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Using AMQ Streams on OpenShift

Red Hat AMQ 2021.q2

For use with AMQ Streams 1.7 on OpenShift Container Platform

Abstract

This guide describes how to install, configure, and manage Red Hat AMQ Streams to build a large-scale messaging network.

Making open source more inclusive

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

Chapter 1. Overview of AMQ Streams

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

This guide provides instructions for configuring Kafka components and using AMQ Streams Operators. Procedures relate to how you might want to modify your deployment and introduce additional features, such as Cruise Control or distributed tracing.

You can configure your deployment using AMQ Streams custom resources. The Custom resource API reference describes the properties you can use in your configuration.

Note

Looking to get started with AMQ Streams? For step-by-step deployment instructions, see the Deploying and Upgrading AMQ Streams on OpenShift guide.

1.1. Kafka capabilities

The underlying data stream-processing capabilities and component architecture of Kafka can deliver:

Microservices and other applications to share data with extremely high throughput and low latency
Message ordering guarantees
Message rewind/replay from data storage to reconstruct an application state
Message compaction to remove old records when using a key-value log
Horizontal scalability in a cluster configuration
Replication of data to control fault tolerance
Retention of high volumes of data for immediate access

1.2. Kafka use cases

Kafka’s capabilities make it suitable for:

Event-driven architectures
Event sourcing to capture changes to the state of an application as a log of events
Message brokering
Website activity tracking
Operational monitoring through metrics
Log collection and aggregation
Commit logs for distributed systems
Stream processing so that applications can respond to data in real time

1.3. How AMQ Streams supports Kafka

AMQ Streams provides container images and Operators for running Kafka on OpenShift. AMQ Streams Operators are fundamental to the running of AMQ Streams. The Operators provided with AMQ Streams are purpose-built with specialist operational knowledge to effectively manage Kafka.

Operators simplify the process of:

Deploying and running Kafka clusters
Deploying and running Kafka components
Configuring access to Kafka
Securing access to Kafka
Upgrading Kafka
Managing brokers
Creating and managing topics
Creating and managing users

1.4. AMQ Streams Operators

AMQ Streams supports Kafka using Operators to deploy and manage the components and dependencies of Kafka to OpenShift.

Operators are a method of packaging, deploying, and managing an OpenShift application. AMQ Streams Operators extend OpenShift functionality, automating common and complex tasks related to a Kafka deployment. By implementing knowledge of Kafka operations in code, Kafka administration tasks are simplified and require less manual intervention.

Operators

AMQ Streams provides Operators for managing a Kafka cluster running within an OpenShift cluster.

Cluster Operator: Deploys and manages Apache Kafka clusters, Kafka Connect, Kafka MirrorMaker, Kafka Bridge, Kafka Exporter, and the Entity Operator
Entity Operator: Comprises the Topic Operator and User Operator
Topic Operator: Manages Kafka topics
User Operator: Manages Kafka users

The Cluster Operator can deploy the Topic Operator and User Operator as part of an Entity Operator configuration at the same time as a Kafka cluster.

Operators within the AMQ Streams architecture

Operators within the AMQ Streams architecture

1.4.1. Cluster Operator

AMQ Streams uses the Cluster Operator to deploy and manage clusters for:

Kafka (including ZooKeeper, Entity Operator, Kafka Exporter, and Cruise Control)
Kafka Connect
Kafka MirrorMaker
Kafka Bridge

Custom resources are used to deploy the clusters.

For example, to deploy a Kafka cluster:

A Kafka resource with the cluster configuration is created within the OpenShift cluster.
The Cluster Operator deploys a corresponding Kafka cluster, based on what is declared in the Kafka resource.

The Cluster Operator can also deploy (through configuration of the Kafka resource):

A Topic Operator to provide operator-style topic management through KafkaTopic custom resources
A User Operator to provide operator-style user management through KafkaUser custom resources

The Topic Operator and User Operator function within the Entity Operator on deployment.

Example architecture for the Cluster Operator

The Cluster Operator creates and deploys Kafka and ZooKeeper clusters

1.4.2. Topic Operator

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

Example architecture for the Topic Operator

The Topic Operator manages topics for a Kafka cluster via KafkaTopic resources

The role of the Topic Operator is to keep a set of KafkaTopic OpenShift resources describing Kafka topics in-sync with corresponding Kafka topics.

Specifically, if a KafkaTopic is:

Created, the Topic Operator creates the topic
Deleted, the Topic Operator deletes the topic
Changed, the Topic Operator updates the topic

Working in the other direction, if a topic is:

Created within the Kafka cluster, the Operator creates a KafkaTopic
Deleted from the Kafka cluster, the Operator deletes the KafkaTopic
Changed in the Kafka cluster, the Operator updates the KafkaTopic

This allows you to declare a KafkaTopic as part of your application’s deployment and the Topic Operator will take care of creating the topic for you. Your application just needs to deal with producing or consuming from the necessary topics.

The Topic Operator maintains information about each topic in a topic store, which is continually synchronized with updates from Kafka topics or OpenShift KafkaTopic custom resources. Updates from operations applied to a local in-memory topic store are persisted to a backup topic store on disk. If a topic is reconfigured or reassigned to other brokers, the KafkaTopic will always be up to date.

1.4.3. User Operator

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser resources that describe Kafka users, and ensuring that they are configured properly in the Kafka cluster.

For example, if a KafkaUser is:

Created, the User Operator creates the user it describes
Deleted, the User Operator deletes the user it describes
Changed, the User Operator updates the user it describes

Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the OpenShift resources. Kafka topics can be created by applications directly in Kafka, but it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator.

The User Operator allows you to declare a KafkaUser resource as part of your application’s deployment. You can specify the authentication and authorization mechanism for the user. You can also configure user quotas that control usage of Kafka resources to ensure, for example, that a user does not monopolize access to a broker.

When the user is created, the user credentials are created in a Secret. Your application needs to use the user and its credentials for authentication and to produce or consume messages.

In addition to managing credentials for authentication, the User Operator also manages authorization rules by including a description of the user’s access rights in the KafkaUser declaration.

1.5. AMQ Streams custom resources

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

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

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

Additional resources

Extend the Kubernetes API with CustomResourceDefinitions

1.5.1. AMQ Streams custom resource example

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

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

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

Note

Access to manage custom resources is limited to AMQ Streams administrators. For more information, see Designating AMQ Streams administrators in the Deploying and Upgrading AMQ Streams on OpenShift guide.

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

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

Warning

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

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

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

Kafka topic CRD

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

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

Note

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

Here is a corresponding example of a KafkaTopic custom resource.

Kafka topic custom resource

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

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

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

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

1.6. Listener configuration

Listeners are used to connect to Kafka brokers.

AMQ Streams provides a generic GenericKafkaListener schema with properties to configure listeners through the Kafka resource.

The GenericKafkaListener provides a flexible approach to listener configuration.

You can specify properties to configure internal listeners for connecting within the OpenShift cluster, or external listeners for connecting outside the OpenShift cluster.

Generic listener configuration

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

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

Generic listener configuration replaces the previous approach to listener configuration using the KafkaListeners schema reference, which is deprecated. However, you can convert the old format into the new format with backwards compatibility.

The KafkaListeners schema uses sub-properties for plain, tls and external listeners, with fixed ports for each. Because of the limits inherent in the architecture of the schema, it is only possible to configure three listeners, with configuration options limited to the type of listener.

With the GenericKafkaListener schema, you can configure as many listeners as required, as long as their names and ports are unique.

You might want to configure multiple external listeners, for example, to handle access from networks that require different authentication mechanisms. Or you might need to join your OpenShift network to an outside network. In which case, you can configure internal listeners (using the useServiceDnsDomain property) so that the OpenShift service DNS domain (typically .cluster.local) is not used.

Configuring listeners to secure access to Kafka brokers

You can configure listeners for secure connection using authentication. For more information on securing access to Kafka brokers, see Managing access to Kafka.

Configuring external listeners for client access outside OpenShift

You can configure external listeners for client access outside an OpenShift environment using a specified connection mechanism, such as a loadbalancer. For more information on the configuration options for connecting an external client, see Configuring external listeners.

Listener certificates

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

1.7. Document Conventions

Replaceables

In this document, replaceable text is styled in monospace, with italics, uppercase, and hyphens.

For example, in the following code, you will want to replace MY-NAMESPACE with the name of your namespace:

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

Chapter 2. Deployment configuration

This chapter describes how to configure different aspects of the supported deployments using custom resources:

Kafka clusters
Kafka Connect clusters
Kafka Connect clusters with Source2Image support
Kafka MirrorMaker
Kafka Bridge
Cruise Control

Note

Labels applied to a custom resource are also applied to the OpenShift resources comprising Kafka MirrorMaker. This provides a convenient mechanism for resources to be labeled as required.

2.1. Kafka cluster configuration

This section describes how to configure a Kafka deployment in your AMQ Streams cluster. A Kafka cluster is deployed with a ZooKeeper cluster. The deployment can also include the Topic Operator and User Operator, which manage Kafka topics and users.

You configure Kafka using the Kafka resource. Configuration options are also available for ZooKeeper and the Entity Operator within the Kafka resource. The Entity Operator comprises the Topic Operator and User Operator.

The full schema of the Kafka resource is described in the Section 13.2.1, “Kafka schema reference”.

Listener configuration

You configure listeners for connecting clients to Kafka brokers. For more information on configuring listeners for connecting brokers, see Listener configuration.

Authorizing access to Kafka

You can configure your Kafka cluster to allow or decline actions executed by users. For more information on securing access to Kafka brokers, see Managing access to Kafka.

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 client CA certificates before their renewal period ends. You can also replace the keys used by the cluster and client CA certificates. For more information, see Renewing CA certificates manually and Replacing private keys.

Additional resources

For more information about Apache Kafka, see the Apache Kafka website.

2.1.1. Configuring Kafka

Use the properties of the Kafka resource to configure your Kafka deployment.

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

This procedure shows only some of the possible configuration options, but those that are particularly important include:

Resource requests (CPU / Memory)
JVM options for maximum and minimum memory allocation
Listeners (and authentication of clients)
Authentication
Storage
Rack awareness
Metrics
Cruise Control for cluster rebalancing

Kafka versions

The log.message.format.version and inter.broker.protocol.version properties for the Kafka config must be the versions supported by the specified Kafka version (spec.kafka.version). The properties represent the log format version appended to messages and the version of Kafka protocol used in a Kafka cluster. Updates to these properties are required when upgrading your Kafka version. For more information, see Upgrading Kafka in the Deploying and Upgrading AMQ Streams on OpenShift guide.

Prerequisites

An OpenShift cluster
A running Cluster Operator

See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on deploying a:

Procedure

Edit the spec properties for the Kafka resource.
The properties you can configure are shown in this example configuration:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    replicas: 3 1
    version: 2.7.0 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: external 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
      log.message.format.version: 2.7
      inter.broker.protocol.version: 2.7
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" 19
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
    storage: 20
      type: persistent-claim 21
      size: 10000Gi 22
    rack: 23
      topologyKey: topology.kubernetes.io/zone
    metricsConfig: 24
      type: jmxPrometheusExporter
      valueFrom:
        configMapKeyRef: 25
          name: my-config-map
          key: my-key
    # ...
  zookeeper: 26
    replicas: 3 27
    logging: 28
      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: 29
    tlsSidecar: 30
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging: 31
        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: 32
        type: inline
        loggers:
          rootLogger.level: INFO
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
  kafkaExporter: 33
    # ...
  cruiseControl: 34
    # ...
    tlsSidecar: 35
    # ...
```
1
The number of replica nodes. If your cluster already has topics defined, you can scale clusters.
2
Kafka version, which can be changed to a supported version by following the upgrade procedure.
3
Specified Kafka loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties key. 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 for external listeners, as route, loadbalancer, nodeport or ingress.
12
Enables TLS encryption for each listener. Default is false. TLS encryption is not required for route listeners.
13
Defines whether the fully-qualified DNS names including the cluster service suffix (usually .cluster.local) are assigned.
14
Listener authentication mechanism specified as mutual TLS, 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 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 Kafka plugin.
18
The config specifies the broker configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by AMQ Streams.
19
SSL properties for listeners with TLS encryption enabled to enable a specific cipher suite or TLS version.
20
Storage is configured as ephemeral, persistent-claim or jbod.
21
Storage size for persistent volumes may be increased and additional volumes may be added to JBOD storage.
22
Persistent storage has additional configuration options, such as a storage id and class for dynamic volume provisioning.
23
Rack awareness is configured to spread replicas across different racks. A topologykey must match the label of a cluster node.
24
Prometheus metrics enabled. In this example, metrics are configured for the Prometheus JMX Exporter (the default metrics exporter).
25
Prometheus rules for exporting metrics 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.
26
ZooKeeper-specific configuration, which contains properties similar to the Kafka configuration.
27
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 AMQ Streams.
28
Specified ZooKeeper loggers and log levels.
29
Entity Operator configuration, which specifies the configuration for the Topic Operator and User Operator.
30
Entity Operator TLS sidecar configuration. Entity Operator uses the TLS sidecar for secure communication with ZooKeeper.
31
Specified Topic Operator loggers and log levels. This example uses inline logging.
32
Specified User Operator loggers and log levels.
33
Kafka Exporter configuration. Kafka Exporter is an optional component for extracting metrics data from Kafka brokers, in particular consumer lag data.
34
Optional configuration for Cruise Control, which is used to rebalance the Kafka cluster.
35
Cruise Control TLS sidecar configuration. Cruise Control uses the TLS sidecar for secure communication with ZooKeeper.
Create or update the resource:
```
oc apply -f KAFKA-CONFIG-FILE
```

2.1.2. Configuring the Entity Operator

The Entity Operator is responsible for managing Kafka-related entities in a running Kafka cluster.

The Entity Operator comprises the:

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

Through Kafka resource configuration, the Cluster Operator can deploy the Entity Operator, including one or both operators, when deploying a Kafka cluster.

Note

When deployed, the Entity Operator contains the operators according to the deployment configuration.

The operators are automatically configured to manage the topics and users of the Kafka cluster.

2.1.2.1. Entity Operator configuration properties

Use the entityOperator property in Kafka.spec to configure the Entity Operator.

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 2.6, “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 EntityUserOperatorSpec 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.

2.1.2.2. Topic Operator configuration properties

Topic Operator deployment can be configured using additional options inside the topicOperator object. The following properties are supported:

watchedNamespace: The OpenShift namespace in which the topic operator watches for KafkaTopics. Default is the namespace where the Kafka cluster is deployed.
reconciliationIntervalSeconds: The interval between periodic reconciliations in seconds. Default 90.
zookeeperSessionTimeoutSeconds: The ZooKeeper session timeout in seconds. Default 20.
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 will be used. For more details about configuring custom container images, see Section 13.1.6, “image”.
resources: The resources property configures the amount of resources allocated to the Topic Operator. For more details about resource request and limit configuration, see Section 13.1.5, “resources”.
logging: The logging property configures the logging of the Topic Operator. For more details, see Section 13.2.67.1, “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
    # ...

2.1.2.3. User Operator configuration properties

User Operator deployment can be configured using additional options inside the userOperator object. The following properties are supported:

watchedNamespace: The OpenShift namespace in which the user operator watches for KafkaUsers. 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 6.
image: The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 13.1.6, “image”.
resources: The resources property configures the amount of resources allocated to the User Operator. For more details about resource request and limit configuration, see Section 13.1.5, “resources”.
logging: The logging property configures the logging of the User Operator. For more details, see Section 13.2.67.1, “logging”.
secretPrefix: The secretPrefix property adds a prefix to the name of all Secrets created from the KafkaUser resource. For example, STRIMZI_SECRET_PREFIX=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
    # ...

2.1.3. Kafka and ZooKeeper storage types

As stateful applications, Kafka and ZooKeeper need to store data on disk. AMQ Streams supports three storage types for this data:

Ephemeral
Persistent
JBOD storage

Note

JBOD storage is supported only for Kafka, not for ZooKeeper.

When configuring a Kafka resource, you can specify the type of storage used by the Kafka broker and its corresponding ZooKeeper node. You configure the storage type using the storage property in the following resources:

Kafka.spec.kafka
Kafka.spec.zookeeper

The storage type is configured in the type field.

Warning

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

Additional resources

For more information about ephemeral storage, see ephemeral storage schema reference.
For more information about persistent storage, see persistent storage schema reference.
For more information about JBOD storage, see JBOD schema reference.
For more information about the schema for Kafka, see Kafka schema reference.

2.1.3.1. Data storage considerations

An efficient data storage infrastructure is essential to the optimal performance of AMQ Streams.

Block storage is required. File storage, such as NFS, does not work with Kafka.

For your block storage, you can choose, for example:

Cloud-based block storage solutions, such as Amazon Elastic Block Store (EBS)
Local persistent volumes
Storage Area Network (SAN) volumes accessed by a protocol such as Fibre Channel or iSCSI

Note

AMQ Streams does not require OpenShift raw block volumes.

2.1.3.1.1. File systems

It is recommended that you configure your storage system to use the XFS file system. AMQ Streams is also compatible with the ext4 file system, but this might require additional configuration for best results.

2.1.3.1.2. Apache Kafka and ZooKeeper storage

Use separate disks for Apache Kafka and ZooKeeper.

Three types of data storage are supported:

Ephemeral (Recommended for development only)
Persistent
JBOD (Just a Bunch of Disks, suitable for Kafka only)

For more information, see Kafka and ZooKeeper storage.

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

Note

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

2.1.3.2. Ephemeral storage

Ephemeral storage uses emptyDir volumes to store data. To use ephemeral storage, set the type field to ephemeral.

Important

emptyDir volumes are not persistent and the data stored in them is lost when the pod is restarted. After the new pod is started, it must recover all data from the other nodes of the cluster. Ephemeral storage is not suitable for use with single-node ZooKeeper clusters or for Kafka topics with a replication factor of 1. This configuration will cause data loss.

An example of Ephemeral storage

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

2.1.3.2.1. Log directories

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

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

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

2.1.3.3. Persistent storage

Persistent storage uses Persistent Volume Claims to provision persistent volumes for storing data. Persistent Volume Claims can be used to provision volumes of many different types, depending on the Storage Class which will provision the volume. The data types which can be used with persistent volume claims include many types of SAN storage as well as Local persistent volumes.

To use persistent storage, the type has to be set to persistent-claim. Persistent storage supports additional configuration options:

id (optional): Storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration. Default is 0.
size (required): Defines the size of the persistent volume claim, for example, "1000Gi".
class (optional): The OpenShift Storage Class to use for dynamic volume provisioning.
selector (optional): Allows selecting a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume.
deleteClaim (optional): Boolean value which specifies if the Persistent Volume Claim has to be deleted when the cluster is undeployed. Default is false.

Warning

Increasing the size of persistent volumes in an existing AMQ Streams 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 which 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 fragment of persistent storage configuration with 1000Gi size

# ...
storage:
  type: persistent-claim
  size: 1000Gi
# ...

The following example demonstrates the use of a storage class.

Example fragment of persistent storage configuration with specific Storage Class

# ...
storage:
  type: persistent-claim
  size: 1Gi
  class: my-storage-class
# ...

Finally, a selector can be used to select a specific labeled persistent volume to provide needed features such as an SSD.

Example fragment of persistent storage configuration with selector

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

2.1.3.3.1. Storage class overrides

You can specify a different storage class for one or more Kafka brokers or ZooKeeper nodes, instead of using the default storage class. This is useful if, for example, 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 AMQ Streams cluster using storage class overrides

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  labels:
    app: my-cluster
  name: my-cluster
  namespace: myproject
spec:
  # ...
  kafka:
    replicas: 3
    storage:
      deleteClaim: true
      size: 100Gi
      type: persistent-claim
      class: my-storage-class
      overrides:
        - broker: 0
          class: my-storage-class-zone-1a
        - broker: 1
          class: my-storage-class-zone-1b
        - broker: 2
          class: my-storage-class-zone-1c
  # ...
  zookeeper:
    replicas: 3
    storage:
      deleteClaim: true
      size: 100Gi
      type: persistent-claim
      class: my-storage-class
      overrides:
        - broker: 0
          class: my-storage-class-zone-1a
        - broker: 1
          class: my-storage-class-zone-1b
        - broker: 2
          class: my-storage-class-zone-1c
  # ...

As a result of the configured overrides property, the volumes use the following storage classes:

The persistent volumes of ZooKeeper node 0 will use my-storage-class-zone-1a.
The persistent volumes of ZooKeeper node 1 will use my-storage-class-zone-1b.
The persistent volumes of ZooKeeepr node 2 will use my-storage-class-zone-1c.
The persistent volumes of Kafka broker 0 will use my-storage-class-zone-1a.
The persistent volumes of Kafka broker 1 will use my-storage-class-zone-1b.
The persistent volumes of Kafka broker 2 will use my-storage-class-zone-1c.

The overrides property is currently used only to override storage class configurations. Overriding other storage configuration fields is not currently supported. Other fields from the storage configuration are currently not supported.

2.1.3.3.2. Persistent Volume Claim naming

When persistent storage is used, it creates Persistent Volume Claims with the following names:

data-cluster-name-kafka-idx: Persistent Volume Claim for the volume used for storing data for the Kafka broker pod idx.
data-cluster-name-zookeeper-idx: Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod idx.

2.1.3.3.3. Log directories

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

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

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

2.1.3.4. Resizing persistent volumes

You can provision increased storage capacity by increasing the size of the persistent volumes used by an existing AMQ Streams cluster. Resizing persistent volumes is supported in clusters that use either a single persistent volume or multiple persistent volumes in a JBOD storage configuration.

Note

You can increase but not decrease the size of persistent volumes. Decreasing the size of persistent volumes is not currently supported in OpenShift.

Prerequisites

An OpenShift cluster with support for volume resizing.
The Cluster Operator is running.
A Kafka cluster using persistent volumes created using a storage class that supports volume expansion.

Procedure

In a Kafka resource, increase the size of the persistent volume allocated to the Kafka cluster, the ZooKeeper cluster, or both.
- To increase the volume size allocated to the Kafka cluster, edit the spec.kafka.storage property.
- To increase the volume size allocated to the ZooKeeper cluster, edit the spec.zookeeper.storage property.
  For example, to increase the volume size from 1000Gi to 2000Gi:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: persistent-claim
      size: 2000Gi
      class: my-storage-class
    # ...
  zookeeper:
    # ...
```
Create or update the resource:
```
oc apply -f KAFKA-CONFIG-FILE
```
OpenShift increases the capacity of the selected persistent volumes in response to a request from the Cluster Operator. When the resizing is complete, the Cluster Operator restarts all pods that use the resized persistent volumes. This happens automatically.

