Deploying and Managing Streams for Apache Kafka on OpenShift


Red Hat Streams for Apache Kafka 2.7

Deploy and manage Streams for Apache Kafka 2.7 on OpenShift Container Platform

Abstract

Use the Streams for Apache Kafka operators to deploy Kafka components. Configure Kafka components to build a large-scale messaging network. Set up secure client access to your Kafka clusters and incoprorate features such as metrics and distrubuted tracing. Upgrade to leverage new features, including the latest supported Kafka version.

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Chapter 1. Deployment overview

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

This guide provides instructions for deploying and managing Streams for Apache Kafka. Deployment options and steps are covered using the example installation files included with Streams for Apache Kafka. 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 Streams for Apache Kafka 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 Streams for Apache Kafka Drain Cleaner. You’ll also find recommendations on managing Streams for Apache Kafka and fine-tuning Kafka configuration for optimal performance.

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

Streams for Apache Kafka is designed to be compatible with all types of OpenShift 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. Streams for Apache Kafka custom resources

The deployment of Kafka components onto an OpenShift cluster using Streams for Apache Kafka 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 OpenShift resources.

CRDs act as configuration instructions to describe the custom resources in an OpenShift cluster, and are provided with Streams for Apache Kafka 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 Streams for Apache Kafka distribution.

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

1.1.1. Streams for Apache Kafka custom resource example

CRDs require a one-time installation in a cluster to define the schemas used to instantiate and manage Streams for Apache Kafka-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 Streams for Apache Kafka administrators. For more information, see Section 4.6, “Designating Streams for Apache Kafka administrators”.

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

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

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

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

Kafka topic CRD

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

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

You can identify the CRD YAML files supplied with the Streams for Apache Kafka 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 Streams for Apache Kafka.

1.1.2. Performing oc operations on custom resources

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

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

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

Listing Kafka clusters

oc get k

NAME         DESIRED KAFKA REPLICAS   DESIRED ZK REPLICAS
my-cluster   3                        3

Table 1.1. Long and short names for each Streams for Apache Kafka resource
Streams for Apache Kafka resourceLong nameShort 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 Mirror Maker

kafkamirrormaker

kmm

Kafka Mirror Maker 2

kafkamirrormaker2

kmm2

Kafka Bridge

kafkabridge

kb

Kafka Rebalance

kafkarebalance

kr

1.1.2.1. Resource categories

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

All Streams for Apache Kafka custom resources belong to the category strimzi, so you can use strimzi to get all the Streams for Apache Kafka resources with one command.

For example, running oc get strimzi lists all Streams for Apache Kafka custom resources in a given namespace.

Listing all custom resources

oc 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 oc 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

oc 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 oc delete command to delete all resources in a single command.

Deleting all custom resources

oc delete $(oc 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 Streams for Apache Kafka features.

1.1.2.2. 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 oc 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

oc 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. Streams for Apache Kafka 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 Streams for Apache Kafka Custom Resource API Reference.

Table 1.2. Custom resources that provide status information
Streams for Apache Kafka resourceSchema referencePublishes status information on…​

Kafka

KafkaStatus schema reference

The Kafka cluster, its listeners, and node pools

KafkaNodePool

KafkaNodePoolStatus schema reference

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

KafkaTopic

KafkaTopicStatus schema reference

Kafka topics in the Kafka cluster

KafkaUser

KafkaUserStatus schema reference

Kafka users in the Kafka cluster

KafkaConnect

KafkaConnectStatus schema reference

The Kafka Connect cluster and connector plugins

KafkaConnector

KafkaConnectorStatus schema reference

KafkaConnector resources

KafkaMirrorMaker2

KafkaMirrorMaker2Status schema reference

The Kafka MirrorMaker 2 cluster and internal connectors

KafkaMirrorMaker

KafkaMirrorMakerStatus schema reference

The Kafka MirrorMaker cluster

KafkaBridge

KafkaBridgeStatus schema reference

The Streams for Apache Kafka Bridge

KafkaRebalance

KafkaRebalance schema reference

The status and results of a rebalance

StrimziPodSet

StrimziPodSetStatus schema reference

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 Streams for Apache Kafka versions being used. You can check the values of operatorLastSuccessfulVersion and kafkaVersion to determine whether an upgrade of Streams for Apache Kafka or Kafka has completed

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

Here we see the status 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: 3.7.0 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: 2.7 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 oc with the status subresource of a custom resource to retrieve information about the resource.

Prerequisites

  • An OpenShift 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:

    oc 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. Streams for Apache Kafka operators

Streams for Apache Kafka operators are purpose-built with specialist operational knowledge to effectively manage Kafka on OpenShift. Each operator performs a distinct function.

Cluster Operator
The Cluster Operator handles the deployment and management of Apache Kafka clusters on OpenShift. 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 Streams for Apache Kafka, 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 Streams for Apache Kafka resources in OpenShift namespaces

Operators watch and manage Streams for Apache Kafka resources in OpenShift namespaces. The Cluster Operator can watch a single namespace, multiple namespaces, or all namespaces in an OpenShift cluster. The Topic Operator and User Operator can watch a single namespace.

  • The Cluster Operator watches for Kafka resources
  • The Topic Operator watches for KafkaTopic resources
  • The User Operator watches for KafkaUser resources

The Topic Operator and the User Operator can only watch a single Kafka cluster in a namespace. And they can only be connected to a single Kafka cluster.

If multiple Topic Operators watch the same namespace, name collisions and topic deletion can occur. This is because each Kafka cluster uses Kafka topics that have the same name (such as __consumer_offsets). Make sure that only one Topic Operator watches a given namespace.

When using multiple User Operators with a single namespace, a user with a given username can exist in more than one Kafka cluster.

If you deploy the Topic Operator and User Operator using the Cluster Operator, they watch the Kafka cluster deployed by the Cluster Operator by default. You can also specify a namespace using watchedNamespace in the operator configuration.

For a standalone deployment of each operator, you specify a namespace and connection to the Kafka cluster to watch in the configuration.

1.2.2. Managing RBAC resources

The Cluster Operator creates and manages role-based access control (RBAC) resources for Streams for Apache Kafka components that need access to OpenShift resources.

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

Permission is specified through the following OpenShift RBAC resources:

  • ServiceAccount
  • Role and ClusterRole
  • RoleBinding and ClusterRoleBinding
1.2.2.1. Delegating privileges to Streams for Apache Kafka components

The Cluster Operator runs under a service account called strimzi-cluster-operator. It is assigned cluster roles that give it permission to create the RBAC resources for Streams for Apache Kafka components. Role bindings associate the cluster roles with the service account.

OpenShift prevents components operating under one ServiceAccount from granting another ServiceAccount privileges that the granting ServiceAccount does not have. Because the Cluster Operator creates the RoleBinding and ClusterRoleBinding RBAC resources needed by the resources it manages, it requires a role that gives it the same privileges.

The following sections describe the RBAC resources required by the Cluster Operator.

1.2.2.2. ClusterRole resources

The Cluster Operator uses ClusterRole resources to provide the necessary access to resources. Depending on the OpenShift cluster setup, a cluster administrator might be needed to create the cluster roles.

Note

Cluster administrator rights are only needed for the creation of ClusterRole resources. The Cluster Operator will not run under a cluster admin account.

The RBAC resources follow the principle of least privilege and contain only those privileges needed by the Cluster Operator to operate the cluster of the Kafka component.

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

Table 1.3. ClusterRole resources
NameDescription

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 Streams for Apache Kafka custom resources.

strimzi-kafka-broker

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

1.2.2.3. 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 1.4. ClusterRoleBinding resources
NameDescription

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 1.5. RoleBinding resources
NameDescription

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.

1.2.2.4. 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 1.6. ServiceAccount resources
NameUsed 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 Streams for Apache Kafka 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

OpenShift 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 Streams for Apache 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 Streams for Apache Kafka on a FIPS-enabled OpenShift cluster, the OpenJDK used in Streams for Apache Kafka container images automatically switches to FIPS mode. From version 2.3, Streams for Apache Kafka can run on FIPS-enabled OpenShift 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 OpenShift clusters, you may experience higher memory consumption compared to regular OpenShift clusters. To avoid any issues, we suggest increasing the memory request to at least 512Mi.

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

Note

Compatibility with the technology previews of Streams for Apache Kafka Proxy and Streams for Apache Kafka Console has not been tested regarding FIPS support. While they are expected to function properly, we cannot guarantee full support at this time.

1.4.1. Minimum password length

When running in the FIPS mode, SCRAM-SHA-512 passwords need to be at least 32 characters long. From Streams for Apache Kafka 2.3, the default password length in Streams for Apache Kafka 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.

1.5. Document Conventions

User-replaced values

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

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

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

1.6. Additional resources

Chapter 2. Streams for Apache Kafka installation methods

You can install Streams for Apache Kafka on OpenShift 4.12 to 4.16 in two ways.

Installation methodDescription

Installation artifacts (YAML files)

Download Red Hat Streams for Apache Kafka 2.7 OpenShift Installation and Example Files from the Streams for Apache Kafka software downloads page. Deploy the YAML installation artifacts to your OpenShift cluster using oc. You start by deploying the Cluster Operator from install/cluster-operator to a single namespace, multiple namespaces, or all namespaces.

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

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

OperatorHub

Use the Streams for Apache Kafka operator in the OperatorHub to deploy Streams for Apache Kafka to a single namespace or all namespaces.

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

Note

Installation of Streams for Apache Kafka using Helm is not supported.

Chapter 3. What is deployed with Streams for Apache Kafka

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

A typical deployment incorporating Kafka components might include:

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

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

3.1. Order of deployment

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

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

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

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

3.2. (Preview) Deploying the Streams for Apache Kafka Proxy

Streams for Apache Kafka Proxy is an Apache Kafka protocol-aware proxy designed to enhance Kafka-based systems. Through its filter mechanism it allows additional behavior to be introduced into a Kafka-based system without requiring changes to either your applications or the Kafka cluster itself.

For more information on connecting to and using the Streams for Apache Kafka Proxy, see the proxy guide in the Streams for Apache Kafka documentation.

Note

The Streams for Apache Kafka Proxy is currently available as a technology preview.

3.3. (Preview) Deploying the Streams for Apache Kafka Console

After you have deployed a Kafka cluster that’s managed by Streams for Apache Kafka, you can deploy the Streams for Apache Kafka Console and connect your cluster. The Streams for Apache Kafka Console facilitates the administration of Kafka clusters, providing real-time insights for monitoring, managing, and optimizing each cluster from its user interface.

For more information on connecting to and using the Streams for Apache Kafka Console, see the console guide in the Streams for Apache Kafka documentation.

Note

The Streams for Apache Kafka Console is currently available as a technology preview.

Chapter 4. Preparing for your Streams for Apache Kafka deployment

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

Note

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

4.1. Deployment prerequisites

To deploy Streams for Apache Kafka, you will need the following:

  • An OpenShift 4.12 to 4.16 cluster.

    Streams for Apache Kafka is based on Strimzi 0.40.x.

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

4.2. Operator deployment best practices

Potential issues can arise from installing more than one Streams for Apache Kafka operator in the same OpenShift cluster, especially when using different versions. Each Streams for Apache Kafka operator manages a set of resources in an OpenShift cluster. When you install multiple Streams for Apache Kafka 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 Streams for Apache Kafka operators in different namespaces within the same OpenShift cluster. Although namespaces provide some degree of resource isolation, certain resources managed by the Streams for Apache Kafka 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 Streams for Apache Kafka operators may introduce changes, bug fixes, or improvements that are not backward-compatible.

To avoid the issues associated with installing multiple Streams for Apache Kafka operators in an OpenShift cluster, the following guidelines are recommended:

  • Install the Streams for Apache Kafka 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 Streams for Apache Kafka operator to manage all your Kafka instances within an OpenShift cluster.
  • Update the Streams for Apache Kafka 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 Streams for Apache Kafka operator, you can enhance the stability of managing Kafka instances in an OpenShift cluster. This approach also enables you to make the most of Streams for Apache Kafka’s latest features and capabilities.

