Kafka resource is updated, the operator performs corresponding updates on the Kubernetes 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 Kubernetes resources.

Note
 While the Cluster Operator can watch one, multiple, or all namespaces in a Kubernetes cluster, the Topic Operator and User Operator watch for KafkaTopic and KafkaUser resources in a single namespace. For more information, see Watching Strimzi resources in Kubernetes namespaces.

5.2.2. Deploying the Cluster Operator to watch a single namespace

This procedure shows how to deploy the Cluster Operator to watch Strimzi resources in a single namespace in your Kubernetes cluster.

Prerequisites

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

Procedure

  1. Edit the Strimzi 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:

    kubectl create -f install/cluster-operator -n my-cluster-operator-namespace

  3. Check the status of the deployment:

    kubectl 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.

5.2.3. Deploying the Cluster Operator to watch multiple namespaces

This procedure shows how to deploy the Cluster Operator to watch Strimzi resources across multiple namespaces in your Kubernetes cluster.

Prerequisites

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

Procedure

  1. Edit the Strimzi 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: quay.io/strimzi/operator:0.39.0
        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:

    kubectl create -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n <watched_namespace>
kubectl create -f install/cluster-operator/023-RoleBinding-strimzi-cluster-operator.yaml -n <watched_namespace>
kubectl create -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n <watched_namespace>

  4. Deploy the Cluster Operator:

    kubectl create -f install/cluster-operator -n my-cluster-operator-namespace

  5. Check the status of the deployment:

    kubectl 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.

5.2.4. Deploying the Cluster Operator to watch all namespaces

This procedure shows how to deploy the Cluster Operator to watch Strimzi resources across all namespaces in your Kubernetes 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 Strimzi 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: quay.io/strimzi/operator:0.39.0
        imagePullPolicy: IfNotPresent
        env:
        - name: STRIMZI_NAMESPACE
          value: "*"
        # ...

  3. Create ClusterRoleBindings that grant cluster-wide access for all namespaces to the Cluster Operator.

    kubectl create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
kubectl create clusterrolebinding strimzi-cluster-operator-watched --clusterrole=strimzi-cluster-operator-watched --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
kubectl 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 Kubernetes cluster.

    kubectl create -f install/cluster-operator -n my-cluster-operator-namespace

  5. Check the status of the deployment:

    kubectl 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.

5.3. Deploying Kafka

To be able to manage a Kafka cluster with the Cluster Operator, you must deploy it as a Kafka resource. Strimzi 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:

Node pools provide configuration for a set of Kafka nodes. By using node pools, nodes can have different configuration within the same Kafka cluster.

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 Kubernetes. However, you can use the Topic Operator and User Operator with a Kafka cluster that is not managed by Strimzi, by deploying them as standalone components. You can also deploy and use other Kafka components with a Kafka cluster not managed by Strimzi.

5.3.1. Deploying a Kafka cluster with node pools

This procedure shows how to deploy Kafka with node pools to your Kubernetes cluster using the Cluster Operator. Node pools represent a distinct group of Kafka nodes within a Kafka cluster that share the same configuration. For each Kafka node in the node pool, any configuration not defined in node pool is inherited from the cluster configuration in the kafka resource.

The deployment uses a YAML file to provide the specification to create a KafkaNodePool resource. You can use node pools with Kafka clusters that use KRaft (Kafka Raft metadata) mode or ZooKeeper for cluster management. To deploy a Kafka cluster in KRaft mode, you must use the KafkaNodePool resources.

Important
 KRaft mode is not ready for production in Apache Kafka or in Strimzi.

Strimzi provides the following example files that you can use to create a Kafka cluster that uses node pools:

kafka-with-dual-role-kraft-nodes.yaml

Deploys a Kafka cluster with one pool of KRaft nodes that share the broker and controller roles.

kafka-with-kraft.yaml

Deploys a persistent Kafka cluster with one pool of controller nodes and one pool of broker nodes.

kafka-with-kraft-ephemeral.yaml

Deploys an ephemeral Kafka cluster with one pool of controller nodes and one pool of broker nodes.

kafka.yaml

Deploys ZooKeeper with 3 nodes, and 2 different pools of Kafka brokers. Each of the pools has 3 brokers. The pools in the example use different storage configuration.

Note
 You can perform the steps outlined here to deploy a new Kafka cluster with KafkaNodePool resources or migrate your existing Kafka cluster.
Prerequisites
Procedure

  1. If you want to use KRaft, enable the UseKRaft feature gate from the command line:

    kubectl set env deployment/strimzi-cluster-operator STRIMZI_FEATURE_GATES="+UseKRaft"

    Or by editing the Cluster Operator Deployment and updating the STRIMZI_FEATURE_GATES environment variable:

    env
  - name: STRIMZI_FEATURE_GATES
    value: +UseKRaft

    This updates the Cluster Operator.

  2. Deploy a Kafka cluster with node pools

    • To deploy a Kafka cluster in KRaft mode with a single node pool that uses dual-role nodes:

      kubectl apply -f examples/kafka/nodepools/kafka-with-dual-role-kraft-nodes.yaml

    • To deploy a persistent Kafka cluster in KRaft mode with separate node pools for broker and controller nodes:

      kubectl apply -f examples/kafka/nodepools/kafka-with-kraft.yaml

    • To deploy an ephemeral Kafka cluster in KRaft mode with separate node pools for broker and controller nodes:

      kubectl apply -f examples/kafka/nodepools/kafka-with-kraft-ephemeral.yaml

    • To deploy a Kafka cluster and ZooKeeper cluster with two node pools of three brokers:

      kubectl apply -f examples/kafka/nodepools/kafka.yaml

  3. Check the status of the deployment:

    kubectl get pods -n <my_cluster_operator_namespace>
    Output shows the node pool names and readiness
    NAME                        READY  STATUS   RESTARTS
my-cluster-entity-operator  3/3    Running  0
my-cluster-pool-a-0         1/1    Running  0
my-cluster-pool-a-1         1/1    Running  0
my-cluster-pool-a-4         1/1    Running  0

    • my-cluster is the name of the Kafka cluster.

    • pool-a is the name of the node pool.

      A sequential index number starting with 0 identifies each Kafka pod created. If you are using ZooKeeper, you’ll also see the ZooKeeper pods.

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

      Information on the deployment is also shown in the status of the KafkaNodePool resource, including a list of IDs for nodes in the pool.

      Note
      		 Node IDs are assigned sequentially starting at 0 (zero) across all node pools within a cluster. This means that node IDs might not run sequentially within a specific node pool. If there are gaps in the sequence of node IDs across the cluster, the next node to be added is assigned an ID that fills the gap. When scaling down, the node with the highest node ID within a pool is removed.
Additional resources

Node pool configuration

5.3.2. Deploying a ZooKeeper-based Kafka cluster without node pools

This procedure shows how to deploy a ZooKeeper-based Kafka cluster to your Kubernetes cluster using the Cluster Operator.

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

Strimzi provides the following example files to create a Kafka cluster that uses ZooKeeper for cluster management:

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, Kubernetes will try to use the default StorageClass.

The following examples show some common types of persistent volumes:

  • If your Kubernetes cluster runs on Amazon AWS, Kubernetes can provision Amazon EBS volumes

  • If your Kubernetes cluster runs on Microsoft Azure, Kubernetes can provision Azure Disk Storage volumes

  • If your Kubernetes cluster runs on Google Cloud, Kubernetes can provision Persistent Disk volumes

  • If your Kubernetes cluster runs on bare metal, Kubernetes 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.

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: 3.6.1
    #...
    config:
      #...
      log.message.format.version: "3.6"
      inter.broker.protocol.version: "3.6"
  # ...
Prerequisites
Procedure

  1. Deploy a ZooKeeper-based Kafka cluster.

    • To deploy an ephemeral cluster:

      kubectl apply -f examples/kafka/kafka-ephemeral.yaml

    • To deploy a persistent cluster:

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

  2. Check the status of the deployment:

    kubectl 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 displays as Running.

Additional resources

Kafka cluster configuration

5.3.3. Deploying the Topic Operator using the Cluster Operator

This procedure describes how to deploy the Topic Operator using the Cluster Operator. The Topic Operator can be deployed for use in either bidirectional mode or unidirectional mode. To learn more about bidirectional and unidirectional topic management, see Topic management modes.

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 Strimzi 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 Strimzi, 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.

Prerequisites
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:

    kubectl apply -f <kafka_configuration_file>

  4. Check the status of the deployment:

    kubectl 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 displays as Running.

5.3.4. 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 Strimzi, 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.

Prerequisites
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:

    kubectl apply -f <kafka_configuration_file>

  4. Check the status of the deployment:

    kubectl 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 displays as Running.

5.3.5. List of Kafka cluster resources

The following resources are created by the Cluster Operator in the Kubernetes cluster.

Shared resources
<kafka_cluster_name>-cluster-ca

Secret with the Cluster CA private key used to encrypt the cluster communication.

<kafka_cluster_name>-cluster-ca-cert

Secret with the Cluster CA public key. This key can be used to verify the identity of the Kafka brokers.

<kafka_cluster_name>-clients-ca

Secret with the Clients CA private key used to sign user certificates

<kafka_cluster_name>-clients-ca-cert

Secret with the Clients CA public key. This key can be used to verify the identity of the Kafka users.

<kafka_cluster_name>-cluster-operator-certs

Secret with Cluster operators keys for communication with Kafka and ZooKeeper.

ZooKeeper nodes
<kafka_cluster_name>-zookeeper

Name given to the following ZooKeeper resources:

  • StrimziPodSet for managing the ZooKeeper node pods.

  • Service account used by the ZooKeeper nodes.

  • PodDisruptionBudget configured for the ZooKeeper nodes.

<kafka_cluster_name>-zookeeper-<pod_id>

Pods created by the StrimziPodSet.

<kafka_cluster_name>-zookeeper-nodes

Headless Service needed to have DNS resolve the ZooKeeper pods IP addresses directly.

<kafka_cluster_name>-zookeeper-client

Service used by Kafka brokers to connect to ZooKeeper nodes as clients.

<kafka_cluster_name>-zookeeper-config

ConfigMap that contains the ZooKeeper ancillary configuration, and is mounted as a volume by the ZooKeeper node pods.

<kafka_cluster_name>-zookeeper-nodes

Secret with ZooKeeper node keys.

<kafka_cluster_name>-network-policy-zookeeper

Network policy managing access to the ZooKeeper services.

data-<kafka_cluster_name>-zookeeper-<pod_id>

Persistent Volume Claim for the volume used for storing data for a specific ZooKeeper node. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.

Kafka brokers
<kafka_cluster_name>-kafka

Name given to the following Kafka resources:

  • StrimziPodSet for managing the Kafka broker pods.

  • Service account used by the Kafka pods.

  • PodDisruptionBudget configured for the Kafka brokers.

<kafka_cluster_name>-kafka-<pod_id>

Name given to the following Kafka resources:

  • Pods created by the StrimziPodSet.

  • ConfigMaps with Kafka broker configuration.

<kafka_cluster_name>-kafka-brokers

Service needed to have DNS resolve the Kafka broker pods IP addresses directly.

<kafka_cluster_name>-kafka-bootstrap

Service can be used as bootstrap servers for Kafka clients connecting from within the Kubernetes cluster.

<kafka_cluster_name>-kafka-external-bootstrap

Bootstrap service for clients connecting from outside the Kubernetes cluster. This resource is created only when an external listener is enabled. The old service name will be used for backwards compatibility when the listener name is external and port is 9094.

<kafka_cluster_name>-kafka-<pod_id>

Service used to route traffic from outside the Kubernetes cluster to individual pods. This resource is created only when an external listener is enabled. The old service name will be used for backwards compatibility when the listener name is external and port is 9094.

<kafka_cluster_name>-kafka-external-bootstrap

Bootstrap route for clients connecting from outside the Kubernetes cluster. This resource is created only when an external listener is enabled and set to type route. The old route name will be used for backwards compatibility when the listener name is external and port is 9094.

<kafka_cluster_name>-kafka-<pod_id>

Route for traffic from outside the Kubernetes cluster to individual pods. This resource is created only when an external listener is enabled and set to type route. The old route name will be used for backwards compatibility when the listener name is external and port is 9094.

<kafka_cluster_name>-kafka-<listener_name>-bootstrap

Bootstrap service for clients connecting from outside the Kubernetes cluster. This resource is created only when an external listener is enabled. The new service name will be used for all other external listeners.

<kafka_cluster_name>-kafka-<listener_name>-<pod_id>

Service used to route traffic from outside the Kubernetes cluster to individual pods. This resource is created only when an external listener is enabled. The new service name will be used for all other external listeners.

<kafka_cluster_name>-kafka-<listener_name>-bootstrap

Bootstrap route for clients connecting from outside the Kubernetes cluster. This resource is created only when an external listener is enabled and set to type route. The new route name will be used for all other external listeners.

<kafka_cluster_name>-kafka-<listener_name>-<pod_id>

Route for traffic from outside the Kubernetes cluster to individual pods. This resource is created only when an external listener is enabled and set to type route. The new route name will be used for all other external listeners.

<kafka_cluster_name>-kafka-config

ConfigMap containing the Kafka ancillary configuration, which is mounted as a volume by the broker pods when the UseStrimziPodSets feature gate is disabled.

<kafka_cluster_name>-kafka-brokers

Secret with Kafka broker keys.

<kafka_cluster_name>-network-policy-kafka

Network policy managing access to the Kafka services.

strimzi-namespace-name-<kafka_cluster_name>-kafka-init

Cluster role binding used by the Kafka brokers.

<kafka_cluster_name>-jmx

Secret with JMX username and password used to secure the Kafka broker port. This resource is created only when JMX is enabled in Kafka.

data-<kafka_cluster_name>-kafka-<pod_id>

Persistent Volume Claim for the volume used for storing data for a specific Kafka broker. This resource is created only if persistent storage is selected for provisioning persistent volumes to store data.

data-<id>-<kafka_cluster_name>-kafka-<pod_id>

Persistent Volume Claim for the volume id used for storing data for a specific Kafka broker. This resource is created only if persistent storage is selected for JBOD volumes when provisioning persistent volumes to store data.

Kafka node pools

If you are using Kafka node pools, the resources created apply to the nodes managed in the node pools whether they are operating as brokers, controllers, or both. The naming convention includes the name of the Kafka cluster and the node pool: <kafka_cluster_name>-<pool_name>.

<kafka_cluster_name>-<pool_name>

Name given to the StrimziPodSet for managing the Kafka node pool.

<kafka_cluster_name>-<pool_name>-<pod_id>

Name given to the following Kafka node pool resources:

  • Pods created by the StrimziPodSet.

  • ConfigMaps with Kafka node configuration.

data-<kafka_cluster_name>-<pool_name>-<pod_id>

Persistent Volume Claim for the volume used for storing data for a specific node. This resource is created only if persistent storage is selected for provisioning persistent volumes to store data.

data-<id>-<kafka_cluster_name>-<pool_name>-<pod_id>

Persistent Volume Claim for the volume id used for storing data for a specific node. This resource is created only if persistent storage is selected for JBOD volumes when provisioning persistent volumes to store data.

Entity Operator

These resources are only created if the Entity Operator is deployed using the Cluster Operator.

<kafka_cluster_name>-entity-operator

Name given to the following Entity Operator resources:

  • Deployment with Topic and User Operators.

  • Service account used by the Entity Operator.

  • Network policy managing access to the Entity Operator metrics.

<kafka_cluster_name>-entity-operator-<random_string>

Pod created by the Entity Operator deployment.

<kafka_cluster_name>-entity-topic-operator-config

ConfigMap with ancillary configuration for Topic Operators.

<kafka_cluster_name>-entity-user-operator-config

ConfigMap with ancillary configuration for User Operators.

<kafka_cluster_name>-entity-topic-operator-certs

Secret with Topic Operator keys for communication with Kafka and ZooKeeper.

<kafka_cluster_name>-entity-user-operator-certs

Secret with User Operator keys for communication with Kafka and ZooKeeper.

strimzi-<kafka_cluster_name>-entity-topic-operator

Role binding used by the Entity Topic Operator.

strimzi-<kafka_cluster_name>-entity-user-operator

Role binding used by the Entity User Operator.

Kafka Exporter

These resources are only created if the Kafka Exporter is deployed using the Cluster Operator.

<kafka_cluster_name>-kafka-exporter

Name given to the following Kafka Exporter resources:

  • Deployment with Kafka Exporter.

  • Service used to collect consumer lag metrics.

  • Service account used by the Kafka Exporter.

  • Network policy managing access to the Kafka Exporter metrics.

<kafka_cluster_name>-kafka-exporter-<random_string>

Pod created by the Kafka Exporter deployment.

Cruise Control

These resources are only created if Cruise Control was deployed using the Cluster Operator.

<kafka_cluster_name>-cruise-control

Name given to the following Cruise Control resources:

  • Deployment with Cruise Control.

  • Service used to communicate with Cruise Control.

  • Service account used by the Cruise Control.

<kafka_cluster_name>-cruise-control-<random_string>

Pod created by the Cruise Control deployment.

<kafka_cluster_name>-cruise-control-config

ConfigMap that contains the Cruise Control ancillary configuration, and is mounted as a volume by the Cruise Control pods.

<kafka_cluster_name>-cruise-control-certs

Secret with Cruise Control keys for communication with Kafka and ZooKeeper.

<kafka_cluster_name>-network-policy-cruise-control

Network policy managing access to the Cruise Control service.

5.4. Deploying Kafka Connect

Kafka Connect is an integration toolkit for streaming data between Kafka brokers and other systems using connector plugins. Kafka Connect provides a framework for integrating Kafka with an external data source or target, such as a database or messaging system, for import or export of data using connectors. Connectors are plugins that provide the connection configuration needed.

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

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.

5.4.1. Deploying Kafka Connect to your Kubernetes cluster

This procedure shows how to deploy a Kafka Connect cluster to your Kubernetes 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.

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

  • examples/connect/kafka-connect.yaml

Important
 If deploying Kafka Connect clusters to run in parallel, each instance must use unique names for internal Kafka Connect topics. To do this, configure each Kafka Connect instance to replace the defaults.
Prerequisites
Procedure

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

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

  2. Check the status of the deployment:

    kubectl 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 displays as Running.

Additional resources

Kafka Connect cluster configuration

5.4.2. List of Kafka Connect cluster resources

The following resources are created by the Cluster Operator in the Kubernetes cluster:

<connect_cluster_name>-connect

Name given to the following Kafka Connect resources:

  • StrimziPodSet that creates the Kafka Connect worker node pods.

  • Headless service that provides stable DNS names to the Kafka Connect pods.

  • Service account used by the Kafka Connect pods.

  • Pod disruption budget configured for the Kafka Connect worker nodes.

  • Network policy managing access to the Kafka Connect REST API.

<connect_cluster_name>-connect-<pod_id>

Pods created by the Kafka Connect StrimziPodSet.

<connect_cluster_name>-connect-api

Service which exposes the REST interface for managing the Kafka Connect cluster.

<connect_cluster_name>-connect-config

ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka Connect pods.

strimzi-<namespace-name>-<connect_cluster_name>-connect-init

Cluster role binding used by the Kafka Connect cluster.

<connect_cluster_name>-connect-build

Pod used to build a new container image with additional connector plugins (only when Kafka Connect Build feature is used).

<connect_cluster_name>-connect-dockerfile

ConfigMap with the Dockerfile generated to build the new container image with additional connector plugins (only when the Kafka Connect build feature is used).

5.5. Adding Kafka Connect 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.

5.5.1. Building a new container image with connector plugins automatically

Configure Kafka Connect so that Strimzi 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. Strimzi 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. Strimzi 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: connector-1
        artifacts:
          - type: tgz
            url: <url_to_download_connector_1_artifact>
            sha512sum: <SHA-512_checksum_of_connector_1_artifact>
      - name: connector-2
        artifacts:
          - type: jar
            url: <url_to_download_connector_2_artifact>
            sha512sum: <SHA-512_checksum_of_connector_2_artifact>
  #...

    1. The specification for the Kafka Connect cluster.

    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:

    $ kubectl 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.

Additional resources

5.5.2. 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 Container Registry as a base image for creating your own custom image with additional connector plugins.

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

Prerequisites
Procedure

  1. Create a new Dockerfile using quay.io/strimzi/kafka:0.39.0-kafka-3.6.1 as the base image:

    FROM quay.io/strimzi/kafka:0.39.0-kafka-3.6.1
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. The specification for the Kafka Connect cluster.

      2. The docker image for Kafka Connect 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.

Additional resources

5.5.3. Deploying KafkaConnector resources

Deploy KafkaConnector resources to manage connectors. The KafkaConnector custom resource offers a Kubernetes-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 feature is enabled (enabled: true) for automatic restarts of failed connectors and tasks. You can also 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 Strimzi. 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.

Strimzi 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:

    Example KafkaConnector source connector configuration
    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 a Kubernetes 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 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. By default, the number of restarts is indefinite, but you can set a maximum on the number of automatic restarts using the maxRestarts property.

    6. Connector configuration as key-value pairs.

    7. Location of the external data file. In this example, we’re configuring the FileStreamSourceConnector to read from the /opt/kafka/LICENSE file.

    8. Kafka topic to publish the source data to.

  4. Create the source KafkaConnector in your Kubernetes cluster:

    kubectl 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 Kubernetes cluster:

    kubectl apply -f examples/connect/sink-connector.yaml

  8. Check that the connector resources were created:

    kubectl 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:

    kubectl 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 5. Configuration options for the FileStreamSource connector class
Name Type Default value Description

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. Configuration options for FileStreamSinkConnector class
Name Type Default value Description

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.

5.5.4. 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 Kubernetes cluster. If you want to make the Kafka Connect API accessible to applications running outside of the Kubernetes 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 Kubernetes 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.

5.5.5. 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 Kubernetes 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 Kubernetes RBAC rules permit only Kubernetes cluster administrators to make changes to connectors. You can also designate non-cluster administrators to manage Strimzi 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 Kubernetes 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
# ...

5.5.6. 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.

5.6. Deploying Kafka MirrorMaker

Kafka MirrorMaker replicates data between two or more Kafka clusters, within or across data centers. This process is called mirroring to avoid confusion with the concept of Kafka partition replication. MirrorMaker consumes messages from a source cluster and republishes those messages to a target cluster.

Data replication across clusters supports scenarios that require the following:

  • Recovery of data in the event of a system failure

  • Consolidation of data from multiple source clusters for centralized analysis

  • Restriction of data access to a specific cluster

  • Provision of data at a specific location to improve latency

5.6.1. Deploying Kafka MirrorMaker to your Kubernetes cluster

This procedure shows how to deploy a Kafka MirrorMaker cluster to your Kubernetes 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. MirrorMaker 2 is based on Kafka Connect and uses its configuration properties.

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 Strimzi as well. The KafkaMirrorMaker resource will be removed from Strimzi when we adopt Apache Kafka 4.0.0. As a replacement, use the KafkaMirrorMaker2 custom resource with the IdentityReplicationPolicy.

Strimzi 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

Important
 If deploying MirrorMaker 2 clusters to run in parallel, using the same target Kafka cluster, each instance must use unique names for internal Kafka Connect topics. To do this, configure each MirrorMaker 2 instance to replace the defaults.
Prerequisites
Procedure

  1. Deploy Kafka MirrorMaker to your Kubernetes cluster:

    For MirrorMaker:

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

    For MirrorMaker 2:

    kubectl apply -f examples/mirror-maker/kafka-mirror-maker-2.yaml

  2. Check the status of the deployment:

    kubectl 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 displays as Running.

Additional resources

5.6.2. List of Kafka MirrorMaker 2 cluster resources

The following resources are created by the Cluster Operator in the Kubernetes cluster:

<mirrormaker2_cluster_name>-mirrormaker2

Name given to the following MirrorMaker 2 resources:

  • StrimziPodSet that creates the MirrorMaker 2 worker node pods.

  • Headless service that provides stable DNS names to the MirrorMaker 2 pods.

  • Service account used by the MirrorMaker 2 pods.

  • Pod disruption budget configured for the MirrorMaker 2 worker nodes.

  • Network Policy managing access to the MirrorMaker 2 REST API.

<mirrormaker2_cluster_name>-mirrormaker2-<pod_id>

Pods created by the MirrorMaker 2 StrimziPodSet.

<mirrormaker2_cluster_name>-mirrormaker2-api

Service which exposes the REST interface for managing the MirrorMaker 2 cluster.

<mirrormaker2_cluster_name>-mirrormaker2-config

ConfigMap which contains the MirrorMaker 2 ancillary configuration and is mounted as a volume by the MirrorMaker 2 pods.

strimzi-<namespace-name>-<mirrormaker2_cluster_name>-mirrormaker2-init

Cluster role binding used by the MirrorMaker 2 cluster.

5.6.3. List of Kafka MirrorMaker cluster resources

The following resources are created by the Cluster Operator in the Kubernetes cluster:

<mirrormaker_cluster_name>-mirror-maker

Name given to the following MirrorMaker resources:

  • Deployment which is responsible for creating the MirrorMaker pods.

  • Service account used by the MirrorMaker nodes.

  • Pod Disruption Budget configured for the MirrorMaker worker nodes.

<mirrormaker_cluster_name>-mirror-maker-config

ConfigMap which contains ancillary configuration for MirrorMaker, and is mounted as a volume by the MirrorMaker pods.

5.7. Deploying Kafka Bridge

Kafka Bridge provides an API for integrating HTTP-based clients with a Kafka cluster.

5.7.1. Deploying Kafka Bridge to your Kubernetes cluster

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

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

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

  • examples/bridge/kafka-bridge.yaml

Prerequisites
Procedure

  1. Deploy Kafka Bridge to your Kubernetes cluster:

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

  2. Check the status of the deployment:

    kubectl 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 displays as Running.

Additional resources

5.7.2. Exposing the Kafka Bridge service to your local machine

Use port forwarding to expose the Strimzi 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 Kubernetes cluster:

    kubectl get pods -o name

pod/kafka-consumer
# ...
pod/my-bridge-bridge-<pod_id>

  2. Connect to the Kafka Bridge pod on port 8080:

    kubectl 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.

5.7.3. Accessing the Kafka Bridge outside of Kubernetes

After deployment, the Strimzi Kafka Bridge can only be accessed by applications running in the same Kubernetes 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 Kubernetes 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 Kubernetes cluster.

5.7.4. List of Kafka Bridge cluster resources

The following resources are created by the Cluster Operator in the Kubernetes cluster:

<bridge_cluster_name>-bridge

Deployment which is in charge to create the Kafka Bridge worker node pods.

<bridge_cluster_name>-bridge-service

Service which exposes the REST interface of the Kafka Bridge cluster.

<bridge_cluster_name>-bridge-config

ConfigMap which contains the Kafka Bridge ancillary configuration and is mounted as a volume by the Kafka broker pods.

<bridge_cluster_name>-bridge

Pod Disruption Budget configured for the Kafka Bridge worker nodes.

5.8. Alternative standalone deployment options for Strimzi 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 Kubernetes. Kafka can be running outside of Kubernetes. 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.

5.8.1. Deploying the standalone Topic Operator

This procedure shows how to deploy the Topic Operator in unidirectional mode 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. Unidirectional topic management maintains topics solely through KafkaTopic resources. For more information on unidirectional topic management, see Topic management modes. Alternate configuration is also shown for deploying the Topic Operator in bidirectional mode.

Standalone deployment files are provided with Strimzi. 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_FULL_RECONCILIATION_INTERVAL_MS # (4)
              value: "120000"
            - name: STRIMZI_LOG_LEVEL # (5)
              value: INFO
            - name: STRIMZI_TLS_ENABLED # (6)
              value: "false"
            - name: STRIMZI_JAVA_OPTS # (7)
              value: "-Xmx=512M -Xms=256M"
            - name: STRIMZI_JAVA_SYSTEM_PROPERTIES # (8)
              value: "-Djavax.net.debug=verbose -DpropertyName=value"
            - name: STRIMZI_PUBLIC_CA # (9)
              value: "false"
            - name: STRIMZI_TLS_AUTH_ENABLED # (10)
              value: "false"
            - name: STRIMZI_SASL_ENABLED # (11)
              value: "false"
            - name: STRIMZI_SASL_USERNAME # (12)
              value: "admin"
            - name: STRIMZI_SASL_PASSWORD # (13)
              value: "password"
            - name: STRIMZI_SASL_MECHANISM # (14)
              value: "scram-sha-512"
            - name: STRIMZI_SECURITY_PROTOCOL # (15)
              value: "SSL"
            - name: STRIMZI_USE_FINALIZERS
              value: "false" # (16)

    1. The Kubernetes 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 interval between periodic reconciliations, in milliseconds. The default is 120000 (2 minutes).

    5. The level for printing logging messages. You can set the level to ERROR, WARNING, INFO, DEBUG, or TRACE.

    6. Enables TLS support for encrypted communication with the Kafka brokers.

    7. (Optional) The Java options used by the JVM running the Topic Operator.

    8. (Optional) The debugging (-D) options set for the Topic Operator.

    9. (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.

    10. (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.

    11. (Optional) Enables SASL support for client authentication when connecting to Kafka brokers. The default is false.

    12. (Optional) The SASL username for client authentication. Mandatory only if SASL is enabled through STRIMZI_SASL_ENABLED.

    13. (Optional) The SASL password for client authentication. Mandatory only if SASL is enabled through STRIMZI_SASL_ENABLED.

    14. (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.

    15. (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.

    16. Set STRIMZI_USE_FINALIZERS to false if you do not want to use finalizers to control topic deletion.

  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. Apply the changes to the Deployment configuration to deploy the Topic Operator.

  5. Check the status of the deployment:

    kubectl 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.

Deploying the standalone Topic Operator for bidirectional topic management

Bidirectional topic management requires ZooKeeper for cluster management, and maintains topics through KafkaTopic resources and within the Kafka cluster. If you want to switch to using the Topic Operator in this mode, follow these steps to deploy the standalone Topic Operator.

Note
 As the feature gate enabling the Topic Operator to run in unidirectional mode progresses to General Availability, bidirectional mode will be phased out. This transition is aimed at enhancing the user experience, particularly in supporting Kafka in KRaft mode.

  1. Undeploy the current standalone Topic Operator.

    Retain the KafkaTopic resources, which are picked up by the Topic Operator when it is deployed again.

  2. Edit the Deployment configuration for the standalone Topic Operator to include ZooKeeper-related environment variables:

    • STRIMZI_ZOOKEEPER_CONNECT

    • STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS

    • TC_ZK_CONNECTION_TIMEOUT_MS

    • STRIMZI_USE_ZOOKEEPER_TOPIC_STORE

      It is the presence or absence of the ZooKeeper variables that defines whether the bidirectional Topic Operator is used. Unidirectional topic management does not use ZooKeeper. If ZooKeeper environment variables are not present, the unidirectional Topic Operator is used. Otherwise, the bidirectional Topic Operator is used.

      Other environment variables that are not used in unidirectional mode can be added if required:

    • STRIMZI_REASSIGN_THROTTLE

    • STRIMZI_REASSIGN_VERIFY_INTERVAL_MS

    • STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS

    • STRIMZI_TOPICS_PATH

    • STRIMZI_STORE_TOPIC

    • STRIMZI_STORE_NAME

    • STRIMZI_APPLICATION_ID

    • STRIMZI_STALE_RESULT_TIMEOUT_MS

      Example standalone Topic Operator deployment configuration for bidirectional topic management
      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
              valueFrom:
                fieldRef:
                  fieldPath: metadata.namespace
            - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS
              value: my-kafka-bootstrap-address:9092
            - name: STRIMZI_RESOURCE_LABELS
              value: "strimzi.io/cluster=my-cluster"
            - name: STRIMZI_ZOOKEEPER_CONNECT # (1)
              value: my-cluster-zookeeper-client:2181
            - name: STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS # (2)
              value: "18000"
            - name: STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS # (3)
              value: "6"
            - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
              value: "120000"
            - name: STRIMZI_LOG_LEVEL
              value: INFO
            - name: STRIMZI_TLS_ENABLED
              value: "false"
            - name: STRIMZI_JAVA_OPTS
              value: "-Xmx=512M -Xms=256M"
            - name: STRIMZI_JAVA_SYSTEM_PROPERTIES
              value: "-Djavax.net.debug=verbose -DpropertyName=value"
            - name: STRIMZI_PUBLIC_CA
              value: "false"
            - name: STRIMZI_TLS_AUTH_ENABLED
              value: "false"
            - name: STRIMZI_SASL_ENABLED
              value: "false"
            - name: STRIMZI_SASL_USERNAME
              value: "admin"
            - name: STRIMZI_SASL_PASSWORD
              value: "password"
            - name: STRIMZI_SASL_MECHANISM
              value: "scram-sha-512"
            - name: STRIMZI_SECURITY_PROTOCOL
              value: "SSL"

      1. (ZooKeeper) 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.