Additional resources

For more information about resizing persistent volumes in OpenShift, see Resizing Persistent Volumes using Kubernetes.

2.1.3.5. JBOD storage overview

You can configure AMQ Streams to use JBOD, a data storage configuration of multiple disks or volumes. JBOD is one approach to providing increased data storage for Kafka brokers. It can also improve performance.

A JBOD configuration is described by one or more volumes, each of which can be either ephemeral or persistent. The rules and constraints for JBOD volume declarations are the same as those for ephemeral and persistent storage. For example, you cannot decrease the size of a persistent storage volume after it has been provisioned, or you cannot change the value of sizeLimit when type=ephemeral.

2.1.3.5.1. JBOD configuration

To use JBOD with AMQ Streams, the storage type must be set to jbod. The volumes property allows you to describe the disks that make up your JBOD storage array or configuration. The following fragment shows an example JBOD configuration:

# ...
storage:
  type: jbod
  volumes:
  - id: 0
    type: persistent-claim
    size: 100Gi
    deleteClaim: false
  - id: 1
    type: persistent-claim
    size: 100Gi
    deleteClaim: false
# ...

The ids cannot be changed once the JBOD volumes are created.

Users can add or remove volumes from the JBOD configuration.

2.1.3.5.2. JBOD and Persistent Volume Claims

When persistent storage is used to declare JBOD volumes, the naming scheme of the resulting Persistent Volume Claims is as follows:

data-id-cluster-name-kafka-idx: Where id is the ID of the volume used for storing data for Kafka broker pod idx.

2.1.3.5.3. Log directories

The JBOD volumes will be used by the Kafka brokers as log directories mounted into the following path:

/var/lib/kafka/data-id/kafka-log_idx_: Where id is the ID of the volume used for storing data for Kafka broker pod idx. For example /var/lib/kafka/data-0/kafka-log0.

2.1.3.6. Adding volumes to JBOD storage

This procedure describes how to add volumes to a Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type.

Note

When adding a new volume under an id which was already used in the past and removed, you have to make sure that the previously used PersistentVolumeClaims have been deleted.

Prerequisites

An OpenShift cluster
A running Cluster Operator
A Kafka cluster with JBOD storage

Procedure

Edit the spec.kafka.storage.volumes property in the Kafka resource. Add the new volumes to the volumes array. For example, add the new volume with id 2:

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

Create or update the resource:
```
oc apply -f KAFKA-CONFIG-FILE
```
Create new topics or reassign existing partitions to the new disks.

Additional resources

For more information about reassigning topics, see Section 2.1.4.2, “Partition reassignment”.

2.1.3.7. Removing volumes from JBOD storage

This procedure describes how to remove volumes from Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type. The JBOD storage always has to contain at least one volume.

Important

To avoid data loss, you have to move all partitions before removing the volumes.

Prerequisites

An OpenShift cluster
A running Cluster Operator
A Kafka cluster with JBOD storage with two or more volumes

Procedure

Reassign all partitions from the disks which are you going to remove. Any data in partitions still assigned to the disks which are going to be removed might be lost.

Edit the spec.kafka.storage.volumes property in the Kafka resource. Remove one or more volumes from the volumes array. For example, remove the volumes with ids 1 and 2:

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

Create or update the resource:
```
oc apply -f KAFKA-CONFIG-FILE
```

Additional resources

For more information about reassigning topics, see Section 2.1.4.2, “Partition reassignment”.

2.1.4. Scaling clusters

2.1.4.1. Scaling Kafka clusters

2.1.4.1.1. Adding brokers to a cluster

The primary way of increasing throughput for a topic is to increase the number of partitions for that topic. That works because the extra partitions allow the load of the topic to be shared between the different brokers in the cluster. However, in situations where every broker is constrained by a particular resource (typically I/O) using more partitions will not result in increased throughput. Instead, you need to add brokers to the cluster.

When you add an extra broker to the cluster, Kafka does not assign any partitions to it automatically. You must decide which partitions to move from the existing brokers to the new broker.

Once the partitions have been redistributed between all the brokers, the resource utilization of each broker should be reduced.

2.1.4.1.2. Removing brokers from a cluster

Because AMQ Streams uses StatefulSets to manage broker pods, you cannot remove any pod from the cluster. You can only remove one or more of the highest numbered pods from the cluster. For example, in a cluster of 12 brokers the pods are named cluster-name-kafka-0 up to cluster-name-kafka-11. If you decide to scale down by one broker, the cluster-name-kafka-11 will be removed.

Before you remove a broker from a cluster, ensure that it is not assigned to any partitions. You should also decide which of the remaining brokers will be responsible for each of the partitions on the broker being decommissioned. Once the broker has no assigned partitions, you can scale the cluster down safely.

2.1.4.2. Partition reassignment

The Topic Operator does not currently support reassigning replicas to different brokers, so it is necessary to connect directly to broker pods to reassign replicas to brokers.

Within a broker pod, the kafka-reassign-partitions.sh utility allows you to reassign partitions to different brokers.

It has three different modes:

--generate: Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you only want to reassign some partitions of some topics.
--execute: Takes a reassignment JSON file and applies it to the partitions and brokers in the cluster. Brokers that gain partitions as a result become followers of the partition leader. For a given partition, once the new broker has caught up and joined the ISR (in-sync replicas) the old broker will stop being a follower and will delete its replica.
--verify: Using the same reassignment JSON file as the --execute step, --verify checks whether all the partitions in the file have been moved to their intended brokers. If the reassignment is complete, --verify also removes any throttles that are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.

It is only possible to have one reassignment running in a cluster at any given time, and it is not possible to cancel a running reassignment. If you need to cancel a reassignment, wait for it to complete and then perform another reassignment to revert the effects of the first reassignment. The kafka-reassign-partitions.sh will print the reassignment JSON for this reversion as part of its output. Very large reassignments should be broken down into a number of smaller reassignments in case there is a need to stop in-progress reassignment.

2.1.4.2.1. Reassignment JSON file

The reassignment JSON file has a specific structure:

{
  "version": 1,
  "partitions": [
    <PartitionObjects>
  ]
}

Where <PartitionObjects> is a comma-separated list of objects like:

{
  "topic": <TopicName>,
  "partition": <Partition>,
  "replicas": [ <AssignedBrokerIds> ]
}

Note

Although Kafka also supports a "log_dirs" property this should not be used in AMQ Streams.

The following is an example reassignment JSON file that assigns partition 4 of topic topic-a to brokers 2, 4 and 7, and partition 2 of topic topic-b to brokers 1, 5 and 7:

{
  "version": 1,
  "partitions": [
    {
      "topic": "topic-a",
      "partition": 4,
      "replicas": [2,4,7]
    },
    {
      "topic": "topic-b",
      "partition": 2,
      "replicas": [1,5,7]
    }
  ]
}

Partitions not included in the JSON are not changed.

2.1.4.2.2. Reassigning partitions between JBOD volumes

When using JBOD storage in your Kafka cluster, you can choose to reassign the partitions between specific volumes and their log directories (each volume has a single log directory). To reassign a partition to a specific volume, add the log_dirs option to <PartitionObjects> in the reassignment JSON file.

{
  "topic": <TopicName>,
  "partition": <Partition>,
  "replicas": [ <AssignedBrokerIds> ],
  "log_dirs": [ <AssignedLogDirs> ]
}

The log_dirs object should contain the same number of log directories as the number of replicas specified in the replicas object. The value should be either an absolute path to the log directory, or the any keyword.

For example:

{
      "topic": "topic-a",
      "partition": 4,
      "replicas": [2,4,7].
      "log_dirs": [ "/var/lib/kafka/data-0/kafka-log2", "/var/lib/kafka/data-0/kafka-log4", "/var/lib/kafka/data-0/kafka-log7" ]
}

2.1.4.3. Generating reassignment JSON files

This procedure describes how to generate a reassignment JSON file that reassigns all the partitions for a given set of topics using the kafka-reassign-partitions.sh tool.

Prerequisites

A running Cluster Operator
A Kafka resource
A set of topics to reassign the partitions of

Procedure

Prepare a JSON file named topics.json that lists the topics to move. It must have the following structure:
```
{
  "version": 1,
  "topics": [
    <TopicObjects>
  ]
}
```
where <TopicObjects> is a comma-separated list of objects like:
```
{
  "topic": <TopicName>
}
```
For example if you want to reassign all the partitions of topic-a and topic-b, you would need to prepare a topics.json file like this:
```
{
  "version": 1,
  "topics": [
    { "topic": "topic-a"},
    { "topic": "topic-b"}
  ]
}
```

Copy the topics.json file to one of the broker pods:

cat topics.json | oc exec -c kafka <BrokerPod> -i -- \
  /bin/bash -c \
  'cat > /tmp/topics.json'

Use the kafka-reassign-partitions.sh command to generate the reassignment JSON.

oc exec <BrokerPod> -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --topics-to-move-json-file /tmp/topics.json \
  --broker-list <BrokerList> \
  --generate

For example, to move all the partitions of topic-a and topic-b to brokers 4 and 7

oc exec <BrokerPod> -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --topics-to-move-json-file /tmp/topics.json \
  --broker-list 4,7 \
  --generate

2.1.4.4. Creating reassignment JSON files manually

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

2.1.4.5. Reassignment throttles

Partition reassignment can be a slow process because it involves transferring large amounts of data between brokers. To avoid a detrimental impact on clients, you can throttle the reassignment process. This might cause the reassignment to take longer to complete.

If the throttle is too low then the newly assigned brokers will not be able to keep up with records being published and the reassignment will never complete.
If the throttle is too high then clients will be impacted.

For example, for producers, this could manifest as higher than normal latency waiting for acknowledgement. For consumers, this could manifest as a drop in throughput caused by higher latency between polls.

2.1.4.6. Scaling up a Kafka cluster

This procedure describes how to increase the number of brokers in a Kafka cluster.

Prerequisites

An existing Kafka cluster.
A reassignment JSON file named reassignment.json that describes how partitions should be reassigned to brokers in the enlarged cluster.

Procedure

Add as many new brokers as you need by increasing the Kafka.spec.kafka.replicas configuration option.
Verify that the new broker pods have started.

Copy the reassignment.json file to the broker pod on which you will later execute the commands:

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

For example:

cat reassignment.json | \
  oc exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \
  'cat > /tmp/reassignment.json'

Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool from the same broker pod.
```
oc exec broker-pod -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --execute
```
If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:
```
oc exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 5000000 \
  --execute
```
This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:

oc exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 10000000 \
  --execute

Periodically verify whether the reassignment has completed using the kafka-reassign-partitions.sh command line tool from any of the broker pods. This is the same command as the previous step but with the --verify option instead of the --execute option.

oc exec broker-pod -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify

For example,

oc exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify

The reassignment has finished when the --verify command reports each of the partitions being moved as completed successfully. This final --verify will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.