Note

As Streams for Apache Kafka is based on Strimzi, the same issues can also arise when combining Streams for Apache Kafka operators with Strimzi operators in an OpenShift cluster.

4.3. Downloading Streams for Apache Kafka release artifacts

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

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

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

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

Note

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

4.4. Pushing container images to your own registry

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

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

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

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

Container imageNamespace/RepositoryDescription

Kafka

  • registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0
  • registry.redhat.io/amq-streams/kafka-36-rhel9:2.7.0

Streams for Apache Kafka image for running Kafka, including:

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

Operator

  • registry.redhat.io/amq-streams/strimzi-rhel9-operator:2.7.0

Streams for Apache Kafka image for running the operators:

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

Kafka Bridge

  • registry.redhat.io/amq-streams/bridge-rhel9:2.7.0

Streams for Apache Kafka image for running the Streams for Apache Kafka Bridge

Streams for Apache Kafka Drain Cleaner

  • registry.redhat.io/amq-streams/drain-cleaner-rhel9:2.7.0

Streams for Apache Kafka image for running the Streams for Apache Kafka Drain Cleaner

Streams for Apache Kafka Proxy

  • registry.redhat.io/amq-streams/proxy-rhel9-operator:2.7.0

Streams for Apache Kafka image for running the Streams for Apache Kafka Proxy

Streams for Apache Kafka Console

  • registry.redhat.io/amq-streams/console-rhel9-operator:2.7.0

Streams for Apache Kafka image for running the Streams for Apache Kafka Console

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

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

Note

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

Prerequisites

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

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

Procedure

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

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

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

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

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

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

4.6. Designating Streams for Apache Kafka administrators

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

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

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

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

A system administrator can designate Streams for Apache Kafka administrators after the Cluster Operator is deployed.

Prerequisites

  • The Streams for Apache Kafka Custom Resource Definitions (CRDs) and role-based access control (RBAC) resources to manage the CRDs have been deployed with the Cluster Operator.

Procedure

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

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

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

Chapter 5. Installing Streams for Apache Kafka from the OperatorHub using the web console

Install the Streams for Apache Kafka operator from the OperatorHub in the OpenShift Container Platform web console.

The procedures in this section show how to:

5.1. Installing the Streams for Apache Kafka operator from the OperatorHub

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

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

Warning

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

Prerequisites

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

Procedure

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

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

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

    The operator is located in the Streaming & Messaging category.

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

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

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

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

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

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

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

    The status will show as Succeeded.

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

Note

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

5.2. Deploying Kafka components using the Streams for Apache Kafka operator

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

The following Kafka components are available for installation:

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

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

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

Prerequisites

Procedure

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

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

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

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

  3. Click Create to start the installation of Kafka.

    Wait until the status changes to Ready.

Chapter 6. Deploying Streams for Apache Kafka using installation artifacts

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

Streams for Apache Kafka is based on Strimzi 0.40.x. You can deploy Streams for Apache Kafka 2.7 on OpenShift 4.12 to 4.16.

The steps to deploy Streams for Apache Kafka using the installation files are as follows:

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

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

Note

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

6.1. Basic deployment path

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

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

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

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

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

The basic deployment path is as follows:

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

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

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

6.2. Deploying the Cluster Operator

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

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

By default, a single replica of the Cluster Operator is deployed. You can add replicas with leader election so that additional Cluster Operators are on standby in case of disruption. For more information, see Section 9.5.4, “Running multiple Cluster Operator replicas with leader election”.

6.2.1. Specifying the namespaces the Cluster Operator watches

The Cluster Operator watches for updates in the namespaces where the Kafka resources are deployed. When you deploy the Cluster Operator, you specify which namespaces to watch in the OpenShift 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 OpenShift cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary OpenShift resources, such as Deployments, Pods, Services and ConfigMaps.

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

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

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

Note

While the Cluster Operator can watch one, multiple, or all namespaces in an OpenShift cluster, the Topic Operator and User Operator watch for KafkaTopic and KafkaUser resources in a single namespace. For more information, see Section 1.2.1, “Watching Streams for Apache Kafka resources in OpenShift namespaces”.

6.2.2. Deploying the Cluster Operator to watch a single namespace

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

Prerequisites

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

Procedure

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

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

    On Linux, use:

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

    On MacOS, use:

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

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

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

    Output shows the deployment name and readiness

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

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

6.2.3. Deploying the Cluster Operator to watch multiple namespaces

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

Prerequisites

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

Procedure

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

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

    On Linux, use:

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

    On MacOS, use:

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

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

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq-streams/strimzi-rhel9-operator:2.7.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: watched-namespace-1,watched-namespace-2,watched-namespace-3
  3. For each namespace listed, install the RoleBindings.

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

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

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

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

    Output shows the deployment name and readiness

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

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

6.2.4. Deploying the Cluster Operator to watch all namespaces

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

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

Prerequisites

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

Procedure

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

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

    On Linux, use:

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

    On MacOS, use:

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

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          # ...
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq-streams/strimzi-rhel9-operator:2.7.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: "*"
            # ...
  3. Create ClusterRoleBindings that grant cluster-wide access for all namespaces to the Cluster Operator.

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

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

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

    Output shows the deployment name and readiness

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

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

6.3. Deploying Kafka

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

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

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

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

6.3.1. Deploying a Kafka cluster with node pools

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

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

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

kafka-with-dual-role-kraft-nodes.yaml
Deploys a Kafka cluster with one pool of KRaft nodes that share the broker and controller roles.
kafka-with-kraft.yaml
Deploys a persistent Kafka cluster with one pool of controller nodes and one pool of broker nodes.
kafka-with-kraft-ephemeral.yaml
Deploys an ephemeral Kafka cluster with one pool of controller nodes and one pool of broker nodes.
kafka.yaml
Deploys ZooKeeper with 3 nodes, and 2 different pools of Kafka brokers. Each of the pools has 3 brokers. The pools in the example use different storage configuration.
Note

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

Procedure

  1. Deploy a KRaft-based Kafka cluster.

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

      oc apply -f examples/kafka/kraft/kafka-with-dual-role-nodes.yaml
    • To deploy a persistent Kafka cluster in KRaft mode with separate node pools for broker and controller nodes:

      oc apply -f examples/kafka/kraft/kafka.yaml
    • To deploy an ephemeral Kafka cluster in KRaft mode with separate node pools for broker and controller nodes:

      oc apply -f examples/kafka/kraft/kafka-ephemeral.yaml
    • To deploy a Kafka cluster and ZooKeeper cluster with two node pools of three brokers:

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

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the node pool names and readiness

    NAME                        READY  STATUS   RESTARTS
    my-cluster-entity-operator  3/3    Running  0
    my-cluster-pool-a-0         1/1    Running  0
    my-cluster-pool-a-1         1/1    Running  0
    my-cluster-pool-a-4         1/1    Running  0

    • my-cluster is the name of the Kafka cluster.
    • pool-a is the name of the node pool.

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

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

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

      Note

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

Additional resources

Node pool configuration

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

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

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

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

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

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

Ephemeral cluster
In general, an ephemeral (or temporary) Kafka cluster is suitable for development and testing purposes, not for production. This deployment uses emptyDir volumes for storing broker information (for ZooKeeper) and topics or partitions (for Kafka). Using an emptyDir volume means that its content is strictly related to the pod life cycle and is deleted when the pod goes down.
Persistent cluster

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

The following examples show some common types of persistent volumes:

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

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

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

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

Default cluster name and specified Kafka versions

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    version: 3.7.0
    #...
    config:
      #...
      log.message.format.version: "3.7"
      inter.broker.protocol.version: "3.7"
  # ...

Procedure

  1. Deploy a ZooKeeper-based Kafka cluster.

    • To deploy an ephemeral cluster:

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

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

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the pod names and readiness

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

    my-cluster is the name of the Kafka cluster.

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

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

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

Additional resources

Kafka cluster configuration

6.3.3. Deploying the Topic Operator using the Cluster Operator

This procedure describes how to deploy the Topic Operator using the Cluster Operator. The Topic Operator can be deployed for use in either bidirectional mode or unidirectional mode. To learn more about bidirectional and unidirectional topic management, see Section 10.1, “Topic management modes”.

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

If you use Streams for Apache Kafka 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 Streams for Apache Kafka, you must deploy the Topic Operator as a standalone component.

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

Procedure

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

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

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

  3. Create or update the resource:

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

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the pod name and readiness

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

    my-cluster is the name of the Kafka cluster.

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

6.3.4. Deploying the User Operator using the Cluster Operator

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

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

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

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

Procedure

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

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

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

  3. Create or update the resource:

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

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the pod name and readiness

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

    my-cluster is the name of the Kafka cluster.

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

6.3.5. 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 Streams for Apache Kafka.

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

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

Procedure

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

    For example:

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

    Be sure to use localhost:12181.

6.3.6. List of Kafka cluster resources

The following resources are created by the Cluster Operator in the OpenShift 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 OpenShift cluster.
<kafka_cluster_name>-kafka-external-bootstrap
Bootstrap service for clients connecting from outside the OpenShift 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 OpenShift 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 OpenShift 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 OpenShift 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 OpenShift 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 OpenShift 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 OpenShift 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 OpenShift 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.

6.4. Deploying Kafka Connect

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

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

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

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

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

Note

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

6.4.1. Deploying Kafka Connect to your OpenShift cluster

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

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

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

Streams for Apache Kafka 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.

Procedure

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

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

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the deployment name and readiness

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

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

    A pod ID identifies each pod created.

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

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

6.4.2. List of Kafka Connect cluster resources

The following resources are created by the Cluster Operator in the OpenShift 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).

6.5. Adding Kafka Connect connectors

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

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

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

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

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

You can also create new connector instances using these options.

6.5.1. Building a new container image with connector plugins automatically

Configure Kafka Connect so that Streams for Apache Kafka 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. Streams for Apache Kafka will automatically download and add the connector plugins into a new container image. The container is pushed into the container repository specified in .spec.build.output and automatically used in the Kafka Connect deployment.

Prerequisites

You need to provide your own container registry where images can be pushed to, stored, and pulled from. Streams for Apache Kafka 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
    2
    (Required) Configuration of the container registry where new images are pushed.
    3
    (Required) List of connector plugins and their artifacts to add to the new container image. Each plugin must be configured with at least one artifact.
  2. Create or update the resource:

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

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

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

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

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

Procedure

  1. Create a new Dockerfile using registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0 as the base image:

    FROM registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0
    USER root:root
    COPY ./my-plugins/ /opt/kafka/plugins/
    USER 1001

    Example plugins file

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

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

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

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

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

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

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

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

6.5.3. Deploying KafkaConnector resources

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

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

In the configuration shown in this procedure, the autoRestart 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 Streams for Apache Kafka. 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.

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

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

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

Note

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

Prerequisites

  • A Kafka Connect deployment
  • The Cluster Operator is running

Procedure

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

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

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

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

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

    Example KafkaConnector source connector configuration

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-source-connector  1
      labels:
        strimzi.io/cluster: my-connect-cluster 2
    spec:
      class: org.apache.kafka.connect.file.FileStreamSourceConnector 3
      tasksMax: 2 4
      autoRestart: 5
        enabled: true
      config: 6
        file: "/opt/kafka/LICENSE" 7
        topic: my-topic 8
        # ...

    1
    Name of the KafkaConnector resource, which is used as the name of the connector. Use any name that is valid for an OpenShift resource.
    2
    Name of the Kafka Connect cluster to create the connector instance in. Connectors must be deployed to the same namespace as the Kafka Connect cluster they link to.
    3
    Full name 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 OpenShift cluster:

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

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

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

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

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

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

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

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

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

Source and sink connector configuration options

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

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

Table 6.1. Configuration options for the FileStreamSource connector class
NameTypeDefault valueDescription

file

String

Null

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

topic

List

Null

The Kafka topic to publish data to.

