1. Deployment overview

Strimzi simplifies the process of running Apache Kafka within 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

The deployment of Kafka components onto a Kubernetes cluster using Strimzi is highly configurable through the use of custom resources. These resources are created as instances of APIs introduced by Custom Resource Definitions (CRDs), which 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

1.1.2. Performing kubectl operations on custom resources

You can use kubectl commands to retrieve information and perform other operations on Strimzi custom resources. Use kubectl commands, such as get, describe, edit, or delete, to perform operations on resource types. For example, kubectl get kafkatopics retrieves a list of all Kafka topics and kubectl get kafkas retrieves all deployed Kafka clusters.

When referencing resource types, you can use both singular and plural names: kubectl get kafkas gets the same results as kubectl get kafka.

You can also use the short name of the resource. Learning short names can save you time when managing Strimzi. The short name for Kafka is k, so you can also run kubectl get k to list all Kafka clusters.

Listing Kafka clusters
kubectl get k

NAME         DESIRED KAFKA REPLICAS   DESIRED ZK REPLICAS
my-cluster   3                        3
Table 1. Long and short names for each Strimzi resource
Strimzi resource Long name Short name

Kafka

kafka

k

Kafka Node Pool

kafkanodepool

knp

Kafka Topic

kafkatopic

kt

Kafka User

kafkauser

ku

Kafka Connect

kafkaconnect

kc

Kafka Connector

kafkaconnector

kctr

Kafka MirrorMaker

kafkamirrormaker

kmm

Kafka MirrorMaker 2

kafkamirrormaker2

kmm2

Kafka Bridge

kafkabridge

kb

Kafka Rebalance

kafkarebalance

kr
Resource categories

Categories of custom resources can also be used in kubectl commands.

All Strimzi custom resources belong to the category strimzi, so you can use strimzi to get all the Strimzi resources with one command.

For example, running kubectl get strimzi lists all Strimzi custom resources in a given namespace.

Listing all custom resources
kubectl get strimzi

NAME                                   DESIRED KAFKA REPLICAS DESIRED ZK REPLICAS
kafka.kafka.strimzi.io/my-cluster      3                      3

NAME                                   PARTITIONS REPLICATION FACTOR
kafkatopic.kafka.strimzi.io/kafka-apps 3          3

NAME                                   AUTHENTICATION AUTHORIZATION
kafkauser.kafka.strimzi.io/my-user     tls            simple

The kubectl get strimzi -o name command returns all resource types and resource names. The -o name option fetches the output in the type/name format

Listing all resource types and names
kubectl get strimzi -o name

kafka.kafka.strimzi.io/my-cluster
kafkatopic.kafka.strimzi.io/kafka-apps
kafkauser.kafka.strimzi.io/my-user

You can combine this strimzi command with other commands. For example, you can pass it into a kubectl delete command to delete all resources in a single command.

Deleting all custom resources
kubectl delete $(kubectl get strimzi -o name)

kafka.kafka.strimzi.io "my-cluster" deleted
kafkatopic.kafka.strimzi.io "kafka-apps" deleted
kafkauser.kafka.strimzi.io "my-user" deleted

Deleting all resources in a single operation might be useful, for example, when you are testing new Strimzi features.

Querying the status of sub-resources

There are other values you can pass to the -o option. For example, by using -o yaml you get the output in YAML format. Using -o json will return it as JSON.

You can see all the options in kubectl get --help.

One of the most useful options is the JSONPath support, which allows you to pass JSONPath expressions to query the Kubernetes API. A JSONPath expression can extract or navigate specific parts of any resource.

For example, you can use the JSONPath expression {.status.listeners[?(@.name=="tls")].bootstrapServers} to get the bootstrap address from the status of the Kafka custom resource and use it in your Kafka clients.

Here, the command retrieves the bootstrapServers value of the listener named tls:

Retrieving the bootstrap address
kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="tls")].bootstrapServers}{"\n"}'

my-cluster-kafka-bootstrap.myproject.svc:9093

By changing the name condition you can also get the address of the other Kafka listeners.

You can use jsonpath to extract any other property or group of properties from any custom resource.

1.1.3. Strimzi custom resource status information

Status properties provide status information for certain custom resources.

The following table lists the custom resources that provide status information (when deployed) and the schemas that define the status properties.

For more information on the schemas, see the Strimzi Custom Resource API Reference.

Table 2. Custom resources that provide status information
Strimzi resource Schema reference Publishes status information on…​

Kafka

KafkaStatus
ListenerStatus
UsedNodePoolStatus
KafkaAutoRebalanceStatus

The Kafka cluster, its listeners, node pools, and any auto-rebalances on scaling

KafkaNodePool

KafkaNodePoolStatus

The nodes in the node pool, their roles, and the associated Kafka cluster

KafkaTopic

KafkaTopicStatus

Kafka topics in the Kafka cluster

KafkaUser

KafkaUserStatus

Kafka users in the Kafka cluster

KafkaConnect

KafkaConnectStatus

The Kafka Connect cluster and connector plugins

KafkaConnector

KafkaConnectorStatus

KafkaConnector resources

KafkaMirrorMaker2

KafkaMirrorMaker2Status

The Kafka MirrorMaker 2 cluster and internal connectors

KafkaMirrorMaker

KafkaMirrorMakerStatus

The Kafka MirrorMaker cluster

KafkaBridge

KafkaBridgeStatus

The Kafka Bridge

KafkaRebalance

KafkaRebalanceStatus

The status and results of a rebalance

StrimziPodSet

StrimziPodSetStatus

The number of pods: being managed, using the current version, and in a ready state

The status property of a resource provides information on the state of the resource. The status.conditions and status.observedGeneration properties are common to all resources.

status.conditions

Status conditions describe the current state of a resource. Status condition properties are useful for tracking progress related to the resource achieving its desired state, as defined by the configuration specified in its spec. Status condition properties provide the time and reason the state of the resource changed, and details of events preventing or delaying the operator from realizing the desired state.

status.observedGeneration

Last observed generation denotes the latest reconciliation of the resource by the Cluster Operator. If the value of observedGeneration is different from the value of metadata.generation (the current version of the deployment), the operator has not yet processed the latest update to the resource. If these values are the same, the status information reflects the most recent changes to the resource.

The status properties also provide resource-specific information. For example, KafkaStatus provides information on listener addresses, and the ID of the Kafka cluster.

KafkaStatus also provides information on the Kafka and Strimzi versions being used. You can check the values of operatorLastSuccessfulVersion and kafkaVersion to determine whether an upgrade of Strimzi or Kafka has completed

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

Here we see the status properties for a Kafka custom resource.

Kafka custom resource status
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
spec:
  # ...
status:
  clusterId: XP9FP2P-RByvEy0W4cOEUA # (1)
  conditions: # (2)
    - lastTransitionTime: '2023-01-20T17:56:29.396588Z'
      status: 'True'
      type: Ready # (3)
  kafkaMetadataState: KRaft # (4)
  kafkaVersion: 3.9.0 # (5)
  kafkaNodePools: # (6)
    - name: broker
    - name: controller
  listeners: # (7)
    - addresses:
        - host: my-cluster-kafka-bootstrap.prm-project.svc
          port: 9092
      bootstrapServers: 'my-cluster-kafka-bootstrap.prm-project.svc:9092'
      name: plain
    - addresses:
        - host: my-cluster-kafka-bootstrap.prm-project.svc
          port: 9093
      bootstrapServers: 'my-cluster-kafka-bootstrap.prm-project.svc:9093'
      certificates:
        - |
          -----BEGIN CERTIFICATE-----

          -----END CERTIFICATE-----
      name: tls
    - addresses:
        - host: >-
            2054284155.us-east-2.elb.amazonaws.com
          port: 9095
      bootstrapServers: >-
        2054284155.us-east-2.elb.amazonaws.com:9095
      certificates:
        - |
          -----BEGIN CERTIFICATE-----

          -----END CERTIFICATE-----
      name: external3
    - addresses:
        - host: ip-10-0-172-202.us-east-2.compute.internal
          port: 31644
      bootstrapServers: 'ip-10-0-172-202.us-east-2.compute.internal:31644'
      certificates:
        - |
          -----BEGIN CERTIFICATE-----

          -----END CERTIFICATE-----
      name: external4
  observedGeneration: 3 # (8)
  operatorLastSuccessfulVersion: latest # (9)

  1. The Kafka cluster ID.

  2. Status conditions describe the current state of the Kafka cluster.

  3. The Ready condition indicates that the Cluster Operator considers the Kafka cluster able to handle traffic.

  4. Kafka metadata state that shows the mechanism used (KRaft or ZooKeeper) to manage Kafka metadata and coordinate operations.

  5. The version of Kafka being used by the Kafka cluster.

  6. The node pools belonging to the Kafka cluster.

  7. The listeners describe Kafka bootstrap addresses by type.

  8. The observedGeneration value indicates the last reconciliation of the Kafka custom resource by the Cluster Operator.

  9. The version of the operator that successfully completed the last reconciliation.

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

1.1.4. Finding the status of a custom resource

Use kubectl with the status subresource of a custom resource to retrieve information about the resource.

Prerequisites

  • A Kubernetes cluster.

  • The Cluster Operator is running.

Procedure

  • Specify the custom resource and use the -o jsonpath option to apply a standard JSONPath expression to select the status property:

    kubectl get kafka <kafka_resource_name> -o jsonpath='{.status}' | jq

    This expression returns all the status information for the specified custom resource. You can use dot notation, such as status.listeners or status.observedGeneration, to fine-tune the status information you wish to see.

    Using the jq command line JSON parser tool makes it easier to read the output.

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, which is assigned cluster roles that give it permission to create the necessary RBAC resources for Strimzi components. Role bindings associate the cluster roles with the service account.

Kubernetes enforces privilege escalation prevention, meaning the Cluster Operator cannot grant privileges it does not possess, nor can it grant such privileges in a namespace it cannot access. Consequently, the Cluster Operator must have the necessary privileges for all the components it orchestrates.

The Cluster Operator must be able to do the following:

  • Enable the Topic Operator to manage KafkaTopic resources by creating Role and RoleBinding resources in the relevant namespace.

  • Enable the User Operator to manage KafkaUser resources by creating Role and RoleBinding resources in the relevant namespace.

  • Allow Strimzi to discover the failure domain of a Node by creating a ClusterRoleBinding.

When using rack-aware partition assignment, broker pods need to access information about the Node they are running on, such as the Availability Zone in Amazon AWS. Similarly, when using NodePort type listeners, broker pods need to advertise the address of the Node they are running on. Since a Node is a cluster-scoped resource, this access must be granted through a ClusterRoleBinding, not a namespace-scoped RoleBinding.

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 3. 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 4. 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 5. 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 6. 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

1.2.3. Managing pod resources

The StrimziPodSet custom resource is used by Strimzi to create and manage Kafka, Kafka Connect, and MirrorMaker 2 pods. If you are using ZooKeeper, ZooKeeper pods are also created and managed using StrimziPodSet resources.

You must not create, update, or delete StrimziPodSet resources. The StrimziPodSet custom resource is used internally and resources are managed solely by the Cluster Operator. As a consequence, the Cluster Operator must be running properly to avoid the possibility of pods not starting and Kafka clusters not being available.

Note
 Kubernetes Deployment resources are used for creating and managing the pods of other components: Kafka Bridge, Kafka Exporter, Cruise Control, (deprecated) MirrorMaker 1, User Operator and Topic Operator.

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

You can use the 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.

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.

1.4.1. NIST validation

Strimzi is designed to use FIPS-validated cryptographic libraries for secure communication in a FIPS-enabled Kubernetes cluster. However, it’s important to note that while Strimzi can leverage these libraries in a FIPS environment, the underlying Universal Base Images (UBI) used in Strimzi deployments may not inherently include NIST-validated binaries. This means that while Strimzi can leverage cryptographic libraries for FIPS, the specific binaries within the Strimzi container images might not have undergone NIST validation.

For more information about the NIST validation program and validated modules, see Cryptographic Module Validation Program on the NIST website.

1.4.2. 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.

Additional resources

1.5. Document conventions

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. Using Kafka in KRaft mode

KRaft (Kafka Raft metadata) mode replaces Kafka’s dependency on ZooKeeper for cluster management. KRaft mode simplifies the deployment and management of Kafka clusters by bringing metadata management and coordination of clusters into Kafka.

Kafka in KRaft mode is designed to offer enhanced reliability, scalability, and throughput. Metadata operations become more efficient as they are directly integrated. And by removing the need to maintain a ZooKeeper cluster, there’s also a reduction in the operational and security overhead.

To deploy a Kafka cluster in KRaft mode, you must use Kafka and KafkaNodePool custom resources. The Kafka resource using KRaft mode must also have the annotations strimzi.io/kraft: enabled and strimzi.io/node-pools: enabled. For more details and examples, see Deploying a Kafka cluster in KRaft mode.

Through node pool configuration using KafkaNodePool resources, nodes are assigned the role of broker, controller, or both:

  • Controller nodes operate in the control plane to manage cluster metadata and the state of the cluster using a Raft-based consensus protocol.

  • Broker nodes operate in the data plane to manage the streaming of messages, receiving and storing data in topic partitions.

  • Dual-role nodes fulfill the responsibilities of controllers and brokers.

Controllers use a metadata log, stored as a single-partition topic (__cluster_metadata) on every node, which records the state of the cluster. When requests are made to change the cluster configuration, an active (lead) controller manages updates to the metadata log, and follower controllers replicate these updates. The metadata log stores information on brokers, replicas, topics, and partitions, including the state of in-sync replicas and partition leadership. Kafka uses this metadata to coordinate changes and manage the cluster effectively.

Broker nodes act as observers, storing the metadata log passively to stay up-to-date with the cluster’s state. Each node fetches updates to the log independently. If you are using JBOD storage, you can change the volume that stores the metadata log.

Note
 The KRaft metadata version used in the Kafka cluster must be supported by the Kafka version in use. Both versions are managed through the Kafka resource configuration. For more information, see Configuring Kafka in KRaft mode.

In the following example, a Kafka cluster comprises a quorum of controller and broker nodes for fault tolerance and high availability.

KRaft quorums for broker and controller
Figure 1. Example cluster with separate broker and controller nodes

In a typical production environment, use dedicated broker and controller nodes. However, you might want to use nodes in a dual-role configuration for development or testing.

You can use a combination of nodes that combine roles with nodes that perform a single role. In the following example, three nodes perform a dual role and two nodes act only as brokers.

KRaft cluster with nodes that combine roles
Figure 2. Example cluster with dual-role nodes and dedicated broker nodes

2.1. KRaft limitations

KRaft limitations primarily relate to controller scaling, which impacts cluster operations.

2.1.1. Controller scaling

KRaft mode supports two types of controller quorums:

  • Static controller quorums
    In this mode, the number of controllers is fixed, and scaling requires downtime.

  • Dynamic controller quorums
    This mode enables dynamic scaling of controllers without downtime. New controllers join as observers, replicate the metadata log, and eventually become eligible to join the quorum. If a controller being removed is the active controller, it will step down from the quorum only after the new quorum is confirmed.

Scaling is useful not only for adding or removing controllers, but supports the following operations:

  • Renaming a node pool, which involves adding a new node pool with the desired name and deleting the old one.

  • Changing non-JBOD storage, which requires creating a new node pool with the updated storage configuration and removing the old one.

Dynamic controller quorums provide the flexibility to make these operations significantly easier to perform.

2.1.2. Limitations with static controller quorums

Migration between static and dynamic controller quorums is not currently supported by Apache Kafka, though it is expected to be introduced in a future release. All pre-existing KRaft-based Apache Kafka clusters that use static controller quorums must continue using them. To ensure compatibility with existing KRaft-based clusters, Strimzi continues to use static controller quorums as well.

This limitation means dynamic scaling of controller quorums cannot be used to support the following:

  • Adding or removing node pools with controller roles

  • Adding the controller role to an existing node pool

  • Removing the controller role from an existing node pool

  • Scaling a node pool with the controller role

  • Renaming a node pool with the controller role

Static controller quorums also limit operations that require scaling. For example, changing the storage type for a node pool with a controller role is not possible because it involves scaling the controller quorum. For non-JBOD storage, creating a new node pool with the desired storage type, adding it to the cluster, and removing the old one would require scaling operations, which are not supported. In some cases, workarounds are possible. For instance, when modifying node pool roles to combine controller and broker functions, you can add broker roles to controller nodes instead of adding controller roles to broker nodes to avoid controller scaling. However, this approach would require reassigning more data, which may temporarily affect cluster performance.

Once migration is possible, Strimzi plans to assess introducing support for dynamic quorums.

2.2. Migrating to KRaft mode

If you are using ZooKeeper for metadata management in your Kafka cluster, you can migrate to using Kafka in KRaft mode.

During the migration, you install a quorum of controller nodes as a node pool, which replaces ZooKeeper for management of your cluster. You enable KRaft migration in the cluster configuration by applying the strimzi.io/kraft="migration" annotation. After the migration is complete, you switch the brokers to using KRaft and the controllers out of migration mode using the strimzi.io/kraft="enabled" annotation.

Before starting the migration, verify that your environment can support Kafka in KRaft mode, as there are a number of limitations. Note also, the following:

  • Migration is only supported on dedicated controller nodes, not on nodes with dual roles as brokers and controllers.

  • Throughout the migration process, ZooKeeper and controller nodes operate in parallel for a period, requiring sufficient compute resources in the cluster.

  • Once KRaft mode is enabled, rollback to ZooKeeper is not possible. Consider this carefully before proceeding with the migration.

Prerequisites

  • You must be using Strimzi 0.40 or newer with Kafka 3.7.0 or newer. If you are using an earlier version of Strimzi or Apache Kafka, upgrade before migrating to KRaft mode.

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

  • The Kafka cluster deployment uses Kafka node pools.

    If your ZooKeeper-based cluster is already using node pools, it is ready to migrate. If not, you can migrate the cluster to use node pools. To migrate when the cluster is not using node pools, brokers must be contained in a KafkaNodePool resource configuration that is assigned a broker role and has the name kafka. Support for node pools is enabled in the Kafka resource configuration using the strimzi.io/node-pools: enabled annotation.

Important
 Using a single controller with ephemeral storage for migrating to KRaft will not work. During the migration, controller restart will cause loss of metadata synced from ZooKeeper (such as topics and ACLs). In general, migrating an ephemeral-based ZooKeeper cluster to KRaft is not recommended.

In this procedure, the Kafka cluster name is my-cluster, which is located in the my-project namespace. The name of the controller node pool created is controller. The node pool for the brokers is called kafka.

Procedure

  1. For the Kafka cluster, create a node pool with a controller role.

    The node pool adds a quorum of controller nodes to the cluster.

    Example configuration for a controller node pool
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: controller
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - controller
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 20Gi
        deleteClaim: false
    resources:
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"
    Note
    		 For the migration, you cannot use a node pool of nodes that share the broker and controller roles.

  2. Apply the new KafkaNodePool resource to create the controllers.

    Errors related to using controllers in a ZooKeeper-based environment are expected in the Cluster Operator logs. The errors can block reconciliation. To prevent this, perform the next step immediately.

  3. Enable KRaft migration in the Kafka resource by setting the strimzi.io/kraft annotation to migration:

    kubectl annotate kafka my-cluster strimzi.io/kraft="migration" --overwrite
    Enabling KRaft migration
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  namespace: my-project
  annotations:
    strimzi.io/kraft: migration
# ...