2.1.4.7. Scaling down a Kafka cluster

This procedure describes how to decrease the number of brokers in a Kafka cluster.

Prerequisites

An existing Kafka cluster.
A reassignment JSON file named reassignment.json describing how partitions should be reassigned to brokers in the cluster once the broker(s) in the highest numbered Pod(s) have been removed.

Procedure

Copy the reassignment.json file to the broker pod on which you will later execute the commands:

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

For example:

cat reassignment.json | \
  oc exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \
  'cat > /tmp/reassignment.json'

Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool from the same broker pod.
```
oc exec broker-pod -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --execute
```
If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:
```
oc exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 5000000 \
  --execute
```
This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:

oc exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 10000000 \
  --execute

oc exec broker-pod -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify

For example,

oc exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify

The reassignment has finished when the --verify command reports each of the partitions being moved as completed successfully. This final --verify will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.
Once all the partition reassignments have finished, the broker(s) being removed should not have responsibility for any of the partitions in the cluster. You can verify this by checking that the broker’s data log directory does not contain any live partition logs. If the log directory on the broker contains a directory that does not match the extended regular expression \.[a-z0-9]-delete$ then the broker still has live partitions and it should not be stopped.
You can check this by executing the command:
```
oc exec my-cluster-kafka-0 -c kafka -it -- \
  /bin/bash -c \
  "ls -l /var/lib/kafka/kafka-log_<N>_ | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'"
```
where N is the number of the Pod(s) being deleted.
If the above command prints any output then the broker still has live partitions. In this case, either the reassignment has not finished, or the reassignment JSON file was incorrect.
Once you have confirmed that the broker has no live partitions you can edit the Kafka.spec.kafka.replicas of your Kafka resource, which will scale down the StatefulSet, deleting the highest numbered broker Pod(s).

2.1.5. Maintenance time windows for rolling updates

Maintenance time windows allow you to schedule certain rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time.

2.1.5.1. Maintenance time windows overview

In most cases, the Cluster Operator only updates your Kafka or ZooKeeper clusters in response to changes to the corresponding Kafka resource. This enables you to plan when to apply changes to a Kafka resource to minimize the impact on Kafka client applications.

However, some updates to your Kafka and ZooKeeper clusters can happen without any corresponding change to the Kafka resource. For example, the Cluster Operator will need to perform a rolling restart if a CA (Certificate Authority) certificate that it manages is close to expiry.

While a rolling restart of the pods should not affect availability of the service (assuming correct broker and topic configurations), it could affect performance of the Kafka client applications. Maintenance time windows allow you to schedule such spontaneous rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time. If maintenance time windows are not configured for a cluster then it is possible that such spontaneous rolling updates will happen at an inconvenient time, such as during a predictable period of high load.

2.1.5.2. Maintenance time window definition

You configure maintenance time windows by entering an array of strings in the Kafka.spec.maintenanceTimeWindows property. Each string is a cron expression interpreted as being in UTC (Coordinated Universal Time, which for practical purposes is the same as Greenwich Mean Time).

The following example configures a single maintenance time window that starts at midnight and ends at 01:59am (UTC), on Sundays, Mondays, Tuesdays, Wednesdays, and Thursdays:

# ...
maintenanceTimeWindows:
  - "* * 0-1 ? * SUN,MON,TUE,WED,THU *"
# ...

In practice, maintenance windows should be set in conjunction with the Kafka.spec.clusterCa.renewalDays and Kafka.spec.clientsCa.renewalDays properties of the Kafka resource, to ensure that the necessary CA certificate renewal can be completed in the configured maintenance time windows.

Note

AMQ Streams does not schedule maintenance operations exactly according to the given windows. Instead, for each reconciliation, it checks whether a maintenance window is currently "open". This means that the start of maintenance operations within a given time window can be delayed by up to the Cluster Operator reconciliation interval. Maintenance time windows must therefore be at least this long.

Additional resources

For more information about the Cluster Operator configuration, see Section 5.1.1, “Cluster Operator configuration”.

2.1.5.3. Configuring a maintenance time window

You can configure a maintenance time window for rolling updates triggered by supported processes.

Prerequisites

An OpenShift cluster.
The Cluster Operator is running.

Procedure

Add or edit the maintenanceTimeWindows property in the Kafka resource. For example to allow maintenance between 0800 and 1059 and between 1400 and 1559 you would set the maintenanceTimeWindows as shown below:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  maintenanceTimeWindows:
    - "* * 8-10 * * ?"
    - "* * 14-15 * * ?"
```
Create or update the resource:
```
oc apply -f KAFKA-CONFIG-FILE
```

Additional resources

Performing rolling updates:

Section 12.3.2, “Performing a rolling update using a StatefulSet annotation”
Section 12.3.3, “Performing a rolling update using a Pod annotation”

2.1.6. Connecting to ZooKeeper from a terminal

Most Kafka CLI tools can connect directly to Kafka, so under normal circumstances you should not need to connect to ZooKeeper. ZooKeeper services are secured with encryption and authentication and are not intended to be used by external applications that are not part of AMQ Streams.

However, if you want to use Kafka CLI tools that require a connection to ZooKeeper, you can use a terminal inside a ZooKeeper container 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

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/kafka-topics.sh --list --zookeeper localhost:12181
```
Be sure to use localhost:12181.
You can now run Kafka commands to ZooKeeper.

2.1.7. Deleting Kafka nodes manually

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

Warning

Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.

Prerequisites

See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:

Procedure

Find the name of the Pod that you want to delete.
For example, if the cluster is named cluster-name, the pods are named cluster-name-kafka-index, where index starts at zero and ends at the total number of replicas.

Annotate the Pod resource in OpenShift.

Use oc annotate:

oc annotate pod cluster-name-kafka-index strimzi.io/delete-pod-and-pvc=true

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

2.1.8. Deleting ZooKeeper nodes manually

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

Warning

Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.

Prerequisites

See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:

Procedure

Find the name of the Pod that you want to delete.
For example, if the cluster is named cluster-name, the pods are named cluster-name-zookeeper-index, where index starts at zero and ends at the total number of replicas.

Annotate the Pod resource in OpenShift.

Use oc annotate:

oc annotate pod cluster-name-zookeeper-index strimzi.io/delete-pod-and-pvc=true

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

2.1.9. List of Kafka cluster resources

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

Shared resources

cluster-name-cluster-ca: Secret with the Cluster CA private key used to encrypt the cluster communication.
cluster-name-cluster-ca-cert: Secret with the Cluster CA public key. This key can be used to verify the identity of the Kafka brokers.
cluster-name-clients-ca: Secret with the Clients CA private key used to sign user certificates
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.
cluster-name-cluster-operator-certs: Secret with Cluster operators keys for communication with Kafka and ZooKeeper.

Zookeeper nodes

cluster-name-zookeeper: StatefulSet which is in charge of managing the ZooKeeper node pods.
cluster-name-zookeeper-idx: Pods created by the Zookeeper StatefulSet.
cluster-name-zookeeper-nodes: Headless Service needed to have DNS resolve the ZooKeeper pods IP addresses directly.
cluster-name-zookeeper-client: Service used by Kafka brokers to connect to ZooKeeper nodes as clients.
cluster-name-zookeeper-config: ConfigMap that contains the ZooKeeper ancillary configuration, and is mounted as a volume by the ZooKeeper node pods.
cluster-name-zookeeper-nodes: Secret with ZooKeeper node keys.
cluster-name-zookeeper: Service account used by the Zookeeper nodes.
cluster-name-zookeeper: Pod Disruption Budget configured for the ZooKeeper nodes.
cluster-name-network-policy-zookeeper: Network policy managing access to the ZooKeeper services.
data-cluster-name-zookeeper-idx: Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod idx. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.

Kafka brokers

cluster-name-kafka: StatefulSet which is in charge of managing the Kafka broker pods.
cluster-name-kafka-idx: Pods created by the Kafka StatefulSet.
cluster-name-kafka-brokers: Service needed to have DNS resolve the Kafka broker pods IP addresses directly.
cluster-name-kafka-bootstrap: Service can be used as bootstrap servers for Kafka clients connecting from within the OpenShift cluster.
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.
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.
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.
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.
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.
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.
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.
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.
cluster-name-kafka-config: ConfigMap which contains the Kafka ancillary configuration and is mounted as a volume by the Kafka broker pods.
cluster-name-kafka-brokers: Secret with Kafka broker keys.
cluster-name-kafka: Service account used by the Kafka brokers.
cluster-name-kafka: Pod Disruption Budget configured for the Kafka brokers.
cluster-name-network-policy-kafka: Network policy managing access to the Kafka services.
strimzi-namespace-name-cluster-name-kafka-init: Cluster role binding used by the Kafka brokers.
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-cluster-name-kafka-idx: Persistent Volume Claim for the volume used for storing data for the Kafka broker pod idx. This resource is created only if persistent storage is selected for provisioning persistent volumes to store data.
data-id-cluster-name-kafka-idx: Persistent Volume Claim for the volume id used for storing data for the Kafka broker pod idx. This resource is 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.

cluster-name-entity-operator: Deployment with Topic and User Operators.
cluster-name-entity-operator-random-string: Pod created by the Entity Operator deployment.
cluster-name-entity-topic-operator-config: ConfigMap with ancillary configuration for Topic Operators.
cluster-name-entity-user-operator-config: ConfigMap with ancillary configuration for User Operators.
cluster-name-entity-operator-certs: Secret with Entity Operator keys for communication with Kafka and ZooKeeper.
cluster-name-entity-operator: Service account used by the Entity Operator.
strimzi-cluster-name-entity-topic-operator: Role binding used by the Entity Topic Operator.
strimzi-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.

cluster-name-kafka-exporter: Deployment with Kafka Exporter.
cluster-name-kafka-exporter-random-string: Pod created by the Kafka Exporter deployment.
cluster-name-kafka-exporter: Service used to collect consumer lag metrics.
cluster-name-kafka-exporter: Service account used by the Kafka Exporter.

Cruise Control

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

cluster-name-cruise-control: Deployment with Cruise Control.
cluster-name-cruise-control-random-string: Pod created by the Cruise Control deployment.
cluster-name-cruise-control-config: ConfigMap that contains the Cruise Control ancillary configuration, and is mounted as a volume by the Cruise Control pods.
cluster-name-cruise-control-certs: Secret with Cruise Control keys for communication with Kafka and ZooKeeper.
cluster-name-cruise-control: Service used to communicate with Cruise Control.
cluster-name-cruise-control: Service account used by Cruise Control.
cluster-name-network-policy-cruise-control: Network policy managing access to the Cruise Control service.

2.2. Kafka Connect/S2I cluster configuration

This section describes how to configure a Kafka Connect or Kafka Connect with Source-to-Image (S2I) deployment in your AMQ Streams cluster.

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, for import or export of data using connectors. Connectors are plugins that provide the connection configuration needed.

If you are using Kafka Connect, you configure either the KafkaConnect or the KafkaConnectS2I resource. Use the KafkaConnectS2I resource if you are using the Source-to-Image (S2I) framework to deploy Kafka Connect.

The full schema of the KafkaConnect resource is described in Section 13.2.80, “KafkaConnect schema reference”.
The full schema of the KafkaConnectS2I resource is described in Section 13.2.104, “KafkaConnectS2I schema reference”.

Important

With the introduction of build configuration to the KafkaConnect resource, AMQ Streams can now automatically build a container image with the connector plugins you require for your data connections. As a result, support for Kafka Connect with Source-to-Image (S2I) is deprecated. To prepare for this change, you can migrate Kafka Connect S2I instances to Kafka Connect instances.

Additional resources

2.2.1. Configuring Kafka Connect

Use Kafka Connect to set up external data connections to your Kafka cluster.

Use the properties of the KafkaConnect or KafkaConnectS2I resource to configure your Kafka Connect deployment. The example shown in this procedure is for the KafkaConnect resource, but the properties are the same for the KafkaConnectS2I resource.

Kafka connector 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 AMQ Streams 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 the Kafka Connect REST API, or use KafkaConnector custom resources. 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 Creating and managing connectors in the Deploying and Upgrading AMQ Streams on OpenShift guide.

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.

Prerequisites

An OpenShift cluster
A running Cluster Operator

See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:

Procedure

Edit the spec properties for the KafkaConnect or KafkaConnectS2I resource.

The properties you can configure are shown in this example 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: debezium-postgres-connector
        artifacts:
          - type: tgz
            url: https://repo1.maven.org/maven2/io/debezium/debezium-connector-postgres/1.3.1.Final/debezium-connector-postgres-1.3.1.Final-plugin.tar.gz
            sha512sum: 962a12151bdf9a5a30627eebac739955a4fd95a08d373b86bdcea2b4d0c27dd6e1edd5cb548045e115e33a9e69b1b2a352bee24df035a0447cb820077af00c03
      - name: camel-telegram
        artifacts:
          - type: tgz
            url: https://repo.maven.apache.org/maven2/org/apache/camel/kafkaconnector/camel-telegram-kafka-connector/0.7.0/camel-telegram-kafka-connector-0.7.0-package.tar.gz
            sha512sum: a9b1ac63e3284bea7836d7d24d84208c49cdf5600070e6bd1535de654f6920b74ad950d51733e8020bf4187870699819f54ef5859c7846ee4081507f48873479
  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: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"

1: Use KafkaConnect or KafkaConnectS2I, as required.
2: Enables KafkaConnectors for the Kafka Connect cluster.
3: The number of replica nodes.
4: Authentication for the Kafka Connect cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism. By default, Kafka Connect connects to Kafka brokers using a plain text connection.
5: Bootstrap server for connection to the Kafka Connect 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 AMQ Streams.
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 Kafka connectors using environment variables, as shown here, or volumes.
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 ConfigMap must be placed under the log4j.properties or log4j2.properties key. 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: Rack awareness is configured to spread replicas across different racks. A topologykey must match the label of a cluster node.
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 also set for distributed tracing using Jaeger.

Create or update the resource:
```
oc apply -f KAFKA-CONNECT-CONFIG-FILE
```
If authorization is enabled for Kafka Connect, configure Kafka Connect users to enable access to the Kafka Connect consumer group and topics.

2.2.2. Kafka Connect configuration for multiple instances

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

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

1: Kafka Connect cluster group that the instance belongs to.
2: Kafka topic that stores connector offsets.
3: Kafka topic that stores connector and task status configurations.
4: Kafka topic that stores connector and task status updates.

Note

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

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

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

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

2.2.3. Configuring Kafka Connect user authorization

This procedure describes how to authorize user access to Kafka Connect.

When any type of authorization is being used in Kafka, a Kafka Connect user requires read/write access rights to the consumer group and the internal topics of Kafka Connect.

The properties for the consumer group and internal topics are automatically configured by AMQ Streams, or they can be specified explicitly in the spec of the KafkaConnect or KafkaConnectS2I resource.

Example configuration properties in the KafkaConnect resource

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: Kafka Connect cluster group that the instance belongs to.
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.

This procedure shows how access is provided when simple authorization is being used.

Simple authorization uses ACL rules, handled by the Kafka AclAuthorizer plugin, to provide the right level of access. For more information on configuring a KafkaUser resource to use simple authorization, see the AclRule schema reference.

Note

The default values for the consumer group and topics will differ when running multiple instances.

Prerequisites

An OpenShift cluster
A running Cluster Operator

Procedure

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

In the following example, access rights are configured for the Kafka Connect topics and consumer group using literal name values:

Property	Name
`offset.storage.topic`	`connect-cluster-offsets`
`status.storage.topic`	`connect-cluster-status`
`config.storage.topic`	`connect-cluster-configs`
`group`	`connect-cluster`

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
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Read
        host: "*"
      # access to status.storage.topic
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Read
        host: "*"
      # access to config.storage.topic
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Read
        host: "*"
      # consumer group
      - resource:
          type: group
          name: connect-cluster
          patternType: literal
        operation: Read
        host: "*"

Create or update the resource.
```
oc apply -f KAFKA-USER-CONFIG-FILE
```

2.2.4. Performing a restart of a Kafka connector

This procedure describes how to manually trigger a restart of a Kafka connector by using an OpenShift annotation.

Prerequisites

The Cluster Operator is running.

Procedure

Find the name of the KafkaConnector custom resource that controls the Kafka connector you want to restart:
```
oc get KafkaConnector
```
To restart the connector, annotate the KafkaConnector resource in OpenShift. For example, using oc annotate:
```
oc annotate KafkaConnector KAFKACONNECTOR-NAME strimzi.io/restart=true
```
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.

Additional resources

Creating and managing connectors in the Deploying and Upgrading guide.

2.2.5. Performing a restart of a Kafka connector task

This procedure describes how to manually trigger a restart of a Kafka connector task by using an OpenShift annotation.

Prerequisites

The Cluster Operator is running.