Table 6.2. Configuration options for FileStreamSinkConnector class
NameTypeDefault valueDescription

file

String

Null

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

topics

List

Null

One or more Kafka topics to read data from.

topics.regex

String

Null

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

6.5.4. Exposing the Kafka Connect API

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

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

Note

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

You can add the connector configuration as a JSON object.

Example curl request to add connector configuration

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

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

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

The connection is insecure, so allow external access advisedly.

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

Selector configuration for the service

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

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

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

Example NetworkPolicy to allow requests to the Kafka Connect API

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

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

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

6.5.5. Limiting access to the Kafka Connect API

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

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

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

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

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

org.apache.kafka.disallowed.login.modules

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

Example configuration for disallowing login modules

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

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

connector.client.config.override.policy

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

Example configuration to specify connector override policy

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

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

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

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

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

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

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

6.6. Deploying Kafka MirrorMaker

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

Data replication across clusters supports scenarios that require the following:

  • Recovery of data in the event of a system failure
  • Consolidation of data from multiple source clusters for centralized analysis
  • Restriction of data access to a specific cluster
  • Provision of data at a specific location to improve latency

6.6.1. Deploying Kafka MirrorMaker to your OpenShift cluster

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

The deployment uses a YAML file to provide the specification to create a KafkaMirrorMaker or KafkaMirrorMaker2 resource depending on the version of MirrorMaker deployed. 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 Streams for Apache Kafka as well. The KafkaMirrorMaker resource will be removed from Streams for Apache Kafka when we adopt Apache Kafka 4.0.0. As a replacement, use the KafkaMirrorMaker2 custom resource with the IdentityReplicationPolicy.

Streams for Apache Kafka 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.

Procedure

  1. Deploy Kafka MirrorMaker to your OpenShift cluster:

    For MirrorMaker:

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

    For MirrorMaker 2:

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

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the deployment name and readiness

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

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

    A pod ID identifies each pod created.

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

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

6.6.2. List of Kafka MirrorMaker 2 cluster resources

The following resources are created by the Cluster Operator in the OpenShift 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.

6.6.3. List of Kafka MirrorMaker cluster resources

The following resources are created by the Cluster Operator in the OpenShift 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.

6.7. Deploying Kafka Bridge

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

6.7.1. Deploying Kafka Bridge to your OpenShift cluster

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

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

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

  • examples/bridge/kafka-bridge.yaml

Procedure

  1. Deploy Kafka Bridge to your OpenShift cluster:

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

    oc get pods -n <my_cluster_operator_namespace>

    Output shows the deployment name and readiness

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

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

    A pod ID identifies each pod created.

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

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

6.7.2. Exposing the Kafka Bridge service to your local machine

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

Note

Port forwarding is only suitable for development and testing purposes.

Procedure

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

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

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

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

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

6.7.3. Accessing the Kafka Bridge outside of OpenShift

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

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

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

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

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

6.7.4. List of Kafka Bridge cluster resources

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

<bridge_cluster_name>-bridge
Deployment which is in charge to create the Kafka Bridge worker node pods.
<bridge_cluster_name>-bridge-service
Service which exposes the REST interface of the Kafka Bridge cluster.
<bridge_cluster_name>-bridge-config
ConfigMap which contains the Kafka Bridge ancillary configuration and is mounted as a volume by the Kafka broker pods.
<bridge_cluster_name>-bridge
Pod Disruption Budget configured for the Kafka Bridge worker nodes.

6.8. Alternative standalone deployment options for Streams for Apache Kafka operators

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

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

6.8.1. Deploying the standalone Topic Operator

This procedure shows how to deploy the Topic Operator in unidirectional mode as a standalone component for topic management. You can use a standalone Topic Operator with a Kafka cluster that is not managed by the Cluster Operator. Unidirectional topic management maintains topics solely through KafkaTopic resources. For more information on unidirectional topic management, see Section 10.1, “Topic management modes”. Alternate configuration is also shown for deploying the Topic Operator in bidirectional mode.

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

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

Prerequisites

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

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

Procedure

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

    Example standalone Topic Operator deployment configuration

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

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

    Example mTLS configuration

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

    1
    The truststore contains the public keys of the Certificate Authorities used to sign the Kafka and ZooKeeper server certificates.
    2
    The password for accessing the truststore.
    3
    The keystore contains the private key for mTLS authentication.
    4
    The password for accessing the keystore.
  4. Apply the changes to the Deployment configuration to deploy the Topic Operator.
  5. Check the status of the deployment:

    oc get deployments

    Output shows the deployment name and readiness

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

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

6.8.1.1. Deploying the standalone Topic Operator for bidirectional topic management

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

Note

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

  1. Undeploy the current standalone Topic Operator.

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

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

    • STRIMZI_ZOOKEEPER_CONNECT
    • STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS
    • TC_ZK_CONNECTION_TIMEOUT_MS
    • STRIMZI_USE_ZOOKEEPER_TOPIC_STORE

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

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

    • STRIMZI_REASSIGN_THROTTLE
    • STRIMZI_REASSIGN_VERIFY_INTERVAL_MS
    • STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS
    • STRIMZI_TOPICS_PATH
    • STRIMZI_STORE_TOPIC
    • STRIMZI_STORE_NAME
    • STRIMZI_APPLICATION_ID
    • STRIMZI_STALE_RESULT_TIMEOUT_MS

      Example standalone Topic Operator deployment configuration for bidirectional topic management

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

      1
      (ZooKeeper) The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
      2
      (ZooKeeper) The ZooKeeper session timeout, in milliseconds. The default is 18000 (18 seconds).
      3
      The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential backoff. Consider increasing this value when topic creation takes more time due to the number of partitions or replicas. The default is 6 attempts.
  3. Apply the changes to the Deployment configuration to deploy the Topic Operator.

6.8.2. Deploying the standalone User Operator

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

A standalone deployment can operate with any Kafka cluster.

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

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

Prerequisites

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

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

Procedure

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

    Example standalone User Operator deployment configuration

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

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

    Example mTLS configuration

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

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

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

    oc get deployments

    Output shows the deployment name and readiness

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

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

Chapter 7. Feature gates

Streams for Apache Kafka 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 Streams for Apache Kafka 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 Streams for Apache Kafka deployment. A feature gate at the GA stage cannot be disabled.

7.1. Graduated feature gates (GA)

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

7.1.1. 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 Streams for Apache Kafka 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 Streams for Apache Kafka 1.7 and earlier and Streams for Apache Kafka 2.3 and newer are not possible. You must first upgrade or downgrade through one of the Streams for Apache Kafka versions in-between, disable the ControlPlaneListener feature gate, and then downgrade or upgrade (with the feature gate enabled) to the target version.

7.1.2. 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.

7.1.3. UseStrimziPodSets feature gate

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

Important

With the UseStrimziPodSets feature gate permanently enabled, direct downgrades from Streams for Apache Kafka 2.5 and newer to Streams for Apache Kafka 2.0 or earlier are not possible. You must first downgrade through one of the Streams for Apache Kafka versions in-between, disable the UseStrimziPodSets feature gate, and then downgrade to Streams for Apache Kafka 2.0 or earlier.

7.1.4. 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 OpenShift Deployment resources.

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

Important

With the StableConnectIdentities feature gate permanently enabled, direct downgrades from Streams for Apache Kafka 2.7 and newer to Streams for Apache Kafka 2.3 or earlier are not possible. You must first downgrade through one of the Streams for Apache Kafka versions in-between, disable the StableConnectIdentities feature gate, and then downgrade to Streams for Apache Kafka 2.3 or earlier.

7.2. 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.

7.2.1. UseKRaft feature gate

The UseKRaft feature gate has a default state of enabled.

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

Using the UseKRaft feature gate requires the KafkaNodePools feature gate to be enabled as well. To deploy a Kafka cluster in KRaft mode, you must use the KafkaNodePool resources. For more details and examples, see Section 6.3.1, “Deploying a Kafka cluster with node pools”. The Kafka custom resource using KRaft mode must also have the annotation strimzi.io/kraft="enabled".

Currently, the KRaft mode in Streams for Apache Kafka has the following major limitations:

  • Only the Unidirectional Topic Operator is supported in KRaft mode. The Bidirectional Topic Operator is not supported and when the UnidirectionalTopicOperator feature gate is disabled, the spec.entityOperator.topicOperator property must be removed from the Kafka custom resource.
  • JBOD storage is not supported. The type: jbod storage can be used, but the JBOD array can contain only one disk.
  • Scaling of KRaft controller-only nodes up or down is not supported.
  • Unregistering Kafka nodes removed from the Kafka cluster.

Disabling the UseKRaft feature gate

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

7.2.2. KafkaNodePools feature gate

The KafkaNodePools feature gate has a default state of enabled.

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

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

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

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

Examples of the KafkaNodePool resources can be found in the example configuration files provided by Streams for Apache Kafka.

Disabling the KafkaNodePools feature gate

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

Downgrading from KafkaNodePools

If your cluster already uses KafkaNodePool custom resources, and you wish to downgrade to an older version of Streams for Apache Kafka 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.

7.2.3. UnidirectionalTopicOperator feature gate

The UnidirectionalTopicOperator feature gate has a default state of enabled.

The UnidirectionalTopicOperator feature gate introduces a unidirectional topic management mode for creating Kafka topics using the KafkaTopic resource. Unidirectional mode is compatible with using KRaft for cluster management. With unidirectional mode, you create Kafka topics using the KafkaTopic resource, which are then managed by the Topic Operator. Any configuration changes to a topic outside the KafkaTopic resource are reverted. For more information on topic management, see Section 10.1, “Topic management modes”.

Disabling the UnidirectionalTopicOperator feature gate

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

7.3. 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 Streams for Apache Kafka.

Currently, there are no feature gates in alpha stage.

7.4. 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

7.5. 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 Streams for Apache Kafka 2.3. It is now permanently enabled and cannot be disabled.
  • The ServiceAccountPatching feature gate moved to GA stage in Streams for Apache Kafka 2.3. It is now permanently enabled and cannot be disabled.
  • The UseStrimziPodSets feature gate moved to GA stage in Streams for Apache Kafka 2.5 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 Streams for Apache Kafka 2.7. It is now permanently enabled and cannot be disabled.
  • The UseKRaft feature gate is in beta stage and is enabled by default.
  • The KafkaNodePools feature gate is in beta stage and is enabled by default.
  • The UnidirectionalTopicOperator feature gate is in beta stage and is enabled by default.
Note

Feature gates might be removed when they reach GA. This means that the feature was incorporated into the Streams for Apache Kafka core features and can no longer be disabled.

Table 7.1. Feature gates and the Streams for Apache Kafka versions when they moved to alpha, beta, or GA
Feature gateAlphaBetaGA

ControlPlaneListener

1.8

2.0

2.3

ServiceAccountPatching

1.8

2.0

2.3

UseStrimziPodSets

2.1

2.3

2.5

UseKRaft

2.2

2.7

-

StableConnectIdentities

2.4

2.6

2.7

KafkaNodePools

2.5

2.7

-

UnidirectionalTopicOperator

2.5

2.7

-

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

Table 7.2. Feature gates to disable when upgrading or downgrading Streams for Apache Kafka
Disable Feature gateUpgrading from Streams for Apache Kafka versionDowngrading to Streams for Apache Kafka version

ControlPlaneListener

1.7 and earlier

1.7 and earlier

UseStrimziPodSets

-

2.0 and earlier

StableConnectIdentities

-

2.3 and earlier

Chapter 8. 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. KRaft mode replaces ZooKeeper for distributed coordination, offering enhanced reliability, scalability, and throughput.

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.