    Applying the annotation to the Kafka resource configuration starts the migration.

  4. Check the controllers have started and the brokers have rolled:

    kubectl get pods -n my-project
    Output shows nodes in broker and controller node pools
    NAME                     READY  STATUS   RESTARTS
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-controller-3  1/1    Running  0
my-cluster-controller-4  1/1    Running  0
my-cluster-controller-5  1/1    Running  0
# ...

  5. Check the status of the migration:

    kubectl get kafka my-cluster -n my-project -w
    Updates to the metadata state
    NAME        ...  METADATA STATE
my-cluster  ...  Zookeeper
my-cluster  ...  KRaftMigration
my-cluster  ...  KRaftDualWriting
my-cluster  ...  KRaftPostMigration

    METADATA STATE shows the mechanism used to manage Kafka metadata and coordinate operations. At the start of the migration this is ZooKeeper.

    • ZooKeeper is the initial state when metadata is only stored in ZooKeeper.

    • KRaftMigration is the state when the migration is in progress. The flag to enable ZooKeeper to KRaft migration (zookeeper.metadata.migration.enable) is added to the brokers and they are rolled to register with the controllers. The migration can take some time at this point depending on the number of topics and partitions in the cluster.

    • KRaftDualWriting is the state when the Kafka cluster is working as a KRaft cluster, but metadata are being stored in both Kafka and ZooKeeper. Brokers are rolled a second time to remove the flag to enable migration.

    • KRaftPostMigration is the state when KRaft mode is enabled for brokers. Metadata are still being stored in both Kafka and ZooKeeper.

    The migration status is also represented in the status.kafkaMetadataState property of the Kafka resource.

    Warning
    		 You can roll back to using ZooKeeper from this point. The next step is to enable KRaft. Rollback cannot be performed after enabling KRaft.

  6. When the metadata state has reached KRaftPostMigration, enable KRaft in the Kafka resource configuration by setting the strimzi.io/kraft annotation to enabled:

    kubectl annotate kafka my-cluster strimzi.io/kraft="enabled" --overwrite
    Enabling KRaft migration
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  namespace: my-project
  annotations:
    strimzi.io/kraft: enabled
# ...

  7. Check the status of the move to full KRaft mode:

    kubectl get kafka my-cluster -n my-project -w
    Updates to the metadata state
    NAME        ...  METADATA STATE
my-cluster  ...  Zookeeper
my-cluster  ...  KRaftMigration
my-cluster  ...  KRaftDualWriting
my-cluster  ...  KRaftPostMigration
my-cluster  ...  PreKRaft
my-cluster  ...  KRaft

    • PreKRaft is the state when all ZooKeeper-related resources have been automatically deleted.

    • KRaft is the final state (after the controllers have rolled) when the KRaft migration is finalized.

    Note
    		 Depending on how deleteClaim is configured for ZooKeeper, its Persistent Volume Claims (PVCs) and persistent volumes (PVs) may not be deleted. deleteClaim specifies whether the PVC is deleted when the cluster is uninstalled. The default is false.

  8. Remove any ZooKeeper-related configuration from the Kafka resource.

    Remove the following section:

    • spec.zookeeper

    If present, you can also remove the following options from the .spec.kafka.config section:

    • log.message.format.version

    • inter.broker.protocol.version

    Removing log.message.format.version and inter.broker.protocol.version causes the brokers and controllers to roll again. Removing ZooKeeper properties removes any warning messages related to ZooKeeper configuration being present in a KRaft-operated cluster.

2.2.1. Performing a rollback on the migration

Before the migration is finalized by enabling KRaft in the Kafka resource, and the state has moved to the KRaft state, you can perform a rollback operation as follows:

  1. Apply the strimzi.io/kraft="rollback" annotation to the Kafka resource to roll back the brokers.

    kubectl annotate kafka my-cluster strimzi.io/kraft="rollback" --overwrite
    Rolling back KRaft migration
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  namespace: my-project
  annotations:
    strimzi.io/kraft: rollback
# ...

    The migration process must be in the KRaftPostMigration state to do this. The brokers are rolled back so that they can be connected to ZooKeeper again and the state returns to KRaftDualWriting.

  2. Delete the controllers node pool:

    kubectl delete KafkaNodePool controller -n my-project

  3. Apply the strimzi.io/kraft="disabled" annotation to the Kafka resource to return the metadata state to ZooKeeper.

    kubectl annotate kafka my-cluster strimzi.io/kraft="disabled" --overwrite
    Switching back to using ZooKeeper
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  namespace: my-project
  annotations:
    strimzi.io/kraft: disabled
# ...

3. Deployment methods

You can deploy Strimzi on Kubernetes 1.25 and later using one of the following methods:

Installation method Description

Deployment files (YAML files)

Download the deployment files to manually deploy Strimzi components. For the greatest flexibility, choose this method.

OperatorHub.io

Deploy the Strimzi Cluster operator through the OperatorHub.io, then deploy Strimzi components using custom resources. This method provides a standard configuration and allows you to take advantage of automatic updates.

Helm chart

Use a Helm chart to deploy the Cluster Operator, then deploy Strimzi components using custom resources. Helm charts provide a convenient way to manage the installation of applications.

4. Deployment path

You can configure a deployment where Strimzi manages a single Kafka cluster in the same namespace, suitable for development or testing. Alternatively, Strimzi can manage multiple Kafka clusters across different namespaces in a production environment.

The basic deployment path includes the following steps:

  1. Create a Kubernetes namespace for the Cluster Operator.

  2. Deploy the Cluster Operator based on your chosen deployment method.

  3. Deploy the Kafka cluster, including the Topic Operator and User Operator if desired.

  4. Optionally, deploy additional components:

    • The Topic Operator and User Operator as standalone components, if not deployed with the Kafka cluster

    • Kafka Connect

    • Kafka MirrorMaker

    • Kafka Bridge

    • Metrics monitoring components

The Cluster Operator creates Kubernetes resources such as Deployment, Service, and Pod for each component. The resource names are appended with the name of the deployed component. For example, a Kafka cluster named my-kafka-cluster will have a service named my-kafka-cluster-kafka.

5. Downloading deployment files

To deploy Strimzi components using YAML files, download and extract the latest release archive (strimzi-latest.*) from the GitHub releases page.

The release archive contains sample YAML files for deploying Strimzi components to Kubernetes using kubectl.

Begin by deploying the Cluster Operator from the install/cluster-operator directory to watch a single namespace, multiple namespaces, or all namespaces.

In the install folder, you can also deploy other Strimzi components, including:

  • Strimzi administrator roles (strimzi-admin)

  • Standalone Topic Operator (topic-operator)

  • Standalone User Operator (user-operator)

  • Strimzi Drain Cleaner (drain-cleaner)

The examples folder provides examples of Strimzi custom resources to help you develop your own Kafka configurations.

Note
 Strimzi container images are available through the Container Registry, but we recommend using the provided YAML files for deployment.

6. Preparing for your 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.

6.1. Deployment prerequisites

To deploy Strimzi, you will need the following:

  • A Kubernetes 1.25 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.

6.2. 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.

6.3. 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:latest-kafka-3.8.0

  • quay.io/strimzi/kafka:latest-kafka-3.8.1

  • quay.io/strimzi/kafka:latest-kafka-3.9.0

Strimzi image for running Kafka, including:

  • Kafka Broker

  • Kafka Connect

  • Kafka MirrorMaker

  • ZooKeeper

  • Cruise Control

Operator

  • quay.io/strimzi/operator:latest

Strimzi image for running the operators:

  • Cluster Operator

  • Topic Operator

  • User Operator

  • Kafka Initializer

Kafka Bridge

  • quay.io/strimzi/kafka-bridge:0.31.1

Strimzi image for running the Kafka Bridge

Strimzi Drain Cleaner

  • quay.io/strimzi/drain-cleaner:1.2.0

Strimzi image for running the Strimzi Drain Cleaner

6.4. 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

7. Deploying Strimzi using installation files

Download and use the Strimzi deployment files to deploy Strimzi components to a Kubernetes cluster.

You can deploy Strimzi latest on Kubernetes 1.25 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.

7.1. Deploying the Cluster Operator

The first step for any deployment of Strimzi is to install the Cluster Operator, which is responsible for deploying and managing Kafka clusters within a Kubernetes cluster. A single command applies all the installation files in the install/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 resources:

  • Cluster Operator (Deployment, ConfigMap)

  • Strimzi CRDs (CustomResourceDefinition)

  • RBAC resources (ClusterRole, ClusterRoleBinding, RoleBinding)

  • Service account (ServiceAccount)

Cluster-scoped resources like CustomResourceDefinition, ClusterRole, and ClusterRoleBinding require administrator privileges for installation. Prior to installation, it’s advisable to review the ClusterRole specifications to ensure they do not grant unnecessary privileges.

After installation, the Cluster Operator runs as a regular Deployment to watch for updates of Kafka resources. Any standard (non-admin) Kubernetes user with privileges to access the Deployment can configure it. A cluster administrator can also grant standard users the privileges necessary to manage Strimzi custom 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.

7.1.1. 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.

7.1.2. 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.

7.1.3. 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:latest
        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.

7.1.4. 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:latest
        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.

7.2. 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 are used in the deployment of a Kafka cluster in KRaft (Kafka Raft metadata) mode, and may be used for the deployment of a Kafka cluster with ZooKeeper. Node pools represent a distinct group of Kafka nodes within the 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.

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.

7.2.1. Deploying a Kafka cluster in KRaft mode

This procedure shows how to deploy a Kafka cluster in KRaft mode and associated node pools using the Cluster Operator.

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

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

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

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

kafka/kraft/kafka.yaml

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

kafka/kraft/kafka-ephemeral.yaml

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

kafka/kraft/kafka-single-node.yaml

Deploys a Kafka cluster with a single node.

kafka/kraft/kafka-jbod.yaml

Deploys a Kafka cluster with multiple volumes in each broker node.

In this procedure, we use the example deployment file that deploys a Kafka cluster with one pool of nodes that share the broker and controller roles.

The Kafka resource configuration for each example includes the strimzi.io/node-pools: enabled annotation, which is required when using node pools. Kafka resources using KRaft mode must also have the annotation strimzi.io/kraft: enabled.

The example YAML files specify the latest supported Kafka version and KRaft metadata version used by the Kafka cluster.

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

By default, the example deployment files specify my-cluster as the Kafka cluster name. The name 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.

Procedure

  1. Deploy a KRaft-based Kafka cluster.

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

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

  2. 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

7.2.2. Deploying a ZooKeeper-based Kafka cluster

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 deployment 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.

To deploy a Kafka cluster that uses node pools, the following example YAML file provides the specification to create a Kafka resource and KafkaNodePool resources:

kafka/kafka-with-node-pools.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.

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

The example YAML files specify the latest supported Kafka version and inter-broker protocol version.

Note
 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.
Prerequisites
Before you begin

By default, the example deployment files specify my-cluster as the Kafka cluster name. The name 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.

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

7.2.3. Deploying the Topic Operator using the Cluster Operator

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

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

If you use 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.

7.2.4. 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.

7.2.5. Connecting to ZooKeeper from a terminal

ZooKeeper services are secured with encryption and authentication and are not intended to be used by external applications that are not part of Strimzi.

However, if you want to use CLI tools that require a connection to ZooKeeper, you can use a terminal inside a ZooKeeper pod and connect to localhost:12181 as the ZooKeeper address.

Prerequisites

  • A Kubernetes cluster is available.

  • A Kafka cluster is running.

  • The Cluster Operator is running.

Procedure

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

    For example:

    kubectl exec -ti my-cluster-zookeeper-0 -- bin/zookeeper-shell.sh localhost:12181 ls /

    Be sure to use localhost:12181.

7.2.6. 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.

7.3. 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.

7.3.1. 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

7.3.2. 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).

7.4. 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.

Plugins provide a set of one or more artifacts that define a connector and task implementation for connecting to a given kind of data source. The configuration describes the source input data and target output data to feed into and out of Kafka Connect. The plugins might also contain the libraries and files needed to transform the data.

A Kafka Connect deployment can have one or more plugins, but only one version of each plugin. Plugins for many external systems are available for use with Kafka Connect. You can also create your own plugins.

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.

7.4.1. Building new container images 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 automatically downloads and adds 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.

Rebuilding the container image with new artifacts

A new container image is built automatically when you change the base image (.spec.image) or change the connector plugin artifacts configuration (.spec.build.plugins).

To pull an upgraded base image or to download the latest connector plugin artifacts without changing the KafkaConnect resource, you can trigger a rebuild of the container image associated with the Kafka Connect cluster by applying the annotation strimzi.io/force-rebuild=true to the Kafka Connect StrimziPodSet resource.

The annotation triggers the rebuilding process, fetching any new artifacts for plugins specified in the KafkaConnect custom resource and incorporating them into the container image. The rebuild includes downloads of new plugin artifacts without versions.

Additional resources

7.4.2. Building new container images 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:latest-kafka-3.9.0 as the base image:

    FROM quay.io/strimzi/kafka:latest-kafka-3.9.0
USER root:root
COPY ./my-plugins/ /opt/kafka/plugins/
USER 1001
    Example plugins file
    $ tree ./my-plugins/
./my-plugins/
├── debezium-connector-mongodb
│   ├── bson-<version>.jar
│   ├── CHANGELOG.md
│   ├── CONTRIBUTE.md
│   ├── COPYRIGHT.txt
│   ├── debezium-connector-mongodb-<version>.jar
│   ├── debezium-core-<version>.jar
│   ├── LICENSE.txt
│   ├── mongodb-driver-core-<version>.jar
│   ├── README.md
│   └── # ...
├── debezium-connector-mysql
│   ├── CHANGELOG.md
│   ├── CONTRIBUTE.md
│   ├── COPYRIGHT.txt
│   ├── debezium-connector-mysql-<version>.jar
│   ├── debezium-core-<version>.jar
│   ├── LICENSE.txt
│   ├── mysql-binlog-connector-java-<version>.jar
│   ├── mysql-connector-java-<version>.jar
│   ├── README.md
│   └── # ...
└── debezium-connector-postgres
    ├── CHANGELOG.md
    ├── CONTRIBUTE.md
    ├── COPYRIGHT.txt
    ├── debezium-connector-postgres-<version>.jar
    ├── debezium-core-<version>.jar
    ├── LICENSE.txt
    ├── postgresql-<version>.jar
    ├── protobuf-java-<version>.jar
    ├── README.md
    └── # ...

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

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

  2. Build the container image.

  3. Push your custom image to your container registry.

  4. Point to the new container image.

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

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

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

      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect-cluster
spec: (1)
  #...
  image: my-new-container-image (2)
  config: (3)
    #...

      1. 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

7.4.3. 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 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 7. 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 8. 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.

7.4.4. 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.

7.4.5. 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
# ...

7.4.6. Switching 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.

7.5. 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

7.5.1. 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

7.5.2. 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.

7.5.3. 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.

7.6. Deploying Kafka Bridge

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

7.6.1. 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

7.6.2. Exposing the Kafka Bridge service to your local machine

Use port forwarding to expose the 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.

7.6.3. Accessing the Kafka Bridge outside of Kubernetes

After deployment, the 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.

7.6.4. 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.

7.7. 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.

7.7.1. Deploying the standalone Topic Operator

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

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

  • The standalone Topic Operator deployment files, which are included in the Strimzi deployment files.

  • 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. If you need to configure custom SASL authentication, you can define the necessary authentication properties using the STRIMZI_SASL_CUSTOM_CONFIG_JSON environment variable for the standalone operator. For example, this configuration may be used for accessing a Kafka cluster in a cloud provider with a custom login module like the Amazon MSK Library for AWS Identity and Access Management (aws-msk_iam-auth).

    The property STRIMZI_ALTERABLE_TOPIC_CONFIG defaults to ALL, allowing all .spec.config properties to be set in the KafkaTopic resource. If this setting is not suitable for a managed Kafka service, do as follows:

    • If only a subset of properties is configurable, list them as comma-separated values.

    • If no properties are to be configured, use NONE, which is equivalent to an empty property list.

    Note
    		 Only Kafka configuration properties starting with sasl. can be set with the STRIMZI_SASL_CUSTOM_CONFIG_JSON environment variable.
    Example custom SASL configuration
    # ....
env:
  - name: STRIMZI_SASL_ENABLED
    value: "true"
  - name: STRIMZI_SECURITY_PROTOCOL
    value: SASL_SSL
  - name: STRIMZI_SKIP_CLUSTER_CONFIG_REVIEW # (1)
    value: "true"
  - name: STRIMZI_ALTERABLE_TOPIC_CONFIG # (2)
    value: compression.type, max.message.bytes, message.timestamp.difference.max.ms, message.timestamp.type, retention.bytes, retention.ms
  - name: STRIMZI_SASL_CUSTOM_CONFIG_JSON # (3)
    value: |
      {
        "sasl.mechanism": "AWS_MSK_IAM",
        "sasl.jaas.config": "software.amazon.msk.auth.iam.IAMLoginModule required;",
        "sasl.client.callback.handler.class": "software.amazon.msk.auth.iam.IAMClientCallbackHandler"
      }
  - name: STRIMZI_PUBLIC_CA
    value: "true"
  - name: STRIMZI_TRUSTSTORE_LOCATION
    value: /etc/pki/java/cacerts
  - name: STRIMZI_TRUSTSTORE_PASSWORD
    value: changeit
  - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS
    value: my-kafka-cluster-.kafka-serverless.us-east-1.amazonaws.com:9098
# ...

    1. Disables cluster configuration lookup for managed Kafka services that don’t allow topic configuration changes.

    2. Defines the topic configuration properties that can be updated based on the limitations set by managed Kafka services.

    3. Specifies the SASL properties to be set in JSON format. Only properties starting with sasl. are allowed.

      Example Dockerfile with external jars
      FROM quay.io/strimzi/operator:latest

USER root

RUN mkdir -p ${STRIMZI_HOME}/external-libs
RUN chmod +rx ${STRIMZI_HOME}/external-libs

COPY ./aws-msk-iam-auth-and-dependencies/* ${STRIMZI_HOME}/external-libs/
ENV JAVA_CLASSPATH=${STRIMZI_HOME}/external-libs/*

USER 1001

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

  6. 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.

7.7.2. Deploying the standalone User Operator

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

A standalone deployment can operate with any Kafka cluster.

Standalone deployment files are provided with 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

  • The standalone User Operator deployment files, which are included in the Strimzi deployment files.

  • 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.

8. 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.

9. 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.

10. Feature gates

Strimzi operators use feature gates to enable or disable specific features and functions. Enabling a feature gate alters the behavior of the associated operator, introducing the corresponding feature to your Strimzi deployment.

The purpose of feature gates is to facilitate the trial and testing of a feature before it is fully adopted. The state (enabled or disabled) of a feature gate may vary by default, depending on its maturity level.

As a feature gate graduates and reaches General Availability (GA), it transitions to an enabled state by default and becomes a permanent part of the Strimzi deployment. A feature gate at the GA stage cannot be disabled.

The supported feature gates are applicable to all Strimzi operators. While a particular feature gate might be used by one operator and ignored by the others, it can still be configured in all operators. When deploying the User Operator and Topic Operator within the context of the`Kafka` custom resource, the Cluster Operator automatically propagates the feature gates configuration to them. When the User Operator and Topic Operator are deployed standalone, without a Cluster Operator available to configure the feature gates, they must be directly configured within their deployments.