Procedure

Find the name of the KafkaConnector custom resource that controls the Kafka connector task you want to restart:
```
oc get KafkaConnector
```
Find the ID of the task to be restarted from the KafkaConnector custom resource. Task IDs are non-negative integers, starting from 0.
```
oc describe KafkaConnector KAFKACONNECTOR-NAME
```
To restart the connector task, annotate the KafkaConnector resource in OpenShift. For example, using oc annotate to restart task 0:
```
oc annotate KafkaConnector KAFKACONNECTOR-NAME strimzi.io/restart-task=0
```
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.

Additional resources

Creating and managing connectors in the Deploying and Upgrading guide.

2.2.6. Migrating from Kafka Connect with S2I to Kafka Connect

Support for Kafka Connect with S2I and the KafkaConnectS2I resource is deprecated. This follows the introduction of build configuration properties to the KafkaConnect resource, which are used to build a container image with the connector plugins you require for your data connections automatically.

This procedure describes how to migrate your Kafka Connect with S2I instance to a standard Kafka Connect instance. To do this, you configure a new KafkaConnect custom resource to replace the KafkaConnectS2I resource, which is then deleted.

Warning

The migration process involves downtime from the moment the KafkaConnectS2I instance is deleted until the new KafkaConnect instance has been successfully deployed. During this time, connectors will not be running and processing data. However, after the changeover they should continue from the point at which they stopped.

Prerequisites

Kafka Connect with S2I is deployed using a KafkaConnectS2I configuration
Kafka Connect with S2I is using an image with connectors added using an S2I build
Sink and source connector instances were created using KafkaConnector resources or the Kafka Connect REST API

Procedure

Create a new KafkaConnect custom resource using the same name as the name used for the KafkaconnectS2I resource.
Copy the KafkaConnectS2I resource properties to the KafkaConnect resource.
If specified, make sure you use the same spec.config properties:
- group.id
- offset.storage.topic
- config.storage.topic
- status.storage.topic
  If these properties are not specified, defaults are used. In which case, leave them out of the KafkaConnect resource configuration as well.
Now add configuration specific to the KafkaConnect resource to the new resource.
Add build configuration to configure all the connectors and other libraries you want to add to the Kafka Connect deployment.
Note
Alternatively, you can build a new image with connectors manually, and specify it using the .spec.image property.
Delete the old KafkaConnectS2I resource:
```
oc delete -f MY-KAFKA-CONNECT-S2I-CONFIG-FILE
```
Replace MY-KAFKA-CONNECT-S2I-CONFIG-FILE with the name of the file containing your KafkaConnectS2I resource configuration.
Alternatively, you can specify the name of the resource:
```
oc delete kafkaconnects2i MY-KAFKA-CONNECT-S2I
```
Replace MY-KAFKA-CONNECT-S2I with the name of the KafkaConnectS2I resource.
Wait until the Kafka Connect with S2I deployment and pods are deleted.
Warning
No other resources must be deleted.
Deploy the new KafkaConnect resource:
```
oc apply -f MY-KAFKA-CONNECT-CONFIG-FILE
```
Replace MY-KAFKA-CONNECT-CONFIG-FILE with the name of the file containing your new KafkaConnect resource configuration.
Wait until the new image is built, the deployment is created, and the pods have started.
If you are using KafkaConnector resources for managing Kafka Connect connectors, check that all expected connectors are present and are running:
```
oc get kctr --selector strimzi.io/cluster=MY-KAFKA-CONNECT-CLUSTER -o name
```
Replace MY-KAFKA-CONNECT-CLUSTER with the name of your Kafka Connect cluster.
Connectors automatically recover through Kafka Connect storage. Even if you are using the Kafka Connect REST API to manage them, you should not need to recreate them manually.

Additional resources

2.2.7. List of Kafka Connect cluster resources

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

connect-cluster-name-connect: Deployment which is in charge to create the Kafka Connect worker node pods.
connect-cluster-name-connect-api: Service which exposes the REST interface for managing the Kafka Connect cluster.
connect-cluster-name-config: ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
connect-cluster-name-connect: Pod Disruption Budget configured for the Kafka Connect worker nodes.

2.2.8. List of Kafka Connect (S2I) cluster resources

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

connect-cluster-name-connect-source: ImageStream which is used as the base image for the newly-built Docker images.
connect-cluster-name-connect: BuildConfig which is responsible for building the new Kafka Connect Docker images.
connect-cluster-name-connect: ImageStream where the newly built Docker images will be pushed.
connect-cluster-name-connect: DeploymentConfig which is in charge of creating the Kafka Connect worker node pods.
connect-cluster-name-connect-api: Service which exposes the REST interface for managing the Kafka Connect cluster.
connect-cluster-name-config: ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
connect-cluster-name-connect: Pod Disruption Budget configured for the Kafka Connect worker nodes.

2.2.9. Integrating with Debezium for change data capture

Red Hat Debezium is a distributed change data capture platform. It captures row-level changes in databases, creates change event records, and streams the records to Kafka topics. Debezium is built on Apache Kafka. You can deploy and integrate Debezium with AMQ Streams. Following a deployment of AMQ Streams, you deploy Debezium as a connector configuration through Kafka Connect. Debezium passes change event records to AMQ Streams on OpenShift. Applications can read these change event streams and access the change events in the order in which they occurred.

Debezium has multiple uses, including:

Data replication
Updating caches and search indexes
Simplifying monolithic applications
Data integration
Enabling streaming queries

To capture database changes, deploy Kafka Connect with a Debezium database connector . You configure a KafkaConnector resource to define the connector instance.

For more information on deploying Debezium with AMQ Streams, refer to the product documentation. The Debezium documentation includes a Getting Started with Debezium guide that guides you through the process of setting up the services and connector required to view change event records for database updates.

2.3. Kafka MirrorMaker cluster configuration

This chapter describes how to configure a Kafka MirrorMaker deployment in your AMQ Streams cluster to replicate data between Kafka clusters.

You can use AMQ Streams with MirrorMaker or MirrorMaker 2.0. MirrorMaker 2.0 is the latest version, and offers a more efficient way to mirror data between Kafka clusters.

If you are using MirrorMaker, you configure the KafkaMirrorMaker resource.

The following procedure shows how the resource is configured:

Configuring Kafka MirrorMaker

The full schema of the KafkaMirrorMaker resource is described in the KafkaMirrorMaker schema reference.

2.3.1. Configuring Kafka MirrorMaker

Use the properties of the KafkaMirrorMaker 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 authentication on the consumer and producer side.

Prerequisites

See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
- Cluster Operator
- Kafka cluster
Source and target Kafka clusters must be available

Procedure

Edit the spec properties for the KafkaMirrorMaker resource.

The properties you can configure are shown in this example 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
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" 9
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      ssl.endpoint.identification.algorithm: HTTPS 10
  producer:
    bootstrapServers: my-target-cluster-kafka-bootstrap:9092
    abortOnSendFailure: false 11
    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
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" 12
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      ssl.endpoint.identification.algorithm: HTTPS 13
  whitelist: "my-topic|other-topic" 14
  resources: 15
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: 16
    type: inline
    loggers:
      mirrormaker.root.logger: "INFO"
  readinessProbe: 17
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  metricsConfig: 18
   type: jmxPrometheusExporter
   valueFrom:
     configMapKeyRef:
       name: my-config-map
       key: my-key
  jvmOptions: 19
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest 20
  template: 21
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: 22
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"
  tracing: 23
    type: jaeger

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, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.
8: Kafka configuration options for consumer and producer.
9: SSL properties for external listeners to run with a specific cipher suite for a TLS version.
10: Hostname verification is enabled by setting to HTTPS. An empty string disables the verification.
11: If the abortOnSendFailure property is set to true, Kafka MirrorMaker will exit and the container will restart following a send failure for a message.
12: SSL properties for external listeners to run with a specific cipher suite for a TLS version.
13: Hostname verification is enabled by setting to HTTPS. An empty string disables the verification.
14: A whitelist of topics mirrored from source to target Kafka cluster.
15: Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
16: Specified loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties or log4j2.properties key. MirrorMaker has a single logger called mirrormaker.root.logger. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
17: Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
18: 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.
19: JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
20: ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
21: Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
22: Environment variables are also set for distributed tracing using Jaeger.
23: Distributed tracing is enabled for Jaeger.

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.

Create or update the resource:
```
oc apply -f <your-file>
```

2.3.2. List of Kafka MirrorMaker cluster resources

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

<mirror-maker-name>-mirror-maker: Deployment which is responsible for creating the Kafka MirrorMaker pods.
<mirror-maker-name>-config: ConfigMap which contains ancillary configuration for the Kafka MirrorMaker, and is mounted as a volume by the Kafka broker pods.
<mirror-maker-name>-mirror-maker: Pod Disruption Budget configured for the Kafka MirrorMaker worker nodes.

2.4. Kafka MirrorMaker 2.0 cluster configuration

This section describes how to configure a Kafka MirrorMaker 2.0 deployment in your AMQ Streams cluster.

MirrorMaker 2.0 is used to replicate data between two or more active Kafka clusters, within or across data centers.

Data replication across clusters supports scenarios that require:

Recovery of data in the event of a system failure
Aggregation of data for analysis
Restriction of data access to a specific cluster
Provision of data at a specific location to improve latency

If you are using MirrorMaker 2.0, you configure the KafkaMirrorMaker2 resource.

MirrorMaker 2.0 introduces an entirely new way of replicating data between clusters.

As a result, the resource configuration differs from the previous version of MirrorMaker. If you choose to use MirrorMaker 2.0, there is currently no legacy support, so any resources must be manually converted into the new format.

How MirrorMaker 2.0 replicates data is described here:

MirrorMaker 2.0 data replication

The following procedure shows how the resource is configured for MirrorMaker 2.0:

Synchronizing data between Kafka clusters

The full schema of the KafkaMirrorMaker2 resource is described in the KafkaMirrorMaker2 schema reference.

2.4.1. MirrorMaker 2.0 data replication

MirrorMaker 2.0 consumes messages from a source Kafka cluster and writes them to a target Kafka cluster.

MirrorMaker 2.0 uses:

Source cluster configuration to consume data from the source cluster
Target cluster configuration to output data to the target cluster

MirrorMaker 2.0 is based on the Kafka Connect framework, connectors managing the transfer of data between clusters. A MirrorMaker 2.0 MirrorSourceConnector replicates topics from a source cluster to a target cluster.

The process of mirroring data from one cluster to another cluster is asynchronous. The recommended pattern is for messages to be produced locally alongside the source Kafka cluster, then consumed remotely close to the target Kafka cluster.

MirrorMaker 2.0 can be used with more than one source cluster.

Figure 2.1. Replication across two clusters

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 of the KafkaMirrorMaker2 resource. However, increasing the frequency of the operation might affect overall performance.

2.4.2. Cluster configuration

You can use MirrorMaker 2.0 in active/passive or active/active cluster configurations.

In an active/active configuration, both clusters are active and provide the same data simultaneously, which is useful if you want to make the same data available locally in different geographical locations.
In an active/passive configuration, the data from an active cluster is replicated in a passive cluster, which remains on standby, for example, for data recovery in the event of system failure.

The expectation is that producers and consumers connect to active clusters only.

A MirrorMaker 2.0 cluster is required at each target destination.

2.4.2.1. Bidirectional replication (active/active)

The MirrorMaker 2.0 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 2.0 to represent the source cluster. The name of the originating cluster is prepended to the name of the topic.

Figure 2.2. Topic renaming

MirrorMaker 2.0 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.

2.4.2.2. Unidirectional replication (active/passive)

The MirrorMaker 2.0 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 of the KafkaMirrorMaker2 resource. With this configuration applied, topics retain their original names.

2.4.2.3. Topic configuration synchronization

Topic configuration is automatically synchronized between source and target clusters. By synchronizing configuration properties, the need for rebalancing is reduced.

2.4.2.4. Data integrity

MirrorMaker 2.0 monitors source topics and propagates any configuration changes to remote topics, checking for and creating missing partitions. Only MirrorMaker 2.0 can write to remote topics.

2.4.2.5. Offset tracking

MirrorMaker 2.0 tracks offsets for consumer groups using internal topics.

The offset sync topic maps the source and target offsets for replicated topic partitions from record metadata
The checkpoint topic maps the last committed offset in the source and target cluster for replicated topic partitions in each consumer group

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

MirrorMaker 2.0 uses its MirrorCheckpointConnector to emit checkpoints for offset tracking.

2.4.2.6. 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 of the KafkaMirrorMaker2 resource, and setting the property to true. Synchronization is disabled by default.
Add the IdentityReplicationPolicy to the source and checkpoint connector configuration so that topics in the target cluster retain their original names.

For topic offset synchronization to work, consumer groups in the target cluster cannot use the same ids as groups in the source cluster.

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.

2.4.2.7. Connectivity checks

A heartbeat internal topic checks connectivity between clusters.

The heartbeat topic is replicated from the source cluster.

Target clusters use the topic to check:

The connector managing connectivity between clusters is running
The source cluster is available

MirrorMaker 2.0 uses its MirrorHeartbeatConnector to emit heartbeats that perform these checks.

2.4.3. ACL rules synchronization

ACL access to remote topics is possible if you are not using the User Operator.

If AclAuthorizer is being used, without the User Operator, ACL rules that manage access to brokers also apply to remote topics. Users that can read a source topic can read its remote equivalent.

Note

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

2.4.4. Synchronizing data between Kafka clusters using MirrorMaker 2.0

Use MirrorMaker 2.0 to synchronize data between Kafka clusters through configuration.

The configuration must specify:

Each Kafka cluster
Connection information for each cluster, including TLS authentication
The replication flow and direction
- Cluster to cluster
- Topic to topic

Use the properties of the KafkaMirrorMaker2 resource to configure your Kafka MirrorMaker 2.0 deployment.

Note

The previous version of MirrorMaker continues to be supported. If you wish to use the resources configured for the previous version, they must be updated to the format supported by MirrorMaker 2.0.

MirrorMaker 2.0 provides default configuration values for properties such as replication factors. A minimal configuration, with defaults left unchanged, would be something like this example:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 2.7.0
  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 TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and authentication for the source and target cluster.

Prerequisites

See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
- Cluster Operator
- Kafka cluster
Source and target Kafka clusters must be available

Procedure

Edit the spec properties for the KafkaMirrorMaker2 resource.
The properties you can configure are shown in this example configuration:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 2.7.0 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
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" 13
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      ssl.endpoint.identification.algorithm: HTTPS 14
    tls: 15
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-target-cluster-ca-cert
  mirrors: 16
  - sourceCluster: "my-cluster-source" 17
    targetCluster: "my-cluster-target" 18
    sourceConnector: 19
      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.separator: "" 24
        replication.policy.class: "io.strimzi.kafka.connect.mirror.IdentityReplicationPolicy" 25
    heartbeatConnector: 26
      config:
        heartbeats.topic.replication.factor: 1 27
    checkpointConnector: 28
      config:
        checkpoints.topic.replication.factor: 1 29
        refresh.groups.interval.seconds: 600 30
        sync.group.offsets.enabled: true 31
        sync.group.offsets.interval.seconds: 60 32
        emit.checkpoints.interval.seconds: 60 33
        replication.policy.class: "io.strimzi.kafka.connect.mirror.IdentityReplicationPolicy"
    topicsPattern: ".*" 34
    groupsPattern: "group1|group2|group3" 35
  resources: 36
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: 37
    type: inline
    loggers:
      connect.root.logger.level: "INFO"
  readinessProbe: 38
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  jvmOptions: 39
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest 40
  template: 41
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: 42
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"
  tracing:
    type: jaeger 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 Mirror Maker 2.0 version, which will always be the same.
2
The number of replica nodes.
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, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.
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 AMQ Streams.
13
SSL properties for external listeners to run with a specific cipher suite for a TLS version.
14
Hostname verification is enabled by setting to HTTPS. An empty string disables the verification.
15
TLS encryption for the target Kafka cluster is configured in the same way as for the source Kafka cluster.
16
MirrorMaker 2.0 connectors.
17
Cluster alias for the source cluster used by the MirrorMaker 2.0 connectors.
18
Cluster alias for the target cluster used by the MirrorMaker 2.0 connectors.
19
Configuration for the MirrorSourceConnector that creates remote topics. The config overrides the default configuration options.
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.
23
Optional setting to change the frequency of checks for new topics. The default is for a check every 10 minutes.
24
Defines the separator used for the renaming of remote topics.
25
Adds a policy that overrides the automatic renaming of remote topics. Instead of prepending the name with the name of the source cluster, the topic retains its original name. This optional setting is useful for active/passive backups and data migration. To configure topic offset synchronization, this property must also be set for the checkpointConnector.config.
26
Configuration for the MirrorHeartbeatConnector that performs connectivity checks. The config overrides the default configuration options.
27
Replication factor for the heartbeat topic created at the target cluster.
28
Configuration for the MirrorCheckpointConnector that tracks offsets. The config overrides the default configuration options.
29
Replication factor for the checkpoints topic created at the target cluster.
30
Optional setting to change the frequency of checks for new consumer groups. The default is for a check every 10 minutes.
31
Optional setting to synchronize consumer group offsets, which is useful for recovery in an active/passive configuration. Synchronization is not enabled by default.
32
If the synchronization of consumer group offsets is enabled, you can adjust the frequency of the synchronization.
33
Adjusts the frequency of checks for offset tracking. If you change the frequency of offset synchronization, you might also need to adjust the frequency of these checks.
34
Topic replication from the source cluster defined as regular expression patterns. Here we request all topics.
35
Consumer group replication from the source cluster defined as regular expression patterns. Here we request three consumer groups by name. You can use comma-separated lists.
36
Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
37
Specified Kafka Connect loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties or log4j2.properties key. For the Kafka Connect log4j.rootLogger logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
38
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
39
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
40
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
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 also set for distributed tracing using Jaeger.
43
Distributed tracing is enabled for Jaeger.
44
External configuration for an OpenShift Secret mounted to Kafka MirrorMaker as an environment variable.