      2. (ZooKeeper) The ZooKeeper session timeout, in milliseconds. The default is 18000 (18 seconds).

      3. 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.

  3. Apply the changes to the Deployment configuration to deploy the Topic Operator.

5.8.2. 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 Strimzi. 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

    1. The Kubernetes 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 Kubernetes Secret that contains the public key (ca.crt) value of the CA (certificate authority) that signs new user certificates for mTLS authentication.

    4. The Kubernetes 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 Kubernetes 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 Kubernetes 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.

  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 Kubernetes Secret that contains the public key (ca.crt) value of the CA that signs Kafka broker certificates.

    2. The Kubernetes 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.

    kubectl create -f install/user-operator

  4. Check the status of the deployment:

    kubectl 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.

6. Deploying Strimzi from OperatorHub.io

OperatorHub.io is a catalog of Kubernetes operators sourced from multiple providers. It offers you an alternative way to install a stable version of Strimzi.

The Operator Lifecycle Manager is used for the installation and management of all operators published on OperatorHub.io. Operator Lifecycle Manager is a prerequisite for installing the Strimzi Kafka operator

To install Strimzi, locate Strimzi from OperatorHub.io, and follow the instructions provided to deploy the Cluster Operator. After you have deployed the Cluster Operator, you can deploy Strimzi components using custom resources. For example, you can deploy the Kafka custom resource, and the installed Cluster Operator will create a Kafka cluster.

Upgrades between versions might include manual steps. Always read the release notes before upgrading.

For information on upgrades, see Upgrading Strimzi.

Warning
 Make sure you use the appropriate update channel. Installing Strimzi from the default stable channel is generally safe. However, we do not recommend enabling automatic OLM updates on the stable channel. An automatic upgrade will skip any necessary steps prior to upgrade. For example, to upgrade from 0.22 or earlier you must first update custom resources to support the v1beta2 API version. Use automatic upgrades only on version-specific channels.

7. Deploying Strimzi using Helm

Helm charts are used to package, configure, and deploy Kubernetes resources. Strimzi provides a Helm chart to deploy the Cluster Operator.

After you have deployed the Cluster Operator this way, you can deploy Strimzi components using custom resources. For example, you can deploy the Kafka custom resource, and the installed Cluster Operator will create a Kafka cluster.

For information on upgrades, see Upgrading Strimzi.

Prerequisites

  • The Helm client must be installed on a local machine.

Procedure

  1. Install the Strimzi Cluster Operator using the Helm command line tool:

    helm install strimzi-cluster-operator oci://quay.io/strimzi-helm/strimzi-kafka-operator

    Alternatively, you can use parameter values to install a specific version of the Cluster Operator or specify any changes to the default configuration.

    Example configuration that installs a specific version of the Cluster Operator and changes the number of replicas
    helm install strimzi-cluster-operator --set replicas=2 --version 0.35.0 oci://quay.io/strimzi-helm/strimzi-kafka-operator

  2. Verify that the Cluster Operator has been deployed successfully using the Helm command line tool:

    helm ls

  3. Deploy Kafka and other Kafka components using custom resources.

8. Enabling Strimzi feature gates

Strimzi operators use feature gates to enable or disable specific features and functions. By enabling a feature gate, you alter the behavior of the corresponding operator, thereby introducing the feature to your Strimzi deployment.

A feature gate might be enabled or disabled by default, depending on its level of maturity.

To modify a feature gate’s default state, use the STRIMZI_FEATURE_GATES environment variable in the operator’s configuration. You can modify multiple feature gates using this single environment variable. Specify a comma-separated list of feature gate names and prefixes. A + prefix enables the feature gate and a - prefix disables it.

Example feature gate configuration that enables FeatureGate1 and disables FeatureGate2
env:
  - name: STRIMZI_FEATURE_GATES
    value: +FeatureGate1,-FeatureGate2

8.1. ControlPlaneListener feature gate

The ControlPlaneListener feature gate has moved to GA, which means it is now permanently enabled and cannot be disabled. With ControlPlaneListener enabled, 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 Strimzi operators, Cruise Control, or the Kafka Exporter use the replication listener on port 9091.

Important
 With the ControlPlaneListener feature gate permanently enabled, it is no longer possible to upgrade or downgrade directly between Strimzi 0.22 and earlier and Strimzi 0.32 and newer. You have to first upgrade or downgrade through one of the Strimzi versions in-between, disable the ControlPlaneListener feature gate, and then downgrade or upgrade (with the feature gate enabled) to the target version.

8.2. ServiceAccountPatching feature gate

The ServiceAccountPatching feature gate has moved to GA, which means it is now permanently enabled and cannot be disabled. With ServiceAccountPatching enabled, the Cluster Operator always reconciles service accounts and updates them when needed. For example, when you change service account labels or annotations using the template property of a custom resource, the operator automatically updates them on the existing service account resources.

8.3. UseStrimziPodSets feature gate

The UseStrimziPodSets feature gate has moved to GA, which means it is now permanently enabled and cannot be disabled. Support for StatefulSets has been removed and Strimzi is now always using StrimziPodSets to manage Kafka and ZooKeeper pods.

Important
 With the UseStrimziPodSets feature gate permanently enabled, it is no longer possible to downgrade directly from Strimzi 0.35 and newer to Strimzi 0.27 or earlier. You have to first downgrade through one of the Strimzi versions in-between, disable the UseStrimziPodSets feature gate, and then downgrade to Strimzi 0.27 or earlier.

8.4. StableConnectIdentities feature gate

The StableConnectIdentities feature gate has moved to GA, which means it is now permanently enabled and cannot be disabled. The StrimziPodSet resources are now always used to manage Kafka Connect and Kafka MirrorMaker 2 pods instead of using Kubernetes Deployment resources. StrimziPodSet resources give the pods stable names and stable addresses, which do not change during rolling upgrades. This helps to minimize the number of rebalances of connector tasks.

Important
 With the StableConnectIdentities feature gate permanently enabled, it is no longer possible to downgrade directly from Strimzi 0.39 and newer to Strimzi 0.33 or earlier. You have to first downgrade through one of the Strimzi versions in-between, disable the StableConnectIdentities feature gate, and then downgrade to Strimzi 0.33 or earlier.

8.5. (Preview) UseKRaft feature gate

The UseKRaft feature gate has a default state of disabled.

The UseKRaft feature gate deploys the Kafka cluster in the KRaft (Kafka Raft metadata) mode without ZooKeeper. ZooKeeper and KRaft are mechanisms used to manage metadata and coordinate operations in Kafka clusters. KRaft mode eliminates the need for an external coordination service like ZooKeeper. In KRaft mode, Kafka nodes take on the roles of brokers, controllers, or both. They collectively manage the metadata, which is replicated across partitions. Controllers are responsible for coordinating operations and maintaining the cluster’s state.

This feature gate is currently intended only for development and testing.

Important
 KRaft mode is not ready for production in Apache Kafka or in Strimzi.

Enabling the UseKRaft feature gate requires the KafkaNodePools feature gate to be enabled as well. To deploy a Kafka cluster in KRaft mode, you must use the KafkaNodePool resources. For more details and examples, see Deploying a Kafka cluster with node pools. The Kafka custom resource using KRaft mode must also have the annotation strimzi.io/kraft: enabled.

When the UseKRaft feature gate is enabled and such annotation is set, the Kafka cluster is deployed without ZooKeeper. The .spec.zookeeper properties in the Kafka custom resource are ignored, but still need to be present. The UseKRaft feature gate provides an API that configures Kafka cluster nodes and their roles. The API is still in development and is expected to change before the KRaft mode is production-ready.

Currently, the KRaft mode in Strimzi has the following major limitations:

  • Moving from Kafka clusters with ZooKeeper to KRaft clusters or the other way around is not supported.

  • Controller-only nodes cannot undergo rolling updates or be updated individually.

  • Upgrades and downgrades of Apache Kafka versions or the Strimzi operator are not supported. Users might need to delete the cluster, upgrade the operator and deploy a new Kafka cluster.

  • Only the Unidirectional Topic Operator is supported in KRaft mode. The Bidirectional Topic Operator is not supported and when the UnidirectionalTopicOperator feature gate is not enabled, the spec.entityOperator.topicOperator property must be removed from the Kafka custom resource.

  • JBOD storage is not supported. The type: jbod storage can be used, but the JBOD array can contain only one disk.

Enabling the UseKRaft feature gate

To enable the UseKRaft feature gate, specify +UseKRaft in the STRIMZI_FEATURE_GATES environment variable in the Cluster Operator configuration. The Kafka custom resource using KRaft mode must also have the annotation strimzi.io/kraft: enabled. If this annotation is set to disabled or any other value, or if it is missing, the operator handles the Kafka custom resource as if it is using ZooKeeper for cluster management.

8.6. KafkaNodePools feature gate

The KafkaNodePools feature gate has a default state of enabled.

The KafkaNodePools feature gate introduces a new KafkaNodePool custom resource that enables the configuration of different pools of Apache Kafka nodes.

A node pool refers to a distinct group of Kafka nodes within a Kafka cluster. Each pool has its own unique configuration, which includes mandatory settings such as the number of replicas, storage configuration, and a list of assigned roles. You can assign the controller role, broker role, or both roles to all nodes in the pool in the .spec.roles field. When used with a ZooKeeper-based Apache Kafka cluster, it must be set to the broker role. When used with the UseKRaft feature gate, it can be set to broker, controller, or both.

In addition, a node pool can have its own configuration of resource requests and limits, Java JVM options, and resource templates. Configuration options not set in the KafkaNodePool resource are inherited from the Kafka custom resource.

The KafkaNodePool resources use a strimzi.io/cluster label to indicate to which Kafka cluster they belong. The label must be set to the name of the Kafka custom resource.

Examples of the KafkaNodePool resources can be found in the example configuration files provided by Strimzi.

Disabling the KafkaNodePools feature gate

To disable the KafkaNodePools feature gate, specify -KafkaNodePools in the STRIMZI_FEATURE_GATES environment variable in the Cluster Operator configuration. The Kafka custom resource using the node pools must also have the annotation strimzi.io/node-pools: enabled.

Downgrading from KafkaNodePools

If your cluster already uses KafkaNodePool custom resources, and you wish to downgrade to an older version of Strimzi that does not support them or with the KafkaNodePools feature gate disabled, you must first migrate from KafkaNodePool custom resources to managing Kafka nodes using only Kafka custom resources.

8.7. UnidirectionalTopicOperator feature gate

The UnidirectionalTopicOperator feature gate has a default state of enabled.

The UnidirectionalTopicOperator feature gate introduces a unidirectional topic management mode for creating Kafka topics using the KafkaTopic resource. Unidirectional mode is compatible with using KRaft for cluster management. With unidirectional mode, you create Kafka topics using the KafkaTopic resource, which are then managed by the Topic Operator. Any configuration changes to a topic outside the KafkaTopic resource are reverted. For more information on topic management, see Topic management modes.

Disabling the UnidirectionalTopicOperator feature gate

To disable the UnidirectionalTopicOperator feature gate, specify -UnidirectionalTopicOperator in the STRIMZI_FEATURE_GATES environment variable in the Cluster Operator configuration.

8.8. Feature gate releases

Feature gates have three stages of maturity:

  • Alpha — typically disabled by default

  • Beta — typically enabled by default

  • General Availability (GA) — typically always enabled

Alpha stage features might be experimental or unstable, subject to change, or not sufficiently tested for production use. Beta stage features are well tested and their functionality is not likely to change. GA stage features are stable and should not change in the future. Alpha and beta stage features are removed if they do not prove to be useful.

  • The ControlPlaneListener feature gate moved to GA stage in Strimzi 0.32. It is now permanently enabled and cannot be disabled.

  • The ServiceAccountPatching feature gate moved to GA stage in Strimzi 0.30. It is now permanently enabled and cannot be disabled.

  • The UseStrimziPodSets feature gate moved to GA stage in Strimzi 0.35 and the support for StatefulSets is completely removed. It is now permanently enabled and cannot be disabled.

  • The StableConnectIdentities feature gate moved to GA stage in Strimzi 0.39. It is now permanently enabled and cannot be disabled.

  • The UseKRaft feature gate is available for development only and does not currently have a planned release for moving to the beta phase.

  • The KafkaNodePools feature gate is in beta stage and is enabled by default. It is expected to move to GA phase and be always enabled from Strimzi 0.41.

  • The UnidirectionalTopicOperator feature gate is in beta stage and is enabled by default. It is expected to move to GA phase and be always enabled from Strimzi 0.41.

Note
 Feature gates might be removed when they reach GA. This means that the feature was incorporated into the Strimzi core features and can no longer be disabled.
Table 7. Feature gates and the Strimzi versions when they moved to alpha, beta, or GA
Feature gate Alpha Beta GA

ControlPlaneListener

0.23

0.27

0.32

ServiceAccountPatching

0.24

0.27

0.30

UseStrimziPodSets

0.28

0.30

0.35

UseKRaft

0.29

-

-

StableConnectIdentities

0.34

0.37

0.39

KafkaNodePools

0.36

0.39

0.41 (planned)

UnidirectionalTopicOperator

0.36

0.39

0.41 (planned)

If a feature gate is enabled, you may need to disable it before upgrading or downgrading from a specific Strimzi version (or first upgrade / downgrade to a version of Strimzi where it can be disabled). The following table shows which feature gates you need to disable when upgrading or downgrading Strimzi versions.

Table 8. Feature gates to disable when upgrading or downgrading Strimzi
Disable Feature gate Upgrading from Strimzi version Downgrading to Strimzi version

ControlPlaneListener

0.22 and earlier

0.22 and earlier

UseStrimziPodSets

-

0.27 and earlier

StableConnectIdentities

-

0.33 and earlier

9. Configuring a deployment

Configure and manage a Strimzi deployment to your precise needs using Strimzi custom resources. Strimzi provides example custom resources with each release, allowing you to configure and create instances of supported Kafka components. Fine-tune your deployment by configuring custom resources to include additional features according to your specific requirements. For specific areas of configuration, namely metrics, logging, and external configuration for Kafka Connect connectors, you can also use ConfigMap resources. By using a ConfigMap resource to incorporate configuration, you centralize maintenance. You can also use configuration providers to load configuration from external sources, which we recommend for supplying the credentials for Kafka Connect connector configuration.

Use custom resources to configure and create instances of the following components:

  • Kafka clusters

  • Kafka Connect clusters

  • Kafka MirrorMaker

  • Kafka Bridge

  • Cruise Control

You can also use custom resource configuration to manage your instances or modify your deployment to introduce additional features. This might include configuration that supports the following:

  • Specifying node pools

  • Securing client access to Kafka brokers

  • Accessing Kafka brokers from outside the cluster

  • Creating topics

  • Creating users (clients)

  • Controlling feature gates

  • Changing logging frequency

  • Allocating resource limits and requests

  • Introducing features, such as Strimzi Drain Cleaner, Cruise Control, or distributed tracing.

The Strimzi Custom Resource API Reference describes the properties you can use in your configuration.

Note
 Labels applied to a custom resource are also applied to the Kubernetes resources making up its cluster. This provides a convenient mechanism for resources to be labeled as required.
Applying changes to a custom resource configuration file

You add configuration to a custom resource using spec properties. After adding the configuration, you can use kubectl to apply the changes to a custom resource configuration file:

kubectl apply -f <kafka_configuration_file>

9.1. Using example configuration files

Further enhance your deployment by incorporating additional supported configuration. Example configuration files are provided with the downloadable release artifacts from the GitHub releases page. You can also access the example files directly from the examples directory.

The example files include only the essential properties and values for custom resources by default. You can download and apply the examples using the kubectl command-line tool. The examples can serve as a starting point when building your own Kafka component configuration for deployment.

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

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

Example configuration files provided with Strimzi
examples
├── user (1)
├── topic (2)
├── security (3)
│   ├── tls-auth
│   ├── scram-sha-512-auth
│   └── keycloak-authorization
├── mirror-maker (4)
├── metrics (5)
├── kafka (6)
│   └── nodepools (7)
├── cruise-control (8)
├── connect (9)
└── bridge (10)

  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 Keycloak example includes Kafka custom resource configuration and a Keycloak realm specification. You can use the example to try Keycloak 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. KafkaNodePool configuration for Kafka nodes in a Kafka cluster. Includes example configuration for nodes in clusters that use KRaft (Kafka Raft metadata) mode or ZooKeeper.

  8. Kafka custom resource with a deployment configuration for Cruise Control. Includes KafkaRebalance custom resources to generate optimization proposals from Cruise Control, with example configurations to use the default or user optimization goals.

  9. KafkaConnect and KafkaConnector custom resource configuration for a deployment of Kafka Connect. Includes example configurations for a single or multi-node deployment.

  10. KafkaBridge custom resource configuration for a deployment of Kafka Bridge.

9.2. Configuring Kafka

Update the spec properties of the Kafka custom resource to configure your Kafka deployment.

As well as configuring Kafka, you can add configuration for ZooKeeper and the Strimzi Operators. Common configuration properties, such as logging and healthchecks, are configured independently for each component.

Configuration options that are particularly important include the following:

  • Resource requests (CPU / Memory)

  • JVM options for maximum and minimum memory allocation

  • Listeners for connecting clients to Kafka brokers (and authentication of clients)

  • Authentication

  • Storage

  • Rack awareness

  • Metrics

  • Cruise Control for cluster rebalancing

  • Metadata version for KRaft-based Kafka clusters

  • Inter-broker protocol version for ZooKeeper-based Kafka clusters

The .spec.kafka.metadataVersion property or the inter.broker.protocol.version property in config must be a version supported by the specified Kafka version (spec.kafka.version). The property represents the Kafka metadata or inter-broker protocol version used in a Kafka cluster. If either of these properties is not set in the configuration, the Cluster Operator updates the version to the default for the Kafka version used.

Note
 The oldest supported metadata version is 3.3. Using a metadata version that is older than the Kafka version might cause some features to be disabled.

For a deeper understanding of the Kafka cluster configuration options, refer to the Strimzi Custom Resource API Reference.

Managing TLS certificates

When deploying Kafka, the Cluster Operator automatically sets up and renews TLS certificates to enable encryption and authentication within your cluster. If required, you can manually renew the cluster and clients CA certificates before their renewal period starts. You can also replace the keys used by the cluster and clients CA certificates. For more information, see Renewing CA certificates manually and Replacing private keys.

Example Kafka custom resource configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    replicas: 3 # (1)
    version: 3.6.1 # (2)
    logging: # (3)
      type: inline
      loggers:
        kafka.root.logger.level: INFO
    resources: # (4)
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"
    readinessProbe: # (5)
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    jvmOptions: # (6)
      -Xms: 8192m
      -Xmx: 8192m
    image: my-org/my-image:latest # (7)
    listeners: # (8)
      - name: plain # (9)
        port: 9092 # (10)
        type: internal # (11)
        tls: false # (12)
        configuration:
          useServiceDnsDomain: true # (13)
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication: # (14)
          type: tls
      - name: external1 # (15)
        port: 9094
        type: route
        tls: true
        configuration:
          brokerCertChainAndKey: # (16)
            secretName: my-secret
            certificate: my-certificate.crt
            key: my-key.key
    authorization: # (17)
      type: simple
    config: # (18)
      auto.create.topics.enable: "false"
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
      default.replication.factor: 3
      min.insync.replicas: 2
      inter.broker.protocol.version: "3.6"
    storage: # (19)
      type: persistent-claim # (20)
      size: 10000Gi
    rack: # (21)
      topologyKey: topology.kubernetes.io/zone
    metricsConfig: # (22)
      type: jmxPrometheusExporter
      valueFrom:
        configMapKeyRef: # (23)
          name: my-config-map
          key: my-key
    # ...
  zookeeper: # (24)
    replicas: 3 # (25)
    logging: # (26)
      type: inline
      loggers:
        zookeeper.root.logger: INFO
    resources:
      requests:
        memory: 8Gi
        cpu: "2"
      limits:
        memory: 8Gi
        cpu: "2"
    jvmOptions:
      -Xms: 4096m
      -Xmx: 4096m
    storage:
      type: persistent-claim
      size: 1000Gi
    metricsConfig:
      # ...
  entityOperator: # (27)
    tlsSidecar: # (28)
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging: # (29)
        type: inline
        loggers:
          rootLogger.level: INFO
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
    userOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging: # (30)
        type: inline
        loggers:
          rootLogger.level: INFO
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
  kafkaExporter: # (31)
    # ...
  cruiseControl: # (32)
    # ...

  1. The number of replica nodes.

  2. Kafka version, which can be changed to a supported version by following the upgrade procedure.

  3. Kafka loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties key in the ConfigMap. For the Kafka kafka.root.logger.level logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

  4. Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.

  5. Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).

  6. JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka.

  7. ADVANCED OPTION: Container image configuration, which is recommended only in special situations.

  8. Listeners configure how clients connect to the Kafka cluster via bootstrap addresses. Listeners are configured as internal or external listeners for connection from inside or outside the Kubernetes cluster.

  9. Name to identify the listener. Must be unique within the Kafka cluster.

  10. 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.

  11. Listener type specified as internal or cluster-ip (to expose Kafka using per-broker ClusterIP services), or for external listeners, as route (OpenShift only), loadbalancer, nodeport or ingress (Kubernetes only).

  12. Enables TLS encryption for each listener. Default is false. TLS encryption has to be enabled, by setting it to true, for route and ingress type listeners.

  13. Defines whether the fully-qualified DNS names including the cluster service suffix (usually .cluster.local) are assigned.

  14. Listener authentication mechanism specified as mTLS, SCRAM-SHA-512, or token-based OAuth 2.0.

  15. External listener configuration specifies how the Kafka cluster is exposed outside Kubernetes, such as through a route, loadbalancer or nodeport.

  16. Optional configuration for a Kafka listener certificate managed by an external CA (certificate authority). The brokerCertChainAndKey specifies a Secret that contains a server certificate and a private key. You can configure Kafka listener certificates on any listener with enabled TLS encryption.

  17. Authorization enables simple, OAUTH 2.0, or OPA authorization on the Kafka broker. Simple authorization uses the AclAuthorizer and StandardAuthorizer Kafka plugins.

  18. Broker configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.

  19. Storage size for persistent volumes may be increased and additional volumes may be added to JBOD storage.

  20. Persistent storage has additional configuration options, such as a storage id and class for dynamic volume provisioning.

  21. Rack awareness configuration to spread replicas across different racks, data centers, or availability zones. The topologyKey must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standard topology.kubernetes.io/zone label.

  22. Prometheus metrics enabled. In this example, metrics are configured for the Prometheus JMX Exporter (the default metrics exporter).

  23. Rules for exporting metrics in Prometheus format to a Grafana dashboard through the Prometheus JMX Exporter, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under metricsConfig.valueFrom.configMapKeyRef.key.

  24. ZooKeeper-specific configuration, which contains properties similar to the Kafka configuration.

  25. The number of ZooKeeper nodes. ZooKeeper clusters or ensembles usually run with an odd number of nodes, typically three, five, or seven. The majority of nodes must be available in order to maintain an effective quorum. If the ZooKeeper cluster loses its quorum, it will stop responding to clients and the Kafka brokers will stop working. Having a stable and highly available ZooKeeper cluster is crucial for Strimzi.

  26. ZooKeeper loggers and log levels.

  27. Entity Operator configuration, which specifies the configuration for the Topic Operator and User Operator.

  28. Entity Operator TLS sidecar configuration. Entity Operator uses the TLS sidecar for secure communication with ZooKeeper.

  29. Specified Topic Operator loggers and log levels. This example uses inline logging.

  30. Specified User Operator loggers and log levels.

  31. Kafka Exporter configuration. Kafka Exporter is an optional component for extracting metrics data from Kafka brokers, in particular consumer lag data. For Kafka Exporter to be able to work properly, consumer groups need to be in use.

  32. Optional configuration for Cruise Control, which is used to rebalance the Kafka cluster.

9.2.1. Setting limits on brokers using the Kafka Static Quota plugin

Use the Kafka Static Quota plugin to set throughput and storage limits on brokers in your Kafka cluster. You enable the plugin and set limits by configuring the Kafka resource. You can set a byte-rate threshold and storage quotas to put limits on the clients interacting with your brokers.

You can set byte-rate thresholds for producer and consumer bandwidth. The total limit is distributed across all clients accessing the broker. For example, you can set a byte-rate threshold of 40 MBps for producers. If two producers are running, they are each limited to a throughput of 20 MBps.

Storage quotas throttle Kafka disk storage limits between a soft limit and hard limit. The limits apply to all available disk space. Producers are slowed gradually between the soft and hard limit. The limits prevent disks filling up too quickly and exceeding their capacity. Full disks can lead to issues that are hard to rectify. The hard limit is the maximum storage limit.

Note
 For JBOD storage, the limit applies across all disks. If a broker is using two 1 TB disks and the quota is 1.1 TB, one disk might fill and the other disk will be almost empty.
Prerequisites

  • The Cluster Operator that manages the Kafka cluster is running.

Procedure

  1. Add the plugin properties to the config of the Kafka resource.

    The plugin properties are shown in this example configuration.

    Example Kafka Static Quota plugin configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    config:
      client.quota.callback.class: io.strimzi.kafka.quotas.StaticQuotaCallback (1)
      client.quota.callback.static.produce: 1000000 (2)
      client.quota.callback.static.fetch: 1000000 (3)
      client.quota.callback.static.storage.soft: 400000000000 (4)
      client.quota.callback.static.storage.hard: 500000000000 (5)
      client.quota.callback.static.storage.check-interval: 5 (6)

    1. Loads the Kafka Static Quota plugin.

    2. Sets the producer byte-rate threshold. 1 MBps in this example.

    3. Sets the consumer byte-rate threshold. 1 MBps in this example.

    4. Sets the lower soft limit for storage. 400 GB in this example.

    5. Sets the higher hard limit for storage. 500 GB in this example.

    6. Sets the interval in seconds between checks on storage. 5 seconds in this example. You can set this to 0 to disable the check.

  2. Update the resource.

    kubectl apply -f <kafka_configuration_file>
Additional resources

9.2.2. Default ZooKeeper configuration values

When deploying ZooKeeper with Strimzi, some of the default configuration set by Strimzi differs from the standard ZooKeeper defaults. This is because Strimzi sets a number of ZooKeeper properties with values that are optimized for running ZooKeeper within a Kubernetes environment.

The default configuration for key ZooKeeper properties in Strimzi is as follows:

Table 9. Default ZooKeeper Properties in Strimzi
Property Default value Description

tickTime

2000

The length of a single tick in milliseconds, which determines the length of a session timeout.

initLimit

5

The maximum number of ticks that a follower is allowed to fall behind the leader in a ZooKeeper cluster.

syncLimit

2

The maximum number of ticks that a follower is allowed to be out of sync with the leader in a ZooKeeper cluster.

autopurge.purgeInterval

1

Enables the autopurge feature and sets the time interval in hours for purging the server-side ZooKeeper transaction log.

admin.enableServer

false

Flag to disable the ZooKeeper admin server. The admin server is not used by Strimzi.
Important
 Modifying these default values as zookeeper.config in the Kafka custom resource may impact the behavior and performance of your ZooKeeper cluster.

9.3. Configuring node pools

Update the spec properties of the KafkaNodePool custom resource to configure a node pool deployment.

A node pool refers to a distinct group of Kafka nodes within a Kafka cluster. Each pool has its own unique configuration, which includes mandatory settings for the number of replicas, roles, and storage allocation.

Optionally, you can also specify values for the following properties:

  • resources to specify memory and cpu requests and limits

  • template to specify custom configuration for pods and other Kubernetes resources

  • jvmOptions to specify custom JVM configuration for heap size, runtime and other options

The Kafka resource represents the configuration for all nodes in the Kafka cluster. The KafkaNodePool resource represents the configuration for nodes only in the node pool. If a configuration property is not specified in KafkaNodePool, it is inherited from the Kafka resource. Configuration specified in the KafkaNodePool resource takes precedence if set in both resources. For example, if both the node pool and Kafka configuration includes jvmOptions, the values specified in the node pool configuration are used. When -Xmx: 1024m is set in KafkaNodePool.spec.jvmOptions and -Xms: 512m is set in Kafka.spec.kafka.jvmOptions, the node uses the value from its node pool configuration.

Properties from Kafka and KafkaNodePool schemas are not combined. To clarify, if KafkaNodePool.spec.template includes only podSet.metadata.labels, and Kafka.spec.kafka.template includes podSet.metadata.annotations and pod.metadata.labels, the template values from the Kafka configuration are ignored since there is a template value in the node pool configuration.

Node pools can be used with Kafka clusters that operate in KRaft mode (using Kafka Raft metadata) or use ZooKeeper for cluster management. If you are using KRaft mode, you can specify roles for all nodes in the node pool to operate as brokers, controllers, or both. If you are using ZooKeeper, nodes must be set as brokers only.

Important
 KRaft mode is not ready for production in Apache Kafka or in Strimzi.

For a deeper understanding of the node pool configuration options, refer to the Strimzi Custom Resource API Reference.

Note
 Currently, replica and storage configuration properties in the KafkaNodePool resource must also be present in the Kafka resource. The configuration in the Kafka resource is ignored when node pools are used. Similarly, ZooKeeper configuration properties must also be present in the Kafka resource when using KRaft mode. These properties are also ignored.
Example configuration for a node pool in a cluster using KRaft mode
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: kraft-dual-role # (1)
  labels:
    strimzi.io/cluster: my-cluster # (2)
spec:
  replicas: 3 # (3)
  roles: # (4)
    - controller
    - broker
  storage: # (5)
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  resources: # (6)
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"

  1. Unique name for the node pool.

  2. The Kafka cluster the node pool belongs to. A node pool can only belong to a single cluster.

  3. Number of replicas for the nodes.

  4. Roles for the nodes in the node pool. In this example, the nodes have dual roles as controllers and brokers.

  5. Storage specification for the nodes.

  6. Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.

Note
 The configuration for the Kafka resource must be suitable for KRaft mode. Currently, KRaft mode has a number of limitations.
Example configuration for a node pool in a cluster using ZooKeeper
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-a
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker # (1)
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  resources:
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"

  1. Roles for the nodes in the node pool, which can only be broker when using Kafka with ZooKeeper.

9.3.1. Assigning IDs to node pools for scaling operations

This procedure describes how to use annotations for advanced node ID handling by the Cluster Operator when performing scaling operations on node pools. You specify the node IDs to use, rather than the Cluster Operator using the next ID in sequence. Management of node IDs in this way gives greater control.

To add a range of IDs, you assign the following annotations to the KafkaNodePool resource:

  • strimzi.io/next-node-ids to add a range of IDs that are used for new brokers

  • strimzi.io/remove-node-ids to add a range of IDs for removing existing brokers

You can specify an array of individual node IDs, ID ranges, or a combination of both. For example, you can specify the following range of IDs: [0, 1, 2, 10-20, 30] for scaling up the Kafka node pool. This format allows you to specify a combination of individual node IDs (0, 1, 2, 30) as well as a range of IDs (10-20).

In a typical scenario, you might specify a range of IDs for scaling up and a single node ID to remove a specific node when scaling down.