10.1. Graduated feature gates (GA)

Graduated feature gates have reached General Availability (GA) and are permanently enabled features.

10.1.1. ControlPlaneListener feature gate

The ControlPlaneListener feature gate separates listeners for data replication and coordination:

  • 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 a replication listener on port 9091.

Important
 With the ControlPlaneListener feature gate permanently enabled, direct upgrades or downgrades between Strimzi 0.22 and earlier and Strimzi 0.32 and newer are not possible. You must 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.

10.1.2. ServiceAccountPatching feature gate

The ServiceAccountPatching feature gate ensures that 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.

10.1.3. UseStrimziPodSets feature gate

The UseStrimziPodSets feature gate introduced the StrimziPodSet custom resource for managing Kafka and ZooKeeper pods, replacing the use of Kubernetes StatefulSet resources.

Important
 With the UseStrimziPodSets feature gate permanently enabled, direct downgrades from Strimzi 0.35 and newer to Strimzi 0.27 or earlier are not possible. You must first downgrade through one of the Strimzi versions in-between, disable the UseStrimziPodSets feature gate, and then downgrade to Strimzi 0.27 or earlier.

10.1.4. StableConnectIdentities feature gate

The StableConnectIdentities feature gate introduced the StrimziPodSet custom resource for managing Kafka Connect and Kafka MirrorMaker 2 pods, replacing the use of Kubernetes Deployment resources.

StrimziPodSet resources give the pods stable names and stable addresses, which do not change during rolling upgrades, replacing the use of Kubernetes Deployment resources.

Important
 With the StableConnectIdentities feature gate permanently enabled, direct downgrades from Strimzi 0.39 and newer to Strimzi 0.33 or earlier are not possible. You must first downgrade through one of the Strimzi versions in-between, disable the StableConnectIdentities feature gate, and then downgrade to Strimzi 0.33 or earlier.

10.1.5. KafkaNodePools feature gate

The KafkaNodePools feature gate introduced 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 using the .spec.roles property. When used with a ZooKeeper-based Apache Kafka cluster, it must be set to the broker role. When used with a KRaft-based Apache Kafka cluster, 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. The Kafka resource configuration must also include the strimzi.io/node-pools: enabled annotation, which is required when using node pools.

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

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. For more information, see the instructions for reversing a migration to node pools.

10.1.6. UnidirectionalTopicOperator feature gate

The UnidirectionalTopicOperator feature gate introduced 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.

10.1.7. UseKRaft feature gate

The UseKRaft feature gate introduced the KRaft (Kafka Raft metadata) mode for running Apache Kafka clusters 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. For more information on using KRraft, see Using Kafka in KRaft mode.

10.2. Stable feature gates (Beta)

Stable feature gates have reached a beta level of maturity, and are generally enabled by default for all users. Stable feature gates are production-ready, but they can still be disabled.

10.2.1. ContinueReconciliationOnManualRollingUpdateFailure feature gate

The ContinueReconciliationOnManualRollingUpdateFailure feature gate has a default state of enabled.

The ContinueReconciliationOnManualRollingUpdateFailure feature gate allows the Cluster Operator to continue a reconciliation if the manual rolling update of the operands fails. It applies to the following operands that support manual rolling updates using the strimzi.io/manual-rolling-update annotation:

  • ZooKeeper

  • Kafka

  • Kafka Connect

  • Kafka MirrorMaker 2

Continuing the reconciliation after a manual rolling update failure allows the operator to recover from various situations that might prevent the update from succeeding. For example, a missing Persistent Volume Claim (PVC) or Persistent Volume (PV) might cause the manual rolling update to fail. However, the PVCs and PVs are created only in a later stage of the reconciliation. By continuing the reconciliation after this failure, the process can recreate the missing PVC or PV and recover.

The ContinueReconciliationOnManualRollingUpdateFailure feature gate is used by the Cluster Operator. It is ignored by the User and Topic Operators.

Disabling the ContinueReconciliationOnManualRollingUpdateFailure feature gate

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

10.3. Early access feature gates (Alpha)

Early access feature gates have not yet reached the beta stage, and are disabled by default. An early access feature gate provides an opportunity for assessment before its functionality is permanently incorporated into Strimzi. Currently, there are no alpha level feature gates.

10.4. Enabling feature gates

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

10.5. 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 KafkaNodePools feature gate moved to GA stage in Strimzi 0.41. It is now permanently enabled and cannot be disabled. To use KafkaNodePool resources, you still need to use the strimzi.io/node-pools: enabled annotation on the Kafka custom resources.

  • The UnidirectionalTopicOperator feature gate moved to GA stage in Strimzi 0.41. It is now permanently enabled and cannot be disabled.

  • The UseKRaft feature gate moved to GA stage in Strimzi 0.42. It is now permanently enabled and cannot be disabled. To use KRaft (ZooKeeper-less Apache Kafka), you still need to use the strimzi.io/kraft: enabled annotation on the Kafka custom resources or migrate from an existing ZooKeeper-based cluster.

  • The ContinueReconciliationOnManualRollingUpdateFailure feature was introduced in Strimzi 0.41 and moved to beta stage in Strimzi 0.44.0. It is now enabled by default, but can be disabled if needed.

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 9. 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

0.40

0.42

StableConnectIdentities

0.34

0.37

0.39

KafkaNodePools

0.36

0.39

0.41

UnidirectionalTopicOperator

0.36

0.39

0.41

ContinueReconciliationOnManualRollingUpdateFailure

0.41

0.44

0.47 (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 10. 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

11. 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.

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 use configuration to manage your instances or modify your deployment to introduce additional features. New features are sometimes introduced through feature gates, which are controlled through operator configuration.

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

Important Kafka configuration options

Through configuration of the Kafka resource, you can introduce the following:

  • Data storage

  • Rack awareness

  • Listeners for authenticated client access to the Kafka cluster

  • Topic Operator for managing Kafka topics

  • User Operator for managing Kafka users (clients)

  • Cruise Control for cluster rebalancing

  • Kafka Exporter for collecting lag metrics

Use KafkaNodePool resources to configure distinct groups of nodes within a Kafka cluster.

Common configuration

Common configuration is configured independently for each component, such as the following:

  • Bootstrap servers for host/port connection to a Kafka cluster

  • Metrics configuration

  • Healthchecks and liveness probes

  • Resource limits and requests (CPU/Memory)

  • Logging frequency

  • JVM options for maximum and minimum memory allocation

  • Adding additional volumes and volume mounts

Config maps to centralize configuration

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.

TLS certificate management

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.

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:

Applying changes to a resource configuration file
kubectl apply -f <kafka_configuration_file>
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.

11.1. Using example configuration files

Further enhance your deployment by incorporating additional supported configuration. Example configuration files are included in the Strimzi deployment files. 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)
│   └── kraft (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. KafkaMirrorMaker and KafkaMirrorMaker2 custom resource configurations for a deployment of MirrorMaker. Includes example configuration for replication policy and synchronization frequency.

  5. Metrics configuration, including Prometheus installation and Grafana dashboard files.

  6. Kafka and KafkaNodePool custom resource configurations for a deployment of Kafka clusters that use ZooKeeper mode. Includes example configuration for an ephemeral or persistent single or multi-node deployment.

  7. Kafka and KafkaNodePool configurations for a deployment of Kafka clusters that use KRaft (Kafka Raft metadata) mode.

  8. Kafka and KafkaRebalance configurations for deploying and using Cruise Control to manage clusters. Kafka configuration examples enable auto-rebalancing on scaling events and set default optimization goals. KakaRebalance configuration examples set proposal-specific optimization goals and generate optimization proposals in various supported modes.

  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.

11.2. Configuring Kafka in KRaft mode

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

As well as configuring Kafka, you can add configuration for Strimzi operators.

The KRaft metadata version (.spec.kafka.metadataVersion) must be a version supported by the Kafka version (spec.kafka.version). If the metadata version 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.

Kafka clusters operating in KRaft mode also use node pools. The following must be specified in the node pool configuration:

  • Roles assigned to each node within the Kafka cluster

  • Number of replica nodes used

  • Storage specification for the nodes

Other optional properties may also be set in node pools.

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

Example Kafka custom resource configuration
# Basic configuration (required)
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
# Deployment specifications
spec:
  kafka:
    # Listener configuration (required)
    listeners: # (1)
      - name: plain # (2)
        port: 9092 # (3)
        type: internal # (4)
        tls: false # (5)
        configuration:
          useServiceDnsDomain: true # (6)
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication: # (7)
          type: tls
      - name: external1 # (8)
        port: 9094
        type: route
        tls: true
        configuration:
          brokerCertChainAndKey: # (9)
            secretName: my-secret
            certificate: my-certificate.crt
            key: my-key.key
    # Kafka version (recommended)
    version: 3.9.0 # (10)
    # KRaft metadata version (recommended)
    metadataVersion: 3.9 # (11)
    # Kafka configuration (recommended)
    config: # (12)
      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
    # Resources requests and limits (recommended)
    resources: # (13)
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"
    # Logging configuration (optional)
    logging: # (14)
      type: inline
      loggers:
        kafka.root.logger.level: INFO
    # Readiness probe (optional)
    readinessProbe: # (15)
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # Liveness probe (optional)
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # JVM options (optional)
    jvmOptions: # (16)
      -Xms: 8192m
      -Xmx: 8192m
    # Custom image (optional)
    image: my-org/my-image:latest # (17)
    # Authorization (optional)
    authorization: # (18)
      type: simple
    # Rack awareness (optional)
    rack: # (19)
      topologyKey: topology.kubernetes.io/zone
    # Metrics configuration (optional)
    metricsConfig: # (20)
      type: jmxPrometheusExporter
      valueFrom:
        configMapKeyRef: # (21)
          name: my-config-map
          key: my-key
  # Entity Operator (recommended)
  entityOperator: # (22)
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalMs: 60000
      # Resources requests and limits (recommended)
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
      # Logging configuration (optional)
      logging: # (23)
        type: inline
        loggers:
          rootLogger.level: INFO
    userOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalMs: 60000
      # Resources requests and limits (recommended)
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
      # Logging configuration (optional)
      logging: # (24)
        type: inline
        loggers:
          rootLogger.level: INFO
  # Kafka Exporter (optional)
  kafkaExporter: # (25)
    # ...
  # Cruise Control (optional)
  cruiseControl: # (26)
    # ...

  1. 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.

  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. 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).

  5. Enables or disables TLS encryption for each listener. For route and ingress type listeners, TLS encryption must always be enabled by setting it to true.

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

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

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

  9. 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.

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

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

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

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

  14. 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.

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

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

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

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

  19. 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.

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

  21. 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.

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

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

  24. Specified User Operator loggers and log levels.

  25. 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.

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

11.2.1. Setting throughput and storage limits on brokers

This procedure describes how to set throughput and storage limits on brokers in your Kafka cluster. Enable a quota plugin and configure limits using quotas properties in the Kafka resource.

There are two types of quota plugins available:

  • The strimzi type enables the Strimzi Quotas plugin.

  • The kafka type enables the built-in Kafka plugin.

Only one quota plugin can be enabled at a time. The built-in kafka plugin is enabled by default. Enabling the strimzi plugin automatically disables the built-in plugin.

strimzi plugin

The strimzi plugin provides storage utilization quotas and dynamic distribution of throughput limits.

  • Storage quotas throttle Kafka producers based on disk storage utilization. Limits can be specified in bytes (minAvailableBytesPerVolume) or percentage (minAvailableRatioPerVolume) of available disk space, applying to each disk individually. When any broker in the cluster exceeds the configured disk threshold, clients are throttled to prevent disks from filling up too quickly and exceeding capacity.

  • A total throughput limit is distributed dynamically across all clients. For example, if you set a 40 MBps producer byte-rate threshold, the distribution across two producers is not static. If one producer is using 10 MBps, the other can use up to 30 MBps.

  • Specific users (clients) can be excluded from the restrictions.

Note
 With the strimzi plugin, you see only aggregated quota metrics, not per-client metrics.
kafka plugin

The kafka plugin applies throughput limits on a per-user, per-broker basis and includes additional CPU and operation rate limits.

  • Limits are applied per user and per broker. For example, setting a 20 MBps producer byte-rate threshold limits each user to 20 MBps on a per-broker basis across all producer connections for that user. There is no total throughput limit as there is in the strimzi plugin. Limits can be overridden by user-specific quota configurations.

  • CPU utilization limits for each client can be set as a percentage of the network threads and I/O threads on a per-broker basis.

  • The number of concurrent partition creation and deletion operations (mutations) allowed per second can be set on a per-broker basis.

When using the default Kafka quotas plugin, the default quotas (if set) are applied to all users. This includes internal users such as the Topic Operator and Cruise Control, which may impact their operations. To avoid unduly limiting internal users, consider tuning the quotas effectively.

For example, a quota automatically applied to the Topic Operator by the Kafka quotas plugin could constrain the controller mutation rate, potentially throttling topic creation or deletion operations. Therefore, it is important to understand the minimal quotas required by the Topic Operator to function correctly and explicitly set appropriate quotas to avoid such issues. Monitoring relevant controller and broker metrics can help track and optimize the rate of operations on topics. Cruise Control and its metrics reporter also require sufficient produce and fetch rates to conduct rebalances, depending on the scale and configuration of the Kafka cluster. To prevent issues for Cruise Control, you might start with a rate of at least 1 KB/s for its producers and consumers in small clusters, such as three brokers with moderate traffic, and adjust as needed for larger or more active clusters.

Prerequisites

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

Procedure

  1. Add the plugin configuration to the quotas section of the Kafka resource.

    Example strimzi plugin configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    quotas:
      type: strimzi
      producerByteRate: 1000000 # (1)
      consumerByteRate: 1000000 # (2)
      minAvailableBytesPerVolume: 500000000000 # (3)
      excludedPrincipals: # (4)
        - my-user

    1. Sets a producer byte-rate threshold of 1 MBps.

    2. Sets a consumer byte-rate threshold of 1 MBps.

    3. Sets an available bytes limit for storage of 500 GB.

    4. Excludes my-user from the restrictions.

    minAvailableBytesPerVolume and minAvailableRatioPerVolume are mutually exclusive. Only configure one of these parameters.

    Example kafka plugin configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    quotas:
      type: kafka
      producerByteRate: 1000000
      consumerByteRate: 1000000
      requestPercentage: 55 # (1)
      controllerMutationRate: 50 # (2)

    1. Sets the CPU utilization limit to 55%.

    2. Sets the controller mutation rate to 50 operations per second.

  2. Apply the changes to the Kafka configuration.

Note
 Additional options can be configured in the spec.kafka.config section. The full list of supported options can be found in the plugin documentation.
Additional resources

11.2.2. Deleting Kafka nodes using annotations

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

Warning
 Deleting a PersistentVolumeClaim can cause permanent data loss and the availability of your cluster cannot be guaranteed. The following procedure should only be performed if you have encountered storage issues.
Prerequisites

  • A running Cluster Operator

Procedure

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

    Kafka broker pods are named <cluster_name>-kafka-<index_number>, where <index_number> starts at zero and ends at the total number of replicas minus one. For example, my-cluster-kafka-0.

  2. Use kubectl annotate to annotate the Pod resource in Kubernetes:

    kubectl annotate pod <cluster_name>-kafka-<index_number> strimzi.io/delete-pod-and-pvc="true"

  3. Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.

11.3. Configuring Kafka with ZooKeeper

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

As well as configuring Kafka, you can add configuration for ZooKeeper and the Strimzi operators. The configuration options for Kafka and the Strimzi operators are the same as when using Kafka in KRaft mode. For descriptions of the properties, see Configuring Kafka in KRaft mode.

The inter-broker protocol version (inter.broker.protocol.version) must be a version supported by the Kafka version (spec.kafka.version). If the inter-broker protocol version is not set in the configuration, the Cluster Operator updates the version to the default for the Kafka version used.

If you are also using node pools, the following must be specified in the node pool configuration:

  • Roles assigned to each node within the Kafka cluster

  • Number of replica nodes used

  • Storage specification for the nodes

If set in the node pool configuration, the equivalent configuration in the Kafka resource, such as spec.kafka.replicas, is not required. Other optional properties may also be set in node pools.

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

Example Kafka custom resource configuration when using ZooKeeper
# Basic configuration (required)
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
# Deployment specifications
spec:
  # Kafka configuration (required)
  kafka:
    # Replicas (required)
    replicas: 3
    # Listener configuration (required)
    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
    # Storage configuration (required)
    storage:
      type: persistent-claim
      size: 10000Gi
    # Kafka version (recommended)
    version: 3.9.0
    # Kafka configuration (recommended)
    config:
      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: "3.9"
    # Resources requests and limits (recommended)
    resources:
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"
    # Logging configuration (optional)
    logging:
      type: inline
      loggers:
        kafka.root.logger.level: INFO
    # Readiness probe (optional)
    readinessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # Liveness probe (optional)
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # JVM options (optional)
    jvmOptions:
      -Xms: 8192m
      -Xmx: 8192m
    # Custom image (optional)
    image: my-org/my-image:latest
    # Authorization (optional)
    authorization:
      type: simple
    # Rack awareness (optional)
    rack:
      topologyKey: topology.kubernetes.io/zone
    # Metrics configuration (optional)
    metricsConfig:
      type: jmxPrometheusExporter
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: my-key
    # ...
  # ZooKeeper configuration (required)
  zookeeper: # (1)
    # Replicas (required)
    replicas: 3 # (2)
    # Storage configuration (required)
    storage: # (3)
      type: persistent-claim
      size: 1000Gi
    # Resources requests and limits (recommended)
    resources: # (4)
      requests:
        memory: 8Gi
        cpu: "2"
      limits:
        memory: 8Gi
        cpu: "2"
    # Logging configuration (optional)
    logging: # (5)
      type: inline
      loggers:
        zookeeper.root.logger: INFO
    # JVM options (optional)
    jvmOptions: # (6)
      -Xms: 4096m
      -Xmx: 4096m
    # Metrics configuration (optional)
    metricsConfig: # (7)
      type: jmxPrometheusExporter
      valueFrom:
        configMapKeyRef: # (8)
          name: my-config-map
          key: my-key
    # ...
  # Entity operator (recommended)
  entityOperator:
    topicOperator:
      # Resources requests and limits (recommended)
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
      # Logging configuration (optional)
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
    userOperator:
      # Resources requests and limits (recommended)
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
      # Logging configuration (optional)
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
  # Kafka Exporter (optional)
  kafkaExporter:
    # ...
  # Cruise Control (optional)
  cruiseControl:
    # ...

  1. ZooKeeper-specific configuration contains properties similar to the Kafka configuration.

  2. 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.

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

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

  5. ZooKeeper loggers and log levels.

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

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

  8. 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.

11.3.1. 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 11. 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.

11.3.2. Deleting ZooKeeper nodes using annotations

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

Warning
 Deleting a PersistentVolumeClaim can cause permanent data loss and the availability of your cluster cannot be guaranteed. The following procedure should only be performed if you have encountered storage issues.
Prerequisites

  • A running Cluster Operator

Procedure

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

    ZooKeeper pods are named <cluster_name>-zookeeper-<index_number>, where <index_number> starts at zero and ends at the total number of replicas minus one. For example, my-cluster-zookeeper-0.