Create or update the resource:

oc apply -f MIRRORMAKER-CONFIGURATION-FILE

2.4.5. Performing a restart of a Kafka MirrorMaker 2.0 connector

This procedure describes how to manually trigger a restart of a Kafka MirrorMaker 2.0 connector by using an OpenShift annotation.

Prerequisites

The Cluster Operator is running.

Procedure

Find the name of the KafkaMirrorMaker2 custom resource that controls the Kafka MirrorMaker 2.0 connector you want to restart:
```
oc get KafkaMirrorMaker2
```
Find the name of the Kafka MirrorMaker 2.0 connector to be restarted from the KafkaMirrorMaker2 custom resource.
```
oc describe KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME
```
To restart the connector, annotate the KafkaMirrorMaker2 resource in OpenShift. In this example, oc annotate restarts a connector named my-source->my-target.MirrorSourceConnector:
```
oc annotate KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME "strimzi.io/restart-connector=my-source->my-target.MirrorSourceConnector"
```
Wait for the next reconciliation to occur (every two minutes by default).
The Kafka MirrorMaker 2.0 connector is restarted, as long as the annotation was detected by the reconciliation process. When the restart request is accepted, the annotation is removed from the KafkaMirrorMaker2 custom resource.

Additional resources

Kafka MirrorMaker 2.0 cluster configuration.

2.4.6. Performing a restart of a Kafka MirrorMaker 2.0 connector task

This procedure describes how to manually trigger a restart of a Kafka MirrorMaker 2.0 connector task by using an OpenShift annotation.

Prerequisites

The Cluster Operator is running.

Procedure

Find the name of the KafkaMirrorMaker2 custom resource that controls the Kafka MirrorMaker 2.0 connector you want to restart:
```
oc get KafkaMirrorMaker2
```
Find the name of the Kafka MirrorMaker 2.0 connector and the ID of the task to be restarted from the KafkaMirrorMaker2 custom resource. Task IDs are non-negative integers, starting from 0.
```
oc describe KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME
```
To restart the connector task, annotate the KafkaMirrorMaker2 resource in OpenShift. In this example, oc annotate restarts task 0 of a connector named my-source->my-target.MirrorSourceConnector:
```
oc annotate KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME "strimzi.io/restart-connector-task=my-source->my-target.MirrorSourceConnector:0"
```
Wait for the next reconciliation to occur (every two minutes by default).
The Kafka MirrorMaker 2.0 connector task is restarted, as long as the annotation was detected by the reconciliation process. When the restart task request is accepted, the annotation is removed from the KafkaMirrorMaker2 custom resource.

Additional resources

Kafka MirrorMaker 2.0 cluster configuration.

2.5. Kafka Bridge cluster configuration

This section describes how to configure a Kafka Bridge deployment in your AMQ Streams cluster.

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

If you are using the Kafka Bridge, you configure the KafkaBridge resource.

The full schema of the KafkaBridge resource is described in Section 13.2.130, “KafkaBridge schema reference”.

2.5.1. Configuring the Kafka Bridge

Use the Kafka Bridge to make HTTP-based requests to the Kafka cluster.

Use the properties of the KafkaBridge 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.

Prerequisites

An OpenShift cluster
A running Cluster Operator

See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:

Procedure

Edit the spec properties for the KafkaBridge resource.

The properties you can configure are shown in this example configuration:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  replicas: 3 1
  bootstrapServers: my-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: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"

1: The number of replica nodes.
2: Bootstrap server for connection to the target Kafka cluster.
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, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism. 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 ConfigMap must be placed under the log4j.properties or log4j2.properties key. 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: ADVANCED OPTION: 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 also set for distributed tracing using Jaeger.

Create or update the resource:
```
oc apply -f KAFKA-BRIDGE-CONFIG-FILE
```

2.5.2. 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.

2.6. Customizing OpenShift resources

AMQ Streams creates several OpenShift resources, such as Deployments, StatefulSets, Pods, and Services, which are managed by AMQ Streams operators. Only the operator that is responsible for managing a particular OpenShift resource can change that resource. If you try to manually change an operator-managed OpenShift resource, the operator will revert your changes back.

However, changing an operator-managed OpenShift resource can be useful if you want to perform certain tasks, such as:

Adding custom labels or annotations that control how Pods are treated by Istio or other services
Managing how Loadbalancer-type Services are created by the cluster

You can make such changes using the template property in the AMQ Streams custom resources. The template property is supported in the following resources. The API reference provides more details about the customizable fields.

Kafka.spec.kafka: See Section 13.2.54, “KafkaClusterTemplate schema reference”
Kafka.spec.zookeeper: See Section 13.2.64, “ZookeeperClusterTemplate schema reference”
Kafka.spec.entityOperator: See Section 13.2.69, “EntityOperatorTemplate schema reference”
Kafka.spec.kafkaExporter: See Section 13.2.75, “KafkaExporterTemplate schema reference”
Kafka.spec.cruiseControl: See Section 13.2.72, “CruiseControlTemplate schema reference”
KafkaConnect.spec: See Section 13.2.89, “KafkaConnectTemplate schema reference”
KafkaConnectS2I.spec: See Section 13.2.89, “KafkaConnectTemplate schema reference”
KafkaMirrorMaker.spec: See Section 13.2.128, “KafkaMirrorMakerTemplate schema reference”
KafkaMirrorMaker2.spec: See Section 13.2.89, “KafkaConnectTemplate schema reference”
KafkaBridge.spec: See Section 13.2.137, “KafkaBridgeTemplate schema reference”
KafkaUser.spec: See Section 13.2.121, “KafkaUserTemplate schema reference”

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

Example template customization

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  labels:
    app: my-cluster
spec:
  kafka:
    # ...
    template:
      statefulset:
        metadata:
          labels:
            mylabel: myvalue
    # ...

2.6.1. Customizing the image pull policy

AMQ Streams allows you to customize the image pull policy for containers in all pods deployed by the Cluster Operator. The image pull policy is configured using the environment variable STRIMZI_IMAGE_PULL_POLICY in the Cluster Operator deployment. The STRIMZI_IMAGE_PULL_POLICY environment variable can be set to three different values:

Always: Container images are pulled from the registry every time the pod is started or restarted.
IfNotPresent: Container images are pulled from the registry only when they were not pulled before.
Never: Container images are never pulled from the registry.

The image pull policy can be currently customized only for all Kafka, Kafka Connect, and Kafka MirrorMaker clusters at once. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.

Additional resources

For more information about Cluster Operator configuration, see Section 5.1, “Using the Cluster Operator”.
For more information about Image Pull Policies, see Disruptions.

2.7. Configuring pod scheduling

When two applications are scheduled to the same OpenShift node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.

2.7.1. Specifying affinity, tolerations, and topology spread constraints

Use affinity, tolerations and topology spread constraints to schedule the pods of kafka resources onto nodes. Affinity, tolerations and topology spread constraints are configured using the affinity, tolerations, and topologySpreadConstraint properties in following resources:

Kafka.spec.kafka.template.pod
Kafka.spec.zookeeper.template.pod
Kafka.spec.entityOperator.template.pod
KafkaConnect.spec.template.pod
KafkaConnectS2I.spec.template.pod
KafkaBridge.spec.template.pod
KafkaMirrorMaker.spec.template.pod
KafkaMirrorMaker2.spec.template.pod

The format of the affinity, tolerations, and topologySpreadConstraint properties follows the OpenShift specification. The affinity configuration can include different types of affinity:

Pod affinity and anti-affinity
Node affinity

Note

On OpenShift 1.16 and 1.17, the support for topologySpreadConstraint is disabled by default. In order to use topologySpreadConstraint, you have to enable the EvenPodsSpread feature gate in Kubernetes API server and scheduler.

Additional resources

2.7.1.1. Use pod anti-affinity to avoid critical applications sharing nodes

Use pod anti-affinity to ensure that critical applications are never scheduled on the same disk. When running a Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share nodes with other workloads, such as databases.

2.7.1.2. Use node affinity to schedule workloads onto specific nodes

The OpenShift cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of AMQ Streams components to use the right nodes.

OpenShift uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

2.7.1.3. Use node affinity and tolerations for dedicated nodes

Use taints to create dedicated nodes, then schedule Kafka pods on the dedicated nodes by configuring node affinity and tolerations.

Cluster administrators can mark selected OpenShift nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

2.7.2. Configuring pod anti-affinity in Kafka components

Prerequisites

An OpenShift cluster
A running Cluster Operator

Procedure

Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
    # ...
  zookeeper:
    # ...

Create or update the resource.
This can be done using oc apply:
```
oc apply -f KAFKA-CONFIG-FILE
```

2.7.3. Configuring node affinity in Kafka components

Prerequisites

An OpenShift cluster
A running Cluster Operator

Procedure

Label the nodes where AMQ Streams components should be scheduled.
This can be done using oc label:
```
oc label node NAME-OF-NODE node-type=fast-network
```
Alternatively, some of the existing labels might be reused.

Edit the affinity property in the resource specifying the cluster deployment. For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
    # ...
  zookeeper:
    # ...

Create or update the resource.
This can be done using oc apply:
```
oc apply -f KAFKA-CONFIG-FILE
```

2.7.4. Setting up dedicated nodes and scheduling pods on them

Prerequisites

An OpenShift cluster
A running Cluster Operator

Procedure

Select the nodes which should be used as dedicated.
Make sure there are no workloads scheduled on these nodes.
Set the taints on the selected nodes:
This can be done using oc adm taint:
```
oc adm taint node NAME-OF-NODE dedicated=Kafka:NoSchedule
```
Additionally, add a label to the selected nodes as well.
This can be done using oc label:
```
oc label node NAME-OF-NODE dedicated=Kafka
```

Edit the affinity and tolerations properties in the resource specifying the cluster deployment.

For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        tolerations:
          - key: "dedicated"
            operator: "Equal"
            value: "Kafka"
            effect: "NoSchedule"
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: dedicated
                  operator: In
                  values:
                  - Kafka
    # ...
  zookeeper:
    # ...

Create or update the resource.
This can be done using oc apply:
```
oc apply -f KAFKA-CONFIG-FILE
```

2.8. External logging

When setting the logging levels for a resource, you can specify them inline directly in the spec.logging property of the resource YAML:

spec:
  # ...
  logging:
    type: inline
    loggers:
      kafka.root.logger.level: "INFO"

Or you can specify external logging:

spec:
  # ...
  logging:
    type: external
    valueFrom:
      configMapKeyRef:
        name: customConfigMap
        key: keyInConfigMap

With external logging, logging properties are defined in a ConfigMap. The name of the ConfigMap is referenced in the spec.logging.valueFrom.configMapKeyRef.name property. The spec.logging.valueFrom.configMapKeyRef.name and spec.logging.valueFrom.configMapKeyRef.key properties are mandatory. Default logging is used if the name or key is not set.

The advantages of using a ConfigMap are that the logging properties are maintained in one place and are accessible to more than one resource.

2.8.1. Creating a ConfigMap for logging

To use a ConfigMap to define logging properties, you create the ConfigMap and then reference it as part of the logging definition in the spec of a resource.

The ConfigMap must contain the appropriate logging configuration.

log4j.properties for Kafka components, ZooKeeper, and the Kafka Bridge
log4j2.properties for the Topic Operator and User Operator

The configuration must be placed under these properties.

Here we demonstrate how a ConfigMap defines a root logger for a Kafka resource.

Procedure

Create the ConfigMap.
You can create the ConfigMap as a YAML file or from a properties file using oc at the command line.
ConfigMap example with a root logger definition for Kafka:
```
kind: ConfigMap
apiVersion: kafka.strimzi.io/v1beta2
metadata:
  name: logging-configmap
data:
  log4j.properties:
    kafka.root.logger.level="INFO"
```
From the command line, using a properties file:
```
oc create configmap logging-configmap --from-file=log4j.properties
```
The properties file defines the logging configuration:
```
# Define the logger
kafka.root.logger.level="INFO"
# ...
```
Define external logging in the spec of the resource, setting the logging.valueFrom.configMapKeyRef.name to the name of the ConfigMap and logging.valueFrom.configMapKeyRef.key to the key in this ConfigMap.
```
spec:
  # ...
  logging:
    type: external
    valueFrom:
      configMapKeyRef:
        name: customConfigMap
        key: keyInConfigMap
```
Create or update the resource.
```
oc apply -f kafka.yaml
```

Chapter 3. Configuring external listeners

Use an external listener to expose your AMQ Streams Kafka cluster to a client outside an OpenShift environment.

Specify the connection type to expose Kafka in the external listener configuration.

nodeport uses NodePort type Services
loadbalancer uses Loadbalancer type Services
ingress uses Kubernetes Ingress and the NGINX Ingress Controller for Kubernetes
route uses OpenShift Routes and the HAProxy router

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

Note

route is only supported on OpenShift

Additional resources

Accessing Apache Kafka in Strimzi

3.1. Accessing Kafka using node ports

This procedure describes how to access an AMQ Streams Kafka cluster from an external client using node ports.

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

Prerequisites

An OpenShift cluster
A running Cluster Operator

Procedure

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

For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: external
        port: 9094
        type: nodeport
        tls: true
        authentication:
          type: tls
        # ...
    # ...
  zookeeper:
    # ...

Create or update the resource.
```
oc apply -f KAFKA-CONFIG-FILE
```
NodePort type services are created for each Kafka broker, as well as an external bootstrap service. The bootstrap service routes external traffic to the Kafka brokers. Node addresses used for connection are propagated to the status of the Kafka custom resource.
The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.

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

oc get kafka KAFKA-CLUSTER-NAME -o=jsonpath='{.status.listeners[?(@.type=="external")].bootstrapServers}{"\n"}'

If TLS encryption is enabled, extract the public certificate of the broker certification authority.
```
oc get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
```
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

3.2. Accessing Kafka using loadbalancers

This procedure describes how to access an AMQ Streams Kafka cluster from an external client using loadbalancers.

To connect to a broker, you need the address of the bootstrap loadbalancer, as well as the certificate used for TLS encryption.

Prerequisites

An OpenShift cluster
A running Cluster Operator

Procedure

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

For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: external
        port: 9094
        type: loadbalancer
        tls: true
        # ...
    # ...
  zookeeper:
    # ...

Create or update the resource.
```
oc apply -f KAFKA-CONFIG-FILE
```
loadbalancer type services and loadbalancers are created for each Kafka broker, as well as an external bootstrap service. The bootstrap service routes external traffic to all Kafka brokers. DNS names and IP addresses used for connection are propagated to the status of each service.
The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.
Retrieve the address of the bootstrap service you can use to access the Kafka cluster from the status of the Kafka resource.
```
oc get kafka KAFKA-CLUSTER-NAME -o=jsonpath='{.status.listeners[?(@.type=="external")].bootstrapServers}{"\n"}'
```
If TLS encryption is enabled, extract the public certificate of the broker certification authority.
```
oc get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
```
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

3.3. Accessing Kafka using ingress

This procedure shows how to access an AMQ Streams Kafka cluster from an external client outside of OpenShift using Nginx Ingress.

To connect to a broker, you need a hostname (advertised address) for the Ingress bootstrap address, as well as the certificate used for authentication.

For access using Ingress, the port is always 443.

TLS passthrough

Kafka uses a binary protocol over TCP, but the NGINX Ingress Controller for Kubernetes is designed to work with the HTTP protocol. To be able to pass the Kafka connections through the Ingress, AMQ Streams uses the TLS passthrough feature of the NGINX Ingress Controller for Kubernetes. Ensure TLS passthrough is enabled in your NGINX Ingress Controller for Kubernetes deployment.