Prerequisites

  • You must be using Streams for Apache Kafka 2.7 or newer with Kafka 3.7.0 or newer. If you are using an earlier version of Streams for Apache Kafka or Apache Kafka, upgrade before migrating to KRaft mode.
  • Verify that the ZooKeeper-based deployment is operating without the following, as they are not supported in KRaft mode:

    • The Topic Operator running in bidirectional mode. It should either be in unidirectional mode or disabled.
    • JBOD storage. While the jbod storage type can be used, the JBOD array must contain only one disk.
  • 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.

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:

    oc 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:

    oc 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:

    oc 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:

    oc 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:

    oc 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.

    If present, you can remove the following:

    • log.message.format.version
    • inter.broker.protocol.version
    • spec.zookeeper.* properties

      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.

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.

    oc 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:

    oc 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.

    oc 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"
    # ...

Chapter 9. Configuring a deployment

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

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

  • Kafka clusters
  • Kafka Connect clusters
  • Kafka MirrorMaker
  • Kafka Bridge
  • Cruise Control

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

  • Specifying node pools
  • Securing client access to Kafka brokers
  • Accessing Kafka brokers from outside the cluster
  • Creating topics
  • Creating users (clients)
  • Controlling feature gates
  • Changing logging frequency
  • Allocating resource limits and requests
  • Introducing features, such as Streams for Apache Kafka Drain Cleaner, Cruise Control, or distributed tracing.

The Streams for Apache Kafka Custom Resource API Reference describes the properties you can use in your configuration.

Note

Labels applied to a custom resource are also applied to the OpenShift resources making up its cluster. This provides a convenient mechanism for resources to be labeled as required.

Applying changes to a custom resource configuration file

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

oc apply -f <kafka_configuration_file>

9.1. Using example configuration files

Further enhance your deployment by incorporating additional supported configuration. Example configuration files are provided with the downloadable release artifacts from the Streams for Apache Kafka software downloads page.

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

Note

If you installed Streams for Apache Kafka 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 Streams for Apache Kafka

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

1
KafkaUser custom resource configuration, which is managed by the User Operator.
2
KafkaTopic custom resource configuration, which is managed by Topic Operator.
3
Authentication and authorization configuration for Kafka components. Includes example configuration for TLS and SCRAM-SHA-512 authentication. The Red Hat Single Sign-On example includes Kafka custom resource configuration and a Red Hat Single Sign-On realm specification. You can use the example to try Red Hat Single Sign-On authorization services. There is also an example with enabled oauth authentication and keycloak authorization metrics.
4
Kafka custom resource configuration for a deployment of Mirror Maker. Includes example configuration for replication policy and synchronization frequency.
5
Metrics configuration, including Prometheus installation and Grafana dashboard files.
6
Kafka custom resource configuration for a deployment of Kafka. Includes example configuration for an ephemeral or persistent single or multi-node deployment.
7
KafkaNodePool configuration for Kafka nodes in a Kafka cluster. Includes example configuration for nodes in clusters that use KRaft (Kafka Raft metadata) mode or ZooKeeper.
8
Kafka custom resource with a deployment configuration for Cruise Control. Includes KafkaRebalance custom resources to generate optimization proposals from Cruise Control, with example configurations to use the default or user optimization goals.
9
KafkaConnect and KafkaConnector custom resource configuration for a deployment of Kafka Connect. Includes example configurations for a single or multi-node deployment.
10
KafkaBridge custom resource configuration for a deployment of Kafka Bridge.

9.2. Configuring Kafka

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

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

Configuration options that are particularly important include the following:

  • Resource requests (CPU / Memory)
  • JVM options for maximum and minimum memory allocation
  • Listeners for connecting clients to Kafka brokers (and authentication of clients)
  • Authentication
  • Storage
  • Rack awareness
  • Metrics
  • Cruise Control for cluster rebalancing
  • Metadata version for KRaft-based Kafka clusters
  • Inter-broker protocol version for ZooKeeper-based Kafka clusters

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

Note

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

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

Managing TLS certificates

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

Example Kafka custom resource configuration

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    replicas: 3 1
    version: 3.7.0 2
    logging: 3
      type: inline
      loggers:
        kafka.root.logger.level: INFO
    resources: 4
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"
    readinessProbe: 5
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    jvmOptions: 6
      -Xms: 8192m
      -Xmx: 8192m
    image: my-org/my-image:latest 7
    listeners: 8
      - name: plain 9
        port: 9092 10
        type: internal 11
        tls: false 12
        configuration:
          useServiceDnsDomain: true 13
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication: 14
          type: tls
      - name: external1 15
        port: 9094
        type: route
        tls: true
        configuration:
          brokerCertChainAndKey: 16
            secretName: my-secret
            certificate: my-certificate.crt
            key: my-key.key
    authorization: 17
      type: simple
    config: 18
      auto.create.topics.enable: "false"
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
      default.replication.factor: 3
      min.insync.replicas: 2
      inter.broker.protocol.version: "3.7"
    storage: 19
      type: persistent-claim 20
      size: 10000Gi
    rack: 21
      topologyKey: topology.kubernetes.io/zone
    metricsConfig: 22
      type: jmxPrometheusExporter
      valueFrom:
        configMapKeyRef: 23
          name: my-config-map
          key: my-key
    # ...
  zookeeper: 24
    replicas: 3 25
    logging: 26
      type: inline
      loggers:
        zookeeper.root.logger: INFO
    resources:
      requests:
        memory: 8Gi
        cpu: "2"
      limits:
        memory: 8Gi
        cpu: "2"
    jvmOptions:
      -Xms: 4096m
      -Xmx: 4096m
    storage:
      type: persistent-claim
      size: 1000Gi
    metricsConfig:
      # ...
  entityOperator: 27
    tlsSidecar: 28
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging: 29
        type: inline
        loggers:
          rootLogger.level: INFO
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
    userOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging: 30
        type: inline
        loggers:
          rootLogger.level: INFO
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
  kafkaExporter: 31
    # ...
  cruiseControl: 32
    # ...

1
The number of replica nodes.
2
Kafka version, which can be changed to a supported version by following the upgrade procedure.
3
Kafka loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties key in the ConfigMap. For the Kafka kafka.root.logger.level logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
4
Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
5
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
6
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka.
7
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
8
Listeners configure how clients connect to the Kafka cluster via bootstrap addresses. Listeners are configured as internal or external listeners for connection from inside or outside the OpenShift cluster.
9
Name to identify the listener. Must be unique within the Kafka cluster.
10
Port number used by the listener inside Kafka. The port number has to be unique within a given Kafka cluster. Allowed port numbers are 9092 and higher with the exception of ports 9404 and 9999, which are already used for Prometheus and JMX. Depending on the listener type, the port number might not be the same as the port number that connects Kafka clients.
11
Listener type specified as internal or cluster-ip (to expose Kafka using per-broker ClusterIP services), or for external listeners, as route (OpenShift only), loadbalancer, nodeport or ingress (Kubernetes only).
12
Enables or disables TLS encryption for each listener. For route and ingress type listeners, TLS encryption must always be enabled by setting it to true.
13
Defines whether the fully-qualified DNS names including the cluster service suffix (usually .cluster.local) are assigned.
14
Listener authentication mechanism specified as mTLS, SCRAM-SHA-512, or token-based OAuth 2.0.
15
External listener configuration specifies how the Kafka cluster is exposed outside OpenShift, such as through a route, loadbalancer or nodeport.
16
Optional configuration for a Kafka listener certificate managed by an external CA (certificate authority). The brokerCertChainAndKey specifies a Secret that contains a server certificate and a private key. You can configure Kafka listener certificates on any listener with enabled TLS encryption.
17
Authorization enables simple, OAUTH 2.0, or OPA authorization on the Kafka broker. Simple authorization uses the AclAuthorizer and StandardAuthorizer Kafka plugins.
18
Broker configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Streams for Apache Kafka.
19
Storage size for persistent volumes may be increased and additional volumes may be added to JBOD storage.
20
Persistent storage has additional configuration options, such as a storage id and class for dynamic volume provisioning.
21
Rack awareness configuration to spread replicas across different racks, data centers, or availability zones. The topologyKey must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standard topology.kubernetes.io/zone label.
22
Prometheus metrics enabled. In this example, metrics are configured for the Prometheus JMX Exporter (the default metrics exporter).
23
Rules for exporting metrics in Prometheus format to a Grafana dashboard through the Prometheus JMX Exporter, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under metricsConfig.valueFrom.configMapKeyRef.key.
24
ZooKeeper-specific configuration, which contains properties similar to the Kafka configuration.
25
The number of ZooKeeper nodes. ZooKeeper clusters or ensembles usually run with an odd number of nodes, typically three, five, or seven. The majority of nodes must be available in order to maintain an effective quorum. If the ZooKeeper cluster loses its quorum, it will stop responding to clients and the Kafka brokers will stop working. Having a stable and highly available ZooKeeper cluster is crucial for Streams for Apache Kafka.
26
ZooKeeper loggers and log levels.
27
Entity Operator configuration, which specifies the configuration for the Topic Operator and User Operator.
28
Entity Operator TLS sidecar configuration. Entity Operator uses the TLS sidecar for secure communication with ZooKeeper.
29
Specified Topic Operator loggers and log levels. This example uses inline logging.
30
Specified User Operator loggers and log levels.
31
Kafka Exporter configuration. Kafka Exporter is an optional component for extracting metrics data from Kafka brokers, in particular consumer lag data. For Kafka Exporter to be able to work properly, consumer groups need to be in use.
32
Optional configuration for Cruise Control, which is used to rebalance the Kafka cluster.

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

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

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

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

Note

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

Prerequisites

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

Procedure

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

    The plugin properties are shown in this example configuration.

    Example Kafka Static Quota plugin configuration

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

    1
    Loads the Kafka Static Quota plugin.
    2
    Sets the producer byte-rate threshold. 1 MBps in this example.
    3
    Sets the consumer byte-rate threshold. 1 MBps in this example.
    4
    Sets the lower soft limit for storage. 400 GB in this example.
    5
    Sets the higher hard limit for storage. 500 GB in this example.
    6
    Sets the interval in seconds between checks on storage. 5 seconds in this example. You can set this to 0 to disable the check.
  2. Update the resource.

    oc apply -f <kafka_configuration_file>

Additional resources

9.2.2. Default ZooKeeper configuration values

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

The default configuration for key ZooKeeper properties in Streams for Apache Kafka is as follows:

Table 9.1. Default ZooKeeper Properties in Streams for Apache Kafka
PropertyDefault valueDescription

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 Streams for Apache Kafka.

Important

Modifying these default values as zookeeper.config in the Kafka custom resource may impact the behavior and performance of your ZooKeeper cluster.

9.2.3. Deleting Kafka nodes using annotations

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

Warning

Deleting a PersistentVolumeClaim can cause permanent data loss 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 oc annotate to annotate the Pod resource in OpenShift:

    oc 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.

9.2.4. Deleting ZooKeeper nodes using annotations

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

Warning

Deleting a PersistentVolumeClaim can cause permanent data loss 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 oc annotate to annotate the Pod resource in OpenShift:

    oc 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.

9.3. Configuring node pools

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

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

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

  • resources to specify memory and cpu requests and limits
  • template to specify custom configuration for pods and other OpenShift resources
  • jvmOptions to specify custom JVM configuration for heap size, runtime and other options

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

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

For a deeper understanding of the node pool configuration options, refer to the Streams for Apache Kafka Custom Resource API Reference.

Example configuration for a node pool in a cluster using KRaft mode

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: kraft-dual-role 1
  labels:
    strimzi.io/cluster: my-cluster 2
spec:
  replicas: 3 3
  roles: 4
    - controller
    - broker
  storage: 5
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  resources: 6
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"

1
Unique name for the node pool.
2
The Kafka cluster the node pool belongs to. A node pool can only belong to a single cluster.
3
Number of replicas for the nodes.
4
Roles for the nodes in the node pool. In this example, the nodes have dual roles as controllers and brokers.
5
Storage specification for the nodes.
6
Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
Note

The configuration for the Kafka resource must be suitable for KRaft mode. Currently, KRaft mode has a number of limitations.