In this procedure, we add the scaling annotations to node pools as follows:

  • pool-a is assigned a range of IDs for scaling up

  • pool-b is assigned a range of IDs for scaling down

During the scaling operation, IDs are used as follows:

  • Scale up picks up the lowest available ID in the range for the new node.

  • Scale down removes the node with the highest available ID in the range.

If there are gaps in the sequence of node IDs assigned in the node pool, the next node to be added is assigned an ID that fills the gap.

The annotations don’t need to be updated after every scaling operation. Any unused IDs are still valid for the next scaling event.

The Cluster Operator allows you to specify a range of IDs in either ascending or descending order, so you can define them in the order the nodes are scaled. For example, when scaling up, you can specify a range such as [1000-1999], and the new nodes are assigned the next lowest IDs: 1000, 1001, 1002, 1003, and so on. Conversely, when scaling down, you can specify a range like [1999-1000], ensuring that nodes with the next highest IDs are removed: 1003, 1002, 1001, 1000, and so on.

If you don’t specify an ID range using the annotations, the Cluster Operator follows its default behavior for handling IDs during scaling operations. Node IDs start at 0 (zero) and run sequentially across the Kafka cluster. The next lowest ID is assigned to a new node. Gaps to node IDs are filled across the cluster. This means that they might not run sequentially within a node pool. The default behavior for scaling up is to add the next lowest available node ID across the cluster; and for scaling down, it is to remove the node in the node pool with the highest available node ID. The default approach is also applied if the assigned range of IDs is misformatted, the scaling up range runs out of IDs, or the scaling down range does not apply to any in-use nodes.

Prerequisites

By default, Apache Kafka restricts node IDs to numbers ranging from 0 to 999. To use node ID values greater than 999, add the reserved.broker-max.id configuration property to the Kafka custom resource and specify the required maximum node ID value.

In this example, the maximum node ID is set at 10000. Node IDs can then be assigned up to that value.

Example configuration for the maximum node ID number
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    config:
      reserved.broker.max.id: 10000
  # ...
Procedure

  1. Annotate the node pool with the IDs to use when scaling up or scaling down, as shown in the following examples.

    IDs for scaling up are assigned to node pool pool-a:

    Assigning IDs for scaling up
    kubectl annotate kafkanodepool pool-a strimzi.io/next-node-ids="[0,1,2,10-20,30]"

    The lowest available ID from this range is used when adding a node to pool-a.

    IDs for scaling down are assigned to node pool pool-b:

    Assigning IDs for scaling down
    kubectl annotate kafkanodepool pool-b strimzi.io/remove-node-ids="[60-50,9,8,7]"

    The highest available ID from this range is removed when scaling down pool-b.

    Note
    		 If you want to remove a specific node, you can assign a single node ID to the scaling down annotation: kubectl annotate kafkanodepool pool-b strimzi.io/remove-node-ids="[3]".

  2. You can now scale the node pool.

    For more information, see the following:

    On reconciliation, a warning is given if the annotations are misformatted.

  3. After you have performed the scaling operation, you can remove the annotation if it’s no longer needed.

    Removing the annotation for scaling up
    kubectl annotate kafkanodepool pool-a strimzi.io/next-node-ids-
    Removing the annotation for scaling down
    kubectl annotate kafkanodepool pool-b strimzi.io/remove-node-ids-

9.3.2. Adding nodes to a node pool

This procedure describes how to scale up a node pool to add new nodes.

In this procedure, we start with three nodes for node pool pool-a:

Kafka nodes in the node pool
NAME                 READY  STATUS   RESTARTS
my-cluster-pool-a-0  1/1    Running  0
my-cluster-pool-a-1  1/1    Running  0
my-cluster-pool-a-2  1/1    Running  0

Node IDs are appended to the name of the node on creation. We add node my-cluster-pool-a-3, which has a node ID of 3.

Note
 During this process, the ID of the node that holds the partition replicas changes. Consider any dependencies that reference the node ID.
Prerequisites
Procedure

  1. Create a new node in the node pool.

    For example, node pool pool-a has three replicas. We add a node by increasing the number of replicas:

    kubectl scale kafkanodepool pool-a --replicas=4

  2. Check the status of the deployment and wait for the pods in the node pool to be created and have a status of READY.

    kubectl get pods -n <my_cluster_operator_namespace>
    Output shows four Kafka nodes in the node pool
    NAME                 READY  STATUS   RESTARTS
my-cluster-pool-a-0  1/1    Running  0
my-cluster-pool-a-1  1/1    Running  0
my-cluster-pool-a-2  1/1    Running  0
my-cluster-pool-a-3  1/1    Running  0

  3. Reassign the partitions after increasing the number of nodes in the node pool.

    After scaling up a node pool, you can use the Cruise Control add-brokers mode to move partition replicas from existing brokers to the newly added brokers.

9.3.3. Removing nodes from a node pool

This procedure describes how to scale down a node pool to remove nodes.

In this procedure, we start with four nodes for node pool pool-a:

Kafka nodes in the node pool
NAME                 READY  STATUS   RESTARTS
my-cluster-pool-a-0  1/1    Running  0
my-cluster-pool-a-1  1/1    Running  0
my-cluster-pool-a-2  1/1    Running  0
my-cluster-pool-a-3  1/1    Running  0

Node IDs are appended to the name of the node on creation. We remove node my-cluster-pool-a-3, which has a node ID of 3.

Note
 During this process, the ID of the node that holds the partition replicas changes. Consider any dependencies that reference the node ID.
Prerequisites
Procedure

  1. Reassign the partitions before decreasing the number of nodes in the node pool.

    Before scaling down a node pool, you can use the Cruise Control remove-brokers mode to move partition replicas off the brokers that are going to be removed.

  2. After the reassignment process is complete, and the node being removed has no live partitions, reduce the number of Kafka nodes in the node pool.

    For example, node pool pool-a has four replicas. We remove a node by decreasing the number of replicas:

    kubectl scale kafkanodepool pool-a --replicas=3
    Output shows three Kafka nodes in the node pool
    NAME                       READY  STATUS   RESTARTS
my-cluster-pool-b-kafka-0  1/1    Running  0
my-cluster-pool-b-kafka-1  1/1    Running  0
my-cluster-pool-b-kafka-2  1/1    Running  0

9.3.4. Moving nodes between node pools

This procedure describes how to move nodes between source and target Kafka node pools without downtime. You create a new node on the target node pool and reassign partitions to move data from the old node on the source node pool. When the replicas on the new node are in-sync, you can delete the old node.

In this procedure, we start with two node pools:

  • pool-a with three replicas is the target node pool

  • pool-b with four replicas is the source node pool

We scale up pool-a, and reassign partitions and scale down pool-b, which results in the following:

  • pool-a with four replicas

  • pool-b with three replicas

Note
 During this process, the ID of the node that holds the partition replicas changes. Consider any dependencies that reference the node ID.
Prerequisites
Procedure

  1. Create a new node in the target node pool.

    For example, node pool pool-a has three replicas. We add a node by increasing the number of replicas:

    kubectl scale kafkanodepool pool-a --replicas=4

  2. Check the status of the deployment and wait for the pods in the node pool to be created and have a status of READY.

    kubectl get pods -n <my_cluster_operator_namespace>
    Output shows four Kafka nodes in the target node pool
    NAME                 READY  STATUS   RESTARTS
my-cluster-pool-a-0  1/1    Running  0
my-cluster-pool-a-1  1/1    Running  0
my-cluster-pool-a-4  1/1    Running  0
my-cluster-pool-a-5  1/1    Running  0

    Node IDs are appended to the name of the node on creation. We add node my-cluster-pool-a-5, which has a node ID of 5.

  3. Reassign the partitions from the old node to the new node.

    Before scaling down the source node pool, you can use the Cruise Control remove-brokers mode to move partition replicas off the brokers that are going to be removed.

  4. After the reassignment process is complete, reduce the number of Kafka nodes in the source node pool.

    For example, node pool pool-b has four replicas. We remove a node by decreasing the number of replicas:

    kubectl scale kafkanodepool pool-b --replicas=3

    The node with the highest ID within a pool is removed.

    Output shows three Kafka nodes in the source node pool
    NAME                       READY  STATUS   RESTARTS
my-cluster-pool-b-kafka-2  1/1    Running  0
my-cluster-pool-b-kafka-3  1/1    Running  0
my-cluster-pool-b-kafka-6  1/1    Running  0

9.3.5. Managing storage using node pools

Storage management in Strimzi is usually straightforward, and requires little change when set up, but there might be situations where you need to modify your storage configurations. Node pools simplify this process, because you can set up separate node pools that specify your new storage requirements.

In this procedure we create and manage storage for a node pool called pool-a containing three nodes. We show how to change the storage class (volumes.class) that defines the type of persistent storage it uses. You can use the same steps to change the storage size (volumes.size).

Note
 We strongly recommend using block storage. Strimzi is only tested for use with block storage.
Prerequisites
Procedure

  1. Create the node pool with its own storage settings.

    For example, node pool pool-a uses JBOD storage with persistent volumes:

    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-a
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 500Gi
        class: gp2-ebs
  # ...

    Nodes in pool-a are configured to use Amazon EBS (Elastic Block Store) GP2 volumes.

  2. Apply the node pool configuration for pool-a.

  3. Check the status of the deployment and wait for the pods in pool-a to be created and have a status of READY.

    kubectl get pods -n <my_cluster_operator_namespace>
    Output shows three Kafka nodes in the node pool
    NAME                 READY  STATUS   RESTARTS
my-cluster-pool-a-0  1/1    Running  0
my-cluster-pool-a-1  1/1    Running  0
my-cluster-pool-a-2  1/1    Running  0

  4. To migrate to a new storage class, create a new node pool with the required storage configuration:

    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-b
  labels:
    strimzi.io/cluster: my-cluster
spec:
  roles:
    - broker
  replicas: 3
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 1Ti
        class: gp3-ebs
  # ...

    Nodes in pool-b are configured to use Amazon EBS (Elastic Block Store) GP3 volumes.

  5. Apply the node pool configuration for pool-b.

  6. Check the status of the deployment and wait for the pods in pool-b to be created and have a status of READY.

  7. Reassign the partitions from pool-a to pool-b.

    When migrating to a new storage configuration, you can use the Cruise Control remove-brokers mode to move partition replicas off the brokers that are going to be removed.

  8. After the reassignment process is complete, delete the old node pool:

    kubectl delete kafkanodepool pool-a

9.3.6. Managing storage affinity using node pools

In situations where storage resources, such as local persistent volumes, are constrained to specific worker nodes, or availability zones, configuring storage affinity helps to schedule pods to use the right nodes.

Node pools allow you to configure affinity independently. In this procedure, we create and manage storage affinity for two availability zones: zone-1 and zone-2.

You can configure node pools for separate availability zones, but use the same storage class. We define an all-zones persistent storage class representing the storage resources available in each zone.

We also use the .spec.template.pod properties to configure the node affinity and schedule Kafka pods on zone-1 and zone-2 worker nodes.

The storage class and affinity is specified in node pools representing the nodes in each availability zone:

  • pool-zone-1

  • pool-zone-2.

Prerequisites
Procedure

  1. Define the storage class for use with each availability zone:

    apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: all-zones
provisioner: kubernetes.io/my-storage
parameters:
  type: ssd
volumeBindingMode: WaitForFirstConsumer

  2. Create node pools representing the two availability zones, specifying the all-zones storage class and the affinity for each zone:

    Node pool configuration for zone-1
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-zone-1
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 500Gi
        class: all-zones
  template:
    pod:
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                - key: topology.kubernetes.io/zone
                  operator: In
                  values:
                  - zone-1
  # ...
    Node pool configuration for zone-2
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-zone-2
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 4
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 500Gi
        class: all-zones
  template:
    pod:
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                - key: topology.kubernetes.io/zone
                  operator: In
                  values:
                  - zone-2
  # ...

  3. Apply the node pool configuration.

  4. Check the status of the deployment and wait for the pods in the node pools to be created and have a status of READY.

    kubectl get pods -n <my_cluster_operator_namespace>
    Output shows 3 Kafka nodes in pool-zone-1 and 4 Kafka nodes in pool-zone-2:
    NAME                       READY  STATUS   RESTARTS
my-cluster-pool-zone-1-kafka-0  1/1    Running  0
my-cluster-pool-zone-1-kafka-1  1/1    Running  0
my-cluster-pool-zone-1-kafka-2  1/1    Running  0
my-cluster-pool-zone-2-kafka-3  1/1    Running  0
my-cluster-pool-zone-2-kafka-4  1/1    Running  0
my-cluster-pool-zone-2-kafka-5  1/1    Running  0
my-cluster-pool-zone-2-kafka-6  1/1    Running  0

9.3.7. Migrating existing Kafka clusters to use Kafka node pools

This procedure describes how to migrate existing Kafka clusters to use Kafka node pools. After you have updated the Kafka cluster, you can use the node pools to manage the configuration of nodes within each pool.

Note
 Currently, replica and storage configuration in the KafkaNodePool resource must also be present in the Kafka resource. The configuration is ignored when node pools are being used.
Prerequisites
Procedure

  1. Create a new KafkaNodePool resource.

    1. Name the resource kafka.

    2. Point a strimzi.io/cluster label to your existing Kafka resource.

    3. Set the replica count and storage configuration to match your current Kafka cluster.

    4. Set the roles to broker.

    Example configuration for a node pool used in migrating a Kafka cluster
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: kafka
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
    Warning
    		 To migrate a cluster while preserving its data along with the names of its nodes and resources, the node pool name must be kafka, and the strimzi.io/cluster label must use the name of the Kafka resource. Otherwise, nodes and resources are created with new names, including the persistent volume storage used by the nodes. Consequently, your previous data may not be available.

  2. Apply the KafkaNodePool resource:

    kubectl apply -f <node_pool_configuration_file>

    By applying this resource, you switch Kafka to using node pools.

    There is no change or rolling update and resources are identical to how they were before.

  3. Enable support for node pools in the Kafka resource using the strimzi.io/node-pools: enabled annotation.

    Example configuration for a node pool in a cluster using ZooKeeper
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  annotations:
    strimzi.io/node-pools: enabled
spec:
  kafka:
    # ...
  zookeeper:
    # ...

  4. Apply the Kafka resource:

    kubectl apply -f <kafka_configuration_file>

    There is no change or rolling update. The resources remain identical to how they were before.

9.4. Configuring the Entity Operator

Use the entityOperator property in Kafka.spec to configure the Entity Operator. The Entity Operator is responsible for managing Kafka-related entities in a running Kafka cluster. It comprises the following operators:

  • Topic Operator to manage Kafka topics

  • User Operator to manage Kafka users

By configuring the Kafka resource, the Cluster Operator can deploy the Entity Operator, including one or both operators. Once deployed, the operators are automatically configured to handle the topics and users of the Kafka cluster.

Each operator can only monitor a single namespace. For more information, see Watching Strimzi resources in Kubernetes namespaces.

The entityOperator property supports several sub-properties:

  • tlsSidecar

  • topicOperator

  • userOperator

  • template

The tlsSidecar property contains the configuration of the TLS sidecar container, which is used to communicate with ZooKeeper.

The template property contains the configuration of the Entity Operator pod, such as labels, annotations, affinity, and tolerations. For more information on configuring templates, see Customizing Kubernetes resources.

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

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

For more information on the properties used to configure the Entity Operator, see the EntityOperatorSpec schema reference.

Example of basic configuration enabling both operators
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}

If an empty object ({}) is used for the topicOperator and userOperator, all properties use their default values.

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

9.4.1. Configuring the Topic Operator

Use topicOperator properties in Kafka.spec.entityOperator to configure the Topic Operator.

Note
 If you are using unidirectional topic management, which is enabled by default, the following properties are not used and are ignored: Kafka.spec.entityOperator.topicOperator.zookeeperSessionTimeoutSeconds and Kafka.spec.entityOperator.topicOperator.topicMetadataMaxAttempts. For more information on unidirectional topic management, refer to Topic management modes.

The following properties are supported:

watchedNamespace

The Kubernetes namespace in which the Topic Operator watches for KafkaTopic resources. Default is the namespace where the Kafka cluster is deployed.

reconciliationIntervalSeconds

The interval between periodic reconciliations in seconds. Default 120.

zookeeperSessionTimeoutSeconds

The ZooKeeper session timeout in seconds. Default 18.

topicMetadataMaxAttempts

The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation might take more time due to the number of partitions or replicas. Default 6.

image

The image property can be used to configure the container image which is used. To learn more, refer to the information provided on configuring the image property`.

resources

The resources property configures the amount of resources allocated to the Topic Operator. You can specify requests and limits for memory and cpu resources. The requests should be enough to ensure a stable performance of the operator.

logging

The logging property configures the logging of the Topic Operator. To learn more, refer to the information provided on Topic Operator logging.

Example Topic Operator configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi
    # ...

9.4.2. Configuring the User Operator

Use userOperator properties in Kafka.spec.entityOperator to configure the User Operator. The following properties are supported:

watchedNamespace

The Kubernetes namespace in which the User Operator watches for KafkaUser resources. Default is the namespace where the Kafka cluster is deployed.

reconciliationIntervalSeconds

The interval between periodic reconciliations in seconds. Default 120.

image

The image property can be used to configure the container image which will be used. To learn more, refer to the information provided on configuring the image property`.

resources

The resources property configures the amount of resources allocated to the User Operator. You can specify requests and limits for memory and cpu resources. The requests should be enough to ensure a stable performance of the operator.

logging

The logging property configures the logging of the User Operator. To learn more, refer to the information provided on User Operator logging.

secretPrefix

The secretPrefix property adds a prefix to the name of all Secrets created from the KafkaUser resource. For example, secretPrefix: kafka- would prefix all Secret names with kafka-. So a KafkaUser named my-user would create a Secret named kafka-my-user.

Example User Operator configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi
    # ...

9.5. Configuring the Cluster Operator

Use environment variables to configure the Cluster Operator. Specify the environment variables for the container image of the Cluster Operator in its Deployment configuration file. You can use the following environment variables to configure the Cluster Operator. If you are running Cluster Operator replicas in standby mode, there are additional environment variables for enabling leader election.

Kafka, Kafka Connect, and Kafka MirrorMaker support multiple versions. Use their STRIMZI_<COMPONENT_NAME>_IMAGES environment variables to configure the default container images used for each version. The configuration provides a mapping between a version and an image. The required syntax is whitespace or comma-separated <version> = <image> pairs, which determine the image to use for a given version. For example, 3.6.1=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1. Theses default images are overridden if image property values are specified in the configuration of a component. For more information on image configuration of components, see the Strimzi Custom Resource API Reference.

Note
 The Deployment configuration file provided with the Strimzi release artifacts is 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 Strimzi on clusters where regular Kubernetes operations take longer than usual (due to factors such as prolonged download times for container 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 Strimzi instance when emitting Kubernetes 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 Strimzi Cluster Operator is running. Use namespace labels to configure the namespace selector in network policies. Network policies allow the Strimzi 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 Kubernetes 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 matching labels.

env:
  - name: STRIMZI_CUSTOM_RESOURCE_SELECTOR
    value: label1=value1,label2=value2
STRIMZI_KAFKA_IMAGES

Required. The mapping from the Kafka version to the corresponding image containing a Kafka broker for that version. For example 3.5.1=quay.io/strimzi/kafka:0.39.0-kafka-3.5.1, 3.6.1=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1.

STRIMZI_KAFKA_CONNECT_IMAGES

Required. The mapping from the Kafka version to the corresponding image of Kafka Connect for that version. For example 3.5.1=quay.io/strimzi/kafka:0.39.0-kafka-3.5.1, 3.6.1=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1.

STRIMZI_KAFKA_MIRROR_MAKER2_IMAGES

Required. The mapping from the Kafka version to the corresponding image of MirrorMaker 2 for that version. For example 3.5.1=quay.io/strimzi/kafka:0.39.0-kafka-3.5.1, 3.6.1=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1.

(Deprecated) STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

Required. The mapping from the Kafka version to the corresponding image of MirrorMaker for that version. For example 3.5.1=quay.io/strimzi/kafka:0.39.0-kafka-3.5.1, 3.6.1=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1.

STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE

Optional. The default is quay.io/strimzi/operator:0.39.0. The image name to use as the default when deploying the Topic Operator if no image is specified as the Kafka.spec.entityOperator.topicOperator.image in the Kafka resource.

STRIMZI_DEFAULT_USER_OPERATOR_IMAGE

Optional. The default is quay.io/strimzi/operator:0.39.0. The image name to use as the default when deploying the User Operator if no image is specified as the Kafka.spec.entityOperator.userOperator.image in the Kafka resource.

STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE

Optional. The default is quay.io/strimzi/kafka:0.39.0-kafka-3.6.1. The image name to use as the default when deploying the sidecar container for the Entity Operator if no image is specified as the Kafka.spec.entityOperator.tlsSidecar.image in the Kafka resource. The sidecar provides TLS support.

STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE

Optional. The default is quay.io/strimzi/kafka:0.39.0-kafka-3.6.1. The image name to use as the default when deploying the Kafka Exporter if no image is specified as the Kafka.spec.kafkaExporter.image in the Kafka resource.

STRIMZI_DEFAULT_CRUISE_CONTROL_IMAGE

Optional. The default is quay.io/strimzi/kafka:0.39.0-kafka-3.6.1. The image name to use as the default when deploying Cruise Control if no image is specified as the Kafka.spec.cruiseControl.image in the Kafka resource.

STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE

Optional. The default is quay.io/strimzi/kafka-bridge:0.27.0. The image name to use as the default when deploying the Kafka Bridge if no image is specified as the Kafka.spec.kafkaBridge.image in the Kafka resource.

STRIMZI_DEFAULT_KAFKA_INIT_IMAGE

Optional. The default is quay.io/strimzi/operator:0.39.0. The image name to use as the default for the Kafka initializer container if no image is specified in the brokerRackInitImage of the Kafka resource or the clientRackInitImage of the Kafka Connect resource. The init container is started before the Kafka cluster for initial configuration work, such as rack support.

STRIMZI_IMAGE_PULL_POLICY

Optional. The ImagePullPolicy that is applied to containers in all pods managed by the Cluster Operator. The valid values are Always, IfNotPresent, and Never. If not specified, the Kubernetes defaults are used. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.

STRIMZI_IMAGE_PULL_SECRETS

Optional. A comma-separated list of Secret names. The secrets referenced here contain the credentials to the container registries where the container images are pulled from. The secrets are specified in the imagePullSecrets property for all pods created by the Cluster Operator. Changing this list results in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.

STRIMZI_KUBERNETES_VERSION

Optional. Overrides the Kubernetes version information detected from the API server.

Example configuration for Kubernetes version override
env:
  - name: STRIMZI_KUBERNETES_VERSION
    value: |
           major=1
           minor=16
           gitVersion=v1.16.2
           gitCommit=c97fe5036ef3df2967d086711e6c0c405941e14b
           gitTreeState=clean
           buildDate=2019-10-15T19:09:08Z
           goVersion=go1.12.10
           compiler=gc
           platform=linux/amd64
KUBERNETES_SERVICE_DNS_DOMAIN

Optional. Overrides the default Kubernetes DNS domain name suffix.

By default, services assigned in the Kubernetes cluster have a DNS domain name that uses the default suffix cluster.local.

For example, for broker kafka-0:

<cluster-name>-kafka-0.<cluster-name>-kafka-brokers.<namespace>.svc.cluster.local

The DNS domain name is added to the Kafka broker certificates used for hostname verification.

If you are using a different DNS domain name suffix in your cluster, change the KUBERNETES_SERVICE_DNS_DOMAIN environment variable from the default to the one you are using in order to establish a connection with the Kafka brokers.

STRIMZI_CONNECT_BUILD_TIMEOUT_MS

Optional, default 300000 ms. The timeout for building new Kafka Connect images with additional connectors, in milliseconds. Consider increasing this value when using Strimzi to build container images containing many connectors or using a slow container registry.

STRIMZI_NETWORK_POLICY_GENERATION

Optional, default true. Network policy for resources. Network policies allow connections between Kafka components.

Set this environment variable to false to disable network policy generation. You might do this, for example, if you want to use custom network policies. Custom network policies allow more control over maintaining the connections between components.

STRIMZI_DNS_CACHE_TTL

Optional, default 30. Number of seconds to cache successful name lookups in local DNS resolver. Any negative value means cache forever. Zero means do not cache, which can be useful for avoiding connection errors due to long caching policies being applied.

STRIMZI_POD_SET_RECONCILIATION_ONLY

Optional, default false. When set to true, the Cluster Operator reconciles only the StrimziPodSet resources and any changes to the other custom resources (Kafka, KafkaConnect, and so on) are ignored. This mode is useful for ensuring that your pods are recreated if needed, but no other changes happen to the clusters.

STRIMZI_FEATURE_GATES

Optional. Enables or disables the features and functionality controlled by feature gates.

STRIMZI_POD_SECURITY_PROVIDER_CLASS

Optional. Configuration for the pluggable PodSecurityProvider class, which can be used to provide the security context configuration for Pods and containers.

9.5.1. Restricting access to the Cluster Operator using network policy

Use the STRIMZI_OPERATOR_NAMESPACE_LABELS environment variable to establish network policy for the Cluster Operator using namespace labels.

The Cluster Operator can run in the same namespace as the resources it manages, or in a separate namespace. By default, the STRIMZI_OPERATOR_NAMESPACE environment variable is configured to use the downward API to find the namespace the Cluster Operator is running in. If the Cluster Operator is running in the same namespace as the resources, only local access is required and allowed by Strimzi.

If the Cluster Operator is running in a separate namespace to the resources it manages, any namespace in the Kubernetes cluster is allowed access to the Cluster Operator unless network policy is configured. By adding namespace labels, access to the Cluster Operator is restricted to the namespaces specified.

Network policy configured for the Cluster Operator deployment
#...
env:
  # ...
  - name: STRIMZI_OPERATOR_NAMESPACE_LABELS
    value: label1=value1,label2=value2
  #...

9.5.2. Configuring periodic reconciliation by the Cluster Operator

Use the STRIMZI_FULL_RECONCILIATION_INTERVAL_MS variable to set the time interval for periodic reconciliations by the Cluster Operator. Replace its value with the required interval in milliseconds.

Reconciliation period configured for the Cluster Operator deployment
#...
env:
  # ...
  - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
    value: "120000"
  #...

The Cluster Operator reacts to all notifications about applicable cluster resources received from the Kubernetes cluster. If the operator is not running, or if a notification is not received for any reason, resources will get out of sync with the state of the running Kubernetes cluster. In order to handle failovers properly, a periodic reconciliation process is executed by the Cluster Operator so that it can compare the state of the resources with the current cluster deployments in order to have a consistent state across all of them.

Additional resources

9.5.3. Running multiple Cluster Operator replicas with leader election

The default Cluster Operator configuration enables leader election to run multiple parallel replicas of the Cluster Operator. One replica is elected as the active leader and operates the deployed resources. The other replicas run in standby mode. When the leader stops or fails, one of the standby replicas is elected as the new leader and starts operating the deployed resources.

By default, Strimzi runs with a single Cluster Operator replica that is always the leader replica. When a single Cluster Operator replica stops or fails, Kubernetes starts a new replica.

Running the Cluster Operator with multiple replicas is not essential. But it’s useful to have replicas on standby in case of large-scale disruptions caused by major failure. For example, suppose multiple worker nodes or an entire availability zone fails. This failure might cause the Cluster Operator pod and many Kafka pods to go down at the same time. If subsequent pod scheduling causes congestion through lack of resources, this can delay operations when running a single Cluster Operator.

Enabling leader election for Cluster Operator replicas

Configure leader election environment variables when running additional Cluster Operator replicas. The following environment variables are supported:

STRIMZI_LEADER_ELECTION_ENABLED

Optional, disabled (false) by default. Enables or disables leader election, which allows additional Cluster Operator replicas to run on standby.

Note
 Leader election is disabled by default. It is only enabled when applying this environment variable on installation.
STRIMZI_LEADER_ELECTION_LEASE_NAME

Required when leader election is enabled. The name of the Kubernetes Lease resource that is used for the leader election.

STRIMZI_LEADER_ELECTION_LEASE_NAMESPACE

Required when leader election is enabled. The namespace where the Kubernetes Lease resource used for leader election is created. You can use the downward API to configure it to the namespace where the Cluster Operator is deployed.

env:
  - name: STRIMZI_LEADER_ELECTION_LEASE_NAMESPACE
    valueFrom:
      fieldRef:
        fieldPath: metadata.namespace
STRIMZI_LEADER_ELECTION_IDENTITY

Required when leader election is enabled. Configures the identity of a given Cluster Operator instance used during the leader election. The identity must be unique for each operator instance. You can use the downward API to configure it to the name of the pod where the Cluster Operator is deployed.

env:
  - name: STRIMZI_LEADER_ELECTION_IDENTITY
    valueFrom:
      fieldRef:
        fieldPath: metadata.name
STRIMZI_LEADER_ELECTION_LEASE_DURATION_MS

Optional, default 15000 ms. Specifies the duration the acquired lease is valid.

STRIMZI_LEADER_ELECTION_RENEW_DEADLINE_MS

Optional, default 10000 ms. Specifies the period the leader should try to maintain leadership.

STRIMZI_LEADER_ELECTION_RETRY_PERIOD_MS

Optional, default 2000 ms. Specifies the frequency of updates to the lease lock by the leader.

Configuring Cluster Operator replicas

To run additional Cluster Operator replicas in standby mode, you will need to increase the number of replicas and enable leader election. To configure leader election, use the leader election environment variables.

To make the required changes, configure the following Cluster Operator installation files located in install/cluster-operator/:

  • 060-Deployment-strimzi-cluster-operator.yaml

  • 022-ClusterRole-strimzi-cluster-operator-role.yaml

  • 022-RoleBinding-strimzi-cluster-operator.yaml

Leader election has its own ClusterRole and RoleBinding RBAC resources that target the namespace where the Cluster Operator is running, rather than the namespace it is watching.

The default deployment configuration creates a Lease resource called strimzi-cluster-operator in the same namespace as the Cluster Operator. The Cluster Operator uses leases to manage leader election. The RBAC resources provide the permissions to use the Lease resource. If you use a different Lease name or namespace, update the ClusterRole and RoleBinding files accordingly.

Prerequisites

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

Procedure

Edit the Deployment resource that is used to deploy the Cluster Operator, which is defined in the 060-Deployment-strimzi-cluster-operator.yaml file.

  1. Change the replicas property from the default (1) to a value that matches the required number of replicas.

    Increasing the number of Cluster Operator replicas
    apiVersion: apps/v1
kind: Deployment
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
spec:
  replicas: 3

  2. Check that the leader election env properties are set.

    If they are not set, configure them.

    To enable leader election, STRIMZI_LEADER_ELECTION_ENABLED must be set to true (default).

    In this example, the name of the lease is changed to my-strimzi-cluster-operator.

    Configuring leader election environment variables for the Cluster Operator
    # ...
spec
  containers:
    - name: strimzi-cluster-operator
      # ...
      env:
        - name: STRIMZI_LEADER_ELECTION_ENABLED
          value: "true"
        - name: STRIMZI_LEADER_ELECTION_LEASE_NAME
          value: "my-strimzi-cluster-operator"
        - name: STRIMZI_LEADER_ELECTION_LEASE_NAMESPACE
            valueFrom:
              fieldRef:
                fieldPath: metadata.namespace
        - name: STRIMZI_LEADER_ELECTION_IDENTITY
            valueFrom:
              fieldRef:
                fieldPath: metadata.name

    For a description of the available environment variables, see Enabling leader election for Cluster Operator replicas.

    If you specified a different name or namespace for the Lease resource used in leader election, update the RBAC resources.

  3. (optional) Edit the ClusterRole resource in the 022-ClusterRole-strimzi-cluster-operator-role.yaml file.

    Update resourceNames with the name of the Lease resource.

    Updating the ClusterRole references to the lease
    apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-leader-election
  labels:
    app: strimzi
rules:
  - apiGroups:
      - coordination.k8s.io
    resourceNames:
      - my-strimzi-cluster-operator
# ...

  4. (optional) Edit the RoleBinding resource in the 022-RoleBinding-strimzi-cluster-operator.yaml file.