Because it is using the TLS passthrough functionality, TLS encryption cannot be disabled when exposing Kafka using Ingress.

For more information about enabling TLS passthrough, see TLS passthrough documentation.

Prerequisites

OpenShift cluster
Deployed NGINX Ingress Controller for Kubernetes with TLS passthrough enabled
A running Cluster Operator

Procedure

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

Specify the Ingress hosts for the bootstrap service and Kafka brokers.

For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: external
        port: 9094
        type: ingress
        tls: true
        authentication:
          type: tls
        configuration: 1
          bootstrap:
            host: bootstrap.myingress.com
          brokers:
          - broker: 0
            host: broker-0.myingress.com
          - broker: 1
            host: broker-1.myingress.com
          - broker: 2
            host: broker-2.myingress.com
    # ...
  zookeeper:
    # ...

1: Ingress hosts for the bootstrap service and Kafka brokers.

Create or update the resource.
```
oc apply -f KAFKA-CONFIG-FILE
```
ClusterIP type services are created for each Kafka broker, as well as an additional bootstrap service. These services are used by the Ingress controller to route traffic to the Kafka brokers. An Ingress resource is also created for each service to expose them using the Ingress controller. The Ingress hosts are propagated to the status of each service.
The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.
Use the address for the bootstrap host you specified in the configuration and port 443 (BOOTSTRAP-HOST:443) in your Kafka client as the bootstrap address to connect to the Kafka cluster.
Extract the public certificate of the broker certificate authority.
```
oc get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
```
Use the extracted certificate in your Kafka client to configure the TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

3.4. Accessing Kafka using OpenShift routes

This procedure describes how to access an AMQ Streams Kafka cluster from an external client outside of OpenShift using routes.

To connect to a broker, you need a hostname for the route bootstrap address, as well as the certificate used for TLS encryption.

For access using routes, the port is always 443.

Prerequisites

An OpenShift cluster
A running Cluster Operator

Procedure

Configure a Kafka resource with an external listener set to the route type.
For example:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  labels:
    app: my-cluster
  name: my-cluster
  namespace: myproject
spec:
  kafka:
    # ...
    listeners:
      - name: listener1
        port: 9094
        type: route
        tls: true
        # ...
    # ...
  zookeeper:
    # ...
```
Warning
An OpenShift Route address comprises the name of the Kafka cluster, the name of the listener, and the name of the namespace it is created in. For example, my-cluster-kafka-listener1-bootstrap-myproject (CLUSTER-NAME-kafka-LISTENER-NAME-bootstrap-NAMESPACE). Be careful that the whole length of the address does not exceed a maximum limit of 63 characters.
Create or update the resource.
```
oc apply -f KAFKA-CONFIG-FILE
```
ClusterIP type services are created for each Kafka broker, as well as an external bootstrap service. The services route the traffic from the OpenShift Routes to the Kafka brokers. An OpenShift Route resource is also created for each service to expose them using the HAProxy load balancer. DNS addresses used for connection are propagated to the status of each service.
The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.
Retrieve the address of the bootstrap service you can use to access the Kafka cluster from the status of the Kafka resource.
```
oc get kafka KAFKA-CLUSTER-NAME -o=jsonpath='{.status.listeners[?(@.type=="external")].bootstrapServers}{"\n"}'
```
Extract the public certificate of the broker certification authority.
```
oc get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
```
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

Chapter 4. Managing secure access to Kafka

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

A secure connection between Kafka brokers and clients can encompass:

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

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

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

4.1. Security options for Kafka

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

4.1.1. Listener authentication

For clients inside the OpenShift cluster, you can create plain (without encryption) or tls internal listeners.

For clients outside the OpenShift cluster, you create external listeners and specify a connection mechanism, which can be nodeport, loadbalancer, ingress or route (on OpenShift).

For more information on the configuration options for connecting an external client, see Configuring external listeners.

Supported authentication options:

Mutual TLS authentication (only on the listeners with TLS enabled encryption)
SCRAM-SHA-512 authentication
OAuth 2.0 token based authentication

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

Figure 4.1. Kafka listener authentication options

options for listener authentication configuration

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

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

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

The following example shows:

A plain listener configured for SCRAM-SHA-512 authentication
A tls listener with mutual TLS authentication
An external listener with mutual TLS authentication

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

Note

Listeners cannot be configured to use the ports set aside for interbroker communication (9091) and metrics (9404).

An example showing listener authentication configuration

# ...
listeners:
  - name: plain
    port: 9092
    type: internal
    tls: true
    authentication:
      type: scram-sha-512
  - name: tls
    port: 9093
    type: internal
    tls: true
    authentication:
      type: tls
  - name: external
    port: 9094
    type: loadbalancer
    tls: true
    authentication:
      type: tls
# ...

4.1.1.1. Mutual TLS authentication

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

AMQ Streams can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. For mutual, or two-way, authentication, both the server and the client present certificates. When you configure mutual authentication, the broker authenticates the client (client authentication) and the client authenticates the broker (server authentication).

Note

TLS authentication is more commonly one-way, with one party authenticating the identity of another. For example, when HTTPS is used between a web browser and a web server, the browser obtains proof of the identity of the web server.

4.1.1.2. SCRAM-SHA-512 authentication

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

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

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

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

When a KafkaUser.spec.authentication.type is configured with scram-sha-512 the User Operator will generate a random 12-character password consisting of upper and lowercase ASCII letters and numbers.

4.1.1.3. Network policies

AMQ Streams automatically creates a NetworkPolicy resource for every listener that is enabled on a Kafka broker. By default, a NetworkPolicy grants access to a listener to all applications and namespaces.

If you want to restrict access to a listener at the network level to only selected applications or namespaces, use the networkPolicyPeers property.

Use network policies as part of the listener authentication configuration. Each listener can have a different networkPolicyPeers configuration.

For more information, refer to the Listener network policies section and the NetworkPolicyPeer API reference.

Note

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

4.1.1.4. Additional listener configuration options

You can use the properties of the GenericKafkaListenerConfiguration schema to add further configuration to listeners.

4.1.2. Kafka authorization

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

Supported authorization options:

Simple authorization
OAuth 2.0 authorization (if you are using OAuth 2.0 token based authentication)
Open Policy Agent (OPA) authorization

Figure 4.2. Kafka cluster authorization options

options for kafks authorization configuration

4.1.2.1. Super users

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

To designate super users for a Kafka cluster, add a list of user principals to the superUsers property. If a user uses TLS client authentication, their username is the common name from their certificate subject prefixed with CN=.

An example configuration with super users

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  namespace: myproject
spec:
  kafka:
    # ...
    authorization:
      type: simple
      superUsers:
        - CN=client_1
        - user_2
        - CN=client_3
    # ...

4.2. Security options for Kafka clients

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

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

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

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

4.2.1. Identifying a Kafka cluster for user handling

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

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

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

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

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

4.2.2. User authentication

User authentication is configured using the authentication property in KafkaUser.spec. The authentication mechanism enabled for the user is specified using the type field.

Supported authentication mechanisms:

TLS client authentication
SCRAM-SHA-512 authentication

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

Additional resources

When to use mutual TLS authentication or SCRAM-SHA Authentication authentication for clients

4.2.2.1. TLS Client Authentication

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

An example KafkaUser with TLS client authentication enabled

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

When the user is created by the User Operator, it creates a new Secret with the same name as the KafkaUser resource. The Secret contains a private and public key for TLS client authentication. The public key is contained in a user certificate, which is signed by the client Certificate Authority (CA).

All keys are in X.509 format.

Secrets provide private keys and certificates in PEM and PKCS #12 formats.

For more information on securing Kafka communication with Secrets, see Chapter 11, Managing TLS certificates.

An example Secret with user credentials

apiVersion: v1
kind: Secret
metadata:
  name: my-user
  labels:
    strimzi.io/kind: KafkaUser
    strimzi.io/cluster: my-cluster
type: Opaque
data:
  ca.crt: # Public key of the client CA
  user.crt: # User certificate that contains the public key of the user
  user.key: # Private key of the user
  user.p12: # PKCS #12 archive file for storing certificates and keys
  user.password: # Password for protecting the PKCS #12 archive file

4.2.2.2. SCRAM-SHA-512 Authentication

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

An example KafkaUser with SCRAM-SHA-512 authentication enabled

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: scram-sha-512
  # ...

When the user is created by the User Operator, it creates a new secret with the same name as the KafkaUser resource. The secret contains the generated password in the password key, which is encoded with base64. In order to use the password, it must be decoded.

An example Secret with user credentials

apiVersion: v1
kind: Secret
metadata:
  name: my-user
  labels:
    strimzi.io/kind: KafkaUser
    strimzi.io/cluster: my-cluster
type: Opaque
data:
  password: Z2VuZXJhdGVkcGFzc3dvcmQ= 1
  sasl.jaas.config: b3JnLmFwYWNoZS5rYWZrYS5jb21tb24uc2VjdXJpdHkuc2NyYW0uU2NyYW1Mb2dpbk1vZHVsZSByZXF1aXJlZCB1c2VybmFtZT0ibXktdXNlciIgcGFzc3dvcmQ9ImdlbmVyYXRlZHBhc3N3b3JkIjsK 2

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

Decoding the generated password:

echo "Z2VuZXJhdGVkcGFzc3dvcmQ=" | base64 --decode

4.2.3. User authorization

User authorization is configured using the authorization property in KafkaUser.spec. The authorization type enabled for a user is specified using the type field.

To use simple authorization, you set the type property to simple in KafkaUser.spec.authorization. Simple authorization uses the default Kafka authorization plugin, AclAuthorizer.

Alternatively, you can use OPA authorization, or if you are already using OAuth 2.0 token based authentication, you can also use OAuth 2.0 authorization.

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

4.2.3.1. ACL rules

AclAuthorizer uses ACL rules to manage access to Kafka brokers.

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

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

4.2.3.2. Super user access to Kafka brokers

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

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

4.2.3.3. User quotas

You can configure the spec for the KafkaUser resource to enforce quotas so that a user does not exceed access to Kafka brokers based on a byte threshold or a time limit of CPU utilization.

An example KafkaUser with user quotas

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

1: Byte-per-second quota on the amount of data the user can push to a Kafka broker
2: Byte-per-second quota on the amount of data the user can fetch from a Kafka broker
3: CPU utilization limit as a percentage of time for a client group

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

4.3. Securing access to Kafka brokers

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

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

Configure the Kafka resource to set up:

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

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

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

You can replace the certificates generated by the Cluster Operator by installing your own certificates. You can also configure your listener to use a Kafka listener certificate managed by an external Certificate Authority. Certificates are available in PKCS #12 format (.p12) and PEM (.crt) formats.

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

Configure the KafkaUser resource to set up:

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

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

Additional resources

For more information about the schema for:

Kafka, see the Kafka schema reference.
KafkaUser, see the KafkaUser schema reference.

4.3.1. Securing Kafka brokers

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

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

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

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

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

Procedure

Configure the Kafka resource.
1. Configure the authorization property for authorization.
2. Configure the listeners property to create a listener with authentication.
  For example:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    authorization: 1
      type: simple
      superUsers: 2
        - CN=client_1
        - user_2
        - CN=client_3
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls 3
    # ...
  zookeeper:
    # ...
```
  1
  Authorization enables simple authorization on the Kafka broker using the AclAuthorizer Kafka plugin.
  2
  List of user principals with unlimited access to Kafka. CN is the common name from the client certificate when TLS authentication is used.
  3
  Listener authentication mechanisms may be configured for each listener, and specified as mutual TLS, SCRAM-SHA-512 or token-based OAuth 2.0.
  If you are configuring an external listener, the configuration is dependent on the chosen connection mechanism.
Create or update the Kafka resource.
```
oc apply -f KAFKA-CONFIG-FILE
```
The Kafka cluster is configured with a Kafka broker listener using TLS authentication.
A service is created for each Kafka broker pod.
A service is created to serve as the bootstrap address for connection to the Kafka cluster.
The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.

4.3.2. Securing user access to Kafka

Use the properties of the KafkaUser resource to configure a Kafka user.

You can use oc apply to create or modify users, and oc delete to delete existing users.

For example:

oc apply -f USER-CONFIG-FILE
oc delete KafkaUser USER-NAME

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

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

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

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

Note

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

Prerequisites

A running Kafka cluster configured with a Kafka broker listener using TLS authentication and encryption.
A running User Operator (typically deployed with the Entity Operator).

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

Procedure

Configure the KafkaUser resource.

For example:

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

1: User authentication mechanism, defined as mutual tls or scram-sha-512.
2: Simple authorization, which requires an accompanying list of ACL rules.

Create or update the KafkaUser resource.
```
oc apply -f USER-CONFIG-FILE
```
The user is created, as well as a Secret with the same name as the KafkaUser resource. The Secret contains a private and public key for TLS client authentication.

For information on configuring a Kafka client with properties for secure connection to Kafka brokers, see Setting up access for clients outside of OpenShift in the Deploying and Upgrading AMQ Streams on OpenShift guide.

4.3.3. Restricting access to Kafka listeners using network policies

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

Prerequisites

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

Procedure

Open the Kafka resource.

In the networkPolicyPeers property, define the application pods or namespaces that will be allowed to access the Kafka cluster.

For example, to configure a tls listener to allow connections only from application pods with the label app set to kafka-client:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
        networkPolicyPeers:
          - podSelector:
              matchLabels:
                app: kafka-client
    # ...
  zookeeper:
    # ...

Create or update the resource.
Use oc apply:
```
oc apply -f your-file
```

Additional resources

For more information about the schema, see the NetworkPolicyPeer API reference and the GenericKafkaListener schema reference.

4.4. Using OAuth 2.0 token-based authentication

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

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

Kafka brokers and clients both need to be configured to use OAuth 2.0. You can configure OAuth 2.0 authentication, then OAuth 2.0 authorization.

Note

OAuth 2.0 authentication can be used in conjunction with Kafka authorization.

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

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

In the context of AMQ Streams:

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

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

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

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

Additional resources

OAuth 2.0 site

4.4.1. OAuth 2.0 authentication mechanisms

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

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

If necessary, OAUTHBEARER and PLAIN can be enabled together, on the same oauth listener.

OAUTHBEARER overview

Kafka supports the OAUTHBEARER authentication mechanism, however it must be explicitly configured. Also, many Kafka client tools use libraries that provide basic support for OAUTHBEARER at the protocol level.

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

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

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

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

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

Note

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

PLAIN overview

PLAIN is a simple authentication mechanism used by all Kafka client tools (including developer tools such as kafkacat). To enable PLAIN to be used together with OAuth 2.0 authentication, AMQ Streams includes server-side callbacks and calls this OAuth 2.0 over PLAIN.

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

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

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

The client must be enabled to use PLAIN authentication, and provide a username and password. If the password is prefixed with $accessToken: followed by the value of the access token, the Kafka broker will interpret the password as the access token. Otherwise, the Kafka broker will interpret the username as the client ID and the password as the client secret.

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

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

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

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

Additional resources

Section 4.4.6.2, “Configuring OAuth 2.0 support for Kafka brokers”

4.4.2. OAuth 2.0 Kafka broker configuration

Kafka broker configuration for OAuth 2.0 involves:

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

Note

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

4.4.2.1. OAuth 2.0 client configuration on an authorization server

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

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

Note

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

4.4.2.2. OAuth 2.0 authentication configuration in the Kafka cluster

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

Assigining the authentication method type for OAuth 2.0

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

You can configure plain, tls and external listeners, but it is recommended not to use plain listeners or external listeners with disabled TLS encryption with OAuth 2.0 as this creates a vulnerability to network eavesdropping and unauthorized access through token theft.

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

Using OAuth 2.0 with an external listener

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

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

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

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

4.4.2.3. Fast local JWT token validation configuration

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

The local check ensures that a token:

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

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

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

Note

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

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

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

Example configuration for fast local JWT token validation

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

4.4.2.4. OAuth 2.0 introspection endpoint configuration

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

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

Example configuration for an introspection endpoint

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

4.4.3. Session re-authentication for Kafka brokers

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

Session re-authentication is disabled by default. To enable it, you set a time value for maxSecondsWithoutReauthentication in the oauth listener configuration. The same property is used to configure session re-authentication for OAUTHBEARER and PLAIN authentication. For an example configuration, see Section 4.4.6.2, “Configuring OAuth 2.0 support for Kafka brokers”.

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

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

Client re-authentication

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

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

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

Session expiry for OAUTHBEARER and PLAIN

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

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

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

For PLAIN using the long-lived access token method:

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

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

Additional resources

Section 4.4.2, “OAuth 2.0 Kafka broker configuration”
Section 4.4.6.2, “Configuring OAuth 2.0 support for Kafka brokers”
KafkaListenerAuthenticationOAuth schema reference
KIP-368

4.4.4. OAuth 2.0 Kafka client configuration

A Kafka client is configured with either:

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

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

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

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

Note

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

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

Client ID and Secret
Client ID, refresh token, and (optionally) a Secret

4.4.5. OAuth 2.0 client authentication flow

In this section, we explain and visualize the communication flow between Kafka client, Kafka broker, and authorization server during Kafka session initiation. The flow depends on the client and server configuration.