Example configuration for a node pool in a cluster using ZooKeeper

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: pool-a
  labels:
    strimzi.io/cluster: my-cluster
spec:
  replicas: 3
  roles:
    - broker 1
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  resources:
      requests:
        memory: 64Gi
        cpu: "8"
      limits:
        memory: 64Gi
        cpu: "12"

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

9.3.1. Assigning IDs to node pools for scaling operations

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

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

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

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

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

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

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

During the scaling operation, IDs are used as follows:

  • Scale up picks up the lowest available ID in the range for the new node.
  • Scale down removes the node with the highest available ID in the range.

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

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

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

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

Prerequisites

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

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

Example configuration for the maximum node ID number

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    config:
      reserved.broker.max.id: 10000
  # ...

Procedure

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

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

    Assigning IDs for scaling up

    oc 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

    oc 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: oc 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

    oc annotate kafkanodepool pool-a strimzi.io/next-node-ids-

    Removing the annotation for scaling down

    oc annotate kafkanodepool pool-b strimzi.io/remove-node-ids-

9.3.2. 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.

9.3.3. 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:

    oc 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).

    oc get pods -n <my_cluster_operator_namespace>

    Output shows four Kafka nodes in the node pool

    NAME                 READY  STATUS   RESTARTS
    my-cluster-pool-a-0  1/1    Running  0
    my-cluster-pool-a-1  1/1    Running  0
    my-cluster-pool-a-2  1/1    Running  0
    my-cluster-pool-a-3  1/1    Running  0

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

    After scaling up a node pool, 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.

9.3.4. 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.

    Before scaling down a 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: [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:

    oc scale kafkanodepool pool-a --replicas=3

    Output shows three Kafka nodes in the node pool

    NAME                       READY  STATUS   RESTARTS
    my-cluster-pool-b-kafka-0  1/1    Running  0
    my-cluster-pool-b-kafka-1  1/1    Running  0
    my-cluster-pool-b-kafka-2  1/1    Running  0

9.3.5. 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
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:

    oc 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).

    oc 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.

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

    Before scaling down 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:

    oc 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

9.3.6. 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 Section 19.6, “Generating optimization proposals”.

9.3.7. 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.

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).

    oc 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.

9.3.8. 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.

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).

    oc 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.

    oc delete kafkanodepool pool-b -n <my_cluster_operator_namespace>

9.3.9. Managing storage using node pools

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

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

Note

We strongly recommend using block storage. Streams for Apache Kafka is only tested for use with block storage.

Prerequisites

Procedure

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

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

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

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

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

    oc 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:

    oc delete kafkanodepool pool-a

9.3.10. 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).

    oc get pods -n <my_cluster_operator_namespace>

    Output shows 3 Kafka nodes in pool-zone-1 and 4 Kafka nodes in pool-zone-2

    NAME                            READY  STATUS   RESTARTS
    my-cluster-pool-zone-1-kafka-0  1/1    Running  0
    my-cluster-pool-zone-1-kafka-1  1/1    Running  0
    my-cluster-pool-zone-1-kafka-2  1/1    Running  0
    my-cluster-pool-zone-2-kafka-3  1/1    Running  0
    my-cluster-pool-zone-2-kafka-4  1/1    Running  0
    my-cluster-pool-zone-2-kafka-5  1/1    Running  0
    my-cluster-pool-zone-2-kafka-6  1/1    Running  0

9.3.11. 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.

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 must match 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:

    oc 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:

    oc 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.

9.4. Configuring the Entity Operator

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

  • Topic Operator to manage Kafka topics
  • User Operator to manage Kafka users

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

Each operator can only monitor a single namespace. For more information, see Section 1.2.1, “Watching Streams for Apache Kafka resources in OpenShift namespaces”.

The entityOperator property supports several sub-properties:

  • tlsSidecar
  • topicOperator
  • userOperator
  • template

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

The template property contains the configuration of the Entity Operator pod, such as labels, annotations, affinity, and tolerations. For more information on configuring templates, see Section 9.16, “Customizing OpenShift resources”.

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

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

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

Example of basic configuration enabling both operators

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}

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

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

9.4.1. Configuring the Topic Operator

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

Note

If you are using unidirectional topic management, which is enabled by default, the following properties are not used and are ignored: Kafka.spec.entityOperator.topicOperator.zookeeperSessionTimeoutSeconds and Kafka.spec.entityOperator.topicOperator.topicMetadataMaxAttempts. For more information on unidirectional topic management, refer to Section 10.1, “Topic management modes”.

The following properties are supported:

watchedNamespace
The OpenShift namespace in which the Topic Operator watches for KafkaTopic resources. Default is the namespace where the Kafka cluster is deployed.
reconciliationIntervalSeconds
The interval between periodic reconciliations in seconds. Default 120.
zookeeperSessionTimeoutSeconds
The ZooKeeper session timeout in seconds. Default 18.
topicMetadataMaxAttempts
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation might take more time due to the number of partitions or replicas. Default 6.
image
The image property can be used to configure the container image which is used. To learn more, refer to the information provided on configuring the image property`.
resources
The resources property configures the amount of resources allocated to the Topic Operator. You can specify requests and limits for memory and cpu resources. The requests should be enough to ensure a stable performance of the operator.
logging
The logging property configures the logging of the Topic Operator. To learn more, refer to the information provided on Topic Operator logging.

Example Topic Operator configuration

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

9.4.2. Configuring the User Operator

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

watchedNamespace
The OpenShift namespace in which the User Operator watches for KafkaUser resources. Default is the namespace where the Kafka cluster is deployed.
reconciliationIntervalSeconds
The interval between periodic reconciliations in seconds. Default 120.
image
The image property can be used to configure the container image which will be used. To learn more, refer to the information provided on configuring the image property`.
resources
The resources property configures the amount of resources allocated to the User Operator. You can specify requests and limits for memory and cpu resources. The requests should be enough to ensure a stable performance of the operator.
logging
The logging property configures the logging of the User Operator. To learn more, refer to the information provided on User Operator logging.
secretPrefix
The secretPrefix property adds a prefix to the name of all Secrets created from the KafkaUser resource. For example, secretPrefix: kafka- would prefix all Secret names with kafka-. So a KafkaUser named my-user would create a Secret named kafka-my-user.

Example User Operator configuration

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

9.5. Configuring the Cluster Operator

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

Kafka, Kafka Connect, and Kafka MirrorMaker support multiple versions. Use their STRIMZI_<COMPONENT_NAME>_IMAGES environment variables to configure the default container images used for each version. The configuration provides a mapping between a version and an image. The required syntax is whitespace or comma-separated <version> = <image> pairs, which determine the image to use for a given version. For example, 3.7.0=registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0. 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 Streams for Apache Kafka Custom Resource API Reference.

Note

The Deployment configuration file provided with the Streams for Apache Kafka 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 Streams for Apache Kafka on clusters where regular OpenShift 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 Streams for Apache Kafka instance when emitting OpenShift events.
STRIMZI_OPERATOR_NAMESPACE

The name of the namespace where the Cluster Operator is running. Do not configure this variable manually. Use the downward API.

env:
  - name: STRIMZI_OPERATOR_NAMESPACE
    valueFrom:
      fieldRef:
        fieldPath: metadata.namespace
STRIMZI_OPERATOR_NAMESPACE_LABELS

Optional. The labels of the namespace where the Streams for Apache Kafka Cluster Operator is running. Use namespace labels to configure the namespace selector in network policies. Network policies allow the Streams for Apache Kafka Cluster Operator access only to the operands from the namespace with these labels. When not set, the namespace selector in network policies is configured to allow access to the Cluster Operator from any namespace in the OpenShift cluster.

env:
  - name: STRIMZI_OPERATOR_NAMESPACE_LABELS
    value: label1=value1,label2=value2
STRIMZI_LABELS_EXCLUSION_PATTERN

Optional, default regex pattern is ^app.kubernetes.io/(?!part-of).*. The regex exclusion pattern used to filter labels propagation from the main custom resource to its subresources. The labels exclusion filter is not applied to labels in template sections such as spec.kafka.template.pod.metadata.labels.

env:
  - name: STRIMZI_LABELS_EXCLUSION_PATTERN
    value: "^key1.*"
STRIMZI_CUSTOM_<COMPONENT_NAME>_LABELS

Optional. One or more custom labels to apply to all the pods created by the 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 3.6.0=registry.redhat.io/amq-streams/kafka-36-rhel9:2.7.0, 3.7.0=registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0.
STRIMZI_KAFKA_CONNECT_IMAGES
Required. The mapping from the Kafka version to the corresponding image of Kafka Connect for that version. For example 3.6.0=registry.redhat.io/amq-streams/kafka-36-rhel9:2.7.0, 3.7.0=registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0.
STRIMZI_KAFKA_MIRROR_MAKER2_IMAGES
Required. The mapping from the Kafka version to the corresponding image of MirrorMaker 2 for that version. For example 3.6.0=registry.redhat.io/amq-streams/kafka-36-rhel9:2.7.0, 3.7.0=registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0.
(Deprecated) STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
Required. The mapping from the Kafka version to the corresponding image of MirrorMaker for that version. For example 3.6.0=registry.redhat.io/amq-streams/kafka-36-rhel9:2.7.0, 3.7.0=registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0.
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
Optional. The default is registry.redhat.io/amq-streams/strimzi-rhel9-operator:2.7.0. The image name to use as the default when deploying the Topic Operator if no image is specified as the Kafka.spec.entityOperator.topicOperator.image in the Kafka resource.
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
Optional. The default is registry.redhat.io/amq-streams/strimzi-rhel9-operator:2.7.0. The image name to use as the default when deploying the User Operator if no image is specified as the Kafka.spec.entityOperator.userOperator.image in the Kafka resource.
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE
Optional. The default is registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0. The image name to use as the default when deploying the sidecar container for the Entity Operator if no image is specified as the Kafka.spec.entityOperator.tlsSidecar.image in the Kafka resource. The sidecar provides TLS support.
STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE
Optional. The default is registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0. 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 registry.redhat.io/amq-streams/kafka-37-rhel9:2.7.0. 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 registry.redhat.io/amq-streams/bridge-rhel9:2.7.0. The image name to use as the default when deploying the Kafka Bridge if no image is specified as the Kafka.spec.kafkaBridge.image in the Kafka resource.
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
Optional. The default is registry.redhat.io/amq-streams/strimzi-rhel9-operator:2.7.0. The image name to use as the default for the Kafka initializer container if no image is specified in the brokerRackInitImage of the Kafka resource or the clientRackInitImage of the Kafka Connect resource. The init container is started before the Kafka cluster for initial configuration work, such as rack support.
STRIMZI_IMAGE_PULL_POLICY
Optional. The ImagePullPolicy that is applied to containers in all pods managed by the Cluster Operator. The valid values are Always, IfNotPresent, and Never. If not specified, the OpenShift 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 OpenShift version information detected from the API server.

Example configuration for OpenShift 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 OpenShift DNS domain name suffix.

By default, services assigned in the OpenShift 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 Streams for Apache Kafka to build container images containing many connectors or using a slow container registry.
STRIMZI_NETWORK_POLICY_GENERATION

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

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

STRIMZI_DNS_CACHE_TTL
Optional, default 30. Number of seconds to cache successful name lookups in local DNS resolver. Any negative value means cache forever. Zero means do not cache, which can be useful for avoiding connection errors due to long caching policies being applied.
STRIMZI_POD_SET_RECONCILIATION_ONLY
Optional, default false. When set to true, the Cluster Operator reconciles only the StrimziPodSet resources and any changes to the other custom resources (Kafka, KafkaConnect, and so on) are ignored. This mode is useful for ensuring that your pods are recreated if needed, but no other changes happen to the clusters.
STRIMZI_FEATURE_GATES
Optional. Enables or disables the features and functionality controlled by feature gates.
STRIMZI_POD_SECURITY_PROVIDER_CLASS
Optional. Configuration for the pluggable PodSecurityProvider class, which can be used to provide the security context configuration for Pods and containers.