    Update subjects.name and subjects.namespace with the name of the Lease resource and the namespace where it was created.

    Updating the RoleBinding references to the lease
    apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator-leader-election
  labels:
    app: strimzi
subjects:
  - kind: ServiceAccount
    name: my-strimzi-cluster-operator
    namespace: myproject
# ...

  5. Deploy the Cluster Operator:

    kubectl create -f install/cluster-operator -n myproject

  6. Check the status of the deployment:

    kubectl get deployments -n myproject
    Output shows the deployment name and readiness
    NAME                      READY  UP-TO-DATE  AVAILABLE
strimzi-cluster-operator  3/3    3           3

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

9.5.4. Configuring Cluster Operator HTTP proxy settings

If you are running a Kafka cluster behind a HTTP proxy, you can still pass data in and out of the cluster. For example, you can run Kafka Connect with connectors that push and pull data from outside the proxy. Or you can use a proxy to connect with an authorization server.

Configure the Cluster Operator deployment to specify the proxy environment variables. The Cluster Operator accepts standard proxy configuration (HTTP_PROXY, HTTPS_PROXY and NO_PROXY) as environment variables. The proxy settings are applied to all Strimzi containers.

The format for a proxy address is http://<ip_address>:<port_number>. To set up a proxy with a name and password, the format is http://<username>:<password>@<ip-address>:<port_number>.

Prerequisites

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

Procedure

  1. To add proxy environment variables to the Cluster Operator, update its Deployment configuration (install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml).

    Example proxy configuration for the Cluster Operator
    apiVersion: apps/v1
kind: Deployment
spec:
  # ...
  template:
    spec:
      serviceAccountName: strimzi-cluster-operator
      containers:
        # ...
        env:
        # ...
        - name: "HTTP_PROXY"
          value: "http://proxy.com" (1)
        - name: "HTTPS_PROXY"
          value: "https://proxy.com" (2)
        - name: "NO_PROXY"
          value: "internal.com, other.domain.com" (3)
  # ...

    1. Address of the proxy server.

    2. Secure address of the proxy server.

    3. Addresses for servers that are accessed directly as exceptions to the proxy server. The URLs are comma-separated.

    Alternatively, edit the Deployment directly:

    kubectl edit deployment strimzi-cluster-operator

  2. If you updated the YAML file instead of editing the Deployment directly, apply the changes:

    kubectl create -f install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
Additional resources

9.5.5. Disabling FIPS mode using Cluster Operator configuration

Strimzi automatically switches to FIPS mode when running on a FIPS-enabled Kubernetes cluster. Disable FIPS mode by setting the FIPS_MODE environment variable to disabled in the deployment configuration for the Cluster Operator. With FIPS mode disabled, Strimzi automatically disables FIPS in the OpenJDK for all components. With FIPS mode disabled, Strimzi is not FIPS compliant. The Strimzi operators, as well as all operands, run in the same way as if they were running on an Kubernetes cluster without FIPS enabled.

Procedure

  1. To disable the FIPS mode in the Cluster Operator, update its Deployment configuration (install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml) and add the FIPS_MODE environment variable.

    Example FIPS configuration for the Cluster Operator
    apiVersion: apps/v1
kind: Deployment
spec:
  # ...
  template:
    spec:
      serviceAccountName: strimzi-cluster-operator
      containers:
        # ...
        env:
        # ...
        - name: "FIPS_MODE"
          value: "disabled" # (1)
  # ...

    1. Disables the FIPS mode.

    Alternatively, edit the Deployment directly:

    kubectl edit deployment strimzi-cluster-operator

  2. If you updated the YAML file instead of editing the Deployment directly, apply the changes:

    kubectl apply -f install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml

9.6. Configuring Kafka Connect

Update the spec properties of the KafkaConnect custom resource to configure your Kafka Connect deployment.

Use Kafka Connect to set up external data connections to your Kafka cluster. Use the properties of the KafkaConnect resource to configure your Kafka Connect deployment.

For a deeper understanding of the Kafka Connect cluster configuration options, refer to the Strimzi Custom Resource API Reference.

KafkaConnector configuration

KafkaConnector resources allow you to create and manage connector instances for Kafka Connect in a Kubernetes-native way.

In your Kafka Connect configuration, you enable KafkaConnectors for a Kafka Connect cluster by adding the strimzi.io/use-connector-resources annotation. You can also add a build configuration so that Strimzi automatically builds a container image with the connector plugins you require for your data connections. External configuration for Kafka Connect connectors is specified through the externalConfiguration property.

To manage connectors, you can use use KafkaConnector custom resources or the Kafka Connect REST API. KafkaConnector resources must be deployed to the same namespace as the Kafka Connect cluster they link to. For more information on using these methods to create, reconfigure, or delete connectors, see Adding connectors.

Connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself. ConfigMaps and Secrets are standard Kubernetes resources used for storing configurations and confidential data. You can use ConfigMaps and Secrets to configure certain elements of a connector. You can then reference the configuration values in HTTP REST commands, which keeps the configuration separate and more secure, if needed. This method applies especially to confidential data, such as usernames, passwords, or certificates.

Handling high volumes of messages

You can tune the configuration to handle high volumes of messages. For more information, see Handling high volumes of messages.

Example KafkaConnect custom resource configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect # (1)
metadata:
  name: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true" # (2)
spec:
  replicas: 3 # (3)
  authentication: # (4)
    type: tls
    certificateAndKey:
      certificate: source.crt
      key: source.key
      secretName: my-user-source
  bootstrapServers: my-cluster-kafka-bootstrap:9092 # (5)
  tls: # (6)
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        certificate: ca.crt
      - secretName: my-cluster-cluster-cert
        certificate: ca2.crt
  config: # (7)
    group.id: my-connect-cluster
    offset.storage.topic: my-connect-cluster-offsets
    config.storage.topic: my-connect-cluster-configs
    status.storage.topic: my-connect-cluster-status
    key.converter: org.apache.kafka.connect.json.JsonConverter
    value.converter: org.apache.kafka.connect.json.JsonConverter
    key.converter.schemas.enable: true
    value.converter.schemas.enable: true
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  build: # (8)
    output: # (9)
      type: docker
      image: my-registry.io/my-org/my-connect-cluster:latest
      pushSecret: my-registry-credentials
    plugins: # (10)
      - name: connector-1
        artifacts:
          - type: tgz
            url: <url_to_download_connector_1_artifact>
            sha512sum: <SHA-512_checksum_of_connector_1_artifact>
      - name: connector-2
        artifacts:
          - type: jar
            url: <url_to_download_connector_2_artifact>
            sha512sum: <SHA-512_checksum_of_connector_2_artifact>
  externalConfiguration: # (11)
    env:
      - name: AWS_ACCESS_KEY_ID
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsAccessKey
      - name: AWS_SECRET_ACCESS_KEY
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsSecretAccessKey
  resources: # (12)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: # (13)
    type: inline
    loggers:
      log4j.rootLogger: INFO
  readinessProbe: # (14)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  metricsConfig: # (15)
    type: jmxPrometheusExporter
    valueFrom:
      configMapKeyRef:
        name: my-config-map
        key: my-key
  jvmOptions: # (16)
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest # (17)
  rack:
    topologyKey: topology.kubernetes.io/zone # (18)
  template: # (19)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: # (20)
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry # (21)

  1. Use KafkaConnect.

  2. Enables KafkaConnectors for the Kafka Connect cluster.

  3. The number of replica nodes for the workers that run tasks.

  4. Authentication for the Kafka Connect cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN. By default, Kafka Connect connects to Kafka brokers using a plain text connection.

  5. Bootstrap server for connection to the Kafka cluster.

  6. TLS encryption with key names under which TLS certificates are stored in X.509 format for the cluster. If certificates are stored in the same secret, it can be listed multiple times.

  7. Kafka Connect configuration of workers (not connectors). Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.

  8. Build configuration properties for building a container image with connector plugins automatically.

  9. (Required) Configuration of the container registry where new images are pushed.

  10. (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.

  11. External configuration for connectors using environment variables, as shown here, or volumes. You can also use configuration provider plugins to load configuration values from external sources.

  12. Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.

  13. Specified Kafka Connect loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties or log4j2.properties key in the ConfigMap. For the Kafka Connect log4j.rootLogger logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

  14. Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).

  15. Prometheus metrics, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under metricsConfig.valueFrom.configMapKeyRef.key.

  16. JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka Connect.

  17. ADVANCED OPTION: Container image configuration, which is recommended only in special situations.

  18. SPECIALIZED OPTION: Rack awareness configuration for the deployment. This is a specialized option intended for a deployment within the same location, not across regions. Use this option if you want connectors to consume from the closest replica rather than the leader replica. In certain cases, consuming from the closest replica can improve network utilization or reduce costs . The topologyKey must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standard topology.kubernetes.io/zone label. To consume from the closest replica, enable the RackAwareReplicaSelector in the Kafka broker configuration.

  19. Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.

  20. Environment variables are set for distributed tracing.

  21. Distributed tracing is enabled by using OpenTelemetry.

9.6.1. Configuring Kafka Connect for multiple instances

By default, Strimzi configures the group ID and names of the internal topics used by Kafka Connect. When running multiple instances of Kafka Connect, you must change these default settings using the following config properties:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  config:
    group.id: my-connect-cluster # (1)
    offset.storage.topic: my-connect-cluster-offsets # (2)
    config.storage.topic: my-connect-cluster-configs # (3)
    status.storage.topic: my-connect-cluster-status # (4)
    # ...
  # ...

  1. The Kafka Connect cluster group 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 instances with the same group.id.

Unless you modify these default settings, each instance connecting to the same Kafka cluster is deployed with the same values. In practice, this means all instances form a cluster and use the same internal topics.

Multiple instances attempting to use the same internal topics will cause unexpected errors, so you must change the values of these properties for each instance.

9.6.2. Configuring Kafka Connect user authorization

When using authorization in Kafka, a Kafka Connect user requires read/write access to the cluster group and internal topics of Kafka Connect. This procedure outlines how access is granted using simple authorization and ACLs.

Properties for the Kafka Connect cluster group ID and internal topics are configured by Strimzi by default. Alternatively, you can define them explicitly in the spec of the KafkaConnect resource. This is useful when configuring Kafka Connect for multiple instances, as the values for the group ID and topics must differ when running multiple Kafka Connect instances.

Simple authorization uses ACL rules managed by the Kafka AclAuthorizer and StandardAuthorizer plugins to ensure appropriate access levels. For more information on configuring a KafkaUser resource to use simple authorization, see the AclRule schema reference.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the authorization property in the KafkaUser resource to provide access rights to the user.

    Access rights are configured for the Kafka Connect topics and cluster group using literal name values. The following table shows the default names configured for the topics and cluster group ID.

    Table 10. Names for the access rights configuration
    Property Name

    offset.storage.topic

    connect-cluster-offsets

    status.storage.topic

    connect-cluster-status

    config.storage.topic

    connect-cluster-configs

    group

    connect-cluster

    In this example configuration, the default names are used to specify access rights. If you are using different names for a Kafka Connect instance, use those names in the ACLs configuration.

    Example configuration for simple authorization
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  authorization:
    type: simple
    acls:
      # access to offset.storage.topic
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operations:
          - Create
          - Describe
          - Read
          - Write
        host: "*"
      # access to status.storage.topic
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operations:
          - Create
          - Describe
          - Read
          - Write
        host: "*"
      # access to config.storage.topic
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operations:
          - Create
          - Describe
          - Read
          - Write
        host: "*"
      # cluster group
      - resource:
          type: group
          name: connect-cluster
          patternType: literal
        operations:
          - Read
        host: "*"

  2. Create or update the resource.

    kubectl apply -f KAFKA-USER-CONFIG-FILE

9.6.3. Manually stopping or pausing Kafka Connect connectors

If you are using KafkaConnector resources to configure connectors, use the state configuration to either stop or pause a connector. In contrast to the paused state, where the connector and tasks remain instantiated, stopping a connector retains only the configuration, with no active processes. Stopping a connector from running may be more suitable for longer durations than just pausing. While a paused connector is quicker to resume, a stopped connector has the advantages of freeing up memory and resources.

Note
 The state configuration replaces the (deprecated) pause configuration in the KafkaConnectorSpec schema, which allows pauses on connectors. If you were previously using the pause configuration to pause connectors, we encourage you to transition to using the state configuration only to avoid conflicts.
Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaConnector custom resource that controls the connector you want to pause or stop:

    kubectl get KafkaConnector

  2. Edit the KafkaConnector resource to stop or pause the connector.

    Example configuration for stopping a Kafka Connect connector
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  class: org.apache.kafka.connect.file.FileStreamSourceConnector
  tasksMax: 2
  config:
    file: "/opt/kafka/LICENSE"
    topic: my-topic
  state: stopped
  # ...

    Change the state configuration to stopped or paused. The default state for the connector when this property is not set is running.

  3. Apply the changes to the KafkaConnector configuration.

    You can resume the connector by changing state to running or removing the configuration.

Note
 Alternatively, you can expose the Kafka Connect API and use the stop and pause endpoints to stop a connector from running. For example, PUT /connectors/<connector_name>/stop. You can then use the resume endpoint to restart it.

9.6.4. Manually restarting Kafka Connect connectors

If you are using KafkaConnector resources to manage connectors, use the strimzi.io/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:

    kubectl get KafkaConnector

  2. Restart the connector by annotating the KafkaConnector resource in Kubernetes:

    kubectl 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.

9.6.5. Manually restarting Kafka Connect connector tasks

If you are using KafkaConnector resources to manage connectors, use the strimzi.io/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:

    kubectl get KafkaConnector

  2. Find the ID of the task to be restarted from the KafkaConnector custom resource:

    kubectl describe KafkaConnector <kafka_connector_name>

    Task IDs are non-negative integers, starting from 0.

  3. Use the ID to restart the connector task by annotating the KafkaConnector resource in Kubernetes:

    kubectl 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.

9.7. Configuring Kafka MirrorMaker 2

Update the spec properties of the KafkaMirrorMaker2 custom resource to configure your MirrorMaker 2 deployment. MirrorMaker 2 uses source cluster configuration for data consumption and target cluster configuration for data output.

MirrorMaker 2 is based on the Kafka Connect framework, connectors managing the transfer of data between clusters.

You configure MirrorMaker 2 to define the Kafka Connect deployment, including the connection details of the source and target clusters, and then run a set of MirrorMaker 2 connectors to make the connection.

MirrorMaker 2 supports topic configuration synchronization between the source and target clusters. You specify source topics in the MirrorMaker 2 configuration. MirrorMaker 2 monitors the source topics. MirrorMaker 2 detects and propagates changes to the source topics to the remote topics. Changes might include automatically creating missing topics and partitions.

Note
 In most cases you write to local topics and read from remote topics. Though write operations are not prevented on remote topics, they should be avoided.

The configuration must specify:

  • Each Kafka cluster

  • Connection information for each cluster, including authentication

  • The replication flow and direction

    • Cluster to cluster

    • Topic to topic

For a deeper understanding of the Kafka MirrorMaker 2 cluster configuration options, refer to the Strimzi Custom Resource API Reference.

Note
 MirrorMaker 2 resource configuration differs from the previous version of MirrorMaker, which is now deprecated. There is currently no legacy support, so any resources must be manually converted into the new format.
Default configuration

MirrorMaker 2 provides default configuration values for properties such as replication factors. A minimal configuration, with defaults left unchanged, would be something like this example:

Minimal configuration for MirrorMaker 2
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 3.6.1
  connectCluster: "my-cluster-target"
  clusters:
  - alias: "my-cluster-source"
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092
  - alias: "my-cluster-target"
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector: {}

You can configure access control for source and target clusters using mTLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and mTLS authentication for the source and target cluster.

You can specify the topics and consumer groups you wish to replicate from a source cluster in the KafkaMirrorMaker2 resource. You use the topicsPattern and groupsPattern properties to do this. You can provide a list of names or use a regular expression. By default, all topics and consumer groups are replicated if you do not set the topicsPattern and groupsPattern properties. You can also replicate all topics and consumer groups by using ".*" as a regular expression. However, try to specify only the topics and consumer groups you need to avoid causing any unnecessary extra load on the cluster.

Handling high volumes of messages

You can tune the configuration to handle high volumes of messages. For more information, see Handling high volumes of messages.

Example KafkaMirrorMaker2 custom resource configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 3.6.1 # (1)
  replicas: 3 # (2)
  connectCluster: "my-cluster-target" # (3)
  clusters: # (4)
  - alias: "my-cluster-source" # (5)
    authentication: # (6)
      certificateAndKey:
        certificate: source.crt
        key: source.key
        secretName: my-user-source
      type: tls
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092 # (7)
    tls: # (8)
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-source-cluster-ca-cert
  - alias: "my-cluster-target" # (9)
    authentication: # (10)
      certificateAndKey:
        certificate: target.crt
        key: target.key
        secretName: my-user-target
      type: tls
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092 # (11)
    config: # (12)
      config.storage.replication.factor: 1
      offset.storage.replication.factor: 1
      status.storage.replication.factor: 1
    tls: # (13)
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-target-cluster-ca-cert
  mirrors: # (14)
  - sourceCluster: "my-cluster-source" # (15)
    targetCluster: "my-cluster-target" # (16)
    sourceConnector: # (17)
      tasksMax: 10 # (18)
      autoRestart: # (19)
        enabled: true
      config
        replication.factor: 1 # (20)
        offset-syncs.topic.replication.factor: 1 # (21)
        sync.topic.acls.enabled: "false" # (22)
        refresh.topics.interval.seconds: 60 # (23)
        replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy" # (24)
    heartbeatConnector: # (25)
      autoRestart:
        enabled: true
      config:
        heartbeats.topic.replication.factor: 1 # (26)
        replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy"
    checkpointConnector: # (27)
      autoRestart:
        enabled: true
      config:
        checkpoints.topic.replication.factor: 1 # (28)
        refresh.groups.interval.seconds: 600 # (29)
        sync.group.offsets.enabled: true # (30)
        sync.group.offsets.interval.seconds: 60 # (31)
        emit.checkpoints.interval.seconds: 60 # (32)
        replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy"
    topicsPattern: "topic1|topic2|topic3" # (33)
    groupsPattern: "group1|group2|group3" # (34)
  resources: # (35)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: # (36)
    type: inline
    loggers:
      connect.root.logger.level: INFO
  readinessProbe: # (37)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  jvmOptions: # (38)
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest # (39)
  rack:
    topologyKey: topology.kubernetes.io/zone # (40)
  template: # (41)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: # (42)
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry # (43)
  externalConfiguration: # (44)
    env:
      - name: AWS_ACCESS_KEY_ID
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsAccessKey
      - name: AWS_SECRET_ACCESS_KEY
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsSecretAccessKey

  1. The Kafka Connect and MirrorMaker 2 version, which will always be the same.

  2. The number of replica nodes for the workers that run tasks.

  3. Kafka cluster alias for Kafka Connect, which must specify the target Kafka cluster. The Kafka cluster is used by Kafka Connect for its internal topics.

  4. Specification for the Kafka clusters being synchronized.

  5. Cluster alias for the source Kafka cluster.

  6. Authentication for the source cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN.

  7. Bootstrap server for connection to the source Kafka cluster.

  8. TLS encryption with key names under which TLS certificates are stored in X.509 format for the source Kafka cluster. If certificates are stored in the same secret, it can be listed multiple times.

  9. Cluster alias for the target Kafka cluster.

  10. Authentication for the target Kafka cluster is configured in the same way as for the source Kafka cluster.

  11. Bootstrap server for connection to the target Kafka cluster.

  12. Kafka Connect configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.

  13. TLS encryption for the target Kafka cluster is configured in the same way as for the source Kafka cluster.

  14. MirrorMaker 2 connectors.

  15. Cluster alias for the source cluster used by the MirrorMaker 2 connectors.

  16. Cluster alias for the target cluster used by the MirrorMaker 2 connectors.

  17. Configuration for the MirrorSourceConnector that creates remote topics. The config overrides the default configuration options.

  18. The maximum number of tasks that the connector may create. Tasks handle the data replication and run in parallel. If the infrastructure supports the processing overhead, increasing this value can improve throughput. Kafka Connect distributes the tasks between members of the cluster. If there are more tasks than workers, workers are assigned multiple tasks. For sink connectors, aim to have one task for each topic partition consumed. For source connectors, the number of tasks that can run in parallel may also depend on the external system. The connector creates fewer than the maximum number of tasks if it cannot achieve the parallelism.

  19. Enables automatic restarts of failed connectors and tasks. By default, the number of restarts is indefinite, but you can set a maximum on the number of automatic restarts using the maxRestarts property.

  20. Replication factor for mirrored topics created at the target cluster.

  21. Replication factor for the MirrorSourceConnector offset-syncs internal topic that maps the offsets of the source and target clusters.

  22. When ACL rules synchronization is enabled, ACLs are applied to synchronized topics. The default is true. This feature is not compatible with the User Operator. If you are using the User Operator, set this property to false.

  23. Optional setting to change the frequency of checks for new topics. The default is for a check every 10 minutes.

  24. Adds a policy that overrides the automatic renaming of remote topics. Instead of prepending the name with the name of the source cluster, the topic retains its original name. This optional setting is useful for active/passive backups and data migration. The property must be specified for all connectors. For bidirectional (active/active) replication, use the DefaultReplicationPolicy class to automatically rename remote topics and specify the replication.policy.separator property for all connectors to add a custom separator.

  25. Configuration for the MirrorHeartbeatConnector that performs connectivity checks. The config overrides the default configuration options.

  26. Replication factor for the heartbeat topic created at the target cluster.

  27. Configuration for the MirrorCheckpointConnector that tracks offsets. The config overrides the default configuration options.

  28. Replication factor for the checkpoints topic created at the target cluster.

  29. Optional setting to change the frequency of checks for new consumer groups. The default is for a check every 10 minutes.

  30. Optional setting to synchronize consumer group offsets, which is useful for recovery in an active/passive configuration. Synchronization is not enabled by default.

  31. If the synchronization of consumer group offsets is enabled, you can adjust the frequency of the synchronization.

  32. Adjusts the frequency of checks for offset tracking. If you change the frequency of offset synchronization, you might also need to adjust the frequency of these checks.

  33. Topic replication from the source cluster defined as a comma-separated list or regular expression pattern. The source connector replicates the specified topics. The checkpoint connector tracks offsets for the specified topics. Here we request three topics by name.

  34. Consumer group replication from the source cluster defined as a comma-separated list or regular expression pattern. The checkpoint connector replicates the specified consumer groups. Here we request three consumer groups by name.

  35. Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.

  36. Specified Kafka Connect loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties or log4j2.properties key in the ConfigMap. For the Kafka Connect log4j.rootLogger logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

  37. Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).

  38. JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.

  39. ADVANCED OPTION: Container image configuration, which is recommended only in special situations.

  40. SPECIALIZED OPTION: Rack awareness configuration for the deployment. This is a specialized option intended for a deployment within the same location, not across regions. Use this option if you want connectors to consume from the closest replica rather than the leader replica. In certain cases, consuming from the closest replica can improve network utilization or reduce costs . The topologyKey must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standard topology.kubernetes.io/zone label. To consume from the closest replica, enable the RackAwareReplicaSelector in the Kafka broker configuration.

  41. Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.

  42. Environment variables are set for distributed tracing.

  43. Distributed tracing is enabled by using OpenTelemetry.

  44. External configuration for a Kubernetes Secret mounted to Kafka MirrorMaker as an environment variable. You can also use configuration provider plugins to load configuration values from external sources.

9.7.1. Configuring active/active or active/passive modes

You can use MirrorMaker 2 in active/passive or active/active cluster configurations.

active/active cluster configuration

An active/active configuration has two active clusters replicating data bidirectionally. Applications can use either cluster. Each cluster can provide the same data. In this way, you can make the same data available in different geographical locations. As consumer groups are active in both clusters, consumer offsets for replicated topics are not synchronized back to the source cluster.

active/passive cluster configuration

An active/passive configuration has an active cluster replicating data to a passive cluster. The passive cluster remains on standby. You might use the passive cluster for data recovery in the event of system failure.

The expectation is that producers and consumers connect to active clusters only. A MirrorMaker 2 cluster is required at each target destination.

Bidirectional replication (active/active)

The MirrorMaker 2 architecture supports bidirectional replication in an active/active cluster configuration.

Each cluster replicates the data of the other cluster using the concept of source and remote topics. As the same topics are stored in each cluster, remote topics are automatically renamed by MirrorMaker 2 to represent the source cluster. The name of the originating cluster is prepended to the name of the topic.

MirrorMaker 2 bidirectional architecture
Figure 1. Topic renaming

By flagging the originating cluster, topics are not replicated back to that cluster.

The concept of replication through remote topics is useful when configuring an architecture that requires data aggregation. Consumers can subscribe to source and remote topics within the same cluster, without the need for a separate aggregation cluster.

Unidirectional replication (active/passive)

The MirrorMaker 2 architecture supports unidirectional replication in an active/passive cluster configuration.

You can use an active/passive cluster configuration to make backups or migrate data to another cluster. In this situation, you might not want automatic renaming of remote topics.

You can override automatic renaming by adding IdentityReplicationPolicy to the source connector configuration. With this configuration applied, topics retain their original names.

9.7.2. Configuring MirrorMaker 2 for multiple instances

By default, Strimzi configures the group ID and names of the internal topics used by the Kafka Connect framework that MirrorMaker 2 runs on. When running multiple instances of MirrorMaker 2, and they share the same connectCluster value, you must change these default settings using the following config properties:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  connectCluster: "my-cluster-target"
  clusters:
  - alias: "my-cluster-target"
    config:
      group.id: my-connect-cluster # (1)
      offset.storage.topic: my-connect-cluster-offsets # (2)
      config.storage.topic: my-connect-cluster-configs # (3)
      status.storage.topic: my-connect-cluster-status # (4)
      # ...
    # ...

  1. The Kafka Connect cluster group 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 instances with the same group.id.

The connectCluster setting specifies the alias of the target Kafka cluster used by Kafka Connect for its internal topics. As a result, modifications to the connectCluster, group ID, and internal topic naming configuration are specific to the target Kafka cluster. You don’t need to make changes if two MirrorMaker 2 instances are using the same source Kafka cluster or in an active-active mode where each MirrorMaker 2 instance has a different connectCluster setting and target cluster.

However, if multiple MirrorMaker 2 instances share the same connectCluster, each instance connecting to the same target Kafka cluster is deployed with the same values. In practice, this means all instances form a cluster and use the same internal topics.

Multiple instances attempting to use the same internal topics will cause unexpected errors, so you must change the values of these properties for each instance.

9.7.3. Configuring MirrorMaker 2 connectors

Use MirrorMaker 2 connector configuration for the internal connectors that orchestrate the synchronization of data between Kafka clusters.

MirrorMaker 2 consists of the following connectors:

MirrorSourceConnector

The source connector replicates topics from a source cluster to a target cluster. It also replicates ACLs and is necessary for the MirrorCheckpointConnector to run.

MirrorCheckpointConnector

The checkpoint connector periodically tracks offsets. If enabled, it also synchronizes consumer group offsets between the source and target cluster.

MirrorHeartbeatConnector

The heartbeat connector periodically checks connectivity between the source and target cluster.

The following table describes connector properties and the connectors you configure to use them.

Table 11. MirrorMaker 2 connector configuration properties
Property sourceConnector checkpointConnector heartbeatConnector
admin.timeout.ms

Timeout for admin tasks, such as detecting new topics. Default is 60000 (1 minute).

✓

✓

✓
replication.policy.class

Policy to define the remote topic naming convention. Default is org.apache.kafka.connect.mirror.DefaultReplicationPolicy.

✓

✓

✓
replication.policy.separator

The separator used for topic naming in the target cluster. By default, the separator is set to a dot (.). Separator configuration is only applicable to the DefaultReplicationPolicy replication policy class, which defines remote topic names. The IdentityReplicationPolicy class does not use the property as topics retain their original names.

✓

✓

✓
consumer.poll.timeout.ms

Timeout when polling the source cluster. Default is 1000 (1 second).

✓

✓
offset-syncs.topic.location

The location of the offset-syncs topic, which can be the source (default) or target cluster.

✓

✓
topic.filter.class

Topic filter to select the topics to replicate. Default is org.apache.kafka.connect.mirror.DefaultTopicFilter.

✓

✓
config.property.filter.class

Topic filter to select the topic configuration properties to replicate. Default is org.apache.kafka.connect.mirror.DefaultConfigPropertyFilter.

✓
config.properties.exclude

Topic configuration properties that should not be replicated. Supports comma-separated property names and regular expressions.

✓
offset.lag.max

Maximum allowable (out-of-sync) offset lag before a remote partition is synchronized. Default is 100.

✓
offset-syncs.topic.replication.factor

Replication factor for the internal offset-syncs topic. Default is 3.

✓
refresh.topics.enabled

Enables check for new topics and partitions. Default is true.

✓
refresh.topics.interval.seconds

Frequency of topic refresh. Default is 600 (10 minutes). By default, a check for new topics in the source cluster is made every 10 minutes. You can change the frequency by adding refresh.topics.interval.seconds to the source connector configuration.

✓
replication.factor

The replication factor for new topics. Default is 2.

✓
sync.topic.acls.enabled

Enables synchronization of ACLs from the source cluster. Default is true. For more information, see Synchronizing ACL rules for remote topics.

✓
sync.topic.acls.interval.seconds

Frequency of ACL synchronization. Default is 600 (10 minutes).

✓
sync.topic.configs.enabled

Enables synchronization of topic configuration from the source cluster. Default is true.

✓
sync.topic.configs.interval.seconds

Frequency of topic configuration synchronization. Default 600 (10 minutes).

✓
checkpoints.topic.replication.factor

Replication factor for the internal checkpoints topic. Default is 3.

✓
emit.checkpoints.enabled

Enables synchronization of consumer offsets to the target cluster. Default is true.

✓
emit.checkpoints.interval.seconds

Frequency of consumer offset synchronization. Default is 60 (1 minute).

✓
group.filter.class

Group filter to select the consumer groups to replicate. Default is org.apache.kafka.connect.mirror.DefaultGroupFilter.

✓
refresh.groups.enabled

Enables check for new consumer groups. Default is true.

✓
refresh.groups.interval.seconds

Frequency of consumer group refresh. Default is 600 (10 minutes).

✓
sync.group.offsets.enabled

Enables synchronization of consumer group offsets to the target cluster __consumer_offsets topic. Default is false.

✓
sync.group.offsets.interval.seconds

Frequency of consumer group offset synchronization. Default is 60 (1 minute).

✓
emit.heartbeats.enabled

Enables connectivity checks on the target cluster. Default is true.

✓
emit.heartbeats.interval.seconds

Frequency of connectivity checks. Default is 1 (1 second).

✓
heartbeats.topic.replication.factor

Replication factor for the internal heartbeats topic. Default is 3.

✓
Changing the location of the consumer group offsets topic

MirrorMaker 2 tracks offsets for consumer groups using internal topics.

offset-syncs topic

The offset-syncs topic maps the source and target offsets for replicated topic partitions from record metadata.

checkpoints topic

The checkpoints topic maps the last committed offset in the source and target cluster for replicated topic partitions in each consumer group.

As they are used internally by MirrorMaker 2, you do not interact directly with these topics.

MirrorCheckpointConnector emits checkpoints for offset tracking. Offsets for the checkpoints topic are tracked at predetermined intervals through configuration. Both topics enable replication to be fully restored from the correct offset position on failover.

The location of the offset-syncs topic is the source cluster by default. You can use the offset-syncs.topic.location connector configuration to change this to the target cluster. You need read/write access to the cluster that contains the topic. Using the target cluster as the location of the offset-syncs topic allows you to use MirrorMaker 2 even if you have only read access to the source cluster.

Synchronizing consumer group offsets

The __consumer_offsets topic stores information on committed offsets for each consumer group. Offset synchronization periodically transfers the consumer offsets for the consumer groups of a source cluster into the consumer offsets topic of a target cluster.