When a Kafka client sends an access token as credentials to a Kafka broker, the token needs to be validated.

Depending on the authorization server used, and the configuration options available, you may prefer to use:

Fast local token validation based on JWT signature checking and local token introspection, without contacting the authorization server
An OAuth 2.0 introspection endpoint provided by the authorization server

Using fast local token validation requires the authorization server to provide a JWKS endpoint with public certificates that are used to validate signatures on the tokens.

Another option is to use an OAuth 2.0 introspection endpoint on the authorization server. Each time a new Kafka broker connection is established, the broker passes the access token received from the client to the authorization server, and checks the response to confirm whether or not the token is valid.

Kafka client credentials can also be configured for:

Direct local access using a previously generated long-lived access token
Contact with the authorization server for a new access token to be issued

Note

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

4.4.5.1. Example client authentication flows

Here you can see the communication flows, for different configurations of Kafka clients and brokers, during Kafka session authentication.

Client using client ID and secret, with broker delegating validation to authorization server
Client using client ID and secret, with broker performing fast local token validation
Client using long-lived access token, with broker delegating validation to authorization server
Client using long-lived access token, with broker performing fast local validation

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

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

Kafka client requests access token from authorization server, using client ID and secret, and optionally a refresh token.
Authorization server generates a new access token.
Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.
Kafka client session is established if the token is valid.

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

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

Kafka client authenticates with authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token.
Authorization server generates a new access token.
Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.

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

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

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

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

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

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

Warning

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

4.4.6. Configuring OAuth 2.0 authentication

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

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

Deploy an authorization server and configure the deployment to integrate with AMQ Streams
Deploy or update the Kafka cluster with Kafka broker listeners configured to use OAuth 2.0
Update your Java-based Kafka clients to use OAuth 2.0
Update Kafka component clients to use OAuth 2.0

4.4.6.1. Configuring Red Hat Single Sign-On as an OAuth 2.0 authorization server

This procedure describes how to deploy Red Hat Single Sign-On as an authorization server and configure it for integration with AMQ Streams.

The authorization server provides a central point for authentication and authorization, and management of users, clients, and permissions. Red Hat Single Sign-On has a concept of realms where a realm represents a separate set of users, clients, permissions, and other configuration. You can use a default master realm, or create a new one. Each realm exposes its own OAuth 2.0 endpoints, which means that application clients and application servers all need to use the same realm.

To use OAuth 2.0 with AMQ Streams, you use a deployment of Red Hat Single Sign-On to create and manage authentication realms.

Note

If you already have Red Hat Single Sign-On deployed, you can skip the deployment step and use your current deployment.

Before you begin

You will need to be familiar with using Red Hat Single Sign-On.

For deployment and administration instructions, see:

Prerequisites

AMQ Streams and Kafka is running

For the Red Hat Single Sign-On deployment:

Check the Red Hat Single Sign-On Supported Configurations
Installation requires a user with a cluster-admin role, such as system:admin

Procedure

Deploy Red Hat Single Sign-On to your OpenShift cluster.
Check the progress of the deployment in your OpenShift web console.
Log in to the Red Hat Single Sign-On Admin Console to create the OAuth 2.0 policies for AMQ Streams.
Login details are provided when you deploy Red Hat Single Sign-On.
Create and enable a realm.
You can use an existing master realm.
Adjust the session and token timeouts for the realm, if required.
Create a client called kafka-broker.
From the Settings tab, set:
- Access Type to Confidential
- Standard Flow Enabled to OFF to disable web login for this client
- Service Accounts Enabled to ON to allow this client to authenticate in its own name
Click Save before continuing.
From the Credentials tab, take a note of the secret for using in your AMQ Streams Kafka cluster configuration.
Repeat the client creation steps for any application client that will connect to your Kafka brokers.
Create a definition for each new client.
You will use the names as client IDs in your configuration.

What to do next

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

4.4.6.2. Configuring OAuth 2.0 support for Kafka brokers

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

We advise use of OAuth 2.0 over an encrypted interface through configuration of TLS listeners. Plain listeners are not recommended.

If the authorization server is using certificates signed by the trusted CA and matching the OAuth 2.0 server hostname, TLS connection works using the default settings. Otherwise, you may need to configure the truststore with prober certificates or disable the certificate hostname validation.

When configuring the Kafka broker you have two options for the mechanism used to validate the access token during OAuth 2.0 authentication of the newly connected Kafka client:

Configuring fast local JWT token validation
Configuring token validation using an introspection endpoint

Before you start

For more information on the configuration of OAuth 2.0 authentication for Kafka broker listeners, see:

KafkaListenerAuthenticationOAuth schema reference
Managing access to Kafka

Prerequisites

AMQ Streams and Kafka are running
An OAuth 2.0 authorization server is deployed

Procedure

Update the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.
```
oc edit kafka my-cluster
```
Configure the Kafka broker listeners configuration.
The configuration for each type of listener does not have to be the same, as they are independent.
The examples here show the configuration options as configured for external listeners.
Example 1: Configuring fast local JWT token validation
```
#...
- name: external
  port: 9094
  type: loadbalancer
  tls: true
  authentication:
    type: oauth 1
    validIssuerUri: <https://<auth-server-address>/auth/realms/external> 2
    jwksEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/certs> 3
    userNameClaim: preferred_username 4
    maxSecondsWithoutReauthentication: 3600 5
    tlsTrustedCertificates: 6
    - secretName: oauth-server-cert
      certificate: ca.crt
    disableTlsHostnameVerification: true 7
    jwksExpirySeconds: 360 8
    jwksRefreshSeconds: 300 9
    jwksMinRefreshPauseSeconds: 1 10
    enableECDSA: "true" 11
```
1
Listener type set to oauth.
2
URI of the token issuer used for authentication.
3
URI of the JWKS certificate endpoint used for local JWT validation.
4
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authentication flow and the authorization server used.
5
(Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
6
(Optional) Trusted certificates for TLS connection to the authorization server.
7
(Optional) Disable TLS hostname verification. Default is false.
8
The duration the JWKS certificates are considered valid before they expire. Default is 360 seconds. If you specify a longer time, consider the risk of allowing access to revoked certificates.
9
The period between refreshes of JWKS certificates. The interval must be at least 60 seconds shorter than the expiry interval. Default is 300 seconds.
10
The minimum pause in seconds between consecutive attempts to refresh JWKS public keys. When an unknown signing key is encountered, the JWKS keys refresh is scheduled outside the regular periodic schedule with at least the specified pause since the last refresh attempt. The refreshing of keys follows the rule of exponential backoff, retrying on unsuccessful refreshes with ever increasing pause, until it reaches jwksRefreshSeconds. The default value is 1.
11
(Optional) If ECDSA is used for signing JWT tokens on authorization server, then this needs to be enabled. It installs additional crypto providers using BouncyCastle crypto library. Default is false.
Example 2: Configuring token validation using an introspection endpoint
```
- name: external
  port: 9094
  type: loadbalancer
  tls: true
  authentication:
    type: oauth
    validIssuerUri: <https://<auth-server-address>/auth/realms/external>
    introspectionEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token/introspect> 1
    clientId: kafka-broker 2
    clientSecret: 3
      secretName: my-cluster-oauth
      key: clientSecret
    userNameClaim: preferred_username 4
    maxSecondsWithoutReauthentication: 3600 5
```
1
URI of the token introspection endpoint.
2
Client ID to identify the client.
3
Client Secret and client ID is used for authentication.
4
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authorization server used.
5
(Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional (optional) configuration settings you can use:
```
  # ...
  authentication:
    type: oauth
    # ...
    checkIssuer: false 1
    checkAudience: true 2
    fallbackUserNameClaim: client_id 3
    fallbackUserNamePrefix: client-account- 4
    validTokenType: bearer 5
    userInfoEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/userinfo 6
    enableOauthBearer: false 7
    enablePlain: true 8
    tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token 9
    customClaimCheck: "@.custom == 'custom-value'" 10
```
1
If your authorization server does not provide an iss claim, it is not possible to perform an issuer check. In this situation, set checkIssuer to false and do not specify a validIssuerUri. Default is true.
2
If your authorization server provides an aud (audience) claim, and you want to enforce an audience check, set checkAudience to true. Audience checks identify the intended recipients of tokens. As a result, the Kafka broker will reject tokens that do not have its clientId in their aud claim. Default is false.
3
An authorization server may not provide a single attribute to identify both regular users and clients. When a client authenticates in its own name, the server might provide a client ID. When a user authenticates using a username and password, to obtain a refresh token or an access token, the server might provide a username attribute in addition to a client ID. Use this fallback option to specify the username claim (attribute) to use if a primary user ID attribute is not available.
4
In situations where fallbackUserNameClaim is applicable, it may also be necessary to prevent name collisions between the values of the username claim, and those of the fallback username claim. Consider a situation where a client called producer exists, but also a regular user called producer exists. In order to differentiate between the two, you can use this property to add a prefix to the user ID of the client.
5
(Only applicable when using introspectionEndpointUri) Depending on the authorization server you are using, the introspection endpoint may or may not return the token type attribute, or it may contain different values. You can specify a valid token type value that the response from the introspection endpoint has to contain.
6
(Only applicable when using introspectionEndpointUri) The authorization server may be configured or implemented in such a way to not provide any identifiable information in an Introspection Endpoint response. In order to obtain the user ID, you can configure the URI of the userinfo endpoint as a fallback. The userNameClaim, fallbackUserNameClaim, and fallbackUserNamePrefix settings are applied to the response of userinfo endpoint.
7
Set this to false`to disable the OAUTHBEARER mechanism on the listener. At least one of PLAIN or OAUTHBEARER has to be enabled. Default is `true.
8
Set this to true to enable the PLAIN mechanism on the listener, which is supported by all clients on all platforms. The Kafka client has to enable the PLAIN mechanism and set the username and the password. This mechanism can be used to authenticate either by using the OAuth access token, or by using the OAuth client id and secret (client credentials). If the client sets password to start with the string $accessToken:, the password is interpreted as the access token on the server, and username as the account username, otherwise the user is interpreted as the client id, and password as the client secret. Default is false.
9
This has to be set to support the client credentials authentication when enablePlain is set to true, as described in previous point.
10
Additional custom rules can be imposed on the JWT access token during validation by setting this to a JsonPath filter query. If the access token does not contain the necessary data, it is rejected. When using the introspectionEndpointUri, the custom check is applied to the introspection endpoint response JSON.
Save and exit the editor, then wait for rolling updates to complete.
Check the update in the logs or by watching the pod state transitions:
```
oc logs -f ${POD_NAME} -c ${CONTAINER_NAME}
oc get pod -w
```
The rolling update configures the brokers to use OAuth 2.0 authentication.

What to do next

Configure your Kafka clients to use OAuth 2.0

4.4.6.3. Configuring Kafka Java clients to use OAuth 2.0

This procedure describes how to configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers.

Add a client callback plugin to your pom.xml file, and configure the system properties.

Prerequisites

AMQ Streams and Kafka are running
An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
Kafka brokers are configured for OAuth 2.0

Procedure

Add the client library with OAuth 2.0 support to the pom.xml file for the Kafka client:

<dependency>
 <groupId>io.strimzi</groupId>
 <artifactId>kafka-oauth-client</artifactId>
 <version>0.7.1.redhat-00003</version>
</dependency>

Configure the system properties for the callback:

For example:

System.setProperty(ClientConfig.OAUTH_TOKEN_ENDPOINT_URI, “https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token”); 1
System.setProperty(ClientConfig.OAUTH_CLIENT_ID, "<client-name>"); 2
System.setProperty(ClientConfig.OAUTH_CLIENT_SECRET, "<client-secret>"); 3

1: URI of the authorization server token endpoint.
2: Client ID, which is the name used when creating the client in the authorization server.
3: Client secret created when creating the client in the authorization server.

Enable the SASL OAUTHBEARER mechanism on a TLS encrypted connection in the Kafka client configuration:

For example:

props.put("sasl.jaas.config", "org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required;");
props.put("security.protocol", "SASL_SSL"); 1
props.put("sasl.mechanism", "OAUTHBEARER");
props.put("sasl.login.callback.handler.class", "io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler");

1: Here we use SASL_SSL for use over TLS connections. Use SASL_PLAINTEXT over unencrypted connections.

Verify that the Kafka client can access the Kafka brokers.

What to do next

Configure Kafka components to use OAuth 2.0

4.4.6.4. Configuring OAuth 2.0 for Kafka components

This procedure describes how to configure Kafka components to use OAuth 2.0 authentication using an authorization server.

You can configure authentication for:

Kafka Connect
Kafka MirrorMaker
Kafka Bridge

In this scenario, the Kafka component and the authorization server are running in the same cluster.

Before you start

For more information on the configuration of OAuth 2.0 authentication for Kafka components, see:

KafkaClientAuthenticationOAuth schema reference

Prerequisites

AMQ Streams and Kafka are running
An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
Kafka brokers are configured for OAuth 2.0

Procedure

Create a client secret and mount it to the component as an environment variable.
For example, here we are creating a client Secret for the Kafka Bridge:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: Secret
metadata:
 name: my-bridge-oauth
type: Opaque
data:
 clientSecret: MGQ1OTRmMzYtZTllZS00MDY2LWI5OGEtMTM5MzM2NjdlZjQw 1
```
1
The clientSecret key must be in base64 format.
Create or edit the resource for the Kafka component so that OAuth 2.0 authentication is configured for the authentication property.
For OAuth 2.0 authentication, you can use:
- Client ID and secret
- Client ID and refresh token
- Access token
- TLS
KafkaClientAuthenticationOAuth schema reference provides examples of each.
For example, here OAuth 2.0 is assigned to the Kafka Bridge client using a client ID and secret, and TLS:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: oauth 1
    tokenEndpointUri: https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token 2
    clientId: kafka-bridge
    clientSecret:
      secretName: my-bridge-oauth
      key: clientSecret
    tlsTrustedCertificates: 3
    - secretName: oauth-server-cert
      certificate: tls.crt
```
1
Authentication type set to oauth.
2
URI of the token endpoint for authentication.
3
Trusted certificates for TLS connection to the authorization server.
Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional configuration options you can use:
```
# ...
spec:
  # ...
  authentication:
    # ...
    disableTlsHostnameVerification: true 1
    checkAccessTokenType: false 2
    accessTokenIsJwt: false 3
    scope: any 4
```
1
(Optional) Disable TLS hostname verification. Default is false.
2
If the authorization server does not return a typ (type) claim inside the JWT token, you can apply checkAccessTokenType: false to skip the token type check. Default is true.
3
If you are using opaque tokens, you can apply accessTokenIsJwt: false so that access tokens are not treated as JWT tokens.
4
(Optional) The scope for requesting the token from the token endpoint. An authorization server may require a client to specify the scope. In this case it is any.
Apply the changes to the deployment of your Kafka resource.
```
oc apply -f your-file
```
Check the update in the logs or by watching the pod state transitions:
```
oc logs -f ${POD_NAME} -c ${CONTAINER_NAME}
oc get pod -w
```
The rolling updates configure the component for interaction with Kafka brokers using OAuth 2.0 authentication.

4.5. Using OAuth 2.0 token-based authorization

If you are using OAuth 2.0 with Red Hat Single Sign-On for token-based authentication, you can also use Red Hat Single Sign-On to configure authorization rules to constrain client access to Kafka brokers. Authentication establishes the identity of a user. Authorization decides the level of access for that user.

AMQ Streams supports the use of OAuth 2.0 token-based authorization through Red Hat Single Sign-On Authorization Services, which allows you to manage security policies and permissions centrally.

Security policies and permissions defined in Red Hat Single Sign-On are used to grant access to resources on Kafka brokers. Users and clients are matched against policies that permit access to perform specific actions on Kafka brokers.

Kafka allows all users full access to brokers by default, and also provides the AclAuthorizer plugin to configure authorization based on Access Control Lists (ACLs).

ZooKeeper stores ACL rules that grant or deny access to resources based on username. However, OAuth 2.0 token-based authorization with Red Hat Single Sign-On offers far greater flexibility on how you wish to implement access control to Kafka brokers. In addition, you can configure your Kafka brokers to use OAuth 2.0 authorization and ACLs.