9.5.1. Restricting access to the Cluster Operator using network policy

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

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

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

Network policy configured for the Cluster Operator deployment

#...
env:
  # ...
  - name: STRIMZI_OPERATOR_NAMESPACE_LABELS
    value: label1=value1,label2=value2
  #...

9.5.2. 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 OpenShift 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 OpenShift cluster. In order to handle failovers properly, a periodic reconciliation process is executed by the Cluster Operator so that it can compare the state of the resources with the current cluster deployments in order to have a consistent state across all of them.

Additional resources

9.5.3. Pausing reconciliation of custom resources using annotations

Sometimes it is useful to pause the reconciliation of custom resources managed by Streams for Apache Kafka 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 Streams for Apache Kafka Operator that manages the custom resource is running.

Procedure

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

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

    For example, for the KafkaConnect custom resource:

    oc annotate KafkaConnect my-connect strimzi.io/pause-reconciliation="true"
  2. Check that the status conditions of the custom resource show a change to ReconciliationPaused:

    oc 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.

9.5.4. 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, Streams for Apache Kafka runs with a single Cluster Operator replica that is always the leader replica. When a single Cluster Operator replica stops or fails, OpenShift 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.

9.5.4.1. 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 OpenShift Lease resource that is used for the leader election.
STRIMZI_LEADER_ELECTION_LEASE_NAMESPACE

Required when leader election is enabled. The namespace where the OpenShift 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.
9.5.4.2. 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 Section 9.5.4.1, “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:

    oc create -f install/cluster-operator -n myproject
  6. Check the status of the deployment:

    oc get deployments -n myproject

    Output shows the deployment name and readiness

    NAME                      READY  UP-TO-DATE  AVAILABLE
    strimzi-cluster-operator  3/3    3           3

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

9.5.5. 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 Streams for Apache Kafka 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:

    oc edit deployment strimzi-cluster-operator
  2. If you updated the YAML file instead of editing the Deployment directly, apply the changes:

    oc create -f install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml

9.5.6. Disabling FIPS mode using Cluster Operator configuration

Streams for Apache Kafka automatically switches to FIPS mode when running on a FIPS-enabled OpenShift 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, Streams for Apache Kafka automatically disables FIPS in the OpenJDK for all components. With FIPS mode disabled, Streams for Apache Kafka is not FIPS compliant. The Streams for Apache Kafka operators, as well as all operands, run in the same way as if they were running on an OpenShift 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:

    oc edit deployment strimzi-cluster-operator
  2. If you updated the YAML file instead of editing the Deployment directly, apply the changes:

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

9.6. Configuring Kafka Connect

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

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

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

KafkaConnector configuration

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

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

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

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

Handling high volumes of messages

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

Example KafkaConnect custom resource configuration

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

1
Use KafkaConnect.
2
Enables KafkaConnectors for the Kafka Connect cluster.
3
The number of replica nodes for the workers that run tasks.
4
Authentication for the Kafka Connect cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN. By default, Kafka Connect connects to Kafka brokers using a plain text connection.
5
Bootstrap server for connection to the Kafka cluster.
6
TLS encryption with key names under which TLS certificates are stored in X.509 format for the cluster. If certificates are stored in the same secret, it can be listed multiple times.
7
Kafka Connect configuration of workers (not connectors). Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Streams for Apache Kafka.
8
Build configuration properties for building a container image with connector plugins automatically.
9
(Required) Configuration of the container registry where new images are pushed.
10
(Required) List of connector plugins and their artifacts to add to the new container image. Each plugin must be configured with at least one artifact.
11
External configuration for connectors using environment variables, as shown here, or volumes. You can also use configuration provider plugins to load configuration values from external sources.
12
Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
13
Specified Kafka Connect loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties or log4j2.properties key in the ConfigMap. For the Kafka Connect log4j.rootLogger logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
14
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
15
Prometheus metrics, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under metricsConfig.valueFrom.configMapKeyRef.key.
16
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka Connect.
17
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
18
SPECIALIZED OPTION: Rack awareness configuration for the deployment. This is a specialized option intended for a deployment within the same location, not across regions. Use this option if you want connectors to consume from the closest replica rather than the leader replica. In certain cases, consuming from the closest replica can improve network utilization or reduce costs . The topologyKey must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standard topology.kubernetes.io/zone label. To consume from the closest replica, enable the RackAwareReplicaSelector in the Kafka broker configuration.
19
Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
20
Environment variables are set for distributed tracing.
21
Distributed tracing is enabled by using OpenTelemetry.

9.6.1. Configuring Kafka Connect for multiple instances

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

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

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

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

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

9.6.2. Configuring Kafka Connect user authorization

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

Properties for the Kafka Connect cluster group ID and internal topics are configured by Streams for Apache Kafka 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

  • An OpenShift 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 9.2. Names for the access rights configuration
    PropertyName

    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.

    oc apply -f KAFKA-USER-CONFIG-FILE

9.6.3. Manually stopping or pausing Kafka Connect connectors

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

Note

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

Prerequisites

  • The Cluster Operator is running.

Procedure

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

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

    Example configuration for stopping a Kafka Connect connector

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

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

  3. Apply the changes to the KafkaConnector configuration.

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

Note

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

9.6.4. Manually restarting Kafka Connect connectors

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

Prerequisites

  • The Cluster Operator is running.

Procedure

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

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

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

    The restart annotation is set to true.

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

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

9.6.5. Manually restarting Kafka Connect connector tasks

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

Prerequisites

  • The Cluster Operator is running.

Procedure

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

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

    oc 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 OpenShift:

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

    In this example, task 0 is restarted.

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

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

9.7. Configuring Kafka MirrorMaker 2

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

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

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

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

Note

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

The configuration must specify:

  • Each Kafka cluster
  • Connection information for each cluster, including authentication
  • The replication flow and direction

    • Cluster to cluster
    • Topic to topic

For a deeper understanding of the Kafka MirrorMaker 2 cluster configuration options, refer to the Streams for Apache Kafka 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: 3.7.0
  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

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 3.7.0 1
  replicas: 3 2
  connectCluster: "my-cluster-target" 3
  clusters: 4
  - alias: "my-cluster-source" 5
    authentication: 6
      certificateAndKey:
        certificate: source.crt
        key: source.key
        secretName: my-user-source
      type: tls
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092 7
    tls: 8
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-source-cluster-ca-cert
  - alias: "my-cluster-target" 9
    authentication: 10
      certificateAndKey:
        certificate: target.crt
        key: target.key
        secretName: my-user-target
      type: tls
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092 11
    config: 12
      config.storage.replication.factor: 1
      offset.storage.replication.factor: 1
      status.storage.replication.factor: 1
    tls: 13
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-target-cluster-ca-cert
  mirrors: 14
  - sourceCluster: "my-cluster-source" 15
    targetCluster: "my-cluster-target" 16
    sourceConnector: 17
      tasksMax: 10 18
      autoRestart: 19
        enabled: true
      config
        replication.factor: 1 20
        offset-syncs.topic.replication.factor: 1 21
        sync.topic.acls.enabled: "false" 22
        refresh.topics.interval.seconds: 60 23
        replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy" 24
    heartbeatConnector: 25
      autoRestart:
        enabled: true
      config:
        heartbeats.topic.replication.factor: 1 26
        replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy"
    checkpointConnector: 27
      autoRestart:
        enabled: true
      config:
        checkpoints.topic.replication.factor: 1 28
        refresh.groups.interval.seconds: 600 29
        sync.group.offsets.enabled: true 30
        sync.group.offsets.interval.seconds: 60 31
        emit.checkpoints.interval.seconds: 60 32
        replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy"
    topicsPattern: "topic1|topic2|topic3" 33
    groupsPattern: "group1|group2|group3" 34
  resources: 35
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: 36
    type: inline
    loggers:
      connect.root.logger.level: INFO
  readinessProbe: 37
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  jvmOptions: 38
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest 39
  rack:
    topologyKey: topology.kubernetes.io/zone 40
  template: 41
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: 42
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry 43
  externalConfiguration: 44
    env:
      - name: AWS_ACCESS_KEY_ID
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsAccessKey
      - name: AWS_SECRET_ACCESS_KEY
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsSecretAccessKey

1
The Kafka Connect and MirrorMaker 2 version, which will always be the same.
2
The number of replica nodes for the workers that run tasks.
3
Kafka cluster alias for Kafka Connect, which must specify the target Kafka cluster. The Kafka cluster is used by Kafka Connect for its internal topics.
4
Specification for the Kafka clusters being synchronized.
5
Cluster alias for the source Kafka cluster.
6
Authentication for the source cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN.
7
Bootstrap server for connection to the source Kafka cluster.
8
TLS encryption with key names under which TLS certificates are stored in X.509 format for the source Kafka cluster. If certificates are stored in the same secret, it can be listed multiple times.
9
Cluster alias for the target Kafka cluster.
10
Authentication for the target Kafka cluster is configured in the same way as for the source Kafka cluster.
11
Bootstrap server for connection to the target Kafka cluster.
12
Kafka Connect configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Streams for Apache Kafka.
13
TLS encryption for the target Kafka cluster is configured in the same way as for the source Kafka cluster.
14
MirrorMaker 2 connectors.
15
Cluster alias for the source cluster used by the MirrorMaker 2 connectors.
16
Cluster alias for the target cluster used by the MirrorMaker 2 connectors.
17
Configuration for the MirrorSourceConnector that creates remote topics. The config overrides the default configuration options.
18
The maximum number of tasks that the connector may create. Tasks handle the data replication and run in parallel. If the infrastructure supports the processing overhead, increasing this value can improve throughput. Kafka Connect distributes the tasks between members of the cluster. If there are more tasks than workers, workers are assigned multiple tasks. For sink connectors, aim to have one task for each topic partition consumed. For source connectors, the number of tasks that can run in parallel may also depend on the external system. The connector creates fewer than the maximum number of tasks if it cannot achieve the parallelism.
19
Enables automatic restarts of failed connectors and tasks. By default, the number of restarts is indefinite, but you can set a maximum on the number of automatic restarts using the maxRestarts property.
20
Replication factor for mirrored topics created at the target cluster.
21
Replication factor for the MirrorSourceConnector offset-syncs internal topic that maps the offsets of the source and target clusters.
22
When ACL rules synchronization is enabled, ACLs are applied to synchronized topics. The default is true. This feature is not compatible with the User Operator. If you are using the User Operator, set this property to false.
23
Optional setting to change the frequency of checks for new topics. The default is for a check every 10 minutes.
24
Adds a policy that overrides the automatic renaming of remote topics. Instead of prepending the name with the name of the source cluster, the topic retains its original name. This optional setting is useful for active/passive backups and data migration. The property must be specified for all connectors. For bidirectional (active/active) replication, use the DefaultReplicationPolicy class to automatically rename remote topics and specify the replication.policy.separator property for all connectors to add a custom separator.
25
Configuration for the MirrorHeartbeatConnector that performs connectivity checks. The config overrides the default configuration options.
26
Replication factor for the heartbeat topic created at the target cluster.
27
Configuration for the MirrorCheckpointConnector that tracks offsets. The config overrides the default configuration options.
28
Replication factor for the checkpoints topic created at the target cluster.
29
Optional setting to change the frequency of checks for new consumer groups. The default is for a check every 10 minutes.
30
Optional setting to synchronize consumer group offsets, which is useful for recovery in an active/passive configuration. Synchronization is not enabled by default.
31
If the synchronization of consumer group offsets is enabled, you can adjust the frequency of the synchronization.
32
Adjusts the frequency of checks for offset tracking. If you change the frequency of offset synchronization, you might also need to adjust the frequency of these checks.
33
Topic replication from the source cluster defined as a comma-separated list or regular expression pattern. The source connector replicates the specified topics. The checkpoint connector tracks offsets for the specified topics. Here we request three topics by name.
34
Consumer group replication from the source cluster defined as a comma-separated list or regular expression pattern. The checkpoint connector replicates the specified consumer groups. Here we request three consumer groups by name.
35
Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
36
Specified Kafka Connect loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties or log4j2.properties key in the ConfigMap. For the Kafka Connect log4j.rootLogger logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
37
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
38
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
39
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
40
SPECIALIZED OPTION: Rack awareness configuration for the deployment. This is a specialized option intended for a deployment within the same location, not across regions. Use this option if you want connectors to consume from the closest replica rather than the leader replica. In certain cases, consuming from the closest replica can improve network utilization or reduce costs . The topologyKey must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standard topology.kubernetes.io/zone label. To consume from the closest replica, enable the RackAwareReplicaSelector in the Kafka broker configuration.
41
Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
42
Environment variables are set for distributed tracing.
43
Distributed tracing is enabled by using OpenTelemetry.
44
External configuration for an OpenShift Secret mounted to Kafka MirrorMaker as an environment variable. You can also use configuration provider plugins to load configuration values from external sources.