  2. Use kubectl annotate to annotate the Pod resource in Kubernetes:

    kubectl annotate pod <cluster_name>-zookeeper-<index_number> strimzi.io/delete-pod-and-pvc="true"

  3. Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.

11.4. 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 relationship between Kafka and KafkaNodePool resources is as follows:

  • Kafka resources represent the configuration for all nodes in a Kafka cluster.

  • KafkaNodePool resources represent 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.

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

Example configuration for a node pool in a cluster using KRaft mode
# Basic configuration (required)
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: kraft-dual-role # (1)
  labels:
    strimzi.io/cluster: my-cluster # (2)
# Node pool specifications
spec:
  # Replicas (required)
  replicas: 3 # (3)
  # Roles (required)
  roles: # (4)
    - controller
    - broker
  # Storage configuration (required)
  storage: # (5)
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  # Resources requests and limits (recommended)
  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.

11.4.1. 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-

11.4.2. Impact on racks when moving nodes from node pools

If rack awareness is enabled on a Kafka cluster, replicas can be spread across different racks, data centers, or availability zones. When moving nodes from node pools, consider the implications on the cluster topology, particularly regarding rack awareness. Removing specific pods from node pools, especially out of order, may break the cluster topology or cause an imbalance in distribution across racks. An imbalance can impact both the distribution of nodes themselves and the partition replicas within the cluster. An uneven distribution of nodes and partitions across racks can affect the performance and resilience of the Kafka cluster.

Plan the removal of nodes strategically to maintain the required balance and resilience across racks. Use the strimzi.io/remove-node-ids annotation to move nodes with specific IDs with caution. Ensure that configuration to spread partition replicas across racks and for clients to consume from the closest replicas is not broken.

Tip
 Use Cruise Control and the KafkaRebalance resource with the RackAwareGoal to make sure that replicas remain distributed across different racks.

11.4.3. Adding nodes to a node pool

This procedure describes how to scale up a node pool to add new nodes. Currently, scale up is only possible for broker-only node pools containing nodes that run as dedicated brokers.

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 ready (1/1).

    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.

    • If auto-rebalancing is enabled, partitions are reassigned to new nodes automatically, so you can skip this step.

    • If auto-rebalancing is not enabled, use the Cruise Control add-brokers mode to move partition replicas from existing brokers to the newly added brokers.

      Using Cruise Control to reassign partition replicas
      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  # ...
spec:
  mode: add-brokers
  brokers: [3]

      We are reassigning partitions to node my-cluster-pool-a-3. The reassignment can take some time depending on the number of topics and partitions in the cluster.

11.4.4. Removing nodes from a node pool

This procedure describes how to scale down a node pool to remove nodes. Currently, scale down is only possible for broker-only node pools containing nodes that run as dedicated brokers.

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.

    • If auto-rebalancing is enabled, partitions are moved off brokers that are going to be removed automatically, so you can skip this step.

    • If auto-rebalancing is not enabled, use the Cruise Control remove-brokers mode to move partition replicas off the brokers that are going to be removed.

      Using Cruise Control to reassign partition replicas
      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  # ...
spec:
  mode: remove-brokers
  brokers: [3]

      We are reassigning partitions from node my-cluster-pool-a-3. The reassignment can take some time depending on the number of topics and partitions in the cluster.

  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

11.4.5. 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

Currently, scaling is only possible for broker-only node pools containing nodes that run as dedicated brokers.

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 ready (1/1).

    kubectl get pods -n <my_cluster_operator_namespace>
    Output shows four Kafka nodes in the source and target node pools
    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-7  1/1    Running  0
my-cluster-pool-b-2  1/1    Running  0
my-cluster-pool-b-3  1/1    Running  0
my-cluster-pool-b-5  1/1    Running  0
my-cluster-pool-b-6  1/1    Running  0

    Node IDs are appended to the name of the node on creation. We add node my-cluster-pool-a-7, which has a node ID of 7.

    If auto-rebalancing is enabled, partitions are reassigned to new nodes and moved off brokers that are going to be removed automatically, so you can skip the next step.

  3. If auto-rebalancing is not enabled, reassign partitions before decreasing the number of nodes in the source node pool.

    Use the Cruise Control remove-brokers mode to move partition replicas off the brokers that are going to be removed.

    Using Cruise Control to reassign partition replicas
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  # ...
spec:
  mode: remove-brokers
  brokers: [6]

    We are reassigning partitions from node my-cluster-pool-b-6. The reassignment can take some time depending on the number of topics and partitions in the cluster.

  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 (6) within the 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-5  1/1    Running  0

11.4.6. Changing node pool roles

Node pools can be used with Kafka clusters that operate in KRaft mode (using Kafka Raft metadata) or use ZooKeeper for metadata 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.

In certain circumstances you might want to change the roles assigned to a node pool. For example, you may have a node pool that contains nodes that perform dual broker and controller roles, and then decide to split the roles between two node pools. In this case, you create a new node pool with nodes that act only as brokers, and then reassign partitions from the dual-role nodes to the new brokers. You can then switch the old node pool to a controller-only role.

You can also perform the reverse operation by moving from node pools with controller-only and broker-only roles to a node pool that contains nodes that perform dual broker and controller roles. In this case, you add the broker role to the existing controller-only node pool, reassign partitions from the broker-only nodes to the dual-role nodes, and then delete the broker-only node pool.

When removing broker roles in the node pool configuration, keep in mind that Kafka does not automatically reassign partitions. Before removing the broker role, ensure that nodes changing to controller-only roles do not have any assigned partitions. If partitions are assigned, the change is prevented. No replicas must be left on the node before removing the broker role. The best way to reassign partitions before changing roles is to apply a Cruise Control optimization proposal in remove-brokers mode. For more information, see Generating optimization proposals.

11.4.7. Transitioning to separate broker and controller roles

This procedure describes how to transition to using node pools with separate roles. If your Kafka cluster is using a node pool with combined controller and broker roles, you can transition to using two node pools with separate roles. To do this, rebalance the cluster to move partition replicas to a node pool with a broker-only role, and then switch the old node pool to a controller-only role.

In this procedure, we start with node pool pool-a, which has controller and broker roles:

Dual-role node pool
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-a
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - controller
    - broker
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 20Gi
        deleteClaim: false
  # ...

The node pool has three nodes:

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

Each node performs a combined role of broker and controller. We create a second node pool called pool-b, with three nodes that act as brokers only.

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 node pool with a broker role.

    Example node pool configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-b
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  # ...

    The new node pool also has three nodes. If you already have a broker-only node pool, you can skip this step.

  2. Apply the new KafkaNodePool resource to create the brokers.

  3. Check the status of the deployment and wait for the pods in the node pool to be created and ready (1/1).

    kubectl get pods -n <my_cluster_operator_namespace>
    Output shows pods running in two node pools
    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-b-3  1/1    Running  0
my-cluster-pool-b-4  1/1    Running  0
my-cluster-pool-b-5  1/1    Running  0

    Node IDs are appended to the name of the node on creation.

  4. Use the Cruise Control remove-brokers mode to reassign partition replicas from the dual-role nodes to the newly added brokers.

    Using Cruise Control to reassign partition replicas
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  # ...
spec:
  mode: remove-brokers
  brokers: [0, 1, 2]

    The reassignment can take some time depending on the number of topics and partitions in the cluster.

    Note
    		 If nodes changing to controller-only roles have any assigned partitions, the change is prevented. The status.conditions of the Kafka resource provide details of events preventing the change.

  5. Remove the broker role from the node pool that originally had a combined role.

    Dual-role nodes switched to controllers
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-a
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - controller
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 20Gi
        deleteClaim: false
  # ...

  6. Apply the configuration change so that the node pool switches to a controller-only role.

11.4.8. Transitioning to dual-role nodes

This procedure describes how to transition from separate node pools with broker-only and controller-only roles to using a dual-role node pool. If your Kafka cluster is using node pools with dedicated controller and broker nodes, you can transition to using a single node pool with both roles. To do this, add the broker role to the controller-only node pool, rebalance the cluster to move partition replicas to the dual-role node pool, and then delete the old broker-only node pool.

In this procedure, we start with two node pools pool-a, which has only the controller role and pool-b which has only the broker role:

Single role node pools
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-a
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - controller
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  # ...
---
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-b
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  # ...

The Kafka cluster has six nodes:

Kafka nodes in the node pools
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-b-3  1/1    Running  0
my-cluster-pool-b-4  1/1    Running  0
my-cluster-pool-b-5  1/1    Running  0

The pool-a nodes perform the role of controller. The pool-b nodes perform the role of broker.

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. Edit the node pool pool-a and add the broker role to it.

    Example node pool configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-a
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - controller
    - broker
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  # ...

  2. Check the status and wait for the pods in the node pool to be restarted and ready (1/1).

    kubectl get pods -n <my_cluster_operator_namespace>
    Output shows pods running in two node pools
    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-b-3  1/1    Running  0
my-cluster-pool-b-4  1/1    Running  0
my-cluster-pool-b-5  1/1    Running  0

    Node IDs are appended to the name of the node on creation.

  3. Use the Cruise Control remove-brokers mode to reassign partition replicas from the broker-only nodes to the dual-role nodes.

    Using Cruise Control to reassign partition replicas
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  # ...
spec:
  mode: remove-brokers
  brokers: [3, 4, 5]

    The reassignment can take some time depending on the number of topics and partitions in the cluster.

  4. Remove the pool-b node pool that has the old broker-only nodes.

    kubectl delete kafkanodepool pool-b -n <my_cluster_operator_namespace>

11.4.9. 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 preserve cluster data and the names of its nodes and resources, the node pool name must be kafka, and the strimzi.io/cluster label matches the Kafka resource name. 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.

  5. Remove the replicated properties from the Kafka custom resource. When the KafkaNodePool resource is in use, you can remove the properties that you copied to the KafkaNodePool resource, such as the .spec.kafka.replicas and .spec.kafka.storage properties.

Reversing the migration

To revert to managing Kafka nodes using only Kafka custom resources:

  1. If you have multiple node pools, consolidate them into a single KafkaNodePool named kafka with node IDs from 0 to N (where N is the number of replicas).

  2. Ensure that the .spec.kafka configuration in the Kafka resource matches the KafkaNodePool configuration, including storage, resources, and replicas.

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

  4. Delete the Kafka node pool named kafka.

11.5. Configuring Kafka storage

Strimzi supports different Kafka storage options. You can choose between the following basic types:

Ephemeral storage

Ephemeral storage is temporary and only persists while a pod is running. When a pod is deleted, the data is lost, though data can be recovered in a highly available environment. Due to its transient nature, ephemeral storage is only recommended for development and testing environments.

Persistent storage

Persistent storage retains data across pod restarts and system disruptions, making it ideal for production environments.

JBOD (Just a Bunch of Disks) storage allows you to configure your Kafka cluster to use multiple disks or volumes as ephemeral or persistent storage.

JBOD storage (multiple volumes)

When specifying JBOD storage, you must still decide between using ephemeral or persistent volumes for each disk. Even if you start with only one volume, using JBOD allows for future scaling by adding more volumes as needed, and that is why it is always recommended.

Note
 Persistent, ephemeral, and JBOD storage types cannot be changed after a Kafka cluster is deployed. However, you can add or remove volumes of different types from the JBOD storage. You can also create and migrate to node pools with new storage specifications.
Tiered storage (advanced)

Tiered storage, currently available as an early access feature, provides additional flexibility for managing Kafka data by combining different storage types with varying performance and cost characteristics. It allows Kafka to offload older data to cheaper, long-term storage (such as object storage) while keeping recent, frequently accessed data on faster, more expensive storage (such as block storage).

Tiered storage is an add-on capability. After configuring storage (ephemeral, persistent, or JBOD) for Kafka nodes, you can configure tiered storage at the cluster level and enable it for specific topics using the remote.storage.enable topic-level configuration.

11.5.1. Storage considerations

Efficient data storage is essential for Strimzi to operate effectively, and block storage is strongly recommended. Strimzi has been tested only with block storage, and file storage solutions like NFS are not guaranteed to work.

Common block storage types supported by Kubernetes include:

  • Cloud-based block storage solutions:

    • Amazon EBS (for AWS)

    • Azure Disk Storage (for Microsoft Azure)

    • Persistent Disk (for Google Cloud)

  • Persistent storage (for bare metal deployments) using local persistent volumes

  • Storage Area Network (SAN) volumes accessed by protocols like Fibre Channel or iSCSI

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

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.

Note
 Replicated storage is not required, as Kafka provides built-in data replication.

11.5.2. Configuring Kafka storage in KRaft mode

Use the storage properties of the KafkaNodePool custom resource to configure storage for a deployment of Kafka in KRaft mode.

Configuring ephemeral storage

To use ephemeral storage, specify ephemeral as the storage type.

Example configuration for ephemeral storage
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: my-node-pool
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker
  storage:
    type: ephemeral
  # ...

Ephemeral storage uses emptyDir volumes, which are created when a pod is assigned to a node. You can limit the size of the emptyDir volume with the sizeLimit property.

The ephemeral volume used by Kafka brokers for log directories is mounted at /var/lib/kafka/data/kafka-log<pod_id>.

Important
 Ephemeral storage is not suitable for Kafka topics with a replication factor of 1.

For more information on ephemeral storage configuration options, see the EphemeralStorage schema reference.

Configuring persistent storage

To use persistent storage, specify one of the following as the storage type:

  • persistent-claim for a single persistent volume

  • jbod for multiple persistent volumes in a Kafka cluster (Recommended for Kafka in a production environment)

Example configuration for persistent storage
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: my-node-pool
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker
  storage:
    type: persistent-claim
    size: 500Gi
    deleteClaim: true
  # ...

Strimzi uses Persistent Volume Claims (PVCs) to request storage on persistent volumes (PVs). The PVC binds to a PV that meets the requested storage criteria, without needing to know the underlying storage infrastructure.

PVCs created for Kafka pods follow the naming convention data-<kafka_cluster_name>-<pool_name>-<pod_id>, and the persistent volumes for Kafka logs are mounted at /var/lib/kafka/data/kafka-log<pod_id>.

You can also specify custom storage classes (StorageClass) and volume selectors in the storage configuration.

Example class and selector configuration
# ...
  storage:
    type: persistent-claim
    size: 500Gi
    class: my-storage-class
    selector:
      hdd-type: ssd
    deleteClaim: true
# ...

Storage classes define storage profiles and dynamically provision persistent volumes (PVs) based on those profiles. This is useful, for example, when storage classes are restricted to different availability zones or data centers. If a storage class is not specified, the default storage class in the Kubernetes cluster is used. Selectors specify persistent volumes that offer specific features, such as solid-state drive (SSD) volumes.

For more information on persistent storage configuration options, see the PersistentClaimStorage schema reference.

Resizing persistent volumes

Persistent volumes can be resized by changing the size storage property 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. Decreasing the size of persistent volumes is not supported in Kubernetes. For more information about resizing persistent volumes in Kubernetes, see Resizing Persistent Volumes using Kubernetes.

After increasing the value of the size property, 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.

In this example, the volumes are increased to 2000Gi.

Kafka configuration to increase volume size to 2000Gi
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: my-node-pool
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 2000Gi
        deleteClaim: false
      - id: 1
        type: persistent-claim
        size: 2000Gi
        deleteClaim: false
      - id: 2
        type: persistent-claim
        size: 2000Gi
        deleteClaim: false
  # ...

Returning information on the PVs verifies the changes:

kubectl get pv
Storage capacity of PVs
NAME               CAPACITY   CLAIM
pvc-0ca459ce-...   2000Gi     my-project/data-my-cluster-my-node-pool-2
pvc-6e1810be-...   2000Gi     my-project/data-my-cluster-my-node-pool-0
pvc-82dc78c9-...   2000Gi     my-project/data-my-cluster-my-node-pool-1

The output shows the names of each PVC associated with a broker pod.

Note
 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).
Configuring JBOD storage

To use JBOD storage, specify jbod as the storage type and add configuration for the JBOD volumes. JBOD volumes can be persistent or ephemeral, with the configuration options and constraints applicable to each type.

Example configuration for JBOD storage
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: my-node-pool
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker
  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
  # ...

PVCs are created for the JBOD volumes using the naming convention data-<volume_id>-<kafka_cluster_name>-<pool_name>-<pod_id>, and the JBOD volumes used for log directories are mounted at /var/lib/kafka/data-<volume_id>/kafka-log<pod_id>.

Adding or removing volumes from JBOD storage

Volume IDs cannot be changed once JBOD volumes are created, though you can add or remove volumes. When adding a new volume to the to the volumes array under an id which was already used in the past and removed, make sure that the previously used PersistentVolumeClaims have been deleted.

Use Cruise Control to reassign partitions when adding or removing volumes. For information on intra-broker disk balancing, see Tuning options for rebalances.

Configuring KRaft metadata log storage

In KRaft mode, each node (including brokers and controllers) stores a copy of the Kafka cluster’s metadata log on one of its data volumes. By default, the log is stored on the volume with the lowest ID, but you can specify a different volume using the kraftMetadata property.

For controller-only nodes, storage is exclusively for the metadata log. Since the log is always stored on a single volume, using JBOD storage with multiple volumes does not improve performance or increase available disk space.

In contrast, broker nodes or nodes that combine broker and controller roles can share the same volume for both the metadata log and partition replica data, optimizing disk utilization. They can also use JBOD storage, where one volume is shared for the metadata log and partition replica data, while additional volumes are used solely for partition replica data.

Changing the volume that stores the metadata log triggers a rolling update of the cluster nodes, involving the deletion of the old log and the creation of a new one in the specified location. If kraftMetadata isn’t specified, adding a new volume with a lower ID also prompts an update and relocation of the metadata log.

Example JBOD storage configuration using volume with ID 1 to store the KRaft metadata
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-a
  # ...
spec:
  storage:
    type: jbod
    volumes:
    - id: 0
      type: persistent-claim
      size: 100Gi
      deleteClaim: false
    - id: 1
      type: persistent-claim
      size: 100Gi
      kraftMetadata: shared
      deleteClaim: false
  # ...
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. The steps require a scaling operation to add a new node pool. Currently, scaling is only possible for broker-only node pools containing nodes that run as dedicated brokers.

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). This approach is particularly useful if you want to reduce disk sizes. When increasing disk sizes, you have the option to dynamically resize persistent volumes.

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:
  roles:
    - broker
  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 ready (1/1).

    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 ready.

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

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

    Using Cruise Control to reassign partition replicas
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  # ...
spec:
  mode: remove-brokers
  brokers: [0, 1, 2]

    We are reassigning partitions from pool-a. The reassignment can take some time depending on the number of topics and partitions in the cluster.

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

    kubectl delete kafkanodepool pool-a
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 ready (1/1).

    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

11.5.3. Configuring Kafka storage with ZooKeeper

If you are using ZooKeeper, configure its storage in the Kafka resource. Depending on whether the deployment uses node pools, configure storage for the Kafka cluster in Kafka or KafkaNodePool resources.

This section focuses only on ZooKeeper storage and Kafka storage configuration in the Kafka resource. For detailed information on Kafka storage, refer to the section describing storage configuration using node pools. The same configuration options for storage are available in the Kafka resource.

Note
 Replicated storage is not required for ZooKeeper, as it has built-in data replication.
Configuring ephemeral storage

To use ephemeral storage, specify ephemeral as the storage type.