Offset synchronization is particularly useful in an active/passive configuration. If the active cluster goes down, consumer applications can switch to the passive (standby) cluster and pick up from the last transferred offset position.

To use topic offset synchronization, enable the synchronization by adding sync.group.offsets.enabled to the checkpoint connector configuration, and setting the property to true. Synchronization is disabled by default.

When using the IdentityReplicationPolicy in the source connector, it also has to be configured in the checkpoint connector configuration. This ensures that the mirrored consumer offsets will be applied for the correct topics.

Consumer offsets are only synchronized for consumer groups that are not active in the target cluster. If the consumer groups are in the target cluster, the synchronization cannot be performed and an UNKNOWN_MEMBER_ID error is returned.

If enabled, the synchronization of offsets from the source cluster is made periodically. You can change the frequency by adding sync.group.offsets.interval.seconds and emit.checkpoints.interval.seconds to the checkpoint connector configuration. The properties specify the frequency in seconds that the consumer group offsets are synchronized, and the frequency of checkpoints emitted for offset tracking. The default for both properties is 60 seconds. You can also change the frequency of checks for new consumer groups using the refresh.groups.interval.seconds property, which is performed every 10 minutes by default.

Because the synchronization is time-based, any switchover by consumers to a passive cluster will likely result in some duplication of messages.

Note
 If you have an application written in Java, you can use the RemoteClusterUtils.java utility to synchronize offsets through the application. The utility fetches remote offsets for a consumer group from the checkpoints topic.
Deciding when to use the heartbeat connector

The heartbeat connector emits heartbeats to check connectivity between source and target Kafka clusters. An internal heartbeat topic is replicated from the source cluster, which means that the heartbeat connector must be connected to the source cluster. The heartbeat topic is located on the target cluster, which allows it to do the following:

  • Identify all source clusters it is mirroring data from

  • Verify the liveness and latency of the mirroring process

This helps to make sure that the process is not stuck or has stopped for any reason. While the heartbeat connector can be a valuable tool for monitoring the mirroring processes between Kafka clusters, it’s not always necessary to use it. For example, if your deployment has low network latency or a small number of topics, you might prefer to monitor the mirroring process using log messages or other monitoring tools. If you decide not to use the heartbeat connector, simply omit it from your MirrorMaker 2 configuration.

Aligning the configuration of MirrorMaker 2 connectors

To ensure that MirrorMaker 2 connectors work properly, make sure to align certain configuration settings across connectors. Specifically, ensure that the following properties have the same value across all applicable connectors:

  • replication.policy.class

  • replication.policy.separator

  • offset-syncs.topic.location

  • topic.filter.class

For example, the value for replication.policy.class must be the same for the source, checkpoint, and heartbeat connectors. Mismatched or missing settings cause issues with data replication or offset syncing, so it’s essential to keep all relevant connectors configured with the same settings.

9.7.4. Configuring MirrorMaker 2 connector producers and consumers

MirrorMaker 2 connectors use internal producers and consumers. If needed, you can configure these producers and consumers to override the default settings.

For example, you can increase the batch.size for the source producer that sends topics to the target Kafka cluster to better accommodate large volumes of messages.

Important
 Producer and consumer configuration options depend on the MirrorMaker 2 implementation, and may be subject to change.

The following tables describe the producers and consumers for each of the connectors and where you can add configuration.

Table 12. Source connector producers and consumers
Type Description Configuration

Producer

Sends topic messages to the target Kafka cluster. Consider tuning the configuration of this producer when it is handling large volumes of data.

mirrors.sourceConnector.config: producer.override.*

Producer

Writes to the offset-syncs topic, which maps the source and target offsets for replicated topic partitions.

mirrors.sourceConnector.config: producer.*

Consumer

Retrieves topic messages from the source Kafka cluster.

mirrors.sourceConnector.config: consumer.*
Table 13. Checkpoint connector producers and consumers
Type Description Configuration

Producer

Emits consumer offset checkpoints.

mirrors.checkpointConnector.config: producer.override.*

Consumer

Loads the offset-syncs topic.

mirrors.checkpointConnector.config: consumer.*
Note
 You can set offset-syncs.topic.location to target to use the target Kafka cluster as the location of the offset-syncs topic.
Table 14. Heartbeat connector producer
Type Description Configuration

Producer

Emits heartbeats.

mirrors.heartbeatConnector.config: producer.override.*

The following example shows how you configure the producers and consumers.

Example configuration for connector producers and consumers
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 3.6.1
  # ...
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector:
      tasksMax: 5
      config:
        producer.override.batch.size: 327680
        producer.override.linger.ms: 100
        producer.request.timeout.ms: 30000
        consumer.fetch.max.bytes: 52428800
        # ...
    checkpointConnector:
      config:
        producer.override.request.timeout.ms: 30000
        consumer.max.poll.interval.ms: 300000
        # ...
    heartbeatConnector:
      config:
        producer.override.request.timeout.ms: 30000
        # ...

9.7.5. Specifying a maximum number of data replication tasks

Connectors create the tasks that are responsible for moving data in and out of Kafka. Each connector comprises one or more tasks that are distributed across a group of worker pods that run the tasks. Increasing the number of tasks can help with performance issues when replicating a large number of partitions or synchronizing the offsets of a large number of consumer groups.

Tasks run in parallel. Workers are assigned one or more tasks. A single task is handled by one worker pod, so you don’t need more worker pods than tasks. If there are more tasks than workers, workers handle multiple tasks.

You can specify the maximum number of connector tasks in your MirrorMaker configuration using the tasksMax property. Without specifying a maximum number of tasks, the default setting is a single task.

The heartbeat connector always uses a single task.

The number of tasks that are started for the source and checkpoint connectors is the lower value between the maximum number of possible tasks and the value for tasksMax. For the source connector, the maximum number of tasks possible is one for each partition being replicated from the source cluster. For the checkpoint connector, the maximum number of tasks possible is one for each consumer group being replicated from the source cluster. When setting a maximum number of tasks, consider the number of partitions and the hardware resources that support the process.

If the infrastructure supports the processing overhead, increasing the number of tasks can improve throughput and latency. For example, adding more tasks reduces the time taken to poll the source cluster when there is a high number of partitions or consumer groups.

Increasing the number of tasks for the source connector is useful when you have a large number of partitions.

Increasing the number of tasks for the source connector
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  # ...
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector:
      tasksMax: 10
  # ...

Increasing the number of tasks for the checkpoint connector is useful when you have a large number of consumer groups.

Increasing the number of tasks for the checkpoint connector
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  # ...
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    checkpointConnector:
      tasksMax: 10
  # ...

By default, MirrorMaker 2 checks for new consumer groups every 10 minutes. You can adjust the refresh.groups.interval.seconds configuration to change the frequency. Take care when adjusting lower. More frequent checks can have a negative impact on performance.

Checking connector task operations

If you are using Prometheus and Grafana to monitor your deployment, you can check MirrorMaker 2 performance. The example MirrorMaker 2 Grafana dashboard provided with Strimzi shows the following metrics related to tasks and latency.

  • The number of tasks

  • Replication latency

  • Offset synchronization latency

Additional resources

9.7.6. Synchronizing ACL rules for remote topics

When using MirrorMaker 2 with Strimzi, it is possible to synchronize ACL rules for remote topics. However, this feature is only available if you are not using the User Operator.

If you are using type: simple authorization without the User Operator, the ACL rules that manage access to brokers also apply to remote topics. This means that users who have read access to a source topic can also read its remote equivalent.

Note
 OAuth 2.0 authorization does not support access to remote topics in this way.

9.7.7. Securing a Kafka MirrorMaker 2 deployment

This procedure describes in outline the configuration required to secure a MirrorMaker 2 deployment.

You need separate configuration for the source Kafka cluster and the target Kafka cluster. You also need separate user configuration to provide the credentials required for MirrorMaker to connect to the source and target Kafka clusters.

For the Kafka clusters, you specify internal listeners for secure connections within a Kubernetes cluster and external listeners for connections outside the Kubernetes cluster.

You can configure authentication and authorization mechanisms. The security options implemented for the source and target Kafka clusters must be compatible with the security options implemented for MirrorMaker 2.

After you have created the cluster and user authentication credentials, you specify them in your MirrorMaker configuration for secure connections.

Note
 In this procedure, the 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).
Before you start

Before starting this procedure, take a look at the example configuration files provided by Strimzi. They include examples for securing a deployment of MirrorMaker 2 using mTLS or SCRAM-SHA-512 authentication. The examples specify internal listeners for connecting within a Kubernetes cluster.

The examples also provide the configuration for full authorization, including the ACLs that allow user operations on the source and target Kafka clusters.

When configuring user access to source and target Kafka clusters, ACLs must grant access rights to internal MirrorMaker 2 connectors and read/write access to the cluster group and internal topics used by the underlying Kafka Connect framework in the target cluster. If you’ve renamed the cluster group or internal topics, such as when configuring MirrorMaker 2 for multiple instances, use those names in the ACLs configuration.

Simple authorization uses ACL rules managed by the Kafka AclAuthorizer and StandardAuthorizer plugins to ensure appropriate access levels. For more information on configuring a KafkaUser resource to use simple authorization, see the AclRule schema reference.

Prerequisites

  • Strimzi is running

  • Separate namespaces for source and target clusters

The procedure assumes that the source and target Kafka clusters are installed to separate namespaces. If you want to use the Topic Operator, you’ll need to do this. The Topic Operator only watches a single cluster in a specified namespace.

By separating the clusters into namespaces, you will need to copy the cluster secrets so they can be accessed outside the namespace. You need to reference the secrets in the MirrorMaker configuration.

Procedure

  1. Configure two Kafka resources, one to secure the source Kafka cluster and one to secure the target Kafka cluster.

    You can add listener configuration for authentication and enable authorization.

    In this example, an internal listener is configured for a Kafka cluster with TLS encryption and mTLS authentication. Kafka simple authorization is enabled.

    Example source Kafka cluster configuration with TLS encryption and mTLS authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-source-cluster
spec:
  kafka:
    version: 3.6.1
    replicas: 1
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
    authorization:
      type: simple
    config:
      offsets.topic.replication.factor: 1
      transaction.state.log.replication.factor: 1
      transaction.state.log.min.isr: 1
      default.replication.factor: 1
      min.insync.replicas: 1
      inter.broker.protocol.version: "3.6"
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  zookeeper:
    replicas: 1
    storage:
      type: persistent-claim
      size: 100Gi
      deleteClaim: false
  entityOperator:
    topicOperator: {}
    userOperator: {}
    Example target Kafka cluster configuration with TLS encryption and mTLS authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-target-cluster
spec:
  kafka:
    version: 3.6.1
    replicas: 1
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
    authorization:
      type: simple
    config:
      offsets.topic.replication.factor: 1
      transaction.state.log.replication.factor: 1
      transaction.state.log.min.isr: 1
      default.replication.factor: 1
      min.insync.replicas: 1
      inter.broker.protocol.version: "3.6"
    storage:
      type: jbod
      volumes:
        - id: 0
          type: persistent-claim
          size: 100Gi
          deleteClaim: false
  zookeeper:
    replicas: 1
    storage:
      type: persistent-claim
      size: 100Gi
      deleteClaim: false
  entityOperator:
    topicOperator: {}
    userOperator: {}

  2. Create or update the Kafka resources in separate namespaces.

    kubectl apply -f <kafka_configuration_file> -n <namespace>

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

    The certificates are created in the secret <cluster_name>-cluster-ca-cert.

  3. Configure two KafkaUser resources, one for a user of the source Kafka cluster and one for a user of the target Kafka cluster.

    1. Configure the same authentication and authorization types as the corresponding source and target Kafka cluster. For example, if you used tls authentication and the simple authorization type in the Kafka configuration for the source Kafka cluster, use the same in the KafkaUser configuration.

    2. Configure the ACLs needed by MirrorMaker 2 to allow operations on the source and target Kafka clusters.

    Example source user configuration for mTLS authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-source-user
  labels:
    strimzi.io/cluster: my-source-cluster
spec:
  authentication:
    type: tls
  authorization:
    type: simple
    acls:
      # MirrorSourceConnector
      - resource: # Not needed if offset-syncs.topic.location=target
          type: topic
          name: mm2-offset-syncs.my-target-cluster.internal
        operations:
          - Create
          - DescribeConfigs
          - Read
          - Write
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operations:
          - DescribeConfigs
          - Read
      # MirrorCheckpointConnector
      - resource:
          type: cluster
        operations:
          - Describe
      - resource: # Needed for every group for which offsets are synced
          type: group
          name: "*"
        operations:
          - Describe
      - resource: # Not needed if offset-syncs.topic.location=target
          type: topic
          name: mm2-offset-syncs.my-target-cluster.internal
        operations:
          - Read
    Example target user configuration for mTLS authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-target-user
  labels:
    strimzi.io/cluster: my-target-cluster
spec:
  authentication:
    type: tls
  authorization:
    type: simple
    acls:
      # cluster group
      - resource:
          type: group
          name: mirrormaker2-cluster
        operations:
          - Read
      # access to config.storage.topic
      - resource:
          type: topic
          name: mirrormaker2-cluster-configs
        operations:
          - Create
          - Describe
          - DescribeConfigs
          - Read
          - Write
      # access to status.storage.topic
      - resource:
          type: topic
          name: mirrormaker2-cluster-status
        operations:
          - Create
          - Describe
          - DescribeConfigs
          - Read
          - Write
      # access to offset.storage.topic
      - resource:
          type: topic
          name: mirrormaker2-cluster-offsets
        operations:
          - Create
          - Describe
          - DescribeConfigs
          - Read
          - Write
      # MirrorSourceConnector
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operations:
          - Create
          - Alter
          - AlterConfigs
          - Write
      # MirrorCheckpointConnector
      - resource:
          type: cluster
        operations:
          - Describe
      - resource:
          type: topic
          name: my-source-cluster.checkpoints.internal
        operations:
          - Create
          - Describe
          - Read
          - Write
      - resource: # Needed for every group for which the offset is synced
          type: group
          name: "*"
        operations:
          - Read
          - Describe
      # MirrorHeartbeatConnector
      - resource:
          type: topic
          name: heartbeats
        operations:
          - Create
          - Describe
          - Write
    Note
    		 You can use a certificate issued outside the User Operator by setting type to tls-external. For more information, see the KafkaUserSpec schema reference.

  4. Create or update a KafkaUser resource in each of the namespaces you created for the source and target Kafka clusters.

    kubectl apply -f <kafka_user_configuration_file> -n <namespace>

    The User Operator creates the users representing the client (MirrorMaker), and the security credentials used for client authentication, based on the chosen authentication type.

    The User Operator creates a new secret with the same name as the KafkaUser resource. The secret contains a private and public key for mTLS authentication. The public key is contained in a user certificate, which is signed by the clients CA.

  5. Configure a KafkaMirrorMaker2 resource with the authentication details to connect to the source and target Kafka clusters.

    Example MirrorMaker 2 configuration with TLS encryption and mTLS authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker-2
spec:
  version: 3.6.1
  replicas: 1
  connectCluster: "my-target-cluster"
  clusters:
    - alias: "my-source-cluster"
      bootstrapServers: my-source-cluster-kafka-bootstrap:9093
      tls: # (1)
        trustedCertificates:
          - secretName: my-source-cluster-cluster-ca-cert
            certificate: ca.crt
      authentication: # (2)
        type: tls
        certificateAndKey:
          secretName: my-source-user
          certificate: user.crt
          key: user.key
    - alias: "my-target-cluster"
      bootstrapServers: my-target-cluster-kafka-bootstrap:9093
      tls: # (3)
        trustedCertificates:
          - secretName: my-target-cluster-cluster-ca-cert
            certificate: ca.crt
      authentication: # (4)
        type: tls
        certificateAndKey:
          secretName: my-target-user
          certificate: user.crt
          key: user.key
      config:
        # -1 means it will use the default replication factor configured in the broker
        config.storage.replication.factor: -1
        offset.storage.replication.factor: -1
        status.storage.replication.factor: -1
  mirrors:
    - sourceCluster: "my-source-cluster"
      targetCluster: "my-target-cluster"
      sourceConnector:
        config:
          replication.factor: 1
          offset-syncs.topic.replication.factor: 1
          sync.topic.acls.enabled: "false"
      heartbeatConnector:
        config:
          heartbeats.topic.replication.factor: 1
      checkpointConnector:
        config:
          checkpoints.topic.replication.factor: 1
          sync.group.offsets.enabled: "true"
      topicsPattern: "topic1|topic2|topic3"
      groupsPattern: "group1|group2|group3"

    1. The TLS certificates for the source Kafka cluster. If they are in a separate namespace, copy the cluster secrets from the namespace of the Kafka cluster.

    2. The user authentication for accessing the source Kafka cluster using the TLS mechanism.

    3. The TLS certificates for the target Kafka cluster.

    4. The user authentication for accessing the target Kafka cluster.

  6. Create or update the KafkaMirrorMaker2 resource in the same namespace as the target Kafka cluster.

    kubectl apply -f <mirrormaker2_configuration_file> -n <namespace_of_target_cluster>

9.7.8. Manually stopping or pausing MirrorMaker 2 connectors

If you are using KafkaMirrorMaker2 resources to configure internal MirrorMaker connectors, use the state configuration to either stop or pause a connector. In contrast to the paused state, where the connector and tasks remain instantiated, stopping a connector retains only the configuration, with no active processes. Stopping a connector from running may be more suitable for longer durations than just pausing. While a paused connector is quicker to resume, a stopped connector has the advantages of freeing up memory and resources.

Note
 The state configuration replaces the (deprecated) pause configuration in the KafkaMirrorMaker2ConnectorSpec schema, which allows pauses on connectors. If you were previously using the pause configuration to pause connectors, we encourage you to transition to using the state configuration only to avoid conflicts.
Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaMirrorMaker2 custom resource that controls the MirrorMaker 2 connector you want to pause or stop:

    kubectl get KafkaMirrorMaker2

  2. Edit the KafkaMirrorMaker2 resource to stop or pause the connector.

    Example configuration for stopping a MirrorMaker 2 connector
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 3.6.1
  replicas: 3
  connectCluster: "my-cluster-target"
  clusters:
    # ...
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector:
      tasksMax: 10
      autoRestart:
        enabled: true
      state: stopped
  # ...

    Change the state configuration to stopped or paused. The default state for the connector when this property is not set is running.

  3. Apply the changes to the KafkaMirrorMaker2 configuration.

    You can resume the connector by changing state to running or removing the configuration.

Note
 Alternatively, you can expose the Kafka Connect API and use the stop and pause endpoints to stop a connector from running. For example, PUT /connectors/<connector_name>/stop. You can then use the resume endpoint to restart it.

9.7.9. Manually restarting MirrorMaker 2 connectors

Use the strimzi.io/restart-connector annotation to manually trigger a restart of a MirrorMaker 2 connector.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaMirrorMaker2 custom resource that controls the Kafka MirrorMaker 2 connector you want to restart:

    kubectl get KafkaMirrorMaker2

  2. Find the name of the Kafka MirrorMaker 2 connector to be restarted from the KafkaMirrorMaker2 custom resource:

    kubectl describe KafkaMirrorMaker2 <mirrormaker_cluster_name>

  3. Use the name of the connector to restart the connector by annotating the KafkaMirrorMaker2 resource in Kubernetes:

    kubectl annotate KafkaMirrorMaker2 <mirrormaker_cluster_name> "strimzi.io/restart-connector=<mirrormaker_connector_name>"

    In this example, connector my-connector in the my-mirror-maker-2 cluster is restarted:

    kubectl annotate KafkaMirrorMaker2 my-mirror-maker-2 "strimzi.io/restart-connector=my-connector"

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

    The MirrorMaker 2 connector is restarted, as long as the annotation was detected by the reconciliation process. When MirrorMaker 2 accepts the request, the annotation is removed from the KafkaMirrorMaker2 custom resource.

9.7.10. Manually restarting MirrorMaker 2 connector tasks

Use the strimzi.io/restart-connector-task annotation to manually trigger a restart of a MirrorMaker 2 connector.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaMirrorMaker2 custom resource that controls the MirrorMaker 2 connector task you want to restart:

    kubectl get KafkaMirrorMaker2

  2. Find the name of the connector and the ID of the task to be restarted from the KafkaMirrorMaker2 custom resource:

    kubectl describe KafkaMirrorMaker2 <mirrormaker_cluster_name>

    Task IDs are non-negative integers, starting from 0.

  3. Use the name and ID to restart the connector task by annotating the KafkaMirrorMaker2 resource in Kubernetes:

    kubectl annotate KafkaMirrorMaker2 <mirrormaker_cluster_name> "strimzi.io/restart-connector-task=<mirrormaker_connector_name>:<task_id>"

    In this example, task 0 for connector my-connector in the my-mirror-maker-2 cluster is restarted:

    kubectl annotate KafkaMirrorMaker2 my-mirror-maker-2 "strimzi.io/restart-connector-task=my-connector:0"

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

    The MirrorMaker 2 connector task is restarted, as long as the annotation was detected by the reconciliation process. When MirrorMaker 2 accepts the request, the annotation is removed from the KafkaMirrorMaker2 custom resource.

9.8. Configuring Kafka MirrorMaker (deprecated)

Update the spec properties of the KafkaMirrorMaker custom resource to configure your Kafka MirrorMaker deployment.

You can configure access control for producers and consumers using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and mTLS authentication on the consumer and producer side.

For a deeper understanding of the Kafka MirrorMaker cluster configuration options, refer to the Strimzi Custom Resource API Reference.

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 Strimzi as well. The KafkaMirrorMaker resource will be removed from Strimzi when we adopt Apache Kafka 4.0.0. As a replacement, use the KafkaMirrorMaker2 custom resource with the IdentityReplicationPolicy.
Example KafkaMirrorMaker custom resource configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  replicas: 3 # (1)
  consumer:
    bootstrapServers: my-source-cluster-kafka-bootstrap:9092 # (2)
    groupId: "my-group" # (3)
    numStreams: 2 # (4)
    offsetCommitInterval: 120000 # (5)
    tls: # (6)
      trustedCertificates:
      - secretName: my-source-cluster-ca-cert
        certificate: ca.crt
    authentication: # (7)
      type: tls
      certificateAndKey:
        secretName: my-source-secret
        certificate: public.crt
        key: private.key
    config: # (8)
      max.poll.records: 100
      receive.buffer.bytes: 32768
  producer:
    bootstrapServers: my-target-cluster-kafka-bootstrap:9092
    abortOnSendFailure: false # (9)
    tls:
      trustedCertificates:
      - secretName: my-target-cluster-ca-cert
        certificate: ca.crt
    authentication:
      type: tls
      certificateAndKey:
        secretName: my-target-secret
        certificate: public.crt
        key: private.key
    config:
      compression.type: gzip
      batch.size: 8192
  include: "my-topic|other-topic" # (10)
  resources: # (11)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: # (12)
    type: inline
    loggers:
      mirrormaker.root.logger: INFO
  readinessProbe: # (13)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  metricsConfig: # (14)
   type: jmxPrometheusExporter
   valueFrom:
     configMapKeyRef:
       name: my-config-map
       key: my-key
  jvmOptions: # (15)
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest # (16)
  template: # (17)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    mirrorMakerContainer: # (18)
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing: # (19)
    type: opentelemetry

  1. The number of replica nodes.

  2. Bootstrap servers for consumer and producer.

  3. Group ID for the consumer.

  4. The number of consumer streams.

  5. The offset auto-commit interval in milliseconds.

  6. TLS encryption with key names under which TLS certificates are stored in X.509 format for consumer or producer. If certificates are stored in the same secret, it can be listed multiple times.

  7. Authentication for consumer or producer, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN.

  8. Kafka configuration options for consumer and producer.

  9. If the abortOnSendFailure property is set to true, Kafka MirrorMaker will exit and the container will restart following a send failure for a message.

  10. A list of included topics mirrored from source to target Kafka cluster.

  11. Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.

  12. Specified loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties or log4j2.properties key in the ConfigMap. MirrorMaker has a single logger called mirrormaker.root.logger. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

  13. Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).

  14. Prometheus metrics, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under metricsConfig.valueFrom.configMapKeyRef.key.

  15. JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.

  16. ADVANCED OPTION: Container image configuration, which is recommended only in special situations.

  17. Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.

  18. Environment variables are set for distributed tracing.

  19. Distributed tracing is enabled by using OpenTelemetry.

    Warning
    		 With the abortOnSendFailure property set to false, the producer attempts to send the next message in a topic. The original message might be lost, as there is no attempt to resend a failed message.

9.9. Configuring the Kafka Bridge

Update the spec properties of the KafkaBridge custom resource to configure your Kafka Bridge deployment.

In order to prevent issues arising when client consumer requests are processed by different Kafka Bridge instances, address-based routing must be employed to ensure that requests are routed to the right Kafka Bridge instance. Additionally, each independent Kafka Bridge instance must have a replica. A Kafka Bridge instance has its own state which is not shared with another instances.

For a deeper understanding of the Kafka Bridge cluster configuration options, refer to the Strimzi Custom Resource API Reference.

Example KafkaBridge custom resource configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  replicas: 3 # (1)
  bootstrapServers: <cluster_name>-cluster-kafka-bootstrap:9092 # (2)
  tls: # (3)
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        certificate: ca.crt
      - secretName: my-cluster-cluster-cert
        certificate: ca2.crt
  authentication: # (4)
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  http: # (5)
    port: 8080
    cors: # (6)
      allowedOrigins: "https://strimzi.io"
      allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  consumer: # (7)
    config:
      auto.offset.reset: earliest
  producer: # (8)
    config:
      delivery.timeout.ms: 300000
  resources: # (9)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: # (10)
    type: inline
    loggers:
      logger.bridge.level: INFO
      # enabling DEBUG just for send operation
      logger.send.name: "http.openapi.operation.send"
      logger.send.level: DEBUG
  jvmOptions: # (11)
    "-Xmx": "1g"
    "-Xms": "1g"
  readinessProbe: # (12)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  image: my-org/my-image:latest # (13)
  template: # (14)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    bridgeContainer: # (15)
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry # (16)

  1. The number of replica nodes.

  2. Bootstrap server for connection to the target Kafka cluster. Use the name of the Kafka cluster as the <cluster_name>.

  3. TLS encryption with key names under which TLS certificates are stored in X.509 format for the source Kafka cluster. If certificates are stored in the same secret, it can be listed multiple times.

  4. Authentication for the Kafka Bridge cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN. By default, the Kafka Bridge connects to Kafka brokers without authentication.

  5. HTTP access to Kafka brokers.

  6. CORS access specifying selected resources and access methods. Additional HTTP headers in requests describe the origins that are permitted access to the Kafka cluster.

  7. Consumer configuration options.

  8. Producer configuration options.

  9. Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.

  10. Specified Kafka Bridge loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties or log4j2.properties key in the ConfigMap. For the Kafka Bridge loggers, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

  11. JVM configuration options to optimize performance for the Virtual Machine (VM) running the Kafka Bridge.

  12. Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).

  13. Optional: Container image configuration, which is recommended only in special situations.

  14. Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.

  15. Environment variables are set for distributed tracing.

  16. Distributed tracing is enabled by using OpenTelemetry.

Additional resources

9.10. Configuring Kafka and ZooKeeper storage

Strimzi provides flexibility in configuring the data storage options of Kafka and ZooKeeper.

The supported storage types are:

  • Ephemeral (Recommended for development only)

  • Persistent

  • JBOD (Kafka only; not available for ZooKeeper)

To configure storage, you specify storage properties in the custom resource of the component. The storage type is set using the storage.type property.

You can also use the preview of the node pools feature for advanced storage management of the Kafka cluster. You can specify storage configuration unique to each node pool used in the cluster. The same storage properties available to the Kafka resource are also available to the KafkaNodePool pool resource.

The storage-related schema references provide more information on the storage configuration properties:

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

9.10.1. Data storage considerations

For Strimzi to work well, an efficient data storage infrastructure is essential. We strongly recommend using block storage. Strimzi is only tested for use with block storage. File storage, such as NFS, is not tested and there is no guarantee it will work.

Choose one of the following options for your block storage:

Note
 Strimzi does not require Kubernetes raw block volumes.
File systems

Kafka uses a file system for storing messages. Strimzi is compatible with the XFS and ext4 file systems, which are commonly used with Kafka. Consider the underlying architecture and requirements of your deployment when choosing and setting up your file system.

For more information, refer to Filesystem Selection in the Kafka documentation.

Disk usage

Use separate disks for Apache Kafka and ZooKeeper.

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

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

9.10.2. Ephemeral storage

Ephemeral data storage is transient. All pods on a node share a local ephemeral storage space. Data is retained for as long as the pod that uses it is running. The data is lost when a pod is deleted. Although a pod can recover data in a highly available environment.

Because of its transient nature, ephemeral storage is only recommended for development and testing.

Ephemeral storage uses emptyDir volumes to store data. An emptyDir volume is created when a pod is assigned to a node. You can set the total amount of storage for the emptyDir using the sizeLimit property .

Important
 Ephemeral storage is not suitable for single-node ZooKeeper clusters or Kafka topics with a replication factor of 1.

To use ephemeral storage, you set the storage type configuration in the Kafka or ZooKeeper resource to ephemeral. If you are using the preview of the node pools feature, you can also specify ephemeral in the storage configuration of individual node pools.

Example ephemeral storage configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    storage:
      type: ephemeral
    # ...
  zookeeper:
    storage:
      type: ephemeral
    # ...
Mount path of Kafka log directories

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

/var/lib/kafka/data/kafka-logIDX

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

9.10.3. Persistent storage

Persistent data storage retains data in the event of system disruption. For pods that use persistent data storage, data is persisted across pod failures and restarts. Because of its permanent nature, persistent storage is recommended for production environments.

To use persistent storage in Strimzi, you specify persistent-claim in the storage configuration of the Kafka or ZooKeeper resources. If you are using the preview of the node pools feature, you can also specify persistent-claim in the storage configuration of individual node pools.

You configure the resource so that pods use Persistent Volume Claims (PVCs) to make storage requests on persistent volumes (PVs). PVs represent storage volumes that are created on demand and are independent of the pods that use them. The PVC requests the amount of storage required when a pod is being created. The underlying storage infrastructure of the PV does not need to be understood. If a PV matches the storage criteria, the PVC is bound to the PV.

You have two options for specifying the storage type:

storage.type: persistent-claim

If you choose persistent-claim as the storage type, a single persistent storage volume is defined.

storage.type: jbod

When you select jbod as the storage type, you have the flexibility to define an array of persistent storage volumes using unique IDs.

In a production environment, it is recommended to configure the following:

  • For Kafka or node pools, set storage.type to jbod with one or more persistent volumes.

  • For ZooKeeper, set storage.type as persistent-claim for a single persistent volume.

Persistent storage also has the following configuration options:

id (optional)

A storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration. Default is 0.

size (required)

The size of the persistent volume claim, for example, "1000Gi".

class (optional)

PVCs can request different types of persistent storage by specifying a StorageClass. Storage classes define storage profiles and dynamically provision PVs based on that profile. If a storage class is not specified, the storage class marked as default in the Kubernetes cluster is used. Persistent storage options might include SAN storage types or local persistent volumes.

selector (optional)

Configuration to specify a specific PV. Provides key:value pairs representing the labels of the volume selected.

deleteClaim (optional)

Boolean value to specify whether the PVC is deleted when the cluster is uninstalled. Default is false.

Warning
 Increasing the size of persistent volumes in an existing Strimzi cluster is only supported in Kubernetes versions that support persistent volume resizing. The persistent volume to be resized must use a storage class that supports volume expansion. For other versions of Kubernetes and storage classes that do not support volume expansion, you must decide the necessary storage size before deploying the cluster. Decreasing the size of existing persistent volumes is not possible.
Example persistent storage configuration for Kafka and ZooKeeper
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 1
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 2
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
    # ...
  zookeeper:
    storage:
      type: persistent-claim
      size: 1000Gi
    # ...
Example persistent storage configuration with specific storage class
# ...
storage:
  type: persistent-claim
  size: 500Gi
  class: my-storage-class
# ...