9.7.1. Configuring active/active or active/passive modes

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

active/active cluster configuration
An active/active configuration has two active clusters replicating data bidirectionally. Applications can use either cluster. Each cluster can provide the same data. In this way, you can make the same data available in different geographical locations. As consumer groups are active in both clusters, consumer offsets for replicated topics are not synchronized back to the source cluster.
active/passive cluster configuration
An active/passive configuration has an active cluster replicating data to a passive cluster. The passive cluster remains on standby. You might use the passive cluster for data recovery in the event of system failure.

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

9.7.1.1. 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.

Figure 9.1. Topic renaming

MirrorMaker 2 bidirectional architecture

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.

9.7.1.2. Unidirectional replication (active/passive)

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

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

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

9.7.2. Configuring MirrorMaker 2 for multiple instances

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

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  connectCluster: "my-cluster-target"
  clusters:
  - alias: "my-cluster-target"
    config:
      group.id: my-connect-cluster 1
      offset.storage.topic: my-connect-cluster-offsets 2
      config.storage.topic: my-connect-cluster-configs 3
      status.storage.topic: my-connect-cluster-status 4
      # ...
    # ...
1
The Kafka Connect cluster group ID within Kafka.
2
Kafka topic that stores connector offsets.
3
Kafka topic that stores connector and task status configurations.
4
Kafka topic that stores connector and task status updates.
Note

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

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

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

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

9.7.3. Configuring MirrorMaker 2 connectors

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

MirrorMaker 2 consists of the following connectors:

MirrorSourceConnector
The source connector replicates topics from a source cluster to a target cluster. It also replicates ACLs and is necessary for the MirrorCheckpointConnector to run.
MirrorCheckpointConnector
The checkpoint connector periodically tracks offsets. If enabled, it also synchronizes consumer group offsets between the source and target cluster.
MirrorHeartbeatConnector
The heartbeat connector periodically checks connectivity between the source and target cluster.

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

Table 9.3. MirrorMaker 2 connector configuration properties
PropertysourceConnectorcheckpointConnectorheartbeatConnector
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 Section 9.7.6, “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.
  

9.7.3.1. 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.

9.7.3.2. 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.

9.7.3.3. 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.

9.7.3.4. Aligning the configuration of MirrorMaker 2 connectors

To ensure that MirrorMaker 2 connectors work properly, make sure to align certain configuration settings across connectors. Specifically, ensure that the following properties have the same value across all applicable connectors:

  • replication.policy.class
  • replication.policy.separator
  • offset-syncs.topic.location
  • topic.filter.class

For example, the value for replication.policy.class must be the same for the source, checkpoint, and heartbeat connectors. Mismatched or missing settings cause issues with data replication or offset syncing, so it’s essential to keep all relevant connectors configured with the same settings.

9.7.4. Configuring MirrorMaker 2 connector producers and consumers

MirrorMaker 2 connectors use internal producers and consumers. If needed, you can configure these producers and consumers to override the default settings.

For example, you can increase the batch.size for the source producer that sends topics to the target Kafka cluster to better accommodate large volumes of messages.

Important

Producer and consumer configuration options depend on the MirrorMaker 2 implementation, and may be subject to change.

The following tables describe the producers and consumers for each of the connectors and where you can add configuration.

Table 9.4. Source connector producers and consumers
TypeDescriptionConfiguration

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 9.5. Checkpoint connector producers and consumers
TypeDescriptionConfiguration

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 9.6. Heartbeat connector producer
TypeDescriptionConfiguration

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: 3.7.0
  # ...
  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
        # ...

9.7.5. Specifying a maximum number of data replication tasks

Connectors create the tasks that are responsible for moving data in and out of Kafka. Each connector comprises one or more tasks that are distributed across a group of worker pods that run the tasks. Increasing the number of tasks can help with performance issues when replicating a large number of partitions or synchronizing the offsets of a large number of consumer groups.

Tasks run in parallel. Workers are assigned one or more tasks. A single task is handled by one worker pod, so you don’t need more worker pods than tasks. If there are more tasks than workers, workers handle multiple tasks.

You can specify the maximum number of connector tasks in your MirrorMaker configuration using the tasksMax property. Without specifying a maximum number of tasks, the default setting is a single task.

The heartbeat connector always uses a single task.

The number of tasks that are started for the source and checkpoint connectors is the lower value between the maximum number of possible tasks and the value for tasksMax. For the source connector, the maximum number of tasks possible is one for each partition being replicated from the source cluster. For the checkpoint connector, the maximum number of tasks possible is one for each consumer group being replicated from the source cluster. When setting a maximum number of tasks, consider the number of partitions and the hardware resources that support the process.

If the infrastructure supports the processing overhead, increasing the number of tasks can improve throughput and latency. For example, adding more tasks reduces the time taken to poll the source cluster when there is a high number of partitions or consumer groups.

Increasing the number of tasks for the source connector is useful when you have a large number of partitions.

Increasing the number of tasks for the source connector

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  # ...
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector:
      tasksMax: 10
  # ...

Increasing the number of tasks for the checkpoint connector is useful when you have a large number of consumer groups.

Increasing the number of tasks for the checkpoint connector

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  # ...
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    checkpointConnector:
      tasksMax: 10
  # ...

By default, MirrorMaker 2 checks for new consumer groups every 10 minutes. You can adjust the refresh.groups.interval.seconds configuration to change the frequency. Take care when adjusting lower. More frequent checks can have a negative impact on performance.

9.7.5.1. 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 Streams for Apache Kafka shows the following metrics related to tasks and latency.

  • The number of tasks
  • Replication latency
  • Offset synchronization latency

9.7.6. Synchronizing ACL rules for remote topics

When using MirrorMaker 2 with Streams for Apache Kafka, it is possible to synchronize ACL rules for remote topics. However, this feature is only available if you are not using the User Operator.

If you are using type: simple authorization without the User Operator, the ACL rules that manage access to brokers also apply to remote topics. This means that users who have read access to a source topic can also read its remote equivalent.

Note

OAuth 2.0 authorization does not support access to remote topics in this way.

9.7.7. Securing a Kafka MirrorMaker 2 deployment

This procedure describes in outline the configuration required to secure a MirrorMaker 2 deployment.

You need separate configuration for the source Kafka cluster and the target Kafka cluster. You also need separate user configuration to provide the credentials required for MirrorMaker to connect to the source and target Kafka clusters.

For the Kafka clusters, you specify internal listeners for secure connections within an OpenShift cluster and external listeners for connections outside the OpenShift 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 Streams for Apache Kafka. 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 an OpenShift 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

  • Streams for Apache Kafka 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: 3.7.0
        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: "3.7"
        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: 3.7.0
        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: "3.7"
        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.

    oc 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.

    oc 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: 3.7.0
      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
                certificate: ca.crt
          authentication: 2
            type: tls
            certificateAndKey:
              secretName: my-source-user
              certificate: user.crt
              key: user.key
        - alias: "my-target-cluster"
          bootstrapServers: my-target-cluster-kafka-bootstrap:9093
          tls: 3
            trustedCertificates:
              - secretName: my-target-cluster-cluster-ca-cert
                certificate: ca.crt
          authentication: 4
            type: tls
            certificateAndKey:
              secretName: my-target-user
              certificate: user.crt
              key: user.key
          config:
            # -1 means it will use the default replication factor configured in the broker
            config.storage.replication.factor: -1
            offset.storage.replication.factor: -1
            status.storage.replication.factor: -1
      mirrors:
        - sourceCluster: "my-source-cluster"
          targetCluster: "my-target-cluster"
          sourceConnector:
            config:
              replication.factor: 1
              offset-syncs.topic.replication.factor: 1
              sync.topic.acls.enabled: "false"
          heartbeatConnector:
            config:
              heartbeats.topic.replication.factor: 1
          checkpointConnector:
            config:
              checkpoints.topic.replication.factor: 1
              sync.group.offsets.enabled: "true"
          topicsPattern: "topic1|topic2|topic3"
          groupsPattern: "group1|group2|group3"

    1
    The TLS certificates for the source Kafka cluster. If they are in a separate namespace, copy the cluster secrets from the namespace of the Kafka cluster.
    2
    The user authentication for accessing the source Kafka cluster using the TLS mechanism.
    3
    The TLS certificates for the target Kafka cluster.
    4
    The user authentication for accessing the target Kafka cluster.
  6. Create or update the KafkaMirrorMaker2 resource in the same namespace as the target Kafka cluster.

    oc apply -f <mirrormaker2_configuration_file> -n <namespace_of_target_cluster>

9.7.8. Manually stopping or pausing MirrorMaker 2 connectors

If you are using KafkaMirrorMaker2 resources to configure internal MirrorMaker connectors, use the state configuration to either stop or pause a connector. In contrast to the paused state, where the connector and tasks remain instantiated, stopping a connector retains only the configuration, with no active processes. Stopping a connector from running may be more suitable for longer durations than just pausing. While a paused connector is quicker to resume, a stopped connector has the advantages of freeing up memory and resources.

Note

The state configuration replaces the (deprecated) pause configuration in the KafkaMirrorMaker2ConnectorSpec schema, which allows pauses on connectors. If you were previously using the pause configuration to pause connectors, we encourage you to transition to using the state configuration only to avoid conflicts.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaMirrorMaker2 custom resource that controls the MirrorMaker 2 connector you want to pause or stop:

    oc 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: 3.7.0
      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.

9.7.9. Manually restarting MirrorMaker 2 connectors

Use the strimzi.io/restart-connector annotation to manually trigger a restart of a MirrorMaker 2 connector.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaMirrorMaker2 custom resource that controls the Kafka MirrorMaker 2 connector you want to restart:

    oc get KafkaMirrorMaker2
  2. Find the name of the Kafka MirrorMaker 2 connector to be restarted from the KafkaMirrorMaker2 custom resource:

    oc describe KafkaMirrorMaker2 <mirrormaker_cluster_name>
  3. Use the name of the connector to restart the connector by annotating the KafkaMirrorMaker2 resource in OpenShift:

    oc 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:

    oc annotate KafkaMirrorMaker2 my-mirror-maker-2 "strimzi.io/restart-connector=my-connector"
  4. Wait for the next reconciliation to occur (every two minutes by default).

    The MirrorMaker 2 connector is restarted, as long as the annotation was detected by the reconciliation process. When MirrorMaker 2 accepts the request, the annotation is removed from the KafkaMirrorMaker2 custom resource.