Example configuration for ephemeral storage
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    storage:
      type: ephemeral
  zookeeper:
    storage:
      type: ephemeral
    # ...

The ephemeral volume used by Kafka brokers for log directories is mounted at /var/lib/kafka/data/kafka-log<pod_id>.

Important
 Ephemeral storage is unsuitable for single-node ZooKeeper clusters or Kafka topics with a replication factor of 1.
Configuring persistent storage

The same persistent storage configuration options available for node pools can also be specified for Kafka in the Kafka resource. For more information, see the section on configuring Kafka storage using node pools. The size property can also be adjusted to resize persistent volumes.

The storage type must always be persistent-claim for ZooKeeper, as it does not support JBOD storage.

Example configuration for persistent storage
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    storage:
      type: persistent-claim
      size: 500Gi
      deleteClaim: true
  # ...
  zookeeper:
    storage:
      type: persistent-claim
      size: 1000Gi

PVCs created for Kafka pods when storage is configured in the Kafka resource use the naming convention data-<cluster_name>-kafka-<pod_id>, and the persistent volumes for Kafka logs are mounted at /var/lib/kafka/data/kafka-log<pod_id>.

PVCs created for ZooKeeper follow the naming convention data-<cluster_name>-zookeeper-<pod_id>.

Note
 As in KRaft mode, you can also specify custom storage classes and volume selectors.
Configuring JBOD storage

ZooKeeper does not support JBOD storage, but Kafka nodes in a ZooKeeper-based cluster can still be configured to use JBOD storage. The same JBOD configuration options available for node pools can also be specified for Kafka in the Kafka resource. For more information, see the section on configuring Kafka storage using node pools. The volumes array can also be adjusted to add or remove volumes.

Example configuration for JBOD storage
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
Migrating from storage class overrides (deprecated)

The use of node pools to change the storage classes used by volumes replaces the deprecated overrides properties previously used for Kafka and ZooKeeper in the Kafka resource.

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
  # ...

If you are using storage class overrides for Kafka, we encourage you to transition to using node pools instead. To migrate the existing configuration, follow these steps:

  1. Make sure you already use node pools resources. If not, you should migrate the cluster to use node pools first.

  2. Create new node pools with storage configuration using the desired storage class without using the overrides.

  3. Move all partition replicas from the old broker using the storage class overrides. You can do this using Cruise Control or using the partition reassignment tool.

  4. Delete the old node pool with the old brokers using the storage class overrides.

11.5.4. Tiered storage (early access)

Tiered storage introduces a flexible approach to managing Kafka data whereby log segments are moved to a separate storage system. For example, you can combine the use of block storage on brokers for frequently accessed data and offload older or less frequently accessed data from the block storage to more cost-effective, scalable remote storage solutions, such as Amazon S3, without compromising data accessibility and durability.

Warning
 Tiered storage is an early access Kafka feature, which is also available in Strimzi. Due to its current limitations, it is not recommended for production environments.

Tiered storage requires an implementation of Kafka’s RemoteStorageManager interface to handle communication between Kafka and the remote storage system, which is enabled through configuration of the Kafka resource. Strimzi uses Kafka’s TopicBasedRemoteLogMetadataManager for Remote Log Metadata Management (RLMM) when custom tiered storage is enabled. The RLMM manages the metadata related to remote storage.

To use custom tiered storage, do the following:

  • Include a tiered storage plugin for Kafka in the Strimzi image by building a custom container image. The plugin must provide the necessary functionality for a Kafka cluster managed by Strimzi to interact with the tiered storage solution.

  • Configure Kafka for tiered storage using tieredStorage properties in the Kafka resource. Specify the class name and path for the custom RemoteStorageManager implementation, as well as any additional configuration.

  • If required, specify RLMM-specific tiered storage configuration.

Example custom tiered storage configuration for Kafka
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    tieredStorage:
      type: custom # (1)
      remoteStorageManager: # (2)
        className: com.example.kafka.tiered.storage.s3.S3RemoteStorageManager
        classPath: /opt/kafka/plugins/tiered-storage-s3/*
        config:
          storage.bucket.name: my-bucket # (3)
          # ...
    config:
      rlmm.config.remote.log.metadata.topic.replication.factor: 1 # (4)
  # ...

  1. The type must be set to custom.

  2. The configuration for the custom RemoteStorageManager implementation, including class name and path.

  3. Configuration to pass to the custom RemoteStorageManager implementation, which Strimzi automatically prefixes with rsm.config..

  4. Tiered storage configuration to pass to the RLMM, which requires an rlmm.config. prefix. For more information on tiered storage configuration, see the Apache Kafka documentation.

11.6. 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:

  • topicOperator

  • userOperator

  • template

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.

11.6.1. Configuring the Topic Operator

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

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.

reconciliationIntervalMs

The interval between periodic reconciliations in milliseconds. Default 120000.

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
      reconciliationIntervalMs: 60000
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi
    # ...

11.6.2. 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.

reconciliationIntervalMs

The interval between periodic reconciliations in milliseconds. Default 120000.

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
      reconciliationIntervalMs: 60000
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi
    # ...

11.7. 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, 3.9.0=quay.io/strimzi/kafka:latest-kafka-3.9.0. 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_POD_DISRUPTION_BUDGET_GENERATION

Optional, default true. Pod disruption budget for resources. A pod disruption budget with the maxUnavailable value set to zero prevents Kubernetes from evicting pods automatically.

Set this environment variable to false to disable pod disruption budget generation. You might do this, for example, if you want to manage the pod disruption budgets yourself, or if you have a development environment where availability is not important.

STRIMZI_LABELS_EXCLUSION_PATTERN

Optional, default regex pattern is (app.kubernetes.io/(?!part-of).|kustomize.toolkit.fluxcd.io.). 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 custom resource of the component. 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 3.8.0=quay.io/strimzi/kafka:latest-kafka-3.8.0, 3.9.0=quay.io/strimzi/kafka:latest-kafka-3.9.0.

STRIMZI_KAFKA_CONNECT_IMAGES

Required. The mapping from the Kafka version to the corresponding image of Kafka Connect for that version. For example 3.8.0=quay.io/strimzi/kafka:latest-kafka-3.8.0, 3.9.0=quay.io/strimzi/kafka:latest-kafka-3.9.0.

STRIMZI_KAFKA_MIRROR_MAKER2_IMAGES

Required. The mapping from the Kafka version to the corresponding image of MirrorMaker 2 for that version. For example 3.8.0=quay.io/strimzi/kafka:latest-kafka-3.8.0, 3.9.0=quay.io/strimzi/kafka:latest-kafka-3.9.0.

(Deprecated) STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

Required. The mapping from the Kafka version to the corresponding image of MirrorMaker for that version. For example 3.8.0=quay.io/strimzi/kafka:latest-kafka-3.8.0, 3.9.0=quay.io/strimzi/kafka:latest-kafka-3.9.0.

STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE

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

STRIMZI_DEFAULT_USER_OPERATOR_IMAGE

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

STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE

Optional. The default is quay.io/strimzi/kafka:latest-kafka-3.9.0. 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:latest-kafka-3.9.0. 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:0.31.1. 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:latest. 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.

11.7.1. 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
  #...

11.7.2. Setting periodic reconciliation of custom resources

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

11.7.3. Pausing reconciliation of custom resources using annotations

Sometimes it is useful to pause the reconciliation of custom resources managed by Strimzi operators, so that you can perform fixes or make updates. If reconciliations are paused, any changes made to custom resources are ignored by the operators until the pause ends.

If you want to pause reconciliation of a custom resource, set the strimzi.io/pause-reconciliation annotation to true in its configuration. This instructs the appropriate operator to pause reconciliation of the custom resource. For example, you can apply the annotation to the KafkaConnect resource so that reconciliation by the Cluster Operator is paused.

You can also create a custom resource with the pause annotation enabled. The custom resource is created, but it is ignored.

Prerequisites

  • The Strimzi Operator that manages the custom resource is running.

Procedure

  1. Annotate the custom resource in Kubernetes, setting pause-reconciliation to true:

    kubectl annotate <kind_of_custom_resource> <name_of_custom_resource> strimzi.io/pause-reconciliation="true"

    For example, for the KafkaConnect custom resource:

    kubectl annotate KafkaConnect my-connect strimzi.io/pause-reconciliation="true"

  2. Check that the status conditions of the custom resource show a change to ReconciliationPaused:

    kubectl describe <kind_of_custom_resource> <name_of_custom_resource>

    The type condition changes to ReconciliationPaused at the lastTransitionTime.

    Example custom resource with a paused reconciliation condition type
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  annotations:
    strimzi.io/pause-reconciliation: "true"
    strimzi.io/use-connector-resources: "true"
  creationTimestamp: 2021-03-12T10:47:11Z
  #...
spec:
  # ...
status:
  conditions:
  - lastTransitionTime: 2021-03-12T10:47:41.689249Z
    status: "True"
    type: ReconciliationPaused
Resuming from pause

  • To resume reconciliation, you can set the annotation to false, or remove the annotation.

Additional resources

11.7.4. 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.

11.7.5. 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

11.7.6. 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

11.8. 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.

You can also use the KafkaConnect resource to specify the following:

  • Connector plugin configuration to build a container image that includes the plugins to make connections

  • Configuration for the Kafka Connect worker pods that run connectors

  • An annotation to enable use of the KafkaConnector resource to manage connectors

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

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

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
# Basic configuration (required)
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect # (1)
metadata:
  name: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true" # (2)
# Deployment specifications
spec:
  # Replicas (required)
  replicas: 3 # (3)
  # Bootstrap servers (required)
  bootstrapServers: my-cluster-kafka-bootstrap:9092 # (4)
  # Kafka Connect configuration (recommended)
  config: # (5)
    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
  # Resources requests and limits (recommended)
  resources: # (6)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  # Authentication (optional)
  authentication: # (7)
    type: tls
    certificateAndKey:
      certificate: source.crt
      key: source.key
      secretName: my-user-source
  # TLS configuration (optional)
  tls: # (8)
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        pattern: "*.crt"
      - secretName: my-cluster-cluster-cert
        pattern: "*.crt"
  # Build configuration (optional)
  build: # (9)
    output: # (10)
      type: docker
      image: my-registry.io/my-org/my-connect-cluster:latest
      pushSecret: my-registry-credentials
    plugins: # (11)
      - 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>
  # Logging configuration (optional)
  logging: # (12)
    type: inline
    loggers:
      log4j.rootLogger: INFO
  # Readiness probe (optional)
  readinessProbe: # (13)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  # Liveness probe (optional)
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  # Metrics configuration (optional)
  metricsConfig: # (14)
    type: jmxPrometheusExporter
    valueFrom:
      configMapKeyRef:
        name: my-config-map
        key: my-key
  # JVM options (optional)
  jvmOptions: # (15)
    "-Xmx": "1g"
    "-Xms": "1g"
  # Custom image (optional)
  image: my-org/my-image:latest # (16)
  # Rack awareness (optional)
  rack:
    topologyKey: topology.kubernetes.io/zone # (17)
  # Pod and container template (optional)
  template: # (18)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: # (19)
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
        - name: AWS_ACCESS_KEY_ID
          valueFrom:
            secretKeyRef:
              name: aws-creds
              key: awsAccessKey
        - name: AWS_SECRET_ACCESS_KEY
          valueFrom:
            secretKeyRef:
              name: aws-creds
              key: awsSecretAccessKey
  # Tracing configuration (optional)
  tracing:
    type: opentelemetry # (20)

  1. Use KafkaConnect.

  2. Enables the use of KafkaConnector resources to start, stop, and manage connector instances.

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

  4. Bootstrap address for connection to the Kafka cluster. The address takes the format <cluster_name>-kafka-bootstrap:<port_number>. The Kafka cluster doesn’t need to be managed by Strimzi or deployed to a Kubernetes cluster.

  5. Kafka Connect configuration of workers (not connectors) that run connectors and their tasks. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi. In this example, JSON convertors are specified. A replication factor of 3 is set for the internal topics used by Kafka Connect (minimum requirement for production environment). Changing the replication factor after the topics have been created has no effect.

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

  7. 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.

  8. TLS configuration for encrypted connections to the Kafka cluster, with trusted certificates stored in X.509 format within the specified secrets.

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

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

  11. (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.

  12. 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.

  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 Connect.

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

  17. 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.

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

  19. Environment variables are set for distributed tracing and to pass credentials to connectors.

  20. Distributed tracing is enabled by using OpenTelemetry.

11.8.1. 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.

11.8.2. 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 12. 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

11.9. Configuring Kafka Connect connectors

The KafkaConnector resource provides a Kubernetes-native approach to management of connectors by the Cluster Operator. To create, delete, or reconfigure connectors with KafkaConnector resources, you must set the use-connector-resources annotation to true in your KafkaConnect custom resource.

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

When the use-connector-resources annotation is enabled in your KafkaConnect configuration, you must define and manage connectors using KafkaConnector resources.

Note
 Alternatively, you can manage connectors using the Kafka Connect REST API instead of KafkaConnector resources. To use the API, you must remove the strimzi.io/use-connector-resources annotation to use KafkaConnector resources in the KafkaConnect the resource.

KafkaConnector resources provide the configuration needed to create connectors within a Kafka Connect cluster, which interacts with a Kafka cluster as specified in the KafkaConnect configuration. The Kafka cluster does not need to be managed by Strimzi or deployed to a Kubernetes cluster.

Kafka components contained in the same Kubernetes cluster

Kafka and Kafka Connect clusters

The configuration also specifies how the connector instances interact with external data systems, including any required authentication methods. Additionally, you must define the data to watch. For example, in a source connector that reads data from a database, the configuration might include the database name. You can also define where this data should be placed in Kafka by specifying the target topic name.

Use the tasksMax property to specify the maximum number of tasks. For instance, a source connector with tasksMax: 2 might split the import of source data into two tasks.

Example 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.

To include external connector configurations, such as user access credentials stored in a secret, use the template property of the KafkaConnect resource. You can also load values using configuration providers.

11.9.1. 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.

11.9.2. 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.

11.9.3. 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.

11.9.4. Listing connector offsets

To track connector offsets using KafkaConnector resources, add the listOffsets configuration. The offsets, which keep track of the flow of data, are written to a config map specified in the configuration. If the config map does not exist, Strimzi creates it.

After the configuration is in place, annotate the KafkaConnector resource to write the list to the config map.

Sink connectors use Kafka’s standard consumer offset mechanism, while source connectors store offsets in a custom format within a Kafka topic.

  • For sink connectors, the list shows Kafka topic partitions and the last committed offset for each partition.

  • For source connectors, the list shows the source system’s partition and the last offset processed.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Edit the KafkaConnector resource for the connector to include the listOffsets configuration.

    Example configuration to list offsets
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  listOffsets:
    toConfigMap: # (1)
      name: my-connector-offsets # (2)
  # ...

    1. The reference to the config map where the list of offsets will be written to.

    2. The name of the config map, which is named my-connector-offsets in this example.

  2. Run the command to write the list to the config map by annotating the KafkaConnector resource:

    kubectl annotate kafkaconnector my-source-connector strimzi.io/connector-offsets=list -n <namespace>

    The annotation remains until either the list operation succeeds or it is manually removed from the resource.

  3. After the KafkaConnector resource is updated, use the following command to check if the config map with the offsets was created:

    kubectl get configmap my-connector-offsets -n <namespace>

  4. Inspect the contents of the config map to verify the offsets are being listed:

    kubectl describe configmap my-connector-offsets -n <namespace>

    Strimzi puts the offset information into the offsets.json property. This does not overwrite any other properties when updating an existing config map.

    Example source connector offset list
    apiVersion: v1
kind: ConfigMap
metadata:
  # ...
  ownerReferences: # (1)
  - apiVersion: kafka.strimzi.io/v1beta2
    blockOwnerDeletion: false
    controller: false
    kind: KafkaConnector
    name: my-source-connector
    uid: 637e3be7-bd96-43ab-abde-c55b4c4550e0
  resourceVersion: "66951"
  uid: 641d60a9-36eb-4f29-9895-8f2c1eb9638e
data:
  offsets.json: |-
    {
      "offsets" : [ {
        "partition" : {
          "filename" : "/data/myfile.txt" # (2)
        },
        "offset" : {
          "position" : 15295 # (3)
        }
      } ]
    }

    1. The owner reference pointing to the KafkaConnector resource for the source connector. To provide a custom owner reference, create the config map in advance and set the owner reference.

    2. The source partition, represented by the filename /data/myfile.txt in this example for a file-based connector.

    3. The last processed offset position in the source partition.

    Example sink connector offset list
    apiVersion: v1
kind: ConfigMap
metadata:
  # ...
  ownerReferences: # (1)
  - apiVersion: kafka.strimzi.io/v1beta2
    blockOwnerDeletion: false
    controller: false
    kind: KafkaConnector
    name: my-sink-connector
    uid: 84a29d7f-77e6-43ac-bfbb-719f9b9a4b3b
  resourceVersion: "79241"
  uid: 721e30bc-23df-41a2-9b48-fb2b7d9b042c
data:
  offsets.json: |-
    {
      "offsets": [
        {
          "partition": {
            "kafka_topic": "my-topic", # (2)
            "kafka_partition": 2 # (3)
          },
          "offset": {
            "kafka_offset": 4 # (4)
          }
        }
      ]
    }

    1. The owner reference pointing to the KafkaConnector resource for the sink connector.

    2. The Kafka topic that the sink connector is consuming from.

    3. The partition of the Kafka topic.

    4. The last committed Kafka offset for this topic and partition.

11.9.5. Altering connector offsets

To alter connector offsets using KafkaConnector resources, configure the resource to stop the connector and add alterOffsets configuration to specify the offset changes in a config map. You can reuse the same config map used to list offsets.

After the connector is stopped and the configuration is in place, annotate the KafkaConnector resource to apply the offset alteration, then restart the connector.

Altering connector offsets can be useful, for example, to skip a poison record or replay a record.

In this procedure, we alter the offset position for a source connector named my-source-connector.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Edit the KafkaConnector resource to stop the connector and include the alterOffsets configuration.

    Example configuration to stop a connector and alter offsets
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  state: stopped # (1)
  alterOffsets:
    fromConfigMap: # (2)
      name: my-connector-offsets # (3)
  # ...

    1. Changes the state of the connector to stopped. The default state for the connector when this property is not set is running.

    2. The reference to the config map that provides the update.

    3. The name of the config map, which is named my-connector-offsets in this example.

  2. Edit the config map to make the alteration.

    In this example, we’re resetting the offset position for a source connector to 15000.

    Example source connector offset list configuration
    apiVersion: v1
kind: ConfigMap
metadata:
  # ...
data:
  offsets.json: |- # (1)
    {
      "offsets" : [ {
        "partition" : {
          "filename" : "/data/myfile.txt"
        },
        "offset" : {
          "position" : 15000 # (2)
        }
      } ]
    }

    1. Edits must be made within the offsets.json property.

    2. The updated offset position in the source partition.

  3. Run the command to update the offset position by annotating the KafkaConnector resource:

    kubectl annotate kafkaconnector my-source-connector strimzi.io/connector-offsets=alter -n <namespace>

    The annotation remains until either the update operation succeeds or it is manually removed from the resource.