Use a selector to specify a labeled persistent volume that provides certain features, such as an SSD.

Example persistent storage configuration with selector
# ...
storage:
  type: persistent-claim
  size: 1Gi
  selector:
    hdd-type: ssd
  deleteClaim: true
# ...
Storage class overrides

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

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

Example storage configuration with class overrides
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  labels:
    app: my-cluster
  name: my-cluster
  namespace: myproject
spec:
  # ...
  kafka:
    replicas: 3
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
        class: my-storage-class
        overrides:
        - broker: 0
          class: my-storage-class-zone-1a
        - broker: 1
          class: my-storage-class-zone-1b
        - broker: 2
          class: my-storage-class-zone-1c
      # ...
  # ...
  zookeeper:
    replicas: 3
    storage:
      deleteClaim: true
      size: 100Gi
      type: persistent-claim
      class: my-storage-class
      overrides:
        - broker: 0
          class: my-storage-class-zone-1a
        - broker: 1
          class: my-storage-class-zone-1b
        - broker: 2
          class: my-storage-class-zone-1c
  # ...

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

  • The persistent volumes of ZooKeeper node 0 use my-storage-class-zone-1a.

  • The persistent volumes of ZooKeeper node 1 use my-storage-class-zone-1b.

  • The persistent volumes of ZooKeeper node 2 use my-storage-class-zone-1c.

  • The persistent volumes of Kafka broker 0 use my-storage-class-zone-1a.

  • The persistent volumes of Kafka broker 1 use my-storage-class-zone-1b.

  • The persistent volumes of Kafka broker 2 use my-storage-class-zone-1c.

The overrides property is currently used only to override the storage class. Overrides for other storage configuration properties is not currently supported.

PVC resources for persistent storage

When persistent storage is used, it creates PVCs with the following names:

data-cluster-name-kafka-idx

PVC for the volume used for storing data for the Kafka broker pod idx.

data-cluster-name-zookeeper-idx

PVC for the volume used for storing data for the ZooKeeper node pod idx.

Mount path of Kafka log directories

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

/var/lib/kafka/data/kafka-logIDX

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

9.10.4. Resizing persistent volumes

Persistent volumes used by a cluster can be resized without any risk of data loss, as long as the storage infrastructure supports it. Following a configuration update to change the size of the storage, Strimzi instructs the storage infrastructure to make the change. Storage expansion is supported in Strimzi clusters that use persistent-claim volumes.

Storage reduction is only possible when using multiple disks per broker. You can remove a disk after moving all partitions on the disk to other volumes within the same broker (intra-broker) or to other brokers within the same cluster (intra-cluster).

Important
 You cannot decrease the size of persistent volumes because it is not currently supported in Kubernetes.
Prerequisites

  • A Kubernetes cluster with support for volume resizing.

  • The Cluster Operator is running.

  • A Kafka cluster using persistent volumes created using a storage class that supports volume expansion.

Procedure

  1. Edit the Kafka resource for your cluster.

    Change the size property to increase the size of the persistent volume allocated to a Kafka cluster, a ZooKeeper cluster, or both.

    • For Kafka clusters, update the size property under spec.kafka.storage.

    • For ZooKeeper clusters, update the size property under spec.zookeeper.storage.

    Kafka configuration to increase the volume size to 2000Gi
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: persistent-claim
      size: 2000Gi
      class: my-storage-class
    # ...
  zookeeper:
    # ...

  2. Create or update the resource:

    kubectl apply -f <kafka_configuration_file>

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

  3. Verify that the storage capacity has increased for the relevant pods on the cluster:

    kubectl get pv
    Kafka broker pods with increased storage
    NAME               CAPACITY   CLAIM
pvc-0ca459ce-...   2000Gi     my-project/data-my-cluster-kafka-2
pvc-6e1810be-...   2000Gi     my-project/data-my-cluster-kafka-0
pvc-82dc78c9-...   2000Gi     my-project/data-my-cluster-kafka-1

    The output shows the names of each PVC associated with a broker pod.

Additional resources

9.10.5. JBOD storage

JBOD storage allows you to configure your Kafka cluster to use multiple disks or volumes. This approach provides increased data storage capacity for Kafka brokers, and can lead to performance improvements. A JBOD configuration is defined by one or more volumes, each of which can be either ephemeral or persistent. The rules and constraints for JBOD volume declarations are the same as those for ephemeral and persistent storage. For example, you cannot decrease the size of a persistent storage volume after it has been provisioned, nor can you change the value of sizeLimit when the type is ephemeral.

Note
 JBOD storage is supported for Kafka only, not for ZooKeeper.

To use JBOD storage, you set the storage type configuration in the Kafka resource to jbod. If you are using the preview of the node pools feature, you can also specify jbod in the storage configuration of individual node pools.

The volumes property allows you to describe the disks that make up your JBOD storage array or configuration.

Example JBOD storage configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 1
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  # ...

The IDs cannot be changed once the JBOD volumes are created. You can add or remove volumes from the JBOD configuration.

PVC resource for JBOD storage

When persistent storage is used to declare JBOD volumes, it creates a PVC with the following name:

data-id-cluster-name-kafka-idx

PVC for the volume used for storing data for the Kafka broker pod idx. The id is the ID of the volume used for storing data for Kafka broker pod.

Mount path of Kafka log directories

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

/var/lib/kafka/data-id/kafka-logidx

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

9.10.6. Adding volumes to JBOD storage

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

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

  • A Kubernetes cluster

  • A running Cluster Operator

  • A Kafka cluster with JBOD storage

Procedure

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

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 1
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 2
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
    # ...
  zookeeper:
    # ...

  2. Create or update the resource:

    kubectl apply -f <kafka_configuration_file>

  3. Create new topics or reassign existing partitions to the new disks.

    Tip
    		 Cruise Control is an effective tool for reassigning partitions. To perform an intra-broker disk balance, you set rebalanceDisk to true under the KafkaRebalance.spec.

9.10.7. Removing volumes from JBOD storage

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

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

  • A Kubernetes cluster

  • A running Cluster Operator

  • A Kafka cluster with JBOD storage with two or more volumes

Procedure

  1. Reassign all partitions from the disks which are you going to remove. Any data in partitions still assigned to the disks which are going to be removed might be lost.

    Tip
    		 You can use the kafka-reassign-partitions.sh tool to reassign the partitions.

  2. Edit the spec.kafka.storage.volumes property in the Kafka resource. Remove one or more volumes from the volumes array. For example, remove the volumes with ids 1 and 2:

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
    # ...
  zookeeper:
    # ...

  3. Create or update the resource:

    kubectl apply -f <kafka_configuration_file>

9.11. Configuring CPU and memory resource limits and requests

By default, the Strimzi Cluster Operator does not specify CPU and memory resource requests and limits for its deployed operands. Ensuring an adequate allocation of resources is crucial for maintaining stability and achieving optimal performance in Kafka. The ideal resource allocation depends on your specific requirements and use cases.

It is recommended to configure CPU and memory resources for each container by setting appropriate requests and limits.

9.12. Configuring pod scheduling

To avoid performance degradation caused by resource conflicts between applications scheduled on the same Kubernetes node, you can schedule Kafka pods separately from critical workloads. This can be achieved by either selecting specific nodes or dedicating a set of nodes exclusively for Kafka.

9.12.1. Specifying affinity, tolerations, and topology spread constraints

Use affinity, tolerations and topology spread constraints to schedule the pods of kafka resources onto nodes. Affinity, tolerations and topology spread constraints are configured using the affinity, tolerations, and topologySpreadConstraint properties in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaBridge.spec.template.pod

  • KafkaMirrorMaker.spec.template.pod

  • KafkaMirrorMaker2.spec.template.pod

The format of the affinity, tolerations, and topologySpreadConstraint properties follows the Kubernetes specification. The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

Additional resources
Use pod anti-affinity to avoid critical applications sharing nodes

Use pod anti-affinity to ensure that critical applications are never scheduled on the same disk. When running a Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share nodes with other workloads, such as databases.

Use node affinity to schedule workloads onto specific nodes

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

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

Use node affinity and tolerations for dedicated nodes

Use taints to create dedicated nodes, then schedule Kafka pods on the dedicated nodes by configuring node affinity and tolerations.

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

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

9.12.2. Configuring pod anti-affinity to schedule each Kafka broker on a different worker node

Many Kafka brokers or ZooKeeper nodes can run on the same Kubernetes worker node. If the worker node fails, they will all become unavailable at the same time. To improve reliability, you can use podAntiAffinity configuration to schedule each Kafka broker or ZooKeeper node on a different Kubernetes worker node.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. To make sure that no worker nodes are shared by Kafka brokers or ZooKeeper nodes, use the strimzi.io/name label. Set the topologyKey to kubernetes.io/hostname to specify that the selected pods are not scheduled on nodes with the same hostname. This will still allow the same worker node to be shared by a single Kafka broker and a single ZooKeeper node. For example:

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: strimzi.io/name
                      operator: In
                      values:
                        - CLUSTER-NAME-kafka
                topologyKey: "kubernetes.io/hostname"
    # ...
  zookeeper:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: strimzi.io/name
                      operator: In
                      values:
                        - CLUSTER-NAME-zookeeper
                topologyKey: "kubernetes.io/hostname"
    # ...

    Where CLUSTER-NAME is the name of your Kafka custom resource.

  2. If you even want to make sure that a Kafka broker and ZooKeeper node do not share the same worker node, use the strimzi.io/cluster label. For example:

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: strimzi.io/cluster
                      operator: In
                      values:
                        - CLUSTER-NAME
                topologyKey: "kubernetes.io/hostname"
    # ...
  zookeeper:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: strimzi.io/cluster
                      operator: In
                      values:
                        - CLUSTER-NAME
                topologyKey: "kubernetes.io/hostname"
    # ...

    Where CLUSTER-NAME is the name of your Kafka custom resource.

  3. Create or update the resource.

    kubectl apply -f <kafka_configuration_file>

9.12.3. Configuring pod anti-affinity in Kafka components

Pod anti-affinity configuration helps with the stability and performance of Kafka brokers. By using podAntiAffinity, Kubernetes will not schedule Kafka brokers on the same nodes as other workloads. Typically, you want to avoid Kafka running on the same worker node as other network or storage intensive applications such as databases, storage or other messaging platforms.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <kafka_configuration_file>

9.12.4. Configuring node affinity in Kafka components

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Label the nodes where Strimzi components should be scheduled.

    This can be done using kubectl label:

    kubectl label node NAME-OF-NODE node-type=fast-network

    Alternatively, some of the existing labels might be reused.

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

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
    # ...
  zookeeper:
    # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <kafka_configuration_file>

9.12.5. Setting up dedicated nodes and scheduling pods on them

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.

  2. Make sure there are no workloads scheduled on these nodes.

  3. Set the taints on the selected nodes:

    This can be done using kubectl taint:

    kubectl taint node NAME-OF-NODE dedicated=Kafka:NoSchedule

  4. Additionally, add a label to the selected nodes as well.

    This can be done using kubectl label:

    kubectl label node NAME-OF-NODE dedicated=Kafka

  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        tolerations:
          - key: "dedicated"
            operator: "Equal"
            value: "Kafka"
            effect: "NoSchedule"
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: dedicated
                  operator: In
                  values:
                  - Kafka
    # ...
  zookeeper:
    # ...

  6. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <kafka_configuration_file>

9.13. Configuring logging levels

Configure logging levels in the custom resources of Kafka components and Strimzi operators. You can specify the logging levels directly in the spec.logging property of the custom resource. Or you can define the logging properties in a ConfigMap that’s referenced in the custom resource using the configMapKeyRef property.

The advantages of using a ConfigMap are that the logging properties are maintained in one place and are accessible to more than one resource. You can also reuse the ConfigMap for more than one resource. If you are using a ConfigMap to specify loggers for Strimzi Operators, you can also append the logging specification to add filters.

You specify a logging type in your logging specification:

  • inline when specifying logging levels directly

  • external when referencing a ConfigMap

Example inline logging configuration
spec:
  # ...
  logging:
    type: inline
    loggers:
      kafka.root.logger.level: INFO
Example external logging configuration
spec:
  # ...
  logging:
    type: external
    valueFrom:
      configMapKeyRef:
        name: my-config-map
        key: my-config-map-key

Values for the name and key of the ConfigMap are mandatory. Default logging is used if the name or key is not set.

9.13.1. Logging options for Kafka components and operators

For more information on configuring logging for specific Kafka components or operators, see the following sections.

Kafka component logging
Operator logging

9.13.2. Creating a ConfigMap for logging

To use a ConfigMap to define logging properties, you create the ConfigMap and then reference it as part of the logging definition in the spec of a resource.

The ConfigMap must contain the appropriate logging configuration.

  • log4j.properties for Kafka components, ZooKeeper, and the Kafka Bridge

  • log4j2.properties for the Topic Operator and User Operator

The configuration must be placed under these properties.

In this procedure a ConfigMap defines a root logger for a Kafka resource.

Procedure

  1. Create the ConfigMap.

    You can create the ConfigMap as a YAML file or from a properties file.

    ConfigMap example with a root logger definition for Kafka:

    kind: ConfigMap
apiVersion: v1
metadata:
  name: logging-configmap
data:
  log4j.properties:
    kafka.root.logger.level="INFO"

    If you are using a properties file, specify the file at the command line:

    kubectl create configmap logging-configmap --from-file=log4j.properties

    The properties file defines the logging configuration:

    # Define the logger
kafka.root.logger.level="INFO"
# ...

  2. Define external logging in the spec of the resource, setting the logging.valueFrom.configMapKeyRef.name to the name of the ConfigMap and logging.valueFrom.configMapKeyRef.key to the key in this ConfigMap.

    spec:
  # ...
  logging:
    type: external
    valueFrom:
      configMapKeyRef:
        name: logging-configmap
        key: log4j.properties

  3. Create or update the resource.

    kubectl apply -f <kafka_configuration_file>

9.13.3. Configuring 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.log4j2.properties values 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:

kubectl create -f install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml

Alternatively, edit the ConfigMap directly:

kubectl edit configmap strimzi-cluster-operator

With this ConfigMap, you can control various aspects of logging, including the root logger level, log output format, and log levels for different components. The monitorInterval setting, determines how often the logging configuration is reloaded. You can also control the logging levels for the Kafka AdminClient, ZooKeeper ZKTrustManager, Netty, and the OkHttp client. Netty is a framework used in Strimzi for network communication, and OkHttp is a library used for making HTTP requests.

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.

9.13.4. Adding logging filters to Strimzi operators

If you are using a ConfigMap to configure the (log4j2) logging levels for Strimzi operators, you can also define logging filters to limit what’s returned in the log.

Logging filters are useful when you have a large number of logging messages. Suppose you set the log level for the logger as DEBUG (rootLogger.level="DEBUG"). Logging filters reduce the number of logs returned for the logger at that level, so you can focus on a specific resource. When the filter is set, only log messages matching the filter are logged.

Filters use markers to specify what to include in the log. You specify a kind, namespace and name for the marker. For example, if a Kafka cluster is failing, you can isolate the logs by specifying the kind as Kafka, and use the namespace and name of the failing cluster.

This example shows a marker filter for a Kafka cluster named my-kafka-cluster.

Basic logging filter configuration
rootLogger.level="INFO"
appender.console.filter.filter1.type=MarkerFilter (1)
appender.console.filter.filter1.onMatch=ACCEPT (2)
appender.console.filter.filter1.onMismatch=DENY (3)
appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster) (4)

  1. The MarkerFilter type compares a specified marker for filtering.

  2. The onMatch property accepts the log if the marker matches.

  3. The onMismatch property rejects the log if the marker does not match.

  4. The marker used for filtering is in the format KIND(NAMESPACE/NAME-OF-RESOURCE).

You can create one or more filters. Here, the log is filtered for two Kafka clusters.

Multiple logging filter configuration
appender.console.filter.filter1.type=MarkerFilter
appender.console.filter.filter1.onMatch=ACCEPT
appender.console.filter.filter1.onMismatch=DENY
appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster-1)
appender.console.filter.filter2.type=MarkerFilter
appender.console.filter.filter2.onMatch=ACCEPT
appender.console.filter.filter2.onMismatch=DENY
appender.console.filter.filter2.marker=Kafka(my-namespace/my-kafka-cluster-2)
Adding filters to the Cluster Operator

To add filters to the Cluster Operator, update its logging ConfigMap YAML file (install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml).

Procedure

  1. Update the 050-ConfigMap-strimzi-cluster-operator.yaml file to add the filter properties to the ConfigMap.

    In this example, the filter properties return logs only for the my-kafka-cluster Kafka cluster:

    kind: ConfigMap
apiVersion: v1
metadata:
  name: strimzi-cluster-operator
data:
  log4j2.properties:
    #...
    appender.console.filter.filter1.type=MarkerFilter
    appender.console.filter.filter1.onMatch=ACCEPT
    appender.console.filter.filter1.onMismatch=DENY
    appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster)

    Alternatively, edit the ConfigMap directly:

    kubectl edit configmap strimzi-cluster-operator

  2. If you updated the YAML file instead of editing the ConfigMap directly, apply the changes by deploying the ConfigMap:

    kubectl create -f install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml
Adding filters to the Topic Operator or User Operator

To add filters to the Topic Operator or User Operator, create or edit a logging ConfigMap.

In this procedure a logging ConfigMap is created with filters for the Topic Operator. The same approach is used for the User Operator.

Procedure

  1. Create the ConfigMap.

    You can create the ConfigMap as a YAML file or from a properties file.

    In this example, the filter properties return logs only for the my-topic topic:

    kind: ConfigMap
apiVersion: v1
metadata:
  name: logging-configmap
data:
  log4j2.properties:
    rootLogger.level="INFO"
    appender.console.filter.filter1.type=MarkerFilter
    appender.console.filter.filter1.onMatch=ACCEPT
    appender.console.filter.filter1.onMismatch=DENY
    appender.console.filter.filter1.marker=KafkaTopic(my-namespace/my-topic)

    If you are using a properties file, specify the file at the command line:

    kubectl create configmap logging-configmap --from-file=log4j2.properties

    The properties file defines the logging configuration:

    # Define the logger
rootLogger.level="INFO"
# Set the filters
appender.console.filter.filter1.type=MarkerFilter
appender.console.filter.filter1.onMatch=ACCEPT
appender.console.filter.filter1.onMismatch=DENY
appender.console.filter.filter1.marker=KafkaTopic(my-namespace/my-topic)
# ...

  2. Define external logging in the spec of the resource, setting the logging.valueFrom.configMapKeyRef.name to the name of the ConfigMap and logging.valueFrom.configMapKeyRef.key to the key in this ConfigMap.

    For the Topic Operator, logging is specified in the topicOperator configuration of the Kafka resource.

    spec:
  # ...
  entityOperator:
    topicOperator:
      logging:
        type: external
        valueFrom:
          configMapKeyRef:
            name: logging-configmap
            key: log4j2.properties

  3. Apply the changes by deploying the Cluster Operator:

create -f install/cluster-operator -n my-cluster-operator-namespace
Additional resources

9.14. Using ConfigMaps to add configuration

Add specific configuration to your Strimzi deployment using ConfigMap resources. ConfigMaps use key-value pairs to store non-confidential data. Configuration data added to ConfigMaps is maintained in one place and can be reused amongst components.

ConfigMaps can only store the following types of configuration data:

  • Logging configuration

  • Metrics configuration

  • External configuration for Kafka Connect connectors

You can’t use ConfigMaps for other areas of configuration.

When you configure a component, you can add a reference to a ConfigMap using the configMapKeyRef property.

For example, you can use configMapKeyRef to reference a ConfigMap that provides configuration for logging. You might use a ConfigMap to pass a Log4j configuration file. You add the reference to the logging configuration.

Example ConfigMap for logging
spec:
  # ...
  logging:
    type: external
    valueFrom:
      configMapKeyRef:
        name: my-config-map
        key: my-config-map-key

To use a ConfigMap for metrics configuration, you add a reference to the metricsConfig configuration of the component in the same way.

ExternalConfiguration properties make data from a ConfigMap (or Secret) mounted to a pod available as environment variables or volumes. You can use external configuration data for the connectors used by Kafka Connect. The data might be related to an external data source, providing the values needed for the connector to communicate with that data source.

For example, you can use the configMapKeyRef property to pass configuration data from a ConfigMap as an environment variable.

Example ConfigMap providing environment variable values
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          configMapKeyRef:
            name: my-config-map
            key: my-key

If you are using ConfigMaps that are managed externally, use configuration providers to load the data in the ConfigMaps.

9.14.1. Naming custom ConfigMaps

Strimzi creates its own ConfigMaps and other resources when it is deployed to Kubernetes. The ConfigMaps contain data necessary for running components. The ConfigMaps created by Strimzi must not be edited.

Make sure that any custom ConfigMaps you create do not have the same name as these default ConfigMaps. If they have the same name, they will be overwritten. For example, if your ConfigMap has the same name as the ConfigMap for the Kafka cluster, it will be overwritten when there is an update to the Kafka cluster.

Additional resources

9.15. Loading configuration values from external sources

Use configuration providers to load configuration data from external sources. The providers operate independently of Strimzi. You can use them to load configuration data for all Kafka components, including producers and consumers. You reference the external source in the configuration of the component and provide access rights. The provider loads data without needing to restart the Kafka component or extracting files, even when referencing a new external source. For example, use providers to supply the credentials for the Kafka Connect connector configuration. The configuration must include any access rights to the external source.

9.15.1. Enabling configuration providers

You can enable one or more configuration providers using the config.providers properties in the spec configuration of a component.

Example configuration to enable a configuration provider
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
  annotations:
    strimzi.io/use-connector-resources: "true"
spec:
  # ...
  config:
    # ...
    config.providers: env
    config.providers.env.class: org.apache.kafka.common.config.provider.EnvVarConfigProvider
  # ...
KubernetesSecretConfigProvider

Loads configuration data from Kubernetes secrets. You specify the name of the secret and the key within the secret where the configuration data is stored. This provider is useful for storing sensitive configuration data like passwords or other user credentials.

KubernetesConfigMapConfigProvider

Loads configuration data from Kubernetes config maps. You specify the name of the config map and the key within the config map where the configuration data is stored. This provider is useful for storing non-sensitive configuration data.

EnvVarConfigProvider

Loads configuration data from environment variables. You specify the name of the environment variable where the configuration data is stored. This provider is useful for configuring applications running in containers, for example, to load certificates or JAAS configuration from environment variables mapped from secrets.

FileConfigProvider

Loads configuration data from a file. You specify the path to the file where the configuration data is stored. This provider is useful for loading configuration data from files that are mounted into containers.

DirectoryConfigProvider

Loads configuration data from files within a directory. You specify the path to the directory where the configuration files are stored. This provider is useful for loading multiple configuration files and for organizing configuration data into separate files.

To use KubernetesSecretConfigProvider and KubernetesConfigMapConfigProvider, which are part of the Kubernetes Configuration Provider plugin, you must set up access rights to the namespace that contains the configuration file.

You can use the other providers without setting up access rights. You can supply connector configuration for Kafka Connect or MirrorMaker 2 in this way by doing the following:

  • Mount config maps or secrets into the Kafka Connect pod as environment variables or volumes

  • Enable EnvVarConfigProvider, FileConfigProvider, or DirectoryConfigProvider in the Kafka Connect or MirrorMaker 2 configuration

  • Pass connector configuration using the externalConfiguration property in the spec of the KafkaConnect or KafkaMirrorMaker2 resource

Using providers help prevent the passing of restricted information through the Kafka Connect REST interface. You can use this approach in the following scenarios:

  • Mounting environment variables with the values a connector uses to connect and communicate with a data source

  • Mounting a properties file with values that are used to configure Kafka Connect connectors

  • Mounting files in a directory that contains values for the TLS truststore and keystore used by a connector

Note
 A restart is required when using a new Secret or ConfigMap for a connector, which can disrupt other connectors.
Additional resources

ExternalConfiguration schema reference

9.15.2. Loading configuration values from secrets or config maps

Use the KubernetesSecretConfigProvider to provide configuration properties from a secret or the KubernetesConfigMapConfigProvider to provide configuration properties from a config map.

In this procedure, a config map provides configuration properties for a connector. The properties are specified as key values of the config map. The config map is mounted into the Kafka Connect pod as a volume.

Prerequisites

  • A Kafka cluster is running.

  • The Cluster Operator is running.

  • You have a config map containing the connector configuration.

Example config map with connector properties
apiVersion: v1
kind: ConfigMap
metadata:
  name: my-connector-configuration
data:
  option1: value1
  option2: value2
Procedure

  1. Configure the KafkaConnect resource.

    • Enable the KubernetesConfigMapConfigProvider

    The specification shown here can support loading values from config maps and secrets.

    Example Kafka Connect configuration to use config maps and secrets
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
  annotations:
    strimzi.io/use-connector-resources: "true"
spec:
  # ...
  config:
    # ...
    config.providers: secrets,configmaps # (1)
    config.providers.configmaps.class: io.strimzi.kafka.KubernetesConfigMapConfigProvider # (2)
    config.providers.secrets.class: io.strimzi.kafka.KubernetesSecretConfigProvider # (3)
  # ...

    1. The alias for the configuration provider is used to define other configuration parameters. The provider parameters use the alias from config.providers, taking the form config.providers.${alias}.class.

    2. KubernetesConfigMapConfigProvider provides values from config maps.

    3. KubernetesSecretConfigProvider provides values from secrets.

  2. Create or update the resource to enable the provider.

    kubectl apply -f <kafka_connect_configuration_file>

  3. Create a role that permits access to the values in the external config map.

    Example role to access values from a config map
    apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: connector-configuration-role
rules:
- apiGroups: [""]
  resources: ["configmaps"]
  resourceNames: ["my-connector-configuration"]
  verbs: ["get"]
# ...

    The rule gives the role permission to access the my-connector-configuration config map.

  4. Create a role binding to permit access to the namespace that contains the config map.

    Example role binding to access the namespace that contains the config map
    apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: connector-configuration-role-binding
subjects:
- kind: ServiceAccount
  name: my-connect-connect
  namespace: my-project
roleRef:
  kind: Role
  name: connector-configuration-role
  apiGroup: rbac.authorization.k8s.io
# ...

    The role binding gives the role permission to access the my-project namespace.

    The service account must be the same one used by the Kafka Connect deployment. The service account name format is <cluster_name>-connect, where <cluster_name> is the name of the KafkaConnect custom resource.

  5. Reference the config map in the connector configuration.

    Example connector configuration referencing the config map
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-connector
  labels:
    strimzi.io/cluster: my-connect
spec:
  # ...
  config:
    option: ${configmaps:my-project/my-connector-configuration:option1}
    # ...
# ...

    The placeholder structure is configmaps:<path_and_file_name>:<property>. KubernetesConfigMapConfigProvider reads and extracts the option1 property value from the external config map.

9.15.3. Loading configuration values from environment variables

Use the EnvVarConfigProvider to provide configuration properties as environment variables. Environment variables can contain values from config maps or secrets.

In this procedure, environment variables provide configuration properties for a connector to communicate with Amazon AWS. The connector must be able to read the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. The values of the environment variables are derived from a secret mounted into the Kafka Connect pod.

Note
 The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_.
Prerequisites

  • A Kafka cluster is running.

  • The Cluster Operator is running.

  • You have a secret containing the connector configuration.

Example secret with values for environment variables
apiVersion: v1
kind: Secret
metadata:
  name: aws-creds
type: Opaque
data:
  awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg=
  awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=
Procedure

  1. Configure the KafkaConnect resource.

    • Enable the EnvVarConfigProvider

    • Specify the environment variables using the externalConfiguration property.

    Example Kafka Connect configuration to use external environment variables
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
  annotations:
    strimzi.io/use-connector-resources: "true"
spec:
  # ...
  config:
    # ...
    config.providers: env # (1)
    config.providers.env.class: org.apache.kafka.common.config.provider.EnvVarConfigProvider # (2)
  # ...
  externalConfiguration:
    env:
      - name: AWS_ACCESS_KEY_ID # (3)
        valueFrom:
          secretKeyRef:
            name: aws-creds # (4)
            key: awsAccessKey # (5)
      - name: AWS_SECRET_ACCESS_KEY
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsSecretAccessKey
  # ...

    1. The alias for the configuration provider is used to define other configuration parameters. The provider parameters use the alias from config.providers, taking the form config.providers.${alias}.class.

    2. EnvVarConfigProvider provides values from environment variables.

    3. The environment variable takes a value from the secret.

    4. The name of the secret containing the environment variable.

    5. The name of the key stored in the secret.

    Note
    		 The secretKeyRef property references keys in a secret. If you are using a config map instead of a secret, use the configMapKeyRef property.

  2. Create or update the resource to enable the provider.

    kubectl apply -f <kafka_connect_configuration_file>

  3. Reference the environment variable in the connector configuration.

    Example connector configuration referencing the environment variable
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-connector
  labels:
    strimzi.io/cluster: my-connect
spec:
  # ...
  config:
    option: ${env:AWS_ACCESS_KEY_ID}
    option: ${env:AWS_SECRET_ACCESS_KEY}
    # ...
# ...

    The placeholder structure is env:<environment_variable_name>. EnvVarConfigProvider reads and extracts the environment variable values from the mounted secret.

9.15.4. Loading configuration values from a file within a directory

Use the FileConfigProvider to provide configuration properties from a file within a directory. Files can be config maps or secrets.

In this procedure, a file provides configuration properties for a connector. A database name and password are specified as properties of a secret. The secret is mounted to the Kafka Connect pod as a volume. Volumes are mounted on the path /opt/kafka/external-configuration/<volume-name>.

Prerequisites

  • A Kafka cluster is running.

  • The Cluster Operator is running.

  • You have a secret containing the connector configuration.

Example secret with database properties
apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
stringData:
  connector.properties: |- # (1)
    dbUsername: my-username # (2)
    dbPassword: my-password

  1. The connector configuration in properties file format.

  2. Database username and password properties used in the configuration.

Procedure

  1. Configure the KafkaConnect resource.

    • Enable the FileConfigProvider

    • Specify the file using the externalConfiguration property.

    Example Kafka Connect configuration to use an external property file
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    config.providers: file # (1)
    config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider # (2)
  #...
  externalConfiguration:
    volumes:
      - name: connector-config # (3)
        secret:
          secretName: mysecret # (4)

    1. The alias for the configuration provider is used to define other configuration parameters.

    2. FileConfigProvider provides values from properties files. The parameter uses the alias from config.providers, taking the form config.providers.${alias}.class.

    3. The name of the volume containing the secret.

    4. The name of the secret.

  2. Create or update the resource to enable the provider.

    kubectl apply -f <kafka_connect_configuration_file>

  3. Reference the file properties in the connector configuration as placeholders.

    Example connector configuration referencing the file
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  class: io.debezium.connector.mysql.MySqlConnector
  tasksMax: 2
  config:
    database.hostname: 192.168.99.1
    database.port: "3306"
    database.user: "${file:/opt/kafka/external-configuration/connector-config/mysecret:dbUsername}"
    database.password: "${file:/opt/kafka/external-configuration/connector-config/mysecret:dbPassword}"
    database.server.id: "184054"
    #...

    The placeholder structure is file:<path_and_file_name>:<property>. FileConfigProvider reads and extracts the database username and password property values from the mounted secret.

9.15.5. Loading configuration values from multiple files within a directory

Use the DirectoryConfigProvider to provide configuration properties from multiple files within a directory. Files can be config maps or secrets.

In this procedure, a secret provides the TLS keystore and truststore user credentials for a connector. The credentials are in separate files. The secrets are mounted into the Kafka Connect pod as volumes. Volumes are mounted on the path /opt/kafka/external-configuration/<volume-name>.

Prerequisites

  • A Kafka cluster is running.

  • The Cluster Operator is running.

  • You have a secret containing the user credentials.

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

The my-user secret provides the keystore credentials (user.crt and user.key) for the connector.

The <cluster_name>-cluster-ca-cert secret generated when deploying the Kafka cluster provides the cluster CA certificate as truststore credentials (ca.crt).