9.7.10. Manually restarting MirrorMaker 2 connector tasks

Use the strimzi.io/restart-connector-task annotation to manually trigger a restart of a MirrorMaker 2 connector.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaMirrorMaker2 custom resource that controls the MirrorMaker 2 connector task you want to restart:

    oc get KafkaMirrorMaker2
  2. Find the name of the connector and the ID of the task to be restarted from the KafkaMirrorMaker2 custom resource:

    oc 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 OpenShift:

    oc 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:

    oc annotate KafkaMirrorMaker2 my-mirror-maker-2 "strimzi.io/restart-connector-task=my-connector:0"
  4. Wait for the next reconciliation to occur (every two minutes by default).

    The MirrorMaker 2 connector task is restarted, as long as the annotation was detected by the reconciliation process. When MirrorMaker 2 accepts the request, the annotation is removed from the KafkaMirrorMaker2 custom resource.

9.8. Configuring Kafka MirrorMaker (deprecated)

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

You can configure access control for producers and consumers using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and mTLS authentication on the consumer and producer side.

For a deeper understanding of the Kafka MirrorMaker cluster configuration options, refer to the Streams for Apache Kafka 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 Streams for Apache Kafka as well. The KafkaMirrorMaker resource will be removed from Streams for Apache Kafka when we adopt Apache Kafka 4.0.0. As a replacement, use the KafkaMirrorMaker2 custom resource with the IdentityReplicationPolicy.

Example KafkaMirrorMaker custom resource configuration

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  replicas: 3 1
  consumer:
    bootstrapServers: my-source-cluster-kafka-bootstrap:9092 2
    groupId: "my-group" 3
    numStreams: 2 4
    offsetCommitInterval: 120000 5
    tls: 6
      trustedCertificates:
      - secretName: my-source-cluster-ca-cert
        certificate: ca.crt
    authentication: 7
      type: tls
      certificateAndKey:
        secretName: my-source-secret
        certificate: public.crt
        key: private.key
    config: 8
      max.poll.records: 100
      receive.buffer.bytes: 32768
  producer:
    bootstrapServers: my-target-cluster-kafka-bootstrap:9092
    abortOnSendFailure: false 9
    tls:
      trustedCertificates:
      - secretName: my-target-cluster-ca-cert
        certificate: ca.crt
    authentication:
      type: tls
      certificateAndKey:
        secretName: my-target-secret
        certificate: public.crt
        key: private.key
    config:
      compression.type: gzip
      batch.size: 8192
  include: "my-topic|other-topic" 10
  resources: 11
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: 12
    type: inline
    loggers:
      mirrormaker.root.logger: INFO
  readinessProbe: 13
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  metricsConfig: 14
   type: jmxPrometheusExporter
   valueFrom:
     configMapKeyRef:
       name: my-config-map
       key: my-key
  jvmOptions: 15
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest 16
  template: 17
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    mirrorMakerContainer: 18
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing: 19
    type: opentelemetry

1
The number of replica nodes.
2
Bootstrap servers for consumer and producer.
3
Group ID for the consumer.
4
The number of consumer streams.
5
The offset auto-commit interval in milliseconds.
6
TLS encryption with key names under which TLS certificates are stored in X.509 format for consumer or producer. If certificates are stored in the same secret, it can be listed multiple times.
7
Authentication for consumer or producer, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN.
8
Kafka configuration options for consumer and producer.
9
If the abortOnSendFailure property is set to true, Kafka MirrorMaker will exit and the container will restart following a send failure for a message.
10
A list of included topics mirrored from source to target Kafka cluster.
11
Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
12
Specified loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties or log4j2.properties key in the ConfigMap. MirrorMaker has a single logger called mirrormaker.root.logger. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
13
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
14
Prometheus metrics, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under metricsConfig.valueFrom.configMapKeyRef.key.
15
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
16
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
17
Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
18
Environment variables are set for distributed tracing.
19
Distributed tracing is enabled by using OpenTelemetry.
Warning

With the abortOnSendFailure property set to false, the producer attempts to send the next message in a topic. The original message might be lost, as there is no attempt to resend a failed message.

9.9. Configuring the Kafka Bridge

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

In order to prevent issues arising when client consumer requests are processed by different Kafka Bridge instances, address-based routing must be employed to ensure that requests are routed to the right Kafka Bridge instance. Additionally, each independent Kafka Bridge instance must have a replica. A Kafka Bridge instance has its own state which is not shared with another instances.

For a deeper understanding of the Kafka Bridge cluster configuration options, refer to the Streams for Apache Kafka Custom Resource API Reference.

Example KafkaBridge custom resource configuration

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  replicas: 3 1
  bootstrapServers: <cluster_name>-cluster-kafka-bootstrap:9092 2
  tls: 3
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        certificate: ca.crt
      - secretName: my-cluster-cluster-cert
        certificate: ca2.crt
  authentication: 4
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  http: 5
    port: 8080
    cors: 6
      allowedOrigins: "https://strimzi.io"
      allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  consumer: 7
    config:
      auto.offset.reset: earliest
  producer: 8
    config:
      delivery.timeout.ms: 300000
  resources: 9
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: 10
    type: inline
    loggers:
      logger.bridge.level: INFO
      # enabling DEBUG just for send operation
      logger.send.name: "http.openapi.operation.send"
      logger.send.level: DEBUG
  jvmOptions: 11
    "-Xmx": "1g"
    "-Xms": "1g"
  readinessProbe: 12
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  image: my-org/my-image:latest 13
  template: 14
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    bridgeContainer: 15
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry 16

1
The number of replica nodes.
2
Bootstrap server for connection to the target Kafka cluster. Use the name of the Kafka cluster as the <cluster_name>.
3
TLS encryption with key names under which TLS certificates are stored in X.509 format for the source Kafka cluster. If certificates are stored in the same secret, it can be listed multiple times.
4
Authentication for the Kafka Bridge cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN. By default, the Kafka Bridge connects to Kafka brokers without authentication.
5
HTTP access to Kafka brokers.
6
CORS access specifying selected resources and access methods. Additional HTTP headers in requests describe the origins that are permitted access to the Kafka cluster.
7
Consumer configuration options.
8
Producer configuration options.
9
Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
10
Specified Kafka Bridge loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties or log4j2.properties key in the ConfigMap. For the Kafka Bridge loggers, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
11
JVM configuration options to optimize performance for the Virtual Machine (VM) running the Kafka Bridge.
12
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
13
Optional: Container image configuration, which is recommended only in special situations.
14
Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
15
Environment variables are set for distributed tracing.
16
Distributed tracing is enabled by using OpenTelemetry.

9.10. Configuring Kafka and ZooKeeper storage

Streams for Apache Kafka provides flexibility in configuring the data storage options of Kafka and ZooKeeper.

The supported storage types are:

  • Ephemeral (Recommended for development only)
  • Persistent
  • JBOD (Kafka only; not available for ZooKeeper)
  • Tiered storage (Early access)

To configure storage, you specify storage properties in the custom resource of the component. The storage type is set using the storage.type property. When using node pools, you can specify storage configuration unique to each node pool used in a Kafka cluster. The same storage properties available to the Kafka resource are also available to the KafkaNodePool pool resource.

Tiered storage provides more flexibility for data management by leveraging the parallel use of storage types with different characteristics. For example, tiered storage might include the following:

  • Higher performance and higher cost block storage
  • Lower performance and lower cost object storage

Tiered storage is an early access feature in Kafka. To configure tiered storage, you specify tieredStorage properties. Tiered storage is configured only at the cluster level using the Kafka custom resource.

The storage-related schema references provide more information on the storage configuration properties:

Warning

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

9.10.1. Data storage considerations

For Streams for Apache Kafka to work well, an efficient data storage infrastructure is essential. We strongly recommend using block storage. Streams for Apache Kafka is only tested for use with block storage. File storage, such as NFS, is not tested and there is no guarantee it will work.

Choose one of the following options for your block storage:

Note

Streams for Apache Kafka does not require OpenShift raw block volumes.

9.10.1.1. File systems

Kafka uses a file system for storing messages. Streams for Apache Kafka 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.

9.10.1.2. Disk usage

Use separate disks for Apache Kafka and ZooKeeper.

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

Note

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

9.10.2. Ephemeral storage

Ephemeral data storage is transient. All pods on a node share a local ephemeral storage space. Data is retained for as long as the pod that uses it is running. The data is lost when a pod is deleted. Although a pod can recover data in a highly available environment.

Because of its transient nature, ephemeral storage is only recommended for development and testing.

Ephemeral storage uses emptyDir volumes to store data. An emptyDir volume is created when a pod is assigned to a node. You can set the total amount of storage for the emptyDir using the sizeLimit property .

Important

Ephemeral storage is not suitable for single-node ZooKeeper clusters or Kafka topics with a replication factor of 1.

To use ephemeral storage, you set the storage type configuration in the Kafka or ZooKeeper resource to ephemeral. If you are using node pools, you can also specify ephemeral in the storage configuration of individual node pools.

Example ephemeral storage configuration

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

9.10.2.1. Mount path of Kafka log directories

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

/var/lib/kafka/data/kafka-logIDX

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

9.10.3. Persistent storage

Persistent data storage retains data in the event of system disruption. For pods that use persistent data storage, data is persisted across pod failures and restarts. Because of its permanent nature, persistent storage is recommended for production environments.

To use persistent storage in Streams for Apache Kafka, you specify persistent-claim in the storage configuration of the Kafka or ZooKeeper resources. If you are using node pools, you can also specify persistent-claim in the storage configuration of individual node pools.

You configure the resource so that pods use Persistent Volume Claims (PVCs) to make storage requests on persistent volumes (PVs). PVs represent storage volumes that are created on demand and are independent of the pods that use them. The PVC requests the amount of storage required when a pod is being created. The underlying storage infrastructure of the PV does not need to be understood. If a PV matches the storage criteria, the PVC is bound to the PV.

You have two options for specifying the storage type:

storage.type: persistent-claim
If you choose persistent-claim as the storage type, a single persistent storage volume is defined.
storage.type: jbod
When you select jbod as the storage type, you have the flexibility to define an array of persistent storage volumes using unique IDs.

In a production environment, it is recommended to configure the following:

  • For Kafka or node pools, set storage.type to jbod with one or more persistent volumes.
  • For ZooKeeper, set storage.type as persistent-claim for a single persistent volume.

Persistent storage also has the following configuration options:

id (optional)
A storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration. Default is 0.
size (required)
The size of the persistent volume claim, for example, "1000Gi".
class (optional)
PVCs can request different types of persistent storage by specifying a StorageClass. Storage classes define storage profiles and dynamically provision PVs based on that profile. If a storage class is not specified, the storage class marked as default in the OpenShift cluster is used. Persistent storage options might include SAN storage types or local persistent volumes.
selector (optional)
Configuration to specify a specific PV. Provides key:value pairs representing the labels of the volume selected.
deleteClaim (optional)
Boolean value to specify whether the PVC is deleted when the cluster is uninstalled. Default is false.
Warning

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

Example persistent storage configuration for Kafka and ZooKeeper

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 1
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 2
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
    # ...
  zookeeper:
    storage:
      type: persistent-claim
      size: 1000Gi
    # ...

Example persistent storage configuration with specific storage class

# ...
storage:
  type: persistent-claim
  size: 500Gi
  class: my-storage-class
# ...

Use a selector to specify a labeled persistent volume that provides certain features, such as an SSD.

Example persistent storage configuration with selector

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

9.10.3.1. Storage class overrides

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

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

Example storage configuration with class overrides

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  labels:
    app: my-cluster
  name: my-cluster
  namespace: myproject
spec:
  # ...
  kafka:
    replicas: 3
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
        class: my-storage-class
        overrides:
        - broker: 0
          class: my-storage-class-zone-1a
        - broker: 1