  4. Check the changes by using the procedure to list connector offsets.

  5. Restart the connector by changing the state to running.

    Example configuration to start a connector
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  state: running
  # ...

11.9.6. Resetting connector offsets

To reset connector offsets using KafkaConnector resources, configure the resource to stop the connector.

After the connector is stopped, annotate the KafkaConnector resource to clear the offsets, then restart the connector.

In this procedure, we reset the offset position for a source connector named my-source-connector.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Edit the KafkaConnector resource to stop the connector.

    Example configuration to stop a connector
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  # ...
  state: stopped # (1)
  # ...

    1. Changes the state of the connector to stopped. The default state for the connector when this property is not set is running.

  2. Run the command to reset the offset position by annotating the KafkaConnector resource:

    kubectl annotate kafkaconnector my-source-connector strimzi.io/connector-offsets=reset -n <namespace>

    The annotation remains until either the reset operation succeeds or it is manually removed from the resource.

  3. Check the changes by using the procedure to list connector offsets.

    After resetting, the offsets.json property is empty.

    Example source connector offset list
    apiVersion: v1
kind: ConfigMap
metadata:
  # ...
data:
  offsets.json: |-
    {
      "offsets" : []
    }

  4. Restart the connector by changing the state to running.

    Example configuration to start a connector
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  state: running
  # ...

11.10. 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: 3.9.0
  connectCluster: "my-cluster-target"
  clusters:
  - alias: "my-cluster-source"
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092
  - alias: "my-cluster-target"
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector: {}

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
# Basic configuration (required)
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
# Deployment specifications
spec:
  # Replicas (required)
  replicas: 3 # (1)
  # Connect cluster name (required)
  connectCluster: "my-cluster-target" # (2)
  # Cluster configurations (required)
  clusters: # (3)
    - alias: "my-cluster-source" # (4)
      # Authentication (optional)
      authentication: # (5)
        certificateAndKey:
          certificate: source.crt
          key: source.key
          secretName: my-user-source
        type: tls
      bootstrapServers: my-cluster-source-kafka-bootstrap:9092 # (6)
      # TLS configuration (optional)
      tls: # (7)
        trustedCertificates:
          - pattern: "*.crt"
            secretName: my-cluster-source-cluster-ca-cert
    - alias: "my-cluster-target" # (8)
      # Authentication (optional)
      authentication: # (9)
        certificateAndKey:
          certificate: target.crt
          key: target.key
          secretName: my-user-target
        type: tls
      bootstrapServers: my-cluster-target-kafka-bootstrap:9092 # (10)
      # Kafka Connect configuration (optional)
      config: # (11)
        config.storage.replication.factor: 1
        offset.storage.replication.factor: 1
        status.storage.replication.factor: 1
      # TLS configuration (optional)
      tls: # (12)
        trustedCertificates:
          - pattern: "*.crt"
            secretName: my-cluster-target-cluster-ca-cert
  # Mirroring configurations (required)
  mirrors: # (13)
    - sourceCluster: "my-cluster-source" # (14)
      targetCluster: "my-cluster-target" # (15)
      # Topic and group patterns (required)
      topicsPattern: "topic1|topic2|topic3" # (16)
      groupsPattern: "group1|group2|group3" # (17)
      # Source connector configuration (required)
      sourceConnector: # (18)
        tasksMax: 10 # (19)
        autoRestart: # (20)
          enabled: true
        config:
          replication.factor: 1 # (21)
          offset-syncs.topic.replication.factor: 1 # (22)
          sync.topic.acls.enabled: "false" # (23)
          refresh.topics.interval.seconds: 60 # (24)
          replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy" # (25)
      # Heartbeat connector configuration (optional)
      heartbeatConnector: # (26)
        autoRestart:
          enabled: true
        config:
          heartbeats.topic.replication.factor: 1 # (27)
          replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy"
      # Checkpoint connector configuration (optional)
      checkpointConnector: # (28)
        autoRestart:
          enabled: true
        config:
          checkpoints.topic.replication.factor: 1 # (29)
          refresh.groups.interval.seconds: 600 # (30)
          sync.group.offsets.enabled: true # (31)
          sync.group.offsets.interval.seconds: 60 # (32)
          emit.checkpoints.interval.seconds: 60 # (33)
          replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy"
  # Kafka version (recommended)
  version: 3.9.0 # (34)
  # Resources requests and limits (recommended)
  resources: # (35)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  # Logging configuration (optional)
  logging: # (36)
    type: inline
    loggers:
      connect.root.logger.level: INFO
  # Readiness probe (optional)
  readinessProbe: # (37)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  # Liveness probe (optional)
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  # JVM options (optional)
  jvmOptions: # (38)
    "-Xmx": "1g"
    "-Xms": "1g"
  # Custom image (optional)
  image: my-org/my-image:latest # (39)
  # Rack awareness (optional)
  rack:
    topologyKey: topology.kubernetes.io/zone # (40)
  # Pod template (optional)
  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 configuration (optional)
  tracing:
    type: opentelemetry # (43)

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

  2. 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.

  3. Specification for the Kafka clusters being synchronized.

  4. Cluster alias for the source Kafka cluster.

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

  6. Bootstrap address for connection to the source Kafka cluster. The address takes the format <cluster_name>-kafka-bootstrap:<port_number>. The Kafka cluster doesn’t need to be managed by Strimzi or deployed to a Kubernetes cluster.

  7. TLS configuration for encrypted connections to the Kafka cluster, with trusted certificates stored in X.509 format within the specified secrets.

  8. Cluster alias for the target Kafka cluster.

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

  10. Bootstrap address for connection to the target Kafka cluster. The address takes the format <cluster_name>-kafka-bootstrap:<port_number>. The Kafka cluster doesn’t need to be managed by Strimzi or deployed to a Kubernetes cluster.

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

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

  13. MirrorMaker 2 connectors.

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

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

  16. 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.

  17. 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.

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

  19. 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.

  20. 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.

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

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

  23. 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.

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

  25. 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.

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

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

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

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

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

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

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

  33. 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.

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

  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.

11.10.1. 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 3. 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.

11.10.2. 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.

11.10.3. 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 13. 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.

Listing the offsets of MirrorMaker 2 connectors

To list the offset positions of the internal MirrorMaker 2 connectors, use the same configuration that’s used to manage Kafka Connect connectors. For more information on setting up the configuration and listing offsets, see Listing connector offsets.

In this example, the sourceConnector configuration is updated to return the connector offset position. The offset information is written to a specified config map.

Example configuration for MirrorMaker 2 connector
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 3.9.0
  # ...
  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:
      listOffsets:
        toConfigMap:
          name: my-connector-offsets
        # ...

You must apply the following annotations to the KafkaMirrorMaker2 resource be able to manage connector offsets:

  • strimzi.io/connector-offsets

  • strimzi.io/mirrormaker-connector

The strimzi.io/mirrormaker-connector annotation must be set to the name of the connector. These annotations remain until the operation succeeds or they are manually removed from the resource.

MirrorMaker 2 connectors are named using the aliases of the source and target clusters, followed by the connector type: <source_alias>-><target_alias>.<connector_type>.

In the following example, the annotations are applied for a connector named my-cluster-source->my-cluster-target.MirrorSourceConnector.

Example application of annotations for connector
kubectl annotate kafkamirrormaker2 my-mirror-maker-2 strimzi.io/connector-offsets=list strimzi.io/mirrormaker-connector="my-cluster-source->my-cluster-target.MirrorSourceConnector" -n kafka

The offsets are listed in the specified config map. Strimzi puts the offset information into a .json property named after the connector. This does not overwrite any other properties when updating an existing config map.

Example source connector offset list
apiVersion: v1
kind: ConfigMap
metadata:
  # ...
  ownerReferences: # (1)
  - apiVersion: kafka.strimzi.io/v1beta2
    blockOwnerDeletion: false
    controller: false
    kind: KafkaMirrorMaker2
    name: my-mirror-maker2
    uid: 637e3be7-bd96-43ab-abde-c55b4c4550e0
data:
  my-cluster-source--my-cluster-target.MirrorSourceConnector.json: |- # (2)
    {
      "offsets": [
        {
          "partition": {
            "cluster": "east-kafka",
            "partition": 0,
            "topic": "mirrormaker2-cluster-configs"
          },
          "offset": {
            "offset": 0
          }
        }
      ]
    }

  1. The owner reference pointing to the KafkaMirrorMaker2 resource. To provide a custom owner reference, create the config map in advance and set the owner reference.

  2. The .json property uses the connector name. Since -> characters are not allowed in config map keys, -> is changed to -- in the connector name.

Note
 It is possible to use configuration to alter or reset connector offsets, though this is rarely necessary.

11.10.4. 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 14. 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 15. 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 16. 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: 3.9.0
  # ...
  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
        # ...

11.10.5. 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

11.10.6. 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.

11.10.7. 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: 3.9.0
    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: "3.9"
    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: 3.9.0
    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: "3.9"
    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: 3.9.0
  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
            pattern: "*.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
            pattern: "*.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>

11.10.8. 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: 3.9.0
  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.

11.10.9. 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.

11.10.10. 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.

11.11. 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
          pattern: "*.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
          pattern: "*.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 configuration for encrypted connections to the Kafka cluster, with trusted certificates stored in X.509 format within the specified secrets.

  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.

11.12. 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 and its cluster configuration options, refer to the Using the Kafka Bridge guide and the Strimzi Custom Resource API Reference.

Example KafkaBridge custom resource configuration
# Basic configuration (required)
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # Replicas (required)
  replicas: 3 # (1)
  # Kafka bootstrap servers (required)
  bootstrapServers: <cluster_name>-cluster-kafka-bootstrap:9092 # (2)
  # HTTP configuration (required)
  http: # (3)
    port: 8080
    # CORS configuration (optional)
    cors: # (4)
      allowedOrigins: "https://strimzi.io"
      allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  # Resources requests and limits (recommended)
  resources: # (5)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  # TLS configuration (optional)
  tls: # (6)
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        pattern: "*.crt"
      - secretName: my-cluster-cluster-cert
        certificate: ca2.crt
  # Authentication (optional)
  authentication: # (7)
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # Consumer configuration (optional)
  consumer: # (8)
    config:
      auto.offset.reset: earliest
  # Producer configuration (optional)
  producer: # (9)
    config:
      delivery.timeout.ms: 300000
  # Logging configuration (optional)
  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
  # JVM options (optional)
  jvmOptions: # (11)
    "-Xmx": "1g"
    "-Xms": "1g"
  # Readiness probe (optional)
  readinessProbe: # (12)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  # Liveness probe (optional)
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  # Custom image (optional)
  image: my-org/my-image:latest # (13)
  # Pod template (optional)
  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 configuration (optional)
  tracing:
    type: opentelemetry # (16)

  1. The number of replica nodes.

  2. Bootstrap address for connection to the target Kafka cluster. The address takes the format <cluster_name>-kafka-bootstrap:<port_number>. The Kafka cluster doesn’t need to be managed by Strimzi or deployed to a Kubernetes cluster.

  3. HTTP access to Kafka brokers.

  4. CORS access specifying selected resources and access methods. Additional HTTP headers in requests describe the origins that are permitted access to the Kafka cluster.

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

  6. TLS configuration for encrypted connections to the Kafka cluster, with trusted certificates stored in X.509 format within the specified secrets.

  7. 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.

  8. Consumer configuration options.

  9. Producer configuration options.

  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.

11.13. 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.

11.14. Restrictions on Kubernetes labels

Kubernetes labels make it easier to organize, manage, and discover Kubernetes resources within your applications. The Cluster Operator is responsible for applying the following Kubernetes labels to the operands it deploys. These labels cannot be overridden through template configuration of Strimzi resources:

  • app.kubernetes.io/name: Identifies the component type within Strimzi, such as kafka, zookeeper, and`cruise-control`.

  • app.kubernetes.io/instance: Represents the name of the custom resource to which the operand belongs to. For instance, if a Kafka custom resource is named my-cluster, this label will bear that name on the associated pods.

  • app.kubernetes.io/part-of: Similar to app.kubernetes.io/instance, but prefixed with strimzi-.

  • app.kubernetes.io/managed-by: Defines the application responsible for managing the operand, such as strimzi-cluster-operator or strimzi-user-operator.

Example Kubernetes labels on a Kafka pod when deploying a Kafka custom resource named my-cluster
apiVersion: kafka.strimzi.io/v1beta2
kind: Pod
metadata:
  name: my-cluster-kafka-0
  labels:
    app.kubernetes.io/instance: my-cluster
    app.kubernetes.io/managed-by: strimzi-cluster-operator
    app.kubernetes.io/name: kafka
    app.kubernetes.io/part-of: strimzi-my-cluster
spec:
  # ...

11.15. 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.

11.15.1. 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.

11.15.2. 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>

11.15.3. 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>

11.15.4. 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>

11.15.5. 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>

11.16. Disabling pod disruption budget generation

Strimzi generates pod disruption budget resources for Kafka, Kafka Connect worker, MirrorMaker2 worker, and Kafka Bridge worker nodes.

If you want to use custom pod disruption budget resources, you can set the STRIMZI_POD_DISRUPTION_BUDGET_GENERATION environment variable to false in the Cluster Operator configuration. For more information, see Configuring the Cluster Operator.

11.17. 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
# ...
logging:
  type: inline
  loggers:
    kafka.root.logger.level: INFO
# ...
Example external logging configuration
# ...
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.

11.17.1. 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

11.17.2. 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.

    # ...
logging:
  type: external
  valueFrom:
    configMapKeyRef:
      name: logging-configmap
      key: log4j.properties
# ...

  3. Create or update the resource.

    kubectl apply -f <kafka_configuration_file>

11.17.3. 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.

11.17.4. 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

11.17.5. Lock acquisition warnings for cluster operations

The Cluster Operator ensures that only one operation runs at a time for each cluster by using locks. If another operation attempts to start while a lock is held, it waits until the current operation completes.

Operations such as cluster creation, rolling updates, scaling down, and scaling up are managed by the Cluster Operator.

If acquiring a lock takes longer than the configured timeout (STRIMZI_OPERATION_TIMEOUT_MS), a DEBUG message is logged:

Example DEBUG message for lock acquisition
DEBUG AbstractOperator:406 - Reconciliation #55(timer) Kafka(myproject/my-cluster): Failed to acquire lock lock::myproject::Kafka::my-cluster within 10000ms.

Timed-out operations are retried during the next periodic reconciliation in intervals defined by STRIMZI_FULL_RECONCILIATION_INTERVAL_MS (by default 120 seconds).

If an INFO message continues to appear with the same same reconciliation number, it might indicate a lock release error:

Example INFO message for reconciliation
INFO  AbstractOperator:399 - Reconciliation #1(watch) Kafka(myproject/my-cluster): Reconciliation is in progress

Restarting the Cluster Operator can resolve such issues.

11.18. 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
# ...
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.

template properties allow data from a ConfigMap or Secret to be mounted in a pod 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:
  # ...
  template:
    connectContainer:
      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.

11.18.1. 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

11.19. 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.

11.19.1. 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 template 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

KafkaConnectTemplate schema reference

11.19.2. 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.

11.19.3. 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 template 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)
  # ...
  template:
    connectContainer:
      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.

11.19.4. 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 stored in 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 /mnt/<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 additional volume using the template 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)
  #...
  template:
    pod:
      volumes:
        - name: connector-config-volume # (3)
          secret:
            secretName: mysecret # (4)
    connectContainer:
      volumeMounts:
        - name: connector-config-volume # (5)
          mountPath: /mnt/mysecret # (6)

    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.

    5. The name of the mounted volume, which must match the volume name in the volumes list.

    6. The path where the secret is mounted, which must start with /mnt/.

  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: "${file:/mnt/mysecret/connector.properties:dbUsername}"
    database.password: "${file:/mnt/mysecret/connector.properties: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.

11.19.5. 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 /mnt/<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 used to sign this user certificate
  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 additional volume using the template 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)
  #...
  template:
    pod:
      volumes:
        - name: my-user-volume # (3)
          secret:
            secretName: my-user # (4)
        - name: cluster-ca-volume
          secret:
            secretName: my-cluster-cluster-ca-cert
    connectContainer:
      volumeMounts:
        - name: my-user-volume # (5)
          mountPath: /mnt/my-user # (6)
        - name: cluster-ca-volume
          mountPath: /mnt/cluster-ca

    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 name of the volume containing the secret.

    4. The name of the secret.

    5. The name of the mounted volume, which must match the volume name in the volumes list.

    6. The path where the secret is mounted, which must start with /mnt/.

  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: "${directory:/mtn/cluster-ca:ca.crt}"
    database.history.producer.ssl.keystore.type: PEM
    database.history.producer.ssl.keystore.certificate.chain: "${directory:/mnt/my-user:user.crt}"
    database.history.producer.ssl.keystore.key: "${directory:/mnt/my-user:user.key}"
    #...

    The placeholder structure is directory:<path>:<file_name>. DirectoryConfigProvider reads and extracts the credentials from the mounted secrets.

11.20. 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
    # ...

11.20.1. 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

11.20.2. 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
        # ...
    # ...

12. 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.

12.1. Topic management

The KafkaTopic resource is responsible for managing a single topic within a Kafka cluster.

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.

12.2. 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.

A Kafka client application, such as Kafka Streams, can automatically create topics with invalid Kubernetes resource names. If you want to manage these topics, you must create KafkaTopic resources with a different .metadata.name, as shown in the previous example.

Note
 For more information on the requirements for identifiers and names in a cluster, refer to the Kubernetes documentation Object Names and IDs.

12.3. Handling changes to topics

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.

12.3.1. Downgrading to a Strimzi version that uses internal topics to store topic metadata

If you are reverting back to a version of Strimzi earlier than 0.41, which uses internal topics 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.

12.3.2. 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:latest-kafka-3.9.0 --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.

12.3.3. 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.

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.

12.4. 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.

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

12.5. 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. Changing the number of replicas in the topic configuration requires a deployment of Cruise Control. For more information, see Using Cruise Control to modify topic replication factor.

  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. Replicas need to be reassigned when adding or removing brokers (see Scaling clusters by adding or removing brokers).
Additional resources

12.6. Managing KafkaTopic resources without impacting Kafka topics

This procedure describes how to convert Kafka topics that are currently managed through the KafkaTopic resource into unmanaged 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.

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" --overwrite

    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:

    kubectl get kafkatopic my-topic-1 -o yaml
    Example topic with an Unmanaged 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
  annotations:
    strimzi.io/managed: "false"
spec:
  partitions: 10
  replicas: 2
  config:
    retention.ms: 432000000
status:
  observedGeneration: 124 # (1)
  conditions:
  - lastTransitionTime: "2024-08-22T06:07:57.671085635Z"
    status: "True"
    type: Unmanaged # (2)

    1. The value of metadata.generation must match status.observedGeneration.

    2. The Unmanaged condition means that the KafkaTopic is no longer reconciled.

  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.

12.7. 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.

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).

12.8. Deleting managed topics

The Topic Operator 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 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.

12.9. Removing finalizers on topics

If the Topic Operator is not running, and you want to bypass the finalization process when deleting managed topics, you must 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 command line JSON parser 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.

12.10. 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.

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.

12.11. Tuning request batches for topic operations

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.

13. 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.

13.1. 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 a Kafka cluster.

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

14. 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.

14.1. Deploying example clients

Send and receive messages from a Kafka cluster installed on Kubernetes.