Procedure

  1. Configure the KafkaConnect resource.

    • Enable the DirectoryConfigProvider

    • Specify the files using the externalConfiguration property.

    Example Kafka Connect configuration to use external property files
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    config.providers: directory # (1)
    config.providers.directory.class: org.apache.kafka.common.config.provider.DirectoryConfigProvider # (2)
  #...
  externalConfiguration:
    volumes: # (3)
      - name: cluster-ca # (4)
        secret:
          secretName: my-cluster-cluster-ca-cert # (5)
      - name: my-user
        secret:
          secretName: my-user # (6)

    1. The alias for the configuration provider is used to define other configuration parameters.

    2. DirectoryConfigProvider provides values from files in a directory. The parameter uses the alias from config.providers, taking the form config.providers.${alias}.class.

    3. The names of the volumes containing the secrets.

    4. The name of the secret for the cluster CA certificate to supply truststore configuration.

    5. The name of the secret for the user to supply keystore configuration.

  2. Create or update the resource to enable the provider.

    kubectl apply -f <kafka_connect_configuration_file>

  3. Reference the file properties in the connector configuration as placeholders.

    Example connector configuration referencing the files
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  class: io.debezium.connector.mysql.MySqlConnector
  tasksMax: 2
  config:
    # ...
    database.history.producer.security.protocol: SSL
    database.history.producer.ssl.truststore.type: PEM
    database.history.producer.ssl.truststore.certificates: "${directory:/opt/kafka/external-configuration/cluster-ca:ca.crt}"
    database.history.producer.ssl.keystore.type: PEM
    database.history.producer.ssl.keystore.certificate.chain: "${directory:/opt/kafka/external-configuration/my-user:user.crt}"
    database.history.producer.ssl.keystore.key: "${directory:/opt/kafka/external-configuration/my-user:user.key}"
    #...

    The placeholder structure is directory:<path>:<file_name>. DirectoryConfigProvider reads and extracts the credentials from the mounted secrets.

9.16. Customizing Kubernetes resources

A Strimzi deployment creates Kubernetes resources, such as Deployment, Pod, and Service resources. These resources are managed by Strimzi operators. Only the operator that is responsible for managing a particular Kubernetes resource can change that resource. If you try to manually change an operator-managed Kubernetes resource, the operator will revert your changes back.

Changing an operator-managed Kubernetes resource can be useful if you want to perform certain tasks, such as the following:

  • Adding custom labels or annotations that control how Pods are treated by Istio or other services

  • Managing how Loadbalancer-type Services are created by the cluster

To make the changes to a Kubernetes resource, you can use the template property within the spec section of various Strimzi custom resources.

Here is a list of the custom resources where you can apply the changes:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • Kafka.spec.kafkaExporter

  • Kafka.spec.cruiseControl

  • KafkaNodePool.spec

  • KafkaConnect.spec

  • KafkaMirrorMaker.spec

  • KafkaMirrorMaker2.spec

  • KafkaBridge.spec

  • KafkaUser.spec

For more information about these properties, see the Strimzi Custom Resource API Reference.

The Strimzi Custom Resource API Reference provides more details about the customizable fields.

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

Example template customization
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  labels:
    app: my-cluster
spec:
  kafka:
    # ...
    template:
      pod:
        metadata:
          labels:
            mylabel: myvalue
    # ...

9.16.1. Customizing the image pull policy

Strimzi allows you to customize the image pull policy for containers in all pods deployed by the Cluster Operator. The image pull policy is configured using the environment variable STRIMZI_IMAGE_PULL_POLICY in the Cluster Operator deployment. The STRIMZI_IMAGE_PULL_POLICY environment variable can be set to three different values:

Always

Container images are pulled from the registry every time the pod is started or restarted.

IfNotPresent

Container images are pulled from the registry only when they were not pulled before.

Never

Container images are never pulled from the registry.

Currently, the image pull policy can only be customized for all Kafka, Kafka Connect, and Kafka MirrorMaker clusters at once. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.

Additional resources

9.16.2. Applying a termination grace period

Apply a termination grace period to give a Kafka cluster enough time to shut down cleanly.

Specify the time using the terminationGracePeriodSeconds property. Add the property to the template.pod configuration of the Kafka custom resource.

The time you add will depend on the size of your Kafka cluster. The Kubernetes default for the termination grace period is 30 seconds. If you observe that your clusters are not shutting down cleanly, you can increase the termination grace period.

A termination grace period is applied every time a pod is restarted. The period begins when Kubernetes sends a term (termination) signal to the processes running in the pod. The period should reflect the amount of time required to transfer the processes of the terminating pod to another pod before they are stopped. After the period ends, a kill signal stops any processes still running in the pod.

The following example adds a termination grace period of 120 seconds to the Kafka custom resource. You can also specify the configuration in the custom resources of other Kafka components.

Example termination grace period configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    template:
      pod:
        terminationGracePeriodSeconds: 120
        # ...
    # ...

10. Using the Topic Operator to manage Kafka topics

The KafkaTopic resource configures topics, including partition and replication factor settings. When you create, modify, or delete a topic using KafkaTopic, the Topic Operator ensures that these changes are reflected in the Kafka cluster.

For more information on the KafkaTopic resource, see the KafkaTopic schema reference.

10.1. Topic management modes

The KafkaTopic resource is responsible for managing a single topic within a Kafka cluster. The Topic Operator provides two modes for managing KafkaTopic resources and Kafka topics:

Unidirectional mode (default)

Unidirectional mode does not require ZooKeeper for cluster management. It is compatible with using Strimzi in KRaft mode.

Bidirectional mode

Bidirectional mode requires ZooKeeper for cluster management. It is not compatible with using Strimzi in KRaft mode.

Note
 As the feature gate enabling the Topic Operator to run in unidirectional mode progresses to General Availability, bidirectional mode will be phased out. This transition is aimed at enhancing the user experience, particularly in supporting Kafka in KRaft mode.

10.1.1. Unidirectional topic management

In unidirectional mode, the Topic Operator operates as follows:

  • When a KafkaTopic is created, deleted, or changed, the Topic Operator performs the corresponding operation on the Kafka topic.

If a topic is created, deleted, or modified directly within the Kafka cluster, without the presence of a corresponding KafkaTopic resource, the Topic Operator does not manage that topic. The Topic Operator will only manage Kafka topics associated with KafkaTopic resources and does not interfere with topics managed independently within the Kafka cluster. If a KafkaTopic does exist for a Kafka topic, any configuration changes made outside the resource are reverted.

The Topic Operator can detect cases where where multiple KafkaTopic resources are attempting to manage a Kafka topic using the same .spec.topicName. Only the oldest resource is reconciled, while the other resources fail with a resource conflict error.

10.1.2. Bidirectional topic management

In bidirectional mode, the Topic Operator operates as follows:

  • When a KafkaTopic is created, deleted, or changed, the Topic Operator performs the corresponding operation on the Kafka topic.

  • Similarly, when a topic is created, deleted, or changed within the Kafka cluster, the Topic Operator performs the corresponding operation on the KafkaTopic resource.

Tip
 Try to stick to one method of managing topics, either through the KafkaTopic resources or directly in Kafka. Avoid routinely switching between both methods for a given topic.

10.2. Topic naming conventions

A KafkaTopic resource includes a name for the topic and a label that identifies the name of the Kafka cluster it belongs to.

Label identifying a Kafka cluster for topic handling
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: topic-name-1
  labels:
    strimzi.io/cluster: my-cluster
spec:
  topicName: topic-name-1

The label provides the cluster name of the Kafka resource. The Topic Operator uses the label as a mechanism for determining which KafkaTopic resources to manage. If the label does not match the Kafka cluster, the Topic Operator cannot see the KafkaTopic, and the topic is not created.

Kafka and Kubernetes have their own naming validation rules, and a Kafka topic name might not be a valid resource name in Kubernetes. If possible, try and stick to a naming convention that works for both.

Consider the following guidelines:

  • Use topic names that reflect the nature of the topic

  • Be concise and keep the name under 63 characters

  • Use all lower case and hyphens

  • Avoid special characters, spaces or symbols

The KafkaTopic resource allows you to specify the Kafka topic name using the metadata.name field. However, if the desired Kafka topic name is not a valid Kubernetes resource name, you can use the spec.topicName property to specify the actual name. The spec.topicName field is optional, and when it’s absent, the Kafka topic name defaults to the metadata.name of the topic. When a topic is created, the topic name cannot be changed later.

Example of supplying a valid Kafka topic name
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic-1 # (1)
spec:
  topicName: My.Topic.1 # (2)
  # ...

  1. A valid topic name that works in Kubernetes.

  2. A Kafka topic name that uses upper case and periods, which are invalid in Kubernetes.

If more than one KafkaTopic resource refers to the same Kafka topic, the resource that was created first is considered to be the one managing the topic. The status of the newer resources is updated to indicate a conflict, and their Ready status is changed to False.

If a Kafka client application, such as Kafka Streams, automatically creates topics with invalid Kubernetes resource names, the Topic Operator generates a valid metadata.name when used in bidirectional mode. It replaces invalid characters and appends a hash to the name. However, this behavior does not apply in unidirectional mode.

Example of replacing an invalid topic name
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic---c55e57fe2546a33f9e603caf57165db4072e827e
  # ...
Note
 For more information on the requirements for identifiers and names in a cluster, refer to the Kubernetes documentation Object Names and IDs.

10.3. Handling changes to topics

How the Topic Operator handles changes to topics depends on the mode of topic management.

  • For unidirectional topic management, configuration changes only go in one direction: from the KafkaTopic resource to the Kafka topic. Any changes to a Kafka topic managed outside the KafkaTopic resource are reverted.

  • For bidirectional topic management, configuration changes are synchronized between the Kafka topic and the KafkaTopic resource in both directions. Incompatible changes prioritize the Kafka configuration, and the KafkaTopic resource is adjusted accordingly.

10.3.1. Topic store for bidirectional topic management

For bidirectional topic management, the Topic Operator is capable of handling changes to topics when there is no single source of truth. The KafkaTopic resource and the Kafka topic can undergo independent modifications, where real-time observation of changes may not always be feasible, particularly when the Topic Operator is not operational. To handle this, the Topic Operator maintains a topic store that stores topic configuration information about each topic. It compares the state of the Kafka cluster and Kubernetes with the topic store to determine the necessary changes for synchronization. This evaluation takes place during startup and at regular intervals while the Topic Operator is active.

For example, if the Topic Operator is inactive, and a new KafkaTopic named my-topic is created, upon restart, the Topic Operator recognizes the absence of my-topic in the topic store. It recognizes that the KafkaTopic was created after its last operation. Consequently, the Topic Operator generates the corresponding Kafka topic and saves the metadata in the topic store.

The topic store enables the Topic Operator to manage situations where the topic configuration is altered in both Kafka topics and KafkaTopic resources, as long as the changes are compatible. When Kafka topic configuration is updated or changes are made to the KafkaTopic custom resource, the topic store is updated after reconciling with the Kafka cluster, as long as the changes are compatible.

The topic store is based on the Kafka Streams key-value mechanism, which uses Kafka topics to persist the state. Topic metadata is cached in-memory and accessed locally within the Topic Operator. Updates from operations applied to the local in-memory cache are persisted to a backup topic store on disk. The topic store is continually synchronized with updates from Kafka topics or Kubernetes KafkaTopic custom resources. Operations are handled rapidly with the topic store set up this way, but should the in-memory cache crash it is automatically repopulated from the persistent storage.

Internal topics support the handling of topic metadata in the topic store.

__strimzi_store_topic

Input topic for storing the topic metadata

__strimzi-topic-operator-kstreams-topic-store-changelog

Retains a log of compacted topic store values

Warning
 Do not delete these topics, as they are essential to the running of the Topic Operator.

10.3.2. Migrating topic metadata from ZooKeeper to the topic store

In previous releases of Strimzi, topic metadata was stored in ZooKeeper. The topic store removes this requirement, bringing the metadata into the Kafka cluster, and under the control of the Topic Operator.

When upgrading to Strimzi 0.39.0, the transition to Topic Operator control of the topic store is seamless. Metadata is found and migrated from ZooKeeper, and the old store is deleted.

10.3.3. Downgrading to a Strimzi version that uses ZooKeeper to store topic metadata

If you are reverting back to a version of Strimzi earlier than 0.22, which uses ZooKeeper for the storage of topic metadata, you still downgrade your Cluster Operator to the previous version, then downgrade Kafka brokers and client applications to the previous Kafka version as standard.

However, you must also delete the topics that were created for the topic store using a kafka-topics command, specifying the bootstrap address of the Kafka cluster. For example:

kubectl run kafka-admin -ti --image=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1 --rm=true --restart=Never -- ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi-topic-operator-kstreams-topic-store-changelog --delete && ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi_store_topic --delete

The command must correspond to the type of listener and authentication used to access the Kafka cluster.

The Topic Operator will reconstruct the ZooKeeper topic metadata from the state of the topics in Kafka.

10.3.4. Automatic creation of topics

Applications can trigger the automatic creation of topics in the Kafka cluster. By default, the Kafka broker configuration auto.create.topics.enable is set to true, allowing the broker to create topics automatically when an application attempts to produce or consume from a non-existing topic. Applications might also use the Kafka AdminClient to automatically create topics. When an application is deployed along with its KafkaTopic resources, it is possible that automatic topic creation in the cluster happens before the Topic Operator can react to the KafkaTopic.

For bidirectional topic management, the Topic Operator synchronizes the changes between the topics and KafkaTopic resources.

If you are using unidirectional topic management, this can mean that the topics created for an application deployment are initially created with default topic configuration. If the Topic Operator attempts to reconfigure the topics based on KafkaTopic resource specifications included with the application deployment, the operation might fail because the required change to the configuration is not allowed. For example, if the change means lowering the number of topic partitions. For this reason, it is recommended to disable auto.create.topics.enable in the Kafka cluster configuration when using unidirectional topic management.

10.4. Configuring Kafka topics

Use the properties of the KafkaTopic resource to configure Kafka topics. Changes made to topic configuration in the KafkaTopic are propagated to Kafka.

You can use kubectl apply to create or modify topics, and kubectl delete to delete existing topics.

For example:

  • kubectl apply -f <topic_config_file>

  • kubectl delete KafkaTopic <topic_name>

To be able to delete topics, delete.topic.enable must be set to true (default) in the spec.kafka.config of the Kafka resource.

This procedure shows how to create a topic with 10 partitions and 2 replicas.

Note
 The procedure is the same for the unidirectional and bidirectional modes of topic management.
Before you begin

The KafkaTopic resource does not allow the following changes:

  • Renaming the topic defined in spec.topicName. A mismatch between spec.topicName and status.topicName will be detected.

  • Decreasing the number of partitions using spec.partitions (not supported by Kafka).

  • Modifying the number of replicas specified in spec.replicas.

Warning
 Increasing spec.partitions for topics with keys will alter the partitioning of records, which can cause issues, especially when the topic uses semantic partitioning.
Prerequisites

  • A running Kafka cluster configured with a Kafka broker listener using mTLS authentication and TLS encryption.

  • A running Topic Operator (typically deployed with the Entity Operator).

  • For deleting a topic, delete.topic.enable=true (default) in the spec.kafka.config of the Kafka resource.

Procedure

  1. Configure the KafkaTopic resource.

    Example Kafka topic configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic-1
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10
  replicas: 2
    Tip
    		 When modifying a topic, you can get the current version of the resource using kubectl get kafkatopic my-topic-1 -o yaml.

  2. Create the KafkaTopic resource in Kubernetes.

    kubectl apply -f <topic_config_file>

  3. Wait for the ready status of the topic to change to True:

    kubectl get kafkatopics -o wide -w -n <namespace>
    Kafka topic status
    NAME         CLUSTER     PARTITIONS  REPLICATION FACTOR READY
my-topic-1   my-cluster  10          3                  True
my-topic-2   my-cluster  10          3
my-topic-3   my-cluster  10          3                  True

    Topic creation is successful when the READY output shows True.

  4. If the READY column stays blank, get more details on the status from the resource YAML or from the Topic Operator logs.

    Status messages provide details on the reason for the current status.

    oc get kafkatopics my-topic-2 -o yaml
    Details on a topic with a NotReady status
    # ...
status:
  conditions:
  - lastTransitionTime: "2022-06-13T10:14:43.351550Z"
    message: Number of partitions cannot be decreased
    reason: PartitionDecreaseException
    status: "True"
    type: NotReady

    In this example, the reason the topic is not ready is because the original number of partitions was reduced in the KafkaTopic configuration. Kafka does not support this.

    After resetting the topic configuration, the status shows the topic is ready.

    kubectl get kafkatopics my-topic-2 -o wide -w -n <namespace>
    Status update of the topic
    NAME         CLUSTER     PARTITIONS  REPLICATION FACTOR READY
my-topic-2   my-cluster  10          3                  True

    Fetching the details shows no messages

    kubectl get kafkatopics my-topic-2 -o yaml
    Details on a topic with a READY status
    # ...
status:
  conditions:
  - lastTransitionTime: '2022-06-13T10:15:03.761084Z'
    status: 'True'
    type: Ready

10.5. Configuring topics for replication and number of partitions

The recommended configuration for topics managed by the Topic Operator is a topic replication factor of 3, and a minimum of 2 in-sync replicas.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10 # (1)
  replicas: 3 # (2)
  config:
    min.insync.replicas: 2 # (3)
  #...

  1. The number of partitions for the topic.

  2. The number of replica topic partitions. Currently, this cannot be changed in the KafkaTopic resource, but it can be changed using the kafka-reassign-partitions.sh tool.

  3. The minimum number of replica partitions that a message must be successfully written to, or an exception is raised.

Note
 In-sync replicas are used in conjunction with the acks configuration for producer applications. The acks configuration determines the number of follower partitions a message must be replicated to before the message is acknowledged as successfully received. The bidirectional Topic Operator runs with acks=all for its internal topics whereby messages must be acknowledged by all in-sync replicas.

When scaling Kafka clusters by adding or removing brokers, replication factor configuration is not changed and replicas are not reassigned automatically. However, you can use the kafka-reassign-partitions.sh tool to change the replication factor, and manually reassign replicas to brokers.

Alternatively, though the integration of Cruise Control for Strimzi cannot change the replication factor for topics, the optimization proposals it generates for rebalancing Kafka include commands that transfer partition replicas and change partition leadership.

Additional resources

10.6. Managing KafkaTopic resources without impacting Kafka topics

This procedure describes how to convert Kafka topics that are currently managed through the KafkaTopic resource into non-managed topics. This capability can be useful in various scenarios. For instance, you might want to update the metadata.name of a KafkaTopic resource. You can only do that by deleting the original KafkaTopic resource and recreating a new one.

By annotating a KafkaTopic resource with strimzi.io/managed=false, you indicate that the Topic Operator should no longer manage that particular topic. This allows you to retain the Kafka topic while making changes to the resource’s configuration or other administrative tasks.

You can perform this task if you are using unidirectional topic management.

Prerequisites
Procedure

  1. Annotate the KafkaTopic resource in Kubernetes, setting strimzi.io/managed to false:

    kubectl annotate kafkatopic my-topic-1 strimzi.io/managed=false

    Specify the metadata.name of the topic in your KafkaTopic resource, which is my-topic-1 in this example.

  2. Check the status of the KafkaTopic resource to make sure the request was successful:

    oc get kafkatopics my-topic-1 -o yaml
    Example topic with a Ready status
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  generation: 124
  name: my-topic-1
  finalizer:
    strimzi.io/topic-operator
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10
  replicas: 2

# ...
status:
  observedGeneration: 124 # (1)
  topicName: my-topic-1
  conditions:
  - type: Ready
    status: True
    lastTransitionTime: 20230301T103000Z

    1. Successful reconciliation of the resource means the topic is no longer managed.

    The value of metadata.generation (the current version of the deployment) must match status.observedGeneration (the latest reconciliation of the resource).

  3. You can now make changes to the KafkaTopic resource without it affecting the Kafka topic it was managing.

    For example, to change the metadata.name, do as follows:

    1. Delete the original KafkTopic resource:

      kubectl delete kafkatopic <kafka_topic_name>

    2. Recreate the KafkTopic resource with a different metadata.name, but use spec.topicName to refer to the same topic that was managed by the original

  4. If you haven’t deleted the original KafkaTopic resource, and you wish to resume management of the Kafka topic again, set the strimzi.io/managed annotation to true or remove the annotation.

10.7. Enabling topic management for existing Kafka topics

This procedure describes how to enable topic management for topics that are not currently managed through the KafkaTopic resource. You do this by creating a matching KafkaTopic resource.

You can perform this task if you are using unidirectional topic management.

Prerequisites
Procedure

  1. Create a KafkaTopic resource with a metadata.name that is the same as the Kafka topic.

    Or use spec.topicName if the name of the topic in Kafka would not be a legal Kubernetes resource name.

    Example Kafka topic configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic-1
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10
  replicas: 2

    In this example, the Kafka topic is named my-topic-1.

    The Topic Operator checks whether the topic is managed by another KafkaTopic resource. If it is, the older resource takes precedence and a resource conflict error is returned in the status of the new resource.

  2. Apply the KafkaTopic resource:

    kubectl apply -f <topic_configuration_file>

  3. Wait for the operator to update the topic in Kafka.

    The operator updates the Kafka topic with the spec of the KafkaTopic that has the same name.

  4. Check the status of the KafkaTopic resource to make sure the request was successful:

    oc get kafkatopics my-topic-1 -o yaml
    Example topic with a Ready status
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  generation: 1
  name: my-topic-1
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10
  replicas: 2
# ...
status:
  observedGeneration: 1 # (1)
  topicName: my-topic-1
  conditions:
  - type: Ready
    status: True
    lastTransitionTime: 20230301T103000Z

    1. Successful reconciliation of the resource means the topic is now managed.

    The value of metadata.generation (the current version of the deployment) must match status.observedGeneration (the latest reconciliation of the resource).

10.8. Deleting managed topics

Unidirectional topic management supports the deletion of topics managed through the KafkaTopic resource with or without Kubernetes finalizers. This is determined by the STRIMZI_USE_FINALIZERS Topic Operator environment variable. By default, this is set to true, though it can be set to false in the Topic Operator env configuration if you do not want the Topic Operator to add finalizers.

Finalizers ensure orderly and controlled deletion of KafkaTopic resources. A finalizer for the Topic Operator is added to the metadata of the KafkaTopic resource:

Finalizer to control topic deletion
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  generation: 1
  name: my-topic-1
  finalizers:
    - strimzi.io/topic-operator
  labels:
    strimzi.io/cluster: my-cluster

In this example, the finalizer is added for topic my-topic-1. The finalizer prevents the topic from being fully deleted until the finalization process is complete. If you then delete the topic using kubectl delete kafkatopic my-topic-1, a timestamp is added to the metadata:

Finalizer timestamp on deletion
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  generation: 1
  name: my-topic-1
  finalizers:
    - strimzi.io/topic-operator
  labels:
    strimzi.io/cluster: my-cluster
  deletionTimestamp: 20230301T000000.000

The resource is still present. If the deletion fails, it is shown in the status of the resource.

When the finalization tasks are successfully executed, the finalizer is removed from the metadata, and the resource is fully deleted.

Finalizers also serve to prevent related resources from being deleted. If the unidirectional Topic Operator is not running, it won’t be able to remove its finalizer from the metadata.finalizers. And any attempt to directly delete the KafkaTopic resources or the namespace will fail or timeout, leaving the namespace in a stuck terminating state. If this happens, you can bypass the finalization process by removing the finalizers on topics.

10.9. Switching between Topic Operator modes

You can switch between topic management modes when upgrading or downgrading Strimzi, or when using the same version of Strimzi, as long as the mode is supported for that version.

Switching from bidirectional to unidirectional topic management mode

  1. Enable the UnidirectionalTopicOperator feature gate.

    The Cluster Operator deploys the Entity Operator with the Topic Operator in unidirectional topic management mode.

  2. The internal topics that support the Topic Operator running in bidirectional topic management mode are no longer required, so you can delete the KafkaTopic resources to manage them:

    kubectl delete $(kubectl get kt -n <namespace_name> -o name | grep strimzi-store-topic) \
  && kubectl delete $(kubectl get kt -n <namespace_name> -o name | grep strimzi-topic-operator)

    This command deletes the internal topics, which have names starting strimzi-store-topic and strimzi-topic-operator.

  3. The internal topics for storing consumer offsets and transaction states must be retained in Kafka. So, you must first discontinue their management by the Topic Operator before deleting the KafkaTopic resources.

    1. Discontinue management of the topics:

      kubectl annotate $(kubectl get kt -n <namespace_name> -o name | grep consumer-offsets) strimzi.io/managed="false" \
  && kubectl annotate $(kubectl get kt -n <namespace_name> -o name | grep transaction-state) strimzi.io/managed="false"

      By annotating the KafkaTopic resources with strimzi.io/managed="false", you indicate that the Topic Operator should no longer manage those topics. In this case, we are adding the annotation to resources for managing the internal topics with names starting consumer-offsets and transaction-state.

    2. When their management is discontinued, delete the KafkaTopic resources (without deleting the topics inside Kafka):

      kubectl delete $(kubectl get kt -n <namespace_name> -o name | grep consumer-offsets) \
  && kubectl delete $(kubectl get kt -n <namespace_name> -o name | grep transaction-state)
Switching from unidirectional to bidirectional topic management mode

  1. Disable the UnidirectionalTopicOperator feature gate.

    The Cluster Operator deploys the Entity Operator with the Topic Operator in bidirectional topic management mode.

    The internal topics required by the Topic Operator running in bidirectional topic management mode are created.

  2. Check whether finalizers are being used to control topic deletion. If KafkaTopic resources are using finalizers, ensure that you do one of the following after making the switch:

    • Remove all finalizers from topics.

    • Disable the finalizers by setting the STRIMZI_USE_FINALIZERS environment variable to false in the Topic Operator env configuration.

      Use the same configuration for a Topic Operator running in a Strimzi-managed cluster or as a standalone deployment.

      Disabling topic finalizers in a Strimzi-managed cluster
      apiVersion: {KafkaApiVersion}
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
    template:
      topicOperatorContainer:
        env:
          - name: STRIMZI_USE_FINALIZERS
            value: "false"
# ...
      Disabling topic finalizers in a standalone deployment
      apiVersion: apps/v1
kind: Deployment
metadata:
  name: strimzi-topic-operator
spec:
  template:
    spec:
      containers:
        - name: STRIMZI_USE_FINALIZERS
          value: "false"
# ...

      The Topic Operator does not use finalizers in bidirectional mode. If they are retained after making the switch from unidirectional mode, you won’t be able to delete KafkaTopic and related resources.

After switching between between Topic Operator modes, try creating a topic to make sure the operator is running correctly. For more information, see Configuring Kafka topics.

10.10. Removing finalizers on topics

If the unidirectional Topic Operator is not running, and you want to bypass the finalization process when deleting managed topics, you have to remove the finalizers. You can do this manually by editing the resources directly or by using a command.

To remove finalizers on all topics, use the following command:

Removing finalizers on topics
kubectl get kt -o=json | jq '.items[].metadata.finalizers = null' | kubectl apply -f -

The command uses the jq tool to modify the KafkaTopic (kt) resources by setting the finalizers to null. You can also use the command for a specific topic:

Removing a finalizer on a specific topic
kubectl get kt <topic_name> -o=json | jq '.metadata.finalizers = null' | kubectl apply -f -

After running the command, you can go ahead and delete the topics. Alternatively, if the topics were already being deleted but were blocked due to outstanding finalizers then their deletion should complete.

Warning
 Be careful when removing finalizers, as any cleanup operations associated with the finalization process are not performed if the Topic Operator is not running. For example, if you remove the finalizer from a KafkaTopic resource and subsequently delete the resource, the related Kafka topic won’t be deleted.

10.11. Considerations when disabling topic deletion

When the delete.topic.enable configuration in Kafka is set to false, topics cannot be deleted. This might be required in certain scenarios, but it introduces a consideration when using the Topic Operator in unidirectional mode.

As topics cannot be deleted, finalizers added to the metadata of a KafkaTopic resource to control topic deletion are never removed by the Topic Operator (though they can be removed manually). Similarly, any Custom Resource Definitions (CRDs) or namespaces associated with topics cannot be deleted.

Before configuring delete.topic.enable=false, assess these implications to ensure it aligns with your specific requirements.

Note
 To avoid using finalizers, you can set the STRIMZI_USE_FINALIZERS Topic Operator environment variable to false.

10.12. Tuning request batches for topic operations

In unidirectional mode, the Topic Operator uses the request batching capabilities of the Kafka Admin API for operations on topic resources. You can fine-tune the batching mechanism using the following operator configuration properties:

  • STRIMZI_MAX_QUEUE_SIZE to set the maximum size of the topic event queue. The default value is 1024.

  • STRIMZI_MAX_BATCH_SIZE to set the maximum number of topic events allowed in a single batch. The default value is 100.

  • MAX_BATCH_LINGER_MS to specify the maximum time to wait for a batch to accumulate items before processing. The default is 100 milliseconds.

If the maximum size of the request batching queue is exceeded, the Topic Operator shuts down and is restarted. To prevent frequent restarts, consider adjusting the STRIMZI_MAX_QUEUE_SIZE property to accommodate the typical load.

11. Using the User Operator to manage Kafka users

When you create, modify or delete a user using the KafkaUser resource, the User Operator ensures that these changes are reflected in the Kafka cluster.

For more information on the KafkaUser resource, see the KafkaUser schema reference.

11.1. Configuring Kafka users

Use the properties of the KafkaUser resource to configure Kafka users.

You can use kubectl apply to create or modify users, and kubectl delete to delete existing users.

For example:

  • kubectl apply -f <user_config_file>

  • kubectl delete KafkaUser <user_name>

Users represent Kafka clients. When you configure Kafka users, you enable the user authentication and authorization mechanisms required by clients to access Kafka. The mechanism used must match the equivalent Kafka configuration. For more information on using Kafka and KafkaUser resources to secure access to Kafka brokers, see Securing access to Kafka brokers.

Prerequisites

  • A running Kafka cluster configured with a Kafka broker listener using mTLS authentication and TLS encryption.

  • A running User Operator (typically deployed with the Entity Operator).

Procedure

  1. Configure the KafkaUser resource.

    This example specifies mTLS authentication and simple authorization using ACLs.

    Example Kafka user configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user-1
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: tls
  authorization:
    type: simple
    acls:
      # Example consumer Acls for topic my-topic using consumer group my-group
      - resource:
          type: topic
          name: my-topic
          patternType: literal
        operations:
          - Describe
          - Read
        host: "*"
      - resource:
          type: group
          name: my-group
          patternType: literal
        operations:
          - Read
        host: "*"
      # Example Producer Acls for topic my-topic
      - resource:
          type: topic
          name: my-topic
          patternType: literal
        operations:
          - Create
          - Describe
          - Write
        host: "*"

  2. Create the KafkaUser resource in Kubernetes.

    kubectl apply -f <user_config_file>

  3. Wait for the ready status of the user to change to True:

    kubectl get kafkausers -o wide -w -n <namespace>
    Kafka user status
    NAME       CLUSTER     AUTHENTICATION  AUTHORIZATION READY
my-user-1  my-cluster  tls             simple        True
my-user-2  my-cluster  tls             simple
my-user-3  my-cluster  tls             simple        True

    User creation is successful when the READY output shows True.

  4. If the READY column stays blank, get more details on the status from the resource YAML or User Operator logs.

    Messages provide details on the reason for the current status.

    kubectl get kafkausers my-user-2 -o yaml
    Details on a user with a NotReady status
    # ...
status:
  conditions:
  - lastTransitionTime: "2022-06-10T10:07:37.238065Z"
    message: Simple authorization ACL rules are configured but not supported in the
      Kafka cluster configuration.
    reason: InvalidResourceException
    status: "True"
    type: NotReady

    In this example, the reason the user is not ready is because simple authorization is not enabled in the Kafka configuration.