This procedure describes how to deploy Kafka clients to the Kubernetes cluster, then produce and consume messages to test your installation. The clients are deployed using the Kafka container image.

Prerequisites

  • The Kafka cluster is available for the clients.

Procedure

  1. Deploy a Kafka producer.

    This example deploys a Kafka producer that connects to the Kafka cluster my-cluster.

    A topic named my-topic is created.

    Deploying a Kafka producer to Kubernetes
    kubectl run kafka-producer -ti --image=quay.io/strimzi/kafka:latest-kafka-3.9.0 --rm=true --restart=Never -- bin/kafka-console-producer.sh --bootstrap-server my-cluster-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.

    The consumer should consume messages produced to my-topic in the Kafka cluster my-cluster.

    Deploying a Kafka consumer to Kubernetes
    kubectl run kafka-consumer -ti --image=quay.io/strimzi/kafka:latest-kafka-3.9.0 --rm=true --restart=Never -- bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9092 --topic my-topic --from-beginning

  5. Confirm that you see the incoming messages in the consumer console.

14.2. Configuring listeners to connect to Kafka

Use listeners to enable client connections to Kafka. Strimzi provides a generic GenericKafkaListener schema with properties to configure listeners through the Kafka resource.

When configuring a Kafka cluster, you specify a listener type based on your requirements, environment, and infrastructure. Services, routes, load balancers, and ingresses for clients to connect to a cluster are created according to the listener type.

Internal and external listener types are supported.

Internal listeners

Use internal listener types to connect clients within a kubernetes cluster.

  • internal to connect within the same Kubernetes cluster

  • cluster-ip to expose Kafka using per-broker ClusterIP services

    Internal listeners use a headless service and the DNS names assigned to the broker pods. By default, they do not use the Kubernetes service DNS domain (typically .cluster.local). However, you can customize this configuration using the useServiceDnsDomain property. Consider using a cluster-ip type listener if routing through the headless service isn’t feasible or if you require a custom access mechanism, such as when integrating with specific Ingress controllers or the Kubernetes Gateway API.

External listeners

Use external listener types to connect clients outside a Kubernetes cluster.

  • 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)

    External listeners handle access to a Kafka cluster from networks that require different authentication mechanisms. For example, loadbalancers might not be suitable for certain infrastructure, such as bare metal, where node ports provide a better option.

Important
 Do not use the built-in ingress controller 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.

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.

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

14.3. 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 17. 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. The specific combinations of listener name and port configuration values in the following table are backwards compatible.

Table 18. 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>

14.4. Accessing Kafka using node ports

Use node ports to access a 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: 3.9.0
  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: latest
 # ...

  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.

14.5. Accessing Kafka using loadbalancers

Use loadbalancers to access a 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: 3.9.0
  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: latest
 # ...

    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.

14.6. Accessing Kafka using an Ingress NGINX Controller for Kubernetes

Use an Ingress NGINX Controller for Kubernetes to access a 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 for the Kafka brokers in the Kafka cluster.

    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:
          class: nginx # (2)
          hostTemplate: broker-{nodeId}.myingress.com  # (3)
          bootstrap:
            host: bootstrap.myingress.com # (4)
    # ...
  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.

    3. The host template used to generate the hostnames for the per-broker Ingress resources.

    4. The host used as the hostnames for the bootstrap Ingress resource.

  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.

14.7. Accessing Kafka using OpenShift routes

Use OpenShift routes to access a 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.

14.8. Discovering connection details for clients

Service discovery makes it easier for client applications running in the same Kubernetes cluster as Strimzi to interact with a Kafka cluster.

A service discovery label and annotation are created for the following services:

  • Internal Kafka bootstrap service

  • Kafka Bridge service

    Service discovery label

    The service discovery label, strimzi.io/discovery, is set to true for Service resources to make them discoverable for client connections.

    Service discovery annotation

    The service discovery annotation provides connection details in JSON format for each service for client applications to use to establish connections.

Example internal Kafka bootstrap service
apiVersion: v1
kind: Service
metadata:
  annotations:
    strimzi.io/discovery: |-
      [ {
        "port" : 9092,
        "tls" : false,
        "protocol" : "kafka",
        "auth" : "scram-sha-512"
      }, {
        "port" : 9093,
        "tls" : true,
        "protocol" : "kafka",
        "auth" : "tls"
      } ]
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/discovery: "true"
    strimzi.io/kind: Kafka
    strimzi.io/name: my-cluster-kafka-bootstrap
  name: my-cluster-kafka-bootstrap
spec:
  #...
Example Kafka Bridge service
apiVersion: v1
kind: Service
metadata:
  annotations:
    strimzi.io/discovery: |-
      [ {
        "port" : 8080,
        "tls" : false,
        "auth" : "none",
        "protocol" : "http"
      } ]
  labels:
    strimzi.io/cluster: my-bridge
    strimzi.io/discovery: "true"
    strimzi.io/kind: KafkaBridge
    strimzi.io/name: my-bridge-bridge-service

Find services by specifying the discovery label when fetching services from the command line or a corresponding API call.

Returning services using the discovery label
kubectl get service -l strimzi.io/discovery=true

Connection details are returned when retrieving the service discovery label.

15. Securing access to a Kafka cluster

Secure connections by configuring Kafka and Kafka users. Through configuration, you can implement encryption, authentication, and authorization mechanisms.

Kafka configuration

To establish secure access to Kafka, configure the Kafka resource to set up the following configurations based on your specific requirements:

  • Listeners with specified authentication types to define how clients authenticate

    • TLS encryption for communication between Kafka and clients

    • Supported TLS versions and cipher suites for additional security

  • Authorization for the entire Kafka cluster

  • Network policies for restricting access

  • Super users for unconstrained access to brokers

Authentication is configured independently for each listener, while authorization is set up for the whole Kafka cluster.

For more information on access configuration for Kafka, see the Kafka schema reference and GenericKafkaListener schema reference.

User (client-side) configuration

To enable secure client access to Kafka, configure KafkaUser resources. These resources represent clients and determine how they authenticate and authorize with the Kafka cluster.

Configure the KafkaUser resource to set up the following configurations based on your specific requirements:

  • Authentication that must match the enabled listener authentication

    • Supported TLS versions and cipher suites that must match the Kafka configuration

  • Simple authorization to apply Access Control List (ACL) rules

    • ACLs for fine-grained control over user access to topics and actions

  • Quotas to limit client access based on byte rates or CPU utilization

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

For more information on access configuration for users, see the KafkaUser schema reference.

15.1. Configuring client authentication on listeners

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

  5. TLS versions and cipher suites

If you’re using OAuth 2.0 for client access management, user authentication and authorization credentials are handled through the authorization server.

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 4. 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
# ...

15.1.1. 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 Configuring 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.

15.1.2. 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 32-character password consisting of upper and lowercase ASCII letters and numbers.

15.1.3. Restricting access to listeners with network policies

Control listener access by configuring the networkPolicyPeers property in the Kafka resource.

By default, Strimzi automatically creates a NetworkPolicy resource for every enabled Kafka listener, allowing connections from all namespaces.

To restrict listener access to specific applications or namespaces at the network level, configure the networkPolicyPeers property. Each listener can have its own 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.
Prerequisites

  • A Kubernetes cluster with support for Ingress NetworkPolicies.

  • The Cluster Operator is running.

Procedure

  1. Configure the networkPolicyPeers property to define the application pods or namespaces allowed to access the Kafka cluster.

    This example shows configuration for 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:
    # ...

  2. Apply the changes to the Kafka resource configuration.

Additional resources

15.1.4. Using custom listener certificates for TLS encryption

This procedure shows how to configure custom server certificates for TLS listeners or external listeners which have TLS encryption enabled.

By default, Kafka listeners use certificates signed by Strimzi’s internal CA (certificate authority). The Cluster Operator automatically generates a CA certificate when creating a Kafka cluster. To configure a client for TLS, the CA certificate is included in its truststore configuration to authenticate the Kafka cluster. Alternatively, you have the option to install and use your own CA certificates.

However, if you prefer more granular control by using your own custom certificates at the listener-level, you can configure listeners using brokerCertChainAndKey properties. You create a secret with your own private key and server certificate, then specify them in the brokerCertChainAndKey configuration.

User-provided certificates allow you to leverage existing security infrastructure. You can use a certificate signed by a public (external) CA or a private CA. Kafka clients need to trust the CA which was used to sign the listener certificate. 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 Specifying SANs for custom listener certificates.

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 changes to the Kafka resource configuration.

    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.

15.1.5. Specifying SANs for custom listener certificates

In order to use TLS hostname verification with custom Kafka listener certificates, you must specify 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 19. 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.

15.2. Configuring authorized access to Kafka

Configure authorized access to a Kafka cluster 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 5. Kafka cluster authorization options

15.2.1. Designating 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 include the following fields:

  • CN=<common_name>

  • OU=<organizational_unit>

  • O=<organization>

  • L=<locality>

  • ST=<state>

  • C=<country_code>

Omit any fields that are not applicable.

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=user-1
        - user-2
        - CN=user-3
        - CN=user-4,OU=my-ou,O=my-org,L=my-location,ST=my-state,C=US
        - CN=user-5,OU=my-ou,O=my-org,C=GB
        - CN=user-6,O=my-org
    # ...

15.3. Configuring user (client-side) security mechanisms

When configuring security mechanisms in clients, the clients are represented as users. Use the KafkaUser resource to configure the authentication, authorization, and access rights for Kafka clients.

Authentication permits user access, and authorization constrains user 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 Example: Setting up secure client access.

15.3.1. Associating users with Kafka clusters

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 enables the User Operator to identify the KafkaUser resource and create and manager 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 configuration.

15.3.2. Configuring 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 used to sign this user certificate
  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.

15.3.3. Configuring 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.

15.3.4. Configuring user quotas

Configure the spec for the KafkaUser resource to enforce quotas so that a user does not overload Kafka brokers. Set size-based network usage and time-based CPU utilization thresholds.

Partition mutations occur in response to the following types of user requests:

  • Creating partitions for a new topic

  • Adding partitions to an existing topic

  • Deleting partitions from a topic

You can also add a partition mutation quota to control the rate at which requests to change partitions are accepted.

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.

Using quotas for Kafka clients might be useful in a number of situations. Consider a wrongly configured Kafka producer which is sending requests at too high a rate. Such misconfiguration can cause a denial of service to other clients, so the problematic client ought to be blocked. By using a network limiting quota, it is possible to prevent this situation from significantly impacting other clients.

Note
 Strimzi supports user-level quotas, but not client-level quotas.

15.4. Example: Setting up secure client access

This procedure shows how to configure client access to a Kafka cluster from outside Kubernetes or from another Kubernetes cluster. It’s split into two parts:

  • Securing Kafka brokers

  • Securing user access to Kafka

Resource configuration

Client access to the Kafka cluster is secured with the following configuration:

  1. An external listener is configured with TLS encryption and mutual TLS (mTLS) authentication in the Kafka resource, as well as simple authorization.

  2. A KafkaUser is created for the client, utilizing mTLS authentication, and Access Control Lists (ACLs) are defined for simple authorization.

At least one listener supporting the desired authentication must be configured for the KafkaUser.

Listeners can be configured for mutual TLS, SCRAM-SHA-512, or OAuth authentication. While mTLS always uses encryption, it’s also recommended when using SCRAM-SHA-512 and OAuth 2.0 authentication.

Authorization options for Kafka include simple, OAuth, OPA, or custom. When enabled, authorization is applied to all enabled listeners.

To ensure compatibility between Kafka and clients, configuration of the following authentication and authorization mechanisms must align:

  • For type: tls and type: scram-sha-512 authentication types, Kafka.spec.kafka.listeners[*].authentication must match KafkaUser.spec.authentication

  • For type: simple authorization,Kafka.spec.kafka.authorization must match KafkaUser.spec.authorization

For example, mTLS authentication for a user is only possible if it’s also enabled in the Kafka configuration.

Automation and certificate management

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 within 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. Alternatively, you can replace them by installing your own custom CA certificates. You can also configure listeners to use Kafka listener certificates managed by an external CA.

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

15.4.1. Securing Kafka brokers

  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. Apply the changes to the Kafka resource configuration.

    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.

15.4.2. Securing user access to Kafka

  1. 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).

  2. Apply the changes to the KafkaUser resource configuration.

    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 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 used to sign this user certificate
  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

  3. 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

  4. Extract the user CA certificate from the <user_name> secret.

    kubectl get secret <user_name> -o jsonpath='{.data.user\.crt}' | base64 -d > user.crt

  5. 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

  6. Configure your client with the bootstrap address hostname and port for connecting to the Kafka cluster:

    props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "<hostname>:<port>");

  7. 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.

  8. 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-----");

15.5. Troubleshooting TLS hostname verification with node ports

Off-cluster access using node ports with TLS encryption enabled does not support TLS hostname verification. This is because Strimzi does not know the address of the node where the broker pod is scheduled and cannot include it in the broker certificate. Consequently, clients that perform hostname verification will fail to connect.

For example, a Java client will fail with the following exception:

Exception for TLS hostname verification
Caused by: java.security.cert.CertificateException: No subject alternative names matching IP address 168.72.15.231 found
 ...

To connect, you must disable hostname verification. In the Java client, set the ssl.endpoint.identification.algorithm configuration option to an empty string.

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

ssl.endpoint.identification.algorithm=

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

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

Alternatively, if you know the addresses of the worker nodes where the brokers are scheduled, you can add them as additional SANs (Subject Alternative Names) to the broker certificates manually. For example, this might apply if your cluster is running on a bare metal deployment with a limited number of available worker nodes. Use the alternativeNames property to specify additional SANS.

16. Enabling OAuth 2.0 token-based access

Strimzi supports OAuth 2.0 for securing Kafka clusters by integrating with an OAUth 2.0 authorization server. Kafka brokers and clients both need to be configured to use OAuth 2.0.

OAuth 2.0 enables standardized token-based authentication and authorization between applications, using a central authorization server to issue tokens that grant limited access to resources. You can define specific scopes for fine-grained access control. Scopes correspond to different levels of access to Kafka topics or operations within the cluster.

OAuth 2.0 also supports single sign-on and integration with identity providers.

For more information on using OAUth 2.0, see the Strimzi OAuth 2.0 for Apache Kafka project.

16.1. Configuring an OAuth 2.0 authorization server

Before you can use OAuth 2.0 token-based access, you must configure an authorization server for integration with Strimzi. 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.

Prepare the authorization server to work with Strimzi by defining OAUth 2.0 clients for Kafka and each Kafka client component of your application. In relation to the authorization server, the Kafka cluster and Kafka clients are both regarded as OAuth 2.0 clients.

In general, configure OAuth 2.0 clients in the authorization server with the following client credentials enabled:

  • Client ID (for example, kafka for the Kafka cluster)

  • 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.

16.2. Using OAuth 2.0 token-based authentication

Strimzi supports the use of OAuth 2.0 for token-based authentication. An OAuth 2.0 authorization server handles the granting of access and inquiries about access. Kafka clients authenticate to Kafka brokers. Brokers and clients communicate with the authorization server, as necessary, to obtain or validate access tokens.

For a deployment of Strimzi, OAuth 2.0 integration provides the following support:

  • Server-side OAuth 2.0 authentication for Kafka brokers

  • Client-side OAuth 2.0 authentication for Kafka MirrorMaker, Kafka Connect, and the Kafka Bridge

16.2.1. Configuring OAuth 2.0 authentication on listeners

To secure Kafka brokers with OAuth 2.0 authentication, configure a listener in the Kafka resource to use OAUth 2.0 authentication and a client authentication mechanism, and add further configuration depending on the authentication mechanism and type of token validation used in the authentication.

Configuring listeners to use oauth authentication

Specify a listener in the Kafka resource with an oauth authentication type. You can configure internal and external 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.

Example listener configuration with OAuth 2.0 authentication
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
      - name: external3
        port: 9094
        type: loadbalancer
        tls: true
        authentication:
          type: oauth
      #...
Enabling SASL authentication mechanisms

Use one or both of the following SASL mechanisms for clients to exchange credentials and establish authenticated sessions with Kafka.

OAUTHBEARER

Using the OAUTHBEARER authentication mechanism, credentials exchange uses a bearer token provided by an OAuth callback handler. Token provision can be configured to use the following methods:

  • Client ID and secret (using the OAuth 2.0 client credentials mechanism)

  • Client ID and client assertion

  • Long-lived access token or Service account token

  • Long-lived refresh token obtained manually

OAUTHBEARER is recommended as it provides a higher level of security than PLAIN, though it can only be used by Kafka clients that support the OAUTHBEARER mechanism at the protocol level. Client credentials are never shared with Kafka.

PLAIN

PLAIN is a simple authentication mechanism used by all Kafka client tools. Consider using PLAIN only with Kafka clients that do not support OAUTHBEARER. Using the PLAIN authentication mechanism, credentials exchange can be configured to use the following methods:

  • Client ID and secret (using the OAuth 2.0 client credentials mechanism)

  • Long-lived access token
    Regardless of the method used, the client must provide username and password properties to Kafka.

Credentials are handled centrally behind a compliant authorization server, similar to how OAUTHBEARER authentication is used. The username extraction process depends on the authorization server configuration.

OAUTHBEARER is automatically enabled in the oauth listener configuration for the Kafka broker. To use the PLAIN mechanism, you must set the enablePlain property to true.

In the following example, the PLAIN mechanism is enabled, and the OAUTHBEARER mechanism is disabled on a listener using the enableOauthBearer property.

Example listener configuration for the PLAIN mechanism
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
      - name: external3
        port: 9094
        type: loadbalancer
        tls: true
        authentication:
          type: oauth
          enablePlain: true
          enableOauthBearer: false
      #...

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.

Configuring fast local JWT token validation

Fast local JWT token validation involves checking a JWT token signature locally to ensure that the token meets the following criteria:

  • Contains a typ (type) or token_type header claim value of Bearer to indicate it is an access token

  • Is currently valid and 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.

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 the tlsTrustedCertificates property 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, 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
#...
- name: external3
  port: 9094
  type: loadbalancer
  tls: true
  authentication:
    type: oauth # (1)
    validIssuerUri: https://<auth_server_address>/<issuer-context> # (2)
    jwksEndpointUri: https://<auth_server_address>/<path_to_jwks_endpoint> # (3)
    userNameClaim: preferred_username # (4)
    maxSecondsWithoutReauthentication: 3600 # (5)
    tlsTrustedCertificates: # (6)
      - secretName: oauth-server-cert
        pattern: "*.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) Certificates stored in X.509 format within the specified secrets 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.

Configuring fast local JWT token validation with Kubernetes service accounts

To configure the listener for Kubernetes service accounts, the Kubernetes API server must be used as the authorization server.

Example configuration for fast local JWT token validation using Kubernetes API server as authorization server
#...
- name: external3
  port: 9094
  type: loadbalancer
  tls: true
  authentication:
    type: oauth
    validIssuerUri: https://kubernetes.default.svc.cluster.local # (1)
    jwksEndpointUri: https://kubernetes.default.svc.cluster.local/openid/v1/jwks # (2)
    serverBearerTokenLocation: /var/run/secrets/kubernetes.io/serviceaccount/token # (3)
    checkAccessTokenType: false # (4)
    includeAcceptHeader: false # (5)
    tlsTrustedCertificates: # (6)
      - secretName: oauth-server-cert
        pattern: "*.crt"
    maxSecondsWithoutReauthentication: 3600
    customClaimCheck: "@.['kubernetes.io'] && @.['kubernetes.io'].['namespace'] in ['myproject']" # (7)

  1. URI of the token issuer used for authentication. Must use FQDN, including the .cluster.local extension, which may vary based on the Kubernetes cluster configuration.