    Kafka configuration for simple authorization
      apiVersion: kafka.strimzi.io/v1beta2
  kind: Kafka
  metadata:
    name: my-cluster
  spec:
    kafka:
      # ...
      authorization:
        type: simple

    After updating the Kafka configuration, the status shows the user is ready.

    kubectl get kafkausers my-user-2 -o wide -w -n <namespace>
    Status update of the user
    NAME       CLUSTER     AUTHENTICATION  AUTHORIZATION READY
my-user-2  my-cluster  tls             simple        True

    Fetching the details shows no messages.

    kubectl get kafkausers my-user-2 -o yaml
    Details on a user with a READY status
    # ...
status:
  conditions:
  - lastTransitionTime: "2022-06-10T10:33:40.166846Z"
    status: "True"
    type: Ready

12. Setting up client access to a Kafka cluster

After you have deployed Strimzi, 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 Kubernetes cluster.

12.1. Deploying example clients

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

Prerequisites

  • The Kafka cluster is available for the clients.

Procedure

  1. Deploy a Kafka producer.

    kubectl run kafka-producer -ti --image=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1 --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.

    kubectl run kafka-consumer -ti --image=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1 --rm=true --restart=Never -- bin/kafka-console-consumer.sh --bootstrap-server cluster-name-kafka-bootstrap:9092 --topic my-topic --from-beginning

  5. Confirm that you see the incoming messages in the consumer console.

12.2. Configuring listeners to connect to Kafka brokers

Use listeners for client connection to Kafka brokers. Strimzi 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 Kubernetes cluster or external listeners for connecting outside the Kubernetes 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 Kubernetes cluster

  • cluster-ip to expose Kafka using per-broker ClusterIP services

External listeners

  • nodeport to use ports on Kubernetes 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 Kubernetes network to an outside network. In which case, you can configure an internal type listener (using the useServiceDnsDomain property) so that the Kubernetes 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 Kubernetes 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 a Kubernetes 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: external1
        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.
Additional resources

12.3. Listener naming conventions

From the listener configuration, the resulting listener bootstrap and per-broker service names are structured according to the following naming conventions:

Table 15. Listener naming conventions
Listener type Bootstrap service name Per-Broker service name

internal

<cluster_name>-kafka-bootstrap

Not applicable

loadbalancer
nodeport
ingress
route
cluster-ip

<cluster_name>-kafka-<listener-name>-bootstrap

<cluster_name>-kafka-<listener-name>-<idx>

For example, my-cluster-kafka-bootstrap, my-cluster-kafka-external1-bootstrap, and my-cluster-kafka-external1-0. The names are assigned to the services, routes, load balancers, and ingresses created through the listener configuration.

You can use certain backwards compatible names and port numbers to transition listeners initially configured under the retired KafkaListeners schema. The resulting external listener naming convention varies slightly. These specific combinations of listener name and port configuration values are backwards compatible:

Table 16. Backwards compatible listener name and port combinations
Listener name Port Bootstrap service name Per-Broker service name

plain

9092

<cluster_name>-kafka-bootstrap

Not applicable

tls

9093

<cluster-name>-kafka-bootstrap

Not applicable

external

9094

<cluster_name>-kafka-bootstrap

<cluster_name>-kafka-bootstrap-<idx>

12.4. 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 Kubernetes cluster; or provide external access to a client on a different Kubernetes namespace or outside Kubernetes entirely. This procedure shows how to configure client access to a Kafka cluster from outside Kubernetes or from another Kubernetes 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.

Strimzi 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 Kubernetes 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 Kubernetes 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: external1 # (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 and StandardAuthorizer Kafka plugins.

      9. (Optional) Super users can access all brokers regardless of any access restrictions defined in ACLs.

      Warning
      		 An OpenShift route address comprises the Kafka cluster name, the listener name, the project name, and the domain of the router. For example, my-cluster-kafka-external1-bootstrap-my-project.domain.com (<cluster_name>-kafka-<listener_name>-bootstrap-<namespace>.<domain>). Each DNS label (between periods “.”) must not exceed 63 characters, and the total length of the address must not exceed 255 characters.

  2. Create or update the Kafka resource.

    kubectl 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.

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

    For example:

    kubectl 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.

    kubectl 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.

    kubectl 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.

    kubectl 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.

    kubectl 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-----");
    Additional resources

12.5. Accessing Kafka using node ports

Use node ports to access a Strimzi Kafka cluster from an external client outside the Kubernetes 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 external4.

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: external4
        port: 9094
        type: nodeport
        tls: true
        authentication:
          type: tls
        # ...
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    kubectl 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-external4-0          NodePort  172.30.55.13    9094:31789/TCP
my-cluster-kafka-external4-1          NodePort  172.30.250.248  9094:30028/TCP
my-cluster-kafka-external4-2          NodePort  172.30.115.81   9094:32650/TCP
my-cluster-kafka-external4-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
  kafkaVersion: 3.6.1
  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: external4
  observedGeneration: 2
  operatorLastSuccessfulVersion: 0.39.0
 # ...

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

    kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external4")].bootstrapServers}{"\n"}'

ip-10-0-224-199.us-west-2.compute.internal:32650

  4. Extract the cluster CA certificate.

    kubectl 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.

12.6. Accessing Kafka using loadbalancers

Use loadbalancers to access a Strimzi Kafka cluster from an external client outside the Kubernetes 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 external3.

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: external3
        port: 9094
        type: loadbalancer
        tls: true
        authentication:
          type: tls
        # ...
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    kubectl 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-external3-0          LoadBalancer    172.30.204.234  9094:30011/TCP
my-cluster-kafka-external3-1          LoadBalancer    172.30.164.89   9094:32544/TCP
my-cluster-kafka-external3-2          LoadBalancer    172.30.73.151   9094:32504/TCP
my-cluster-kafka-external3-bootstrap  LoadBalancer    172.30.30.228   9094:30371/TCP

NAME                                  EXTERNAL-IP (loadbalancer)
my-cluster-kafka-external3-0          a8a519e464b924000b6c0f0a05e19f0d-1132975133.us-west-2.elb.amazonaws.com
my-cluster-kafka-external3-1          ab6adc22b556343afb0db5ea05d07347-611832211.us-west-2.elb.amazonaws.com
my-cluster-kafka-external3-2          a9173e8ccb1914778aeb17eca98713c0-777597560.us-west-2.elb.amazonaws.com
my-cluster-kafka-external3-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
  kafkaVersion: 3.6.1
  listeners:
    # ...
    - addresses:
        - host: >-
            a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com
          port: 9094
      bootstrapServers: >-
        a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com:9094
      certificates:
        - |
          -----BEGIN CERTIFICATE-----

          -----END CERTIFICATE-----
      name: external3
  observedGeneration: 2
  operatorLastSuccessfulVersion: 0.39.0
 # ...

    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.

    kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external3")].bootstrapServers}{"\n"}'

a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com:9094

  4. Extract the cluster CA certificate.

    kubectl 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:9094.

    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.

12.7. Accessing Kafka using an Ingress NGINX Controller for Kubernetes

Use an Ingress NGINX Controller for Kubernetes to access a Strimzi Kafka cluster from clients outside the Kubernetes cluster.

To be able to use an Ingress NGINX Controller for Kubernetes, add configuration for an ingress type listener in the Kafka custom resource. When applied, the configuration creates a dedicated ingress and service for an external bootstrap and each broker in the cluster. Clients connect to the bootstrap ingress, 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 ingresses and services.

To connect to a broker, you specify a hostname for the ingress bootstrap address, as well as the certificate used for TLS encryption. For access using an ingress, the port used in the Kafka client is typically 443.

The procedure shows basic ingress 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 class configuration property with ingress listeners to specify the ingress controller used.

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

TLS passthrough

Make sure that you enable TLS passthrough in your Ingress NGINX Controller for Kubernetes deployment. Kafka uses a binary protocol over TCP, but the Ingress NGINX Controller for Kubernetes is designed to work with a HTTP protocol. To be able to route TCP traffic through ingresses, Strimzi 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 the 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.

For more information about enabling TLS passthrough, see the TLS passthrough documentation.

Prerequisites

  • An Ingress NGINX Controller for Kubernetes is running with TLS passthrough enabled

  • A running Cluster Operator

In this procedure, the Kafka cluster name is my-cluster. The name of the listener is external2.

Procedure

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

    Specify an ingress hostname for the bootstrap service and each of the Kafka brokers in the Kafka cluster. Add any hostname to the bootstrap and broker-<index> prefixes that identify the bootstrap and brokers.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  labels:
    app: my-cluster
  name: my-cluster
  namespace: myproject
spec:
  kafka:
    # ...
    listeners:
      - name: external2
        port: 9094
        type: ingress
        tls: true # (1)
        authentication:
          type: tls
        configuration:
          bootstrap:
            host: bootstrap.myingress.com
          brokers:
          - broker: 0
            host: broker-0.myingress.com
          - broker: 1
            host: broker-1.myingress.com
          - broker: 2
            host: broker-2.myingress.com
          class: nginx  # (2)
    # ...
  zookeeper:
    # ...

    1. For ingress type listeners, TLS encryption must be enabled (true).

    2. (Optional) Class that specifies the ingress controller to use. You might need to add a class if you have not set up a default and a class name is missing in the ingresses created.

  2. Create or update the resource.

    kubectl 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.

    An ingress is also created for each service, with a DNS address to expose them using the Ingress NGINX Controller for Kubernetes.

    Ingresses created for the bootstrap and brokers
    NAME                                  CLASS  HOSTS                    ADDRESS       PORTS
my-cluster-kafka-external2-0          nginx  broker-0.myingress.com   192.168.49.2  80,443
my-cluster-kafka-external2-1          nginx  broker-1.myingress.com   192.168.49.2  80,443
my-cluster-kafka-external2-2          nginx  broker-2.myingress.com   192.168.49.2  80,443
my-cluster-kafka-external2-bootstrap  nginx  bootstrap.myingress.com  192.168.49.2  80,443

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

    Status for the bootstrap ingress
    status:
  loadBalancer:
    ingress:
    - ip: 192.168.49.2
 # ...

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

    openssl s_client -connect broker-0.myingress.com:443 -servername broker-0.myingress.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. Extract the cluster CA certificate.

    kubectl 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 (from the listener configuration) and port 443 in your Kafka client as the bootstrap address to connect to the Kafka cluster. For example, bootstrap.myingress.com:443.

    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.

12.8. Accessing Kafka using OpenShift routes

Use OpenShift routes to access a Strimzi 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 Kafka cluster name, the listener name, the project name, and the domain of the router. For example, my-cluster-kafka-external1-bootstrap-my-project.domain.com (<cluster_name>-kafka-<listener_name>-bootstrap-<namespace>.<domain>). Each DNS label (between periods “.”) must not exceed 63 characters, and the total length of the address must not exceed 255 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 Strimzi. 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, Strimzi 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 external1.

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: external1
        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.

    kubectl 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-external1-0          my-cluster-kafka-external1-0-my-project.router.com         my-cluster-kafka-external1-0          9094  passthrough
my-cluster-kafka-external1-1          my-cluster-kafka-external1-1-my-project.router.com         my-cluster-kafka-external1-1          9094  passthrough
my-cluster-kafka-external1-2          my-cluster-kafka-external1-2-my-project.router.com         my-cluster-kafka-external1-2          9094  passthrough
my-cluster-kafka-external1-bootstrap  my-cluster-kafka-external1-bootstrap-my-project.router.com my-cluster-kafka-external1-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-external1-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-external1-0-my-project.router.com:443 -servername my-cluster-kafka-external1-0-my-project.router.com -showcerts

    The server name is the Server Name Indication (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.

    kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external1")].bootstrapServers}{"\n"}'

my-cluster-kafka-external1-bootstrap-my-project.router.com:443

    The address comprises the Kafka 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.

    kubectl 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.

13. Managing secure access to Kafka

Secure your Kafka cluster by managing the access a client has to Kafka brokers. Specify configuration options to secure Kafka brokers and clients

A secure connection between Kafka brokers and clients can encompass the following:

  • Encryption for data exchange

  • Authentication to prove identity

  • Authorization to allow or decline actions executed by users

The authentication and authorization mechanisms specified for a client must match those specified for the Kafka brokers. Strimzi operators automate the configuration process and create the certificates required for authentication. The Cluster Operator automatically sets up TLS certificates for data encryption and authentication within your cluster.

13.1. Security options for Kafka

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

13.1.1. 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 Kubernetes 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 Kubernetes 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 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.
options for listener authentication configuration
Figure 2. Kafka listener authentication options

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.

Important
 When configuring listeners for client access to brokers, you can use port 9092 or higher (9093, 9094, and so on), but with a few exceptions. The listeners cannot be configured to use the ports reserved for interbroker communication (9090 and 9091), Prometheus metrics (9404), and JMX (Java Management Extensions) monitoring (9999).
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: external3
        port: 9094
        type: loadbalancer
        tls: true
        authentication:
          type: tls
# ...
mTLS authentication

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

Strimzi 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 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.
SCRAM-SHA-512 authentication

SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. Strimzi 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.

Network policies

By default, Strimzi 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 Configuring the Cluster Operator.

Note
 Your configuration of Kubernetes must support ingress NetworkPolicies in order to use network policies in Strimzi.
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 Providing your own Kafka listener certificates for TLS encryption.

Additional resources

13.1.2. 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:

options for kafka authorization configuration
Figure 3. Kafka cluster authorization options
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
    # ...

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

13.2.1. 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.

13.2.2. 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.

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.

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
  # ...
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
Custom password configuration

When a user is created, Strimzi generates a random password. You can use your own password instead of the one generated by Strimzi. 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.

13.2.3. 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 Keycloak authorization, you can manage the ACL rules directly in Keycloak. 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, Strimzi 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 simple authorization, this is determined by the allow.everyone.if.no.acl.found configuration in the Kafka cluster.

ACL rules

simple authorization 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.

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.

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.

13.3. 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:

13.3.1. Securing Kafka brokers

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

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. Authorization enables simple authorization on the Kafka broker using the AclAuthorizer and StandardAuthorizer Kafka plugins.

      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.

    kubectl 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.

13.3.2. 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.

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

13.3.3. 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

  • A Kubernetes 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 kubectl apply:

    kubectl apply -f your-file
Additional resources

13.3.4. 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 Strimzi. 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 Strimzi, 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:

    • A compatible server certificate signed by an external CA. (Provide an X.509 certificate in PEM format.)

      You can use one listener certificate for multiple listeners.

    • Subject Alternative Names (SANs) are specified in the certificate for each listener. For more information, see Alternative subjects in server certificates for Kafka listeners.

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
 Strimzi 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:

    kubectl 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: external3
    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:

    kubectl 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.

13.3.5. 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.

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 Kubernetes 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
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 17. SANs for each type of external listener
External listener type In 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 Kubernetes worker nodes that the Kafka broker pods might be scheduled to.

You can use a matching wildcard name.
Additional resources

13.4. Using OAuth 2.0 token-based authentication

Strimzi 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.

Kafka brokers and clients both need to be configured to use OAuth 2.0. You can configure OAuth 2.0 authentication, then OAuth 2.0 authorization.

Note

OAuth 2.0 authentication can be used in conjunction with Kafka authorization.

Using OAuth 2.0 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 Strimzi:

  • 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 Strimzi, 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

13.4.1. OAuth 2.0 authentication mechanisms

Strimzi 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, Strimzi 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, Strimzi provides OAuth 2.0 over PLAIN server-side callbacks.

With the Strimzi 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.
Additional resources

13.4.2. 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.
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.
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: external3
    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.

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 a Kubernetes Secret in your Strimzi project namespace, and use a tlsTrustedCertificates attribute to point to the Kubernetes Secret containing the truststore file.

You might want to configure a userNameClaim to properly extract a username from the JWT token. If required, you can use a JsonPath expression like "['user.info'].['user.id']" to retrieve the username from nested JSON attributes within a 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
    #...
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

13.4.3. 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 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.

Additional resources

13.4.4. 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

13.4.5. 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)

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.
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.

13.4.6. Configuring OAuth 2.0 authentication

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

In order to use OAuth 2.0 for Strimzi, you must:

  1. Configure an OAuth 2.0 authorization server for the Strimzi cluster and Kafka clients

  2. Deploy or update the Kafka cluster with Kafka broker listeners configured to use OAuth 2.0

  3. Update your Java-based Kafka clients to use OAuth 2.0

  4. Update Kafka component clients to use OAuth 2.0

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 Strimzi.

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 Strimzi.

    Now prepare the authorization server to work with Strimzi.

  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.

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

  • Strimzi 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.

    kubectl 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: external3
  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 username used to identify the user. Its value depends on the authorization server. If necessary, a JsonPath expression like "['user.info'].['user.id']" can be used to retrieve the username from nested JSON attributes within a token.

    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: external3
  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 username used to identify the user. Its value depends on the authorization server. If necessary, a JsonPath expression like "['user.info'].['user.id']" can be used to retrieve the username from nested JSON attributes within a token.

    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://<auth_server_address>/auth/realms/external/protocol/openid-connect/userinfo # (6)
    enableOauthBearer: false # (7)
    enablePlain: true # (8)
    tokenEndpointUri: https://<auth_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)
    includeAcceptHeader: false # (19)

    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. If necessary, a JsonPath expression like "['client.info'].['client.id']" can be used to retrieve the fallback username to retrieve the username from nested JSON attributes within a token.

    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).

    19. Some authorization servers have issues with client sending Accept: application/json header. By setting includeAcceptHeader: false the header will not be sent. Default is true.

  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:

    kubectl logs -f ${POD_NAME} -c ${CONTAINER_NAME}
kubectl get pod -w

    The rolling update configures the brokers to use OAuth 2.0 authentication.

What to do next
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

    To be able to use the OAuthbearer mechanism, you must also add the custom io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler class as the callback handler. JaasClientOauthLoginCallbackHandler handles OAuth callbacks to the authorization server for access tokens during client login. This enables automatic token renewal, ensuring continuous authentication without user intervention. Additionally, it handles login credentials for clients using the OAuth 2.0 password grant method.

  • 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 and sasl.login.callback.handler.class). 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

  • Strimzi 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>0.14.0</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)
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler

      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>" ;
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler

      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" ;
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler

      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" ;
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler

      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.

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

  • Strimzi 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)
    includeAcceptHeader: false (10)

    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.

    10. (Optional) Some authorization servers have issues with client sending Accept: application/json header. By setting includeAcceptHeader: false the header will not be sent. Default is true.

  3. Apply the changes to the deployment of your Kafka resource.

    kubectl apply -f your-file

  4. Check the update in the logs or by watching the pod state transitions:

    kubectl logs -f ${POD_NAME} -c ${CONTAINER_NAME}
kubectl get pod -w

    The rolling updates configure the component for interaction with Kafka brokers using OAuth 2.0 authentication.

13.4.7. Authorization server examples

When choosing an authorization server, consider the features that best support configuration of your chosen authentication flow.

For the purposes of testing OAuth 2.0 with Strimzi, Keycloak and ORY Hydra were implemented as the OAuth 2.0 authorization server.

For more information, see:

13.5. Using OAuth 2.0 token-based authorization

If you are using OAuth 2.0 with Keycloak for token-based authentication, you can also use Keycloak 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.

Strimzi supports the use of OAuth 2.0 token-based authorization through Keycloak Keycloak Authorization Services, which allows you to manage security policies and permissions centrally.

Security policies and permissions defined in Keycloak 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 and StandardAuthorizer plugins to configure authorization based on Access Control Lists (ACLs). The ACL rules managed by these plugins are used to grant or deny access to resources based on the username, and these rules are stored within the Kafka cluster itself. However, OAuth 2.0 token-based authorization with Keycloak 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.

Additional resources

13.5.1. OAuth 2.0 authorization mechanism

OAuth 2.0 authorization in Strimzi uses Keycloak server Authorization Services REST endpoints to extend token-based authentication with Keycloak 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 Keycloak Authorization Services.

Kafka broker custom authorizer

A Keycloak authorizer (KeycloakAuthorizer) is provided with Strimzi. To be able to use the Keycloak REST endpoints for Authorization Services provided by Keycloak, 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.

13.5.2. Configuring OAuth 2.0 authorization support

This procedure describes how to configure Kafka brokers to use OAuth 2.0 authorization using Keycloak Authorization Services.

Before you begin

Consider the access you require or want to limit for certain users. You can use a combination of Keycloak groups, roles, clients, and users to configure access in Keycloak.

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 Keycloak, 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

  • Strimzi must be configured to use OAuth 2.0 with Keycloak for token-based authentication. You use the same Keycloak 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 Keycloak Admin Console or use the Keycloak 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 Keycloak authorization by updating the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    kubectl 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)
      grantsMaxIdleSeconds: 300 (10)
      grantsGcPeriodSeconds: 300 (11)
      grantsAlwaysLatest: false (12)
      connectTimeoutSeconds: 60 (13)
      readTimeoutSeconds: 60 (14)
      httpRetries: 2 (15)
      enableMetrics: false (16)
      includeAcceptHeader: false (17)
    #...

    1. Type keycloak enables Keycloak authorization.

    2. URI of the Keycloak 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 Keycloak that has Authorization Services enabled. Typically, kafka is used as the ID.

    4. (Optional) Delegate authorization to Kafka AclAuthorizer and StandardAuthorizer if access is denied by Keycloak 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 Keycloak. 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 time, in seconds, after which an idle grant in the cache can be evicted. The default value is 300.

    11. (Optional) The time, in seconds, between consecutive runs of a job that cleans stale grants from the cache. The default value is 300.

    12. (Optional) Controls whether the latest grants are fetched for a new session. When enabled, grants are retrieved from Keycloak and cached for the user. The default value is false.

    13. (Optional) The connect timeout in seconds when connecting to the Keycloak token endpoint. The default value is 60.

    14. (Optional) The read timeout in seconds when connecting to the Keycloak token endpoint. The default value is 60.

    15. (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.

    16. (Optional) Enable or disable OAuth metrics. The default value is false.

    17. (Optional) Some authorization servers have issues with client sending Accept: application/json header. By setting includeAcceptHeader: false the header will not be sent. Default is true.

  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:

    kubectl logs -f ${POD_NAME} -c kafka
kubectl 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.

13.5.3. Managing policies and permissions in Keycloak Authorization Services

This section describes the authorization models used by Keycloak Authorization Services and Kafka, and defines the important concepts in each model.

To grant permissions to access Kafka, you can map Keycloak Authorization Services objects to Kafka resources by creating an OAuth client specification in Keycloak. Kafka permissions are granted to user accounts or service accounts using Keycloak Authorization Services rules.

Examples are shown of the different user permissions required for common Kafka operations, such as creating and listing topics.

Kafka and Keycloak authorization models overview

Kafka and Keycloak 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 KeycloakAuthorizer 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

Keycloak Authorization Services model

The Keycloak 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
Map Keycloak Authorization Services to the Kafka authorization model

The Kafka authorization model is used as a basis for defining the Keycloak 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 Keycloak for the Kafka broker. You then specify Keycloak 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 Strimzi 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 Keycloak 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 Keycloak realm.

When the Kafka client uses OAUTHBEARER authentication, the Keycloak authorizer (KeycloakAuthorizer) uses the access token of the current session to retrieve a list of grants from the Keycloak server. To retrieve the grants, the authorizer evaluates the Keycloak Authorization Services policies and permissions.

Authorization scopes for Kafka permissions

An initial Keycloak 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
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

13.5.4. Trying Keycloak Authorization Services

This example explains how to use Keycloak Authorization Services with keycloak authorization. Use Keycloak Authorization Services to enforce access restrictions on Kafka clients. Keycloak Authorization Services use authorization scopes, policies and permissions to define and apply access control to resources.

Keycloak Authorization Services REST endpoints provide a list of granted permissions on resources for authenticated users. The list of grants (permissions) is fetched from the Keycloak 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.

Strimzi provides example configuration files. These include the following example files for setting up Keycloak:

kafka-ephemeral-oauth-single-keycloak-authz.yaml

An example Kafka custom resource configured for OAuth 2.0 token-based authorization using Keycloak. 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 Keycloak realm configured with sample groups, users, roles and clients. You can import the realm into a Keycloak instance to set up fine-grained permissions to access Kafka.

If you want to try the example with Keycloak, use these files to perform the tasks outlined in this section in the order shown.

  1. Accessing the Keycloak Admin Console

  2. Deploying a Kafka cluster with Keycloak authorization

  3. Preparing TLS connectivity for a CLI Kafka client session

  4. Checking authorized access to Kafka using a CLI Kafka client session

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 Keycloak token endpoint. The hostname for both URIs must be the same.

Targeted permissions with group or role policies

In Keycloak, 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.

Accessing the Keycloak Admin Console

Set up Keycloak, 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 Keycloak realm.

Prerequisites

  • A running Kubernetes cluster.

  • The Strimzi examples/security/keycloak-authorization/kafka-authz-realm.json file that contains the preconfigured realm.

Procedure

  1. Install the Keycloak server using the Keycloak Operator as described in Installing the Keycloak Operator in the Keycloak documentation.

  2. Wait until the Keycloak instance is running.

  3. Get the external hostname to be able to access the Admin Console.

    NS=sso
kubectl get ingress keycloak -n $NS

    In this example, we assume the Keycloak server is running in the sso namespace.

  4. Get the password for the admin user.

    kubectl get -n $NS pod keycloak-0 -o yaml | less

    The password is stored as a secret, so get the configuration YAML file for the Keycloak 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
kubectl 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 Keycloak 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 Keycloak 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 Keycloak 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 Keycloak 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 Keycloak 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_.

Deploying a Kafka cluster with Keycloak authorization

Deploy a Kafka cluster configured to connect to the Keycloak 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 Keycloak authorization server is deployed to your Kubernetes cluster and loaded with the example realm.

  • The Cluster Operator is deployed to your Kubernetes cluster.

  • The Strimzi examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml custom resource.

Procedure

  1. Use the hostname of the Keycloak instance you deployed to prepare a truststore certificate for Kafka brokers to communicate with the Keycloak 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 Kubernetes as a secret.

    kubectl 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#" | kubectl create -n $NS -f -
Preparing TLS connectivity for a CLI Kafka client session

Create a new pod for an interactive CLI session. Set up a truststore with a Keycloak certificate for TLS connectivity. The truststore is to connect to Keycloak and the Kafka broker.

Prerequisites

  • The Keycloak authorization server is deployed to your Kubernetes cluster and loaded with the example realm.

    In the Keycloak Admin Console, check the roles assigned to the clients are displayed in Clients > Service Account Roles.

  • The Kafka cluster configured to connect with Keycloak is deployed to your Kubernetes cluster.

Procedure

  1. Run a new interactive pod container using the Strimzi Kafka image to connect to a running Kafka broker.

    NS=sso
kubectl run -ti --restart=Never --image=quay.io/strimzi/kafka:0.39.0-kafka-3.6.1 kafka-cli -n $NS -- /bin/sh
    Note
    		 If kubectl times out waiting on the image download, subsequent attempts may result in an AlreadyExists error.

  2. Attach to the pod container.

    kubectl attach -ti kafka-cli -n $NS

  3. Use the hostname of the Keycloak 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 Kubernetes cluster, you would have to first determine the bootstrap address. In that case you can also get the cluster certificate directly from the Kubernetes secret, and there is no need for openssl. For more information, see 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.

Checking authorized access to Kafka using a CLI Kafka client session

Check the authorization rules applied through the Keycloak 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 Strimzi 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 Keycloak 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.

    kubectl 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.

Additional resources

14. Managing TLS certificates

Strimzi supports TLS for encrypted communication between Kafka and Strimzi components.

Strimzi establishes encrypted TLS connections for communication between the following components:

  • Kafka brokers and ZooKeeper nodes

  • Kafka brokers (interbroker communication)

  • ZooKeeper nodes (internodal communication)

  • Strimzi operators and Kafka and ZooKeeper

  • Cruise Control and Kafka

  • Kafka Exporter and Kafka

Connections between clients and brokers use listeners that you must configure to use TLS-encrypted communication. You configure these listeners in the Kafka custom resource and each listener name and port number must be unique within the cluster. Communication between Kafka brokers and Kafka clients is encrypted according to how the tls property is configured for the listener. For more information, see Setting up client access to a Kafka cluster.

The following diagram shows the connections for secure communication.

Secure Communication
Figure 4. Kafka and ZooKeeper communication secured by TLS encryption

The ports shown in the diagram are used as follows:

Control plane listener (9090)

Connections between the Kafka controller and brokers use an internal control plane listener on port 9090, facilitating interbroker communication. This listener is not accessible to Kafka clients.

Replication listener (9091)

Data replication between brokers, as well as internal connections from Strimzi operators, Cruise Control, and the Kafka Exporter, use the replication listener on port 9091. This listener is not accessible to Kafka clients.

Listeners for client connections (9092 or higher)

For TLS-encrypted communication (through configuration of the listener), internal and external clients connect to Kafka brokers. External clients (producers and consumers) connect to the Kafka brokers through the advertised listener port.

ZooKeeper Port (2181)

ZooKeeper port for connection to Kafka.

ZooKeeper internodal communication port (2888)

ZooKeeper port for internodal communication between ZooKeeper nodes.

ZooKeeper leader election port (3888)

ZooKeeper port for leader election among ZooKeeper nodes in a ZooKeeper cluster.

Important
 When configuring listeners for client access to brokers, you can use port 9092 or higher (9093, 9094, and so on), but with a few exceptions. The listeners cannot be configured to use the ports reserved for interbroker communication (9090 and 9091), Prometheus metrics (9404), and JMX (Java Management Extensions) monitoring (9999).

14.1. Internal cluster CA and clients CA

To support encryption, each Strimzi 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.

CA (Certificate Authority) certificates are generated by the Cluster Operator to verify the identities of components and clients.

Similarly, each Kafka client application connecting to Strimzi 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, Strimzi automatically generates and renews CA certificates issued by the cluster CA or clients CA. You can configure the management of these CA certificates using Kafka.spec.clusterCa and Kafka.spec.clientsCa properties.

Note
 If you don’t want to use the CAs generated by the Cluster Operator, you can install your own cluster and clients CA certificates. Any certificates you provide are not renewed by the Cluster Operator.

14.2. Secrets generated by the operators

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

Secrets are created when custom resources are deployed, such as Kafka and KafkaUser. Strimzi 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.

Note
 You can 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.

14.2.1. TLS authentication using keys and certificates in PEM or PKCS #12 format

The secrets created by Strimzi 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.

14.2.2. Secrets generated by the Cluster Operator

The Cluster Operator generates the following certificates, which are saved as secrets in the Kubernetes cluster. Strimzi 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. Strimzi 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 Strimzi 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.

14.2.3. 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 Strimzi components. You can enforce this using Kubernetes 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 18. Fields in the <cluster_name>-cluster-ca secret
Field Description

ca.key

The current private key for the cluster CA.
Table 19. Fields in the <cluster_name>-cluster-ca-cert secret
Field Description

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 20. Fields in the <cluster_name>-kafka-brokers secret
Field Description

<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 21. Fields in the <cluster_name>-zookeeper-nodes secret
Field Description

<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 22. Fields in the <cluster_name>-cluster-operator-certs secret
Field Description

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 23. Fields in the <cluster_name>-entity-topic-operator-certs secret
Field Description

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 24. Fields in the <cluster_name>-entity-user-operator-certs secret
Field Description

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 25. Fields in the <cluster_name>-cruise-control-certs secret
Field Description

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 26. Fields in the <cluster_name>-kafka-exporter-certs secret
Field Description

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.

14.2.4. 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 Strimzi components and for administrative access if you are intending to issue application certificates without using the User Operator. You can enforce this using Kubernetes role-based access controls, if necessary.

Table 27. Fields in the <cluster_name>-clients-ca secret
Field Description

ca.key

The current private key for the clients CA.
Table 28. Fields in the <cluster_name>-clients-ca-cert secret
Field Description

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.

14.2.5. 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 29. Fields in the user_name secret
Secret name Field within secret Description

<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

14.2.6. 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 Strimzi 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
    # ...

14.2.7. 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 Kubernetes.

If you want to reuse the CA for a new cluster, you can disable