  2. URI of the JWKS certificate endpoint used for local JWT validation. Must use FQDN, including the .cluster.local extension, which may vary based on the Kubernetes cluster configuration.

  3. Location to the access token used by the Kafka broker to authenticate to the Kubernetes API server in order to access the jwksEndpointUri.

  4. Skip the access token type check, as the claim for this is not present in service account tokens.

  5. Skip sending Accept header in HTTP requests to the JWKS endpoint, as the Kubernetes API server does not support it.

  6. Trusted certificates to connect to authorization server. This should point to a manually created Secret that contains the Kubernetes API server public certificate, which is mounted to the running pods under /var/run/secrets/kubernetes.io/serviceaccount/ca.crt. You can use the following command to create the Secret:

    kubectl get cm kube-root-ca.crt -o jsonpath="{['data']['ca\.crt']}" > /tmp/ca.crt
kubectl create secret generic oauth-server-cert --from-file=ca.crt=/tmp/ca.crt

  7. (Optional) Additional constraints that JWT token has to fulfill in order to be accepted, expressed as JsonPath filter query. In this example the service account has to belong to myproject namespace in order to be allowed to authenticate.

The above configuration uses the sub claim from the service account JWT token as the user ID. For example, the default service account for pods deployed in the myproject namespace has the username: system:serviceaccount:myproject:default.

When configuring ACLs the general form of how to refer to the ServiceAccount user should in that case be: User:system:serviceaccount:<Namespace>:<ServiceAccount-name>

Configuring token validation using an introspection endpoint

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 token validation configuration using an introspection endpoint
- name: external3
  port: 9094
  type: loadbalancer
  tls: true
  authentication:
    type: oauth
    validIssuerUri: https://<auth_server_address>/<issuer-context>
    introspectionEndpointUri: https://<auth_server_address>/<path_to_introspection_endpoint> # (1)
    clientId: kafka-broker # (2)
    clientSecret: # (3)
      secretName: my-cluster-oauth
      key: clientSecret
    userNameClaim: preferred_username # (4)
    maxSecondsWithoutReauthentication: 3600 # (5)
    tlsTrustedCertificates:
      - secretName: oauth-server-cert
        pattern: "*.crt"

  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.

Authenticating brokers to the authorization server protected endpoints

Usually, the certificates endpoint of the authorization server (jwksEndpointUri) is publicly accessible, while the introspection endpoint (introspectionEndpointUri) is protected. However, this may vary depending on the authorization server configuration.

The Kafka broker can authenticate to the authorization server’s protected endpoints in one of two ways using HTTP authentication schemes:

  • HTTP Basic authentication uses a client ID and secret.

  • HTTP Bearer authentication uses a bearer token.

To configure HTTP Basic authentication, set the following properties:

  • clientId

  • clientSecret

For HTTP Bearer authentication, set the following property:

  • serverBearerTokenLocation to specify the file path on disk containing the bearer token.

Including additional configuration options

Specify additional settings depending on the authentication requirements and the authorization server you are using. Some of these properties apply only to certain authentication mechanisms or when used in combination with other properties.

For example, when using OAUth over PLAIN, access tokens are passed as password property values 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 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, usernamePrefix, 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
 The PLAIN mechanism does not support password grant authentication. Use either client credentials (client ID + secret) or an access token for authentication.
Example optional configuration settings
  # ...
  authentication:
    type: oauth
    # ...
    checkIssuer: false # (1)
    checkAudience: true # (2)
    usernamePrefix: user- # (3)
    fallbackUserNameClaim: client_id # (4)
    fallbackUserNamePrefix: client-account- # (5)
    serverBearerTokenLocation: path/to/access/token # (6)
    validTokenType: bearer # (7)
    userInfoEndpointUri: https://<auth_server_address>/<path_to_userinfo_endpoint> # (8)
    enableOauthBearer: false # (9)
    enablePlain: true # (10)
    tokenEndpointUri: https://<auth_server_address>/<path_to_token_endpoint> # (11)
    customClaimCheck: "@.custom == 'custom-value'" # (12)
    clientAudience: audience # (13)
    clientScope: scope # (14)
    connectTimeoutSeconds: 60 # (15)
    readTimeoutSeconds: 60 # (16)
    httpRetries: 2 # (17)
    httpRetryPauseMs: 300 # (18)
    groupsClaim: "$.groups" # (19)
    groupsClaimDelimiter: "," # (20)
    includeAcceptHeader: false # (21)

  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. The prefix used when constructing the user ID. This only takes effect if userNameClaim is configured.

  4. 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.

  5. 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.

  6. The location of the access token used by the Kafka broker to authenticate to the Kubernetes API server for accessing protected endpoints. The authorization server must support OAUTHBEARER authentication. This is an alternative to specifying clientId and clientSecret, which uses PLAIN authentication.

  7. (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.

  8. (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.

  9. 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.

  10. Set to true to enable PLAIN authentication on the listener, which is supported for clients on all platforms.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. The connect timeout in seconds when connecting to the authorization server. The default value is 60.

  16. The read timeout in seconds when connecting to the authorization server. The default value is 60.

  17. 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.

  18. 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.

  19. 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.

  20. A delimiter used to parse groups information when it is returned as a single delimited string. The default value is ',' (comma).

  21. 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.

16.2.2. Configuring OAuth 2.0 on client applications

To configure OAuth 2.0 on client applications, you must specify the following:

  • SASL (Simple Authentication and Security Layer) security protocols

  • SASL mechanisms

  • A JAAS (Java Authentication and Authorization Service) module

  • Authentication properties to access the authorization server

Configuring SASL protocols

Specify SASL protocols in the client configuration:

  • 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.

Configuring SASL authentication mechanisms

Specify SASL mechanisms in the client configuration:

  • OAUTHBEARER for credentials exchange using a bearer token

  • PLAIN to pass client credentials (clientId + secret) or an access token

Configuring a JAAS module

Specify a JAAS module that implements the SASL authentication mechanism as a sasl.jaas.config property value:

  • org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule implements the OAUTHBEARER mechanism

  • org.apache.kafka.common.security.plain.PlainLoginModule implements the PLAIN mechanism

Note
 For the OAUTHBEARER mechanism, Strimzi provides a callback handler for clients that use Kafka Client Java libraries to enable credentials exchange. For clients in other languages, custom code may be required to obtain the access token. For the PLAIN mechanism, Strimzi provides server-side callbacks to enable credentials exchange.

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.

Configuring authentication properties

Configure the client to use credentials or access tokens for OAuth 2.0 authentication.

Using client credentials

Using client credentials involves configuring the client with the necessary credentials (client ID and secret, or client ID and client assertion) to obtain a valid access token from an authorization server. This is the simplest mechanism.

Using access tokens

Using access tokens, the client is configured with a valid long-lived access token or refresh token obtained from an authorization server. Using access tokens adds more complexity because there is an additional dependency on authorization server tools. 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.

The only information ever sent to Kafka is the access token. The credentials used to obtain the token are never sent to Kafka. When a client obtains an access token, no further communication with the authorization server is needed.

SASL authentication properties support the following authentication methods:

  • OAuth 2.0 client credentials

  • Access token or Service account token

  • Refresh token

  • OAuth 2.0 password grant (deprecated)

Add the authentication properties as JAAS configuration (sasl.jaas.config and sasl.login.callback.handler.class).

If the client application is not configured with an access token directly, the client exchanges one of the following sets of credentials for an access token during Kafka session initiation:

  • Client ID and secret

  • Client ID and client assertion

  • Client ID, refresh token, and (optionally) a secret

  • Username and password, with client ID and (optionally) a secret

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.
Example client credentials configuration using the client secret
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.

Example client credentials configuration using the client assertion
security.protocol=SASL_SSL
sasl.mechanism=OAUTHBEARER
ssl.truststore.location=/tmp/truststore.p12
ssl.truststore.password=$STOREPASS
ssl.truststore.type=PKCS12
sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
  oauth.token.endpoint.uri="<token_endpoint_url>" \
  oauth.client.id="<client_id>" \
  oauth.client.assertion.location="<path_to_client_assertion_token_file>" \ # (1)
  oauth.client.assertion.type="urn:ietf:params:oauth:client-assertion-type:jwt-bearer" \ # (2)
  oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \
  oauth.ssl.truststore.password="$STOREPASS" \
  oauth.ssl.truststore.type="PKCS12" \
  oauth.scope="<scope>" \
  oauth.audience="<audience>" ;
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler

  1. Path to the client assertion file used for authenticating the client. This file is a private key file as an alternative to the client secret. Alternatively, use the oauth.client.assertion option to specify the client assertion value in clear text.

  2. (Optional) Sometimes you may need to specify the client assertion type. In not specified, the default value is urn:ietf:params:oauth:client-assertion-type:jwt-bearer.

Example password grants configuration
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.
Example access token configuration
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.access.token="<access_token>" ; # (1)
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler

  1. Long-lived access token for Kafka clients. Alternatively, oauth.access.token.location can be used to specify the file that contains the access token.

Example Kubernetes service account token configuration
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.access.token.location="/var/run/secrets/kubernetes.io/serviceaccount/token";  # (1)
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler

  1. Location to the service account token on the filesystem (assuming that the client is deployed as a Kubernetes pod)

Example refresh token configuration
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.

SASL extensions for custom OAUTHBEARER implementations

If your Kafka broker uses a custom OAUTHBEARER implementation, you may need to pass additional SASL extension options. These extensions can include attributes or information required as client context by the authorization server. The options are passed as key-value pairs and are sent to the Kafka broker when a new session is started.

Pass SASL extension values using oauth.sasl.extension. as a key prefix.

Example configuration to pass SASL extension values
oauth.sasl.extension.key1="value1"
oauth.sasl.extension.key2="value2"

16.2.3. 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 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 credentials, 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 credentials, 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 credentials, 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 credentials, with broker performing fast local token validation

Client using client ID and credentials 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 credentials, 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.

16.2.4. Re-authenticating sessions

Configure oauth listeners to use Kafka session re-authentication for OAuth 2.0 sessions between Kafka clients and Kafka. 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 authentication on listeners.

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

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.

16.2.5. Example: Enabling OAuth 2.0 authentication

This example shows how to configure client access to a Kafka cluster using OAUth 2.0 authentication. The procedures describe the configuration required to set up OAuth 2.0 authentication on Kafka listeners, Kafka Java clients, and Kafka components.

Setting up OAuth 2.0 authentication on listeners

Configure Kafka listeners so that they are enabled to use OAuth 2.0 authentication using an authorization server.

We advise using 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.

For more information on the configuration of OAuth 2.0 authentication for Kafka broker listeners, see the KafkaListenerAuthenticationOAuth schema reference.

Prerequisites

  • Strimzi and Kafka are running

  • An OAuth 2.0 authorization server is deployed

Procedure

  1. Specify a listener in the Kafka resource with an oauth authentication type.

    Example listener configuration with OAuth 2.0 authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
      - name: external3
        port: 9094
        type: loadbalancer
        tls: true
        authentication:
          type: oauth
      #...

  2. Configure the OAuth listener depending on the authorization server and validation type:

  3. Apply the changes to the Kafka configuration.

  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
Setting up OAuth 2.0 on Kafka Java clients

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.

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.

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>0.15.0</version>
</dependency>

  2. Configure the client depending on the OAuth 2.0 authentication method:

    For example, specify the properties for the authentication method in a client.properties file.

  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.

Setting up OAuth 2.0 on Kafka components

This procedure describes how to set up Kafka components to use OAuth 2.0 authentication using an authorization server.

You can configure OAuth 2.0 authentication for the following components:

  • 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 client assertion

    • 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:

    Example OAuth 2.0 authentication configuration using the client secret
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: oauth # (1)
    tokenEndpointUri: https://<auth_server_address>/<path_to_token_endpoint> # (2)
    clientId: kafka-bridge
    clientSecret:
      secretName: my-bridge-oauth
      key: clientSecret
    tlsTrustedCertificates: # (3)
      - secretName: oauth-server-cert
        pattern: "*.crt"

    1. Authentication type set to oauth.

    2. URI of the token endpoint for authentication.

    3. Certificates stored in X.509 format within the specified secrets for TLS connection to the authorization server.

    In this example, OAuth 2.0 is assigned to the Kafka Bridge client using a client ID and the location of a client assertion file, with TLS to connect to the authorization server:

    Example OAuth 2.0 authentication configuration using client assertion
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: oauth
    tokenEndpointUri: https://<auth_server_address>/<path_to_token_endpoint>
    clientId: kafka-bridge
    clientAssertionLocation: /var/run/secrets/sso/assertion # (1)
    tlsTrustedCertificates:
      - secretName: oauth-server-cert
        pattern: "*.crt"

    1. Filesystem path to the client assertion file used for authenticating the client. This file is typically added to the deployed pod by an external operator service. Alternatively, use clientAssertion to refer to a secret containing the client assertion value.

    Here, OAuth 2.0 is assigned to the Kafka Bridge client using a service account token:

    Example OAuth 2.0 authentication configuration using the service account token
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: oauth
    accessTokenLocation: /var/run/secrets/kubernetes.io/serviceaccount/token # (1)

    1. Path to the service account token file location.

    Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional configuration options you can use:

    Additional configuration options
    # ...
spec:
  # ...
  authentication:
    # ...
    disableTlsHostnameVerification: true # (1)
    accessTokenIsJwt: false # (2)
    scope: any # (3)
    audience: kafka # (4)
    connectTimeoutSeconds: 60 # (5)
    readTimeoutSeconds: 60 # (6)
    httpRetries: 2 # (7)
    httpRetryPauseMs: 300 # (8)
    includeAcceptHeader: false # (9)

    1. (Optional) Disable TLS hostname verification. Default is false.

    2. If you are using opaque tokens, you can apply accessTokenIsJwt: false so that access tokens are not treated as JWT tokens.

    3. (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.

    4. (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.

    5. (Optional) The connect timeout in seconds when connecting to the authorization server. The default value is 60.

    6. (Optional) The read timeout in seconds when connecting to the authorization server. The default value is 60.

    7. (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.

    8. (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.

    9. (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 resource configuration of the component.

  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.

16.3. Using OAuth 2.0 token-based authorization

Strimzi supports the use of OAuth 2.0 token-based authorization through Keycloak Authorization Services, which lets you manage security policies and permissions centrally.

Security policies and permissions defined in Keycloak grant access to Kafka resources. 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, but 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 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.

16.3.1. Example: Enabling OAuth 2.0 authorization

This example procedure shows how to configure Kafka to use OAuth 2.0 authorization using Keycloak Authorization Services. To enable OAuth 2.0 authorization using Keycloak, configure the Kafka resource to use keycloak authorization and specify the properties required to access the authorization server and Keycloak Authorization Services.

Keycloak server Authorization Services REST endpoints 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.

A Keycloak authorizer (KeycloakAuthorizer) is provided with Strimzi. The authorizer fetches a list of granted permissions from the authorization server as needed, and enforces authorization locally on Kafka, making rapid authorization decisions for each client request.

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 Kafka regardless of the authorization implemented.
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 OAuth 2.0 client for Kafka 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 resource to use keycloak authorization, and to be able to access the authorization server and Authorization Services.

    Example OAuth 2.0 authorization configuration
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    authorization:
      type: keycloak (1)
      tokenEndpointUri: <https://<auth-server-address>/realms/external/protocol/openid-connect/token> (2)
      clientId: kafka (3)
      delegateToKafkaAcls: false (4)
      disableTlsHostnameVerification: false (5)
      superUsers: (6)
        - CN=user-1
        - user-2
        - CN=user-3
      tlsTrustedCertificates: (7)
        - secretName: oauth-server-cert
          pattern: "*.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) Certificates stored in X.509 format within the specified secrets 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 Kafka 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.

  5. Apply the changes to the Kafka configuration.

  6. 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.

  7. Verify the configured permissions by accessing Kafka brokers as clients or users with specific roles, ensuring they have the necessary access and do not have unauthorized access.

16.4. Setting up permissions in Keycloak

When using Keycloak as the OAuth 2.0 authorization server, Kafka permissions are granted to user accounts or service accounts using authorization permissions. To grant permissions to access Kafka, create an OAuth client specification in Keycloak that maps the authorization models of Keycloak Authorization Services and Kafka.

16.4.1. Kafka and Keycloak authorization models

Kafka and Keycloak use different authorization models.

Kafka authorization model

Kafka’s authorization model uses resource types and operations to describe ACLs for a user. 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.

Each resource type has a set of available permissions for operations. For example, the Topic resource type has Create and Write permissions among others.

Refer to the Kafka authorization model in the Kafka documentation for the full list of resources and permissions.

Keycloak authorization model

Keycloak’s authorization services model has four concepts for defining and granting permissions:

  • Resources

  • Scopes

  • Policies

  • Permissions

For information on these concepts, see the guide to Keycloak Authorization Services.

16.4.2. Mapping authorization models

The Kafka authorization model is used as a basis for defining the Keycloak roles and resources that 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 cluster. You then specify Keycloak Authorization Services rules on the client. Typically, the client ID of the OAuth client that represents the Kafka cluster 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. For information on enabling authorization services, see the guide to Keycloak Authorization Services.

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 grant permissions, the authorizer evaluates the grants list (received and cached) from Keycloak Authorization Services based on the access token owner’s policies and permissions.

Uploading 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 should contain the following Kafka permissions regardless of the resource type:

  • Create

  • Write

  • Read

  • Delete

  • Describe

  • Alter

  • DescribeConfigs

  • AlterConfigs

  • ClusterAction

  • IdempotentWrite

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.

Note

The All permission is not supported.
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

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.

16.4.3. Permissions for common 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

16.4.4. Example: Setting up Keycloak Authorization Services

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. This example explains how to use Keycloak Authorization Services with keycloak authorization. Set up 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. Setting up permissions in Keycloak

  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.

Setting up permissions in Keycloak

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 organize users and set permissions. Groups can be linked to an LDAP identity provider and used to compartmentalize users, such as by region or department. Users can be added to groups through a custom LDAP interface to manage permissions for Kafka resources. For more information on using KeyCloak’s LDAP provider, see the guide to Keycloak Server Administration.

    Users

    Users define individual users. In this example, alice (in the ClusterManager group) and bob (in ClusterManager-my-cluster) are created. Users can also be stored in an LDAP identity provider.

    Roles

    Roles assign specific permissions to users or clients. Roles function like tags to give users certain rights. While roles cannot be stored in LDAP, you can add Keycloak roles to groups to combine both roles and permissions.

    Clients

    Clients define configurations for Kafka interactions.

    • The kafka client handles OAuth 2.0 token validation for brokers and contains authorization policies (which require authorization to be enabled).

    • The kafka-cli client is used by command line tools to obtain access or refresh tokens.

    • team-a-client and team-b-client represent services with partial access to specific 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 Kafka image to connect to a running Kafka broker.

    NS=sso
kubectl run -ti --restart=Never --image=quay.io/strimzi/kafka:latest-kafka-3.9.0 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 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/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/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/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"