Additional resources

Using OAuth 2.0 token-based authentication
Kafka Authorization
Red Hat Single Sign-On documentation

4.5.1. OAuth 2.0 authorization mechanism

OAuth 2.0 authorization in AMQ Streams uses Red Hat Single Sign-On server Authorization Services REST endpoints to extend token-based authentication with Red Hat Single Sign-On by applying defined security policies on a particular user, and providing a list of permissions granted on different resources for that user. Policies use roles and groups to match permissions to users. OAuth 2.0 authorization enforces permissions locally based on the received list of grants for the user from Red Hat Single Sign-On Authorization Services.

4.5.1.1. Kafka broker custom authorizer

A Red Hat Single Sign-On authorizer (KeycloakRBACAuthorizer) is provided with AMQ Streams. To be able to use the Red Hat Single Sign-On REST endpoints for Authorization Services provided by Red Hat Single Sign-On, you configure a custom authorizer on the Kafka broker.

The authorizer fetches a list of granted permissions from the authorization server as needed, and enforces authorization locally on the Kafka Broker, making rapid authorization decisions for each client request.

4.5.2. Configuring OAuth 2.0 authorization support

This procedure describes how to configure Kafka brokers to use OAuth 2.0 authorization using Red Hat Single Sign-On Authorization Services.

Before you begin

Consider the access you require or want to limit for certain users. You can use a combination of Red Hat Single Sign-On groups, roles, clients, and users to configure access in Red Hat Single Sign-On.

Typically, groups are used to match users based on organizational departments or geographical locations. And roles are used to match users based on their function.

With Red Hat Single Sign-On, you can store users and groups in LDAP, whereas clients and roles cannot be stored this way. Storage and access to user data may be a factor in how you choose to configure authorization policies.

Note

Super users always have unconstrained access to a Kafka broker regardless of the authorization implemented on the Kafka broker.

Prerequisites

AMQ Streams must be configured to use OAuth 2.0 with Red Hat Single Sign-On for token-based authentication. You use the same Red Hat Single Sign-On server endpoint when you set up authorization.
OAuth 2.0 authentication must be configured with the maxSecondsWithoutReauthentication option to enable re-authentication.

Procedure

Access the Red Hat Single Sign-On Admin Console or use the Red Hat Single Sign-On Admin CLI to enable Authorization Services for the Kafka broker client you created when setting up OAuth 2.0 authentication.
Use Authorization Services to define resources, authorization scopes, policies, and permissions for the client.
Bind the permissions to users and clients by assigning them roles and groups.
Configure the Kafka brokers to use Red Hat Single Sign-On authorization by updating the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.
```
oc edit kafka my-cluster
```
Configure the Kafka broker kafka configuration to use keycloak authorization, and to be able to access the authorization server and Authorization Services.
For example:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    authorization:
      type: keycloak 1
      tokenEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token> 2
      clientId: kafka 3
      delegateToKafkaAcls: false 4
      disableTlsHostnameVerification: false 5
      superUsers: 6
      - CN=fred
      - sam
      - CN=edward
      tlsTrustedCertificates: 7
      - secretName: oauth-server-cert
        certificate: ca.crt
      grantsRefreshPeriodSeconds: 60 8
      grantsRefreshPoolSize: 5 9
    #...
```
1
Type keycloak enables Red Hat Single Sign-On authorization.
2
URI of the Red Hat Single Sign-On token endpoint. For production, always use HTTPs.
3
The client ID of the OAuth 2.0 client definition in Red Hat Single Sign-On that has Authorization Services enabled. Typically, kafka is used as the ID.
4
(Optional) Delegate authorization to Kafka AclAuthorizer if access is denied by Red Hat Single Sign-On Authorization Services policies. Default is false.
5
(Optional) Disable TLS hostname verification. Default is false.
6
(Optional) Designated super users.
7
(Optional) Trusted certificates for TLS connection to the authorization server.
8
(Optional) The time between two consecutive grants refresh runs. That is the maximum time for active sessions to detect any permissions changes for the user on Red Hat Single Sign-On. The default value is 60.
9
(Optional) The number of threads to use to refresh (in parallel) the grants for the active sessions. The default value is 5.
Save and exit the editor, then wait for rolling updates to complete.
Check the update in the logs or by watching the pod state transitions:
```
oc logs -f ${POD_NAME} -c kafka
oc get pod -w
```
The rolling update configures the brokers to use OAuth 2.0 authorization.
Verify the configured permissions by accessing Kafka brokers as clients or users with specific roles, making sure they have the necessary access, or do not have the access they are not supposed to have.

Chapter 5. Using AMQ Streams Operators

Use the AMQ Streams operators to manage your Kafka cluster, and Kafka topics and users.

5.1. Using the Cluster Operator

The Cluster Operator is used to deploy a Kafka cluster and other Kafka components.

The Cluster Operator is deployed using YAML installation files.

Note

On OpenShift, a Kafka Connect deployment can incorporate a Source2Image feature to provide a convenient way to add additional connectors.

Additional resources

Deploying the Cluster Operator in the Deploying and Upgrading AMQ Streams on OpenShift guide.
Kafka Cluster configuration.

5.1.1. Cluster Operator configuration

You can configure the Cluster Operator using supported environment variables, and through its logging configuration.

The environment variables relate to container configuration for the deployment of the Cluster Operator image. For more information on image configuration, see, Section 13.1.6, “image”.

STRIMZI_NAMESPACE

A comma-separated list of namespaces that the operator should operate in. When not set, set to empty string, or set to *, the Cluster Operator will operate in all namespaces. The Cluster Operator deployment might use the OpenShift 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. This value should be increased when using AMQ Streams on clusters where regular OpenShift operations take longer than usual (because of slow downloading of Docker images, for example).

STRIMZI_OPERATOR_NAMESPACE

The name of the namespace where the AMQ Streams Cluster Operator is running. Do not configure this variable manually. Use the OpenShift Downward API.

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

STRIMZI_OPERATOR_NAMESPACE_LABELS

Optional. The labels of the namespace where the AMQ Streams Cluster Operator is running. Namespace labels are used to configure the namespace selector in network policies to allow the AMQ Streams Cluster Operator to only have access 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 AMQ Streams Cluster Operator from any namespace in the OpenShift cluster.

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

STRIMZI_CUSTOM_RESOURCE_SELECTOR

Optional. Specifies label selector used to filter the custom resources handled by the operator. The operator will operate only on those custom resources which will have the specified labels set. Resources without these labels will not be seen by the operator. The label selector applies to Kafka, KafkaConnect, KafkaConnectS2I, KafkaBridge, KafkaMirrorMaker, and KafkaMirrorMaker2 resources. KafkaRebalance and KafkaConnector resources will be 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. This provides a mapping from Kafka version to the corresponding Docker image containing a Kafka broker of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example

2.6.0=registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.7.0, 2.7.0=registry.redhat.io/amq7/amq-streams-kafka-27-rhel7:1.7.0

. This is used when a Kafka.spec.kafka.version property is specified but not the Kafka.spec.kafka.image in the Kafka resource.

STRIMZI_DEFAULT_KAFKA_INIT_IMAGE

Optional, default registry.redhat.io/amq7/amq-streams-rhel7-operator:1.7.0. The image name to use as default for the init container started before the broker for initial configuration work (that is, rack support), if no image is specified as the kafka-init-image in the Kafka resource.

STRIMZI_KAFKA_CONNECT_IMAGES

Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example

2.6.0=registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.7.0, 2.7.0=registry.redhat.io/amq7/amq-streams-kafka-27-rhel7:1.7.0

. This is used when a KafkaConnect.spec.version property is specified but not the KafkaConnect.spec.image.

STRIMZI_KAFKA_CONNECT_S2I_IMAGES

2.6.0=registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.7.0, 2.7.0=registry.redhat.io/amq7/amq-streams-kafka-27-rhel7:1.7.0

. This is used when a KafkaConnectS2I.spec.version property is specified but not the KafkaConnectS2I.spec.image.

STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka mirror maker of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example

2.6.0=registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.7.0, 2.7.0=registry.redhat.io/amq7/amq-streams-kafka-27-rhel7:1.7.0

. This is used when a KafkaMirrorMaker.spec.version property is specified but not the KafkaMirrorMaker.spec.image.

STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE

Optional, default registry.redhat.io/amq7/amq-streams-rhel7-operator:1.7.0. 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 Kafka resource.

STRIMZI_DEFAULT_USER_OPERATOR_IMAGE

Optional, default registry.redhat.io/amq7/amq-streams-rhel7-operator:1.7.0. 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, default registry.redhat.io/amq7/amq-streams-kafka-27-rhel7:1.7.0. The image name to use as the default when deploying the sidecar container which provides TLS support for the Entity Operator, if no image is specified as the Kafka.spec.entityOperator.tlsSidecar.image in the Kafka resource.

STRIMZI_IMAGE_PULL_POLICY

Optional. The ImagePullPolicy which will be applied to containers in all pods managed by AMQ Streams Cluster Operator. The valid values are Always, IfNotPresent, and Never. If not specified, the OpenShift defaults will be used. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka 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 used in the imagePullSecrets field for all Pods created by the Cluster Operator. Changing this list results in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.

STRIMZI_KUBERNETES_VERSION

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

Example configuration for Kubernetes version override

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

KUBERNETES_SERVICE_DNS_DOMAIN

Optional. Overrides the default 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 connectots, in milliseconds. This value should be increased when using AMQ Streams to build container images containing many connectors or using a slow container registry.

5.1.1.1. Logging configuration by ConfigMap

The Cluster Operator’s logging is configured by the strimzi-cluster-operator ConfigMap.

A ConfigMap containing logging configuration is created when installing the Cluster Operator. This ConfigMap is described in the file install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml. You configure Cluster Operator logging by changing the data field log4j2.properties in this ConfigMap.

To update the logging configuration, you can edit the 050-ConfigMap-strimzi-cluster-operator.yaml file and then run the following command:

oc create -f install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml

Alternatively, edit the ConfigMap directly:

oc edit cm strimzi-cluster-operator

To change the frequency of the reload interval, set a time in seconds in the monitorInterval option in the created ConfigMap.

If the ConfigMap is missing when the Cluster Operator is deployed, the default logging values are used.

If the ConfigMap is accidentally deleted after the Cluster Operator is deployed, the most recently loaded logging configuration is used. Create a new ConfigMap to load a new logging configuration.

Note

Do not remove the monitorInterval option from the ConfigMap.

5.1.1.2. Restricting Cluster Operator access with network policy

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 OpenShift Downward API to find which 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 AMQ Streams.

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

5.1.1.3. Periodic reconciliation

Although the Cluster Operator reacts to all notifications about the desired cluster resources received from the OpenShift cluster, if the operator is not running, or if a notification is not received for any reason, the desired resources will get out of sync with the state of the running OpenShift cluster.

In order to handle failovers properly, a periodic reconciliation process is executed by the Cluster Operator so that it can compare the state of the desired resources with the current cluster deployments in order to have a consistent state across all of them. You can set the time interval for the periodic reconciliations using the [STRIMZI_FULL_RECONCILIATION_INTERVAL_MS] variable.

5.1.2. Provisioning Role-Based Access Control (RBAC)

For the Cluster Operator to function it needs permission within the OpenShift cluster to interact with resources such as Kafka, KafkaConnect, and so on, as well as the managed resources, such as ConfigMaps, Pods, Deployments, StatefulSets and Services. Such permission is described in terms of OpenShift role-based access control (RBAC) resources:

ServiceAccount,
Role and ClusterRole,
RoleBinding and ClusterRoleBinding.

In addition to running under its own ServiceAccount with a ClusterRoleBinding, the Cluster Operator manages some RBAC resources for the components that need access to OpenShift resources.

OpenShift also includes privilege escalation protections that prevent components operating under one ServiceAccount from granting other ServiceAccounts privileges that the granting ServiceAccount does not have. Because the Cluster Operator must be able to create the ClusterRoleBindings, and RoleBindings needed by resources it manages, the Cluster Operator must also have those same privileges.

5.1.2.1. Delegated privileges

When the Cluster Operator deploys resources for a desired Kafka resource it also creates ServiceAccounts, RoleBindings, and ClusterRoleBindings, as follows:

The Kafka broker pods use a ServiceAccount called cluster-name-kafka
- When the rack feature is used, the strimzi-cluster-name-kafka-init ClusterRoleBinding is used to grant this ServiceAccount access to the nodes within the cluster via a ClusterRole called strimzi-kafka-broker
- When the rack feature is not used no binding is created
The ZooKeeper pods use a ServiceAccount called cluster-name-zookeeper
The Entity Operator pod uses a ServiceAccount called cluster-name-entity-operator
- The Topic Operator produces OpenShift events with status information, so the ServiceAccount is bound to a ClusterRole called strimzi-entity-operator which grants this access via the strimzi-entity-operator RoleBinding
The pods for KafkaConnect and KafkaConnectS2I resources use a ServiceAccount called cluster-name-cluster-connect
The pods for KafkaMirrorMaker use a ServiceAccount called cluster-name-mirror-maker
The pods for KafkaMirrorMaker2 use a ServiceAccount called cluster-name-mirrormaker2
The pods for KafkaBridge use a ServiceAccount called cluster-name-bridge

5.1.2.2. `ServiceAccount`

The Cluster Operator is best run using a ServiceAccount:

Example ServiceAccount for the Cluster Operator

apiVersion: v1
kind: ServiceAccount
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi

The Deployment of the operator then needs to specify this in its spec.template.spec.serviceAccountName:

Partial example of Deployment for the Cluster Operator

apiVersion: apps/v1
kind: Deployment
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
spec:
  replicas: 1
  selector:
    matchLabels:
      name: strimzi-cluster-operator
      strimzi.io/kind: cluster-operator
  template:
      # ...

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

5.1.2.3. `ClusterRoles`

The Cluster Operator needs to operate using ClusterRoles that gives access to the necessary resources. Depending on the OpenShift cluster setup, a cluster administrator might be needed to create the ClusterRoles.

Note

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

The ClusterRoles follow the principle of least privilege and contain only those privileges needed by the Cluster Operator to operate Kafka, Kafka Connect, and ZooKeeper clusters. The first set of assigned privileges allow the Cluster Operator to manage OpenShift resources such as StatefulSets, Deployments, Pods, and ConfigMaps.

Cluster Operator uses ClusterRoles to grant permission at the namespace-scoped resources level and cluster-scoped resources level:

ClusterRole with namespaced resources for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-namespaced
  labels:
    app: strimzi
rules:
  - apiGroups:
      - "rbac.authorization.k8s.io"
    resources:
      # The cluster operator needs to access and manage rolebindings to grant Strimzi components cluster permissions
      - rolebindings
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - "rbac.authorization.k8s.io"
    resources:
      # The cluster operator needs to access and manage roles to grant the entity operator permissions
      - roles
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - ""
    resources:
      # The cluster operator needs to access and delete pods, this is to allow it to monitor pod health and coordinate rolling updates
      - pods
      # The cluster operator needs to access and manage service accounts to grant Strimzi components cluster permissions
      - serviceaccounts
      # The cluster operator needs to access and manage config maps for Strimzi components configuration
      - configmaps
      # The cluster operator needs to access and manage services and endpoints to expose Strimzi components to network traffic
      - services
      - endpoints
      # The cluster operator needs to access and manage secrets to handle credentials
      - secrets
      # The cluster operator needs to access and manage persistent volume claims to bind them to Strimzi components for persistent data
      - persistentvolumeclaims
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - "kafka.strimzi.io"
    resources:
      # The cluster operator runs the KafkaAssemblyOperator, which needs to access and manage Kafka resources
      - kafkas
      - kafkas/status
      # The cluster operator runs the KafkaConnectAssemblyOperator, which needs to access and manage KafkaConnect resources
      - kafkaconnects
      - kafkaconnects/status
      # The cluster operator runs the KafkaConnectS2IAssemblyOperator, which needs to access and manage KafkaConnectS2I resources
      - kafkaconnects2is
      - kafkaconnects2is/status
      # The cluster operator runs the KafkaConnectorAssemblyOperator, which needs to access and manage KafkaConnector resources
      - kafkaconnectors
      - kafkaconnectors/status
      # The cluster operator runs the KafkaMirrorMakerAssemblyOperator, which needs to access and manage KafkaMirrorMaker resources
      - kafkamirrormakers
      - kafkamirrormakers/status
      # The cluster operator runs the KafkaBridgeAssemblyOperator, which needs to access and manage BridgeMaker resources
      - kafkabridges
      - kafkabridges/status
      # The cluster operator runs the KafkaMirrorMaker2AssemblyOperator, which needs to access and manage KafkaMirrorMaker2 resources
      - kafkamirrormaker2s
      - kafkamirrormaker2s/status
      # The cluster operator runs the KafkaRebalanceAssemblyOperator, which needs to access and manage KafkaRebalance resources
      - kafkarebalances
      - kafkarebalances/status
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      # The cluster operator needs the extensions api as the operator supports Kubernetes version 1.11+
      # apps/v1 was introduced in Kubernetes 1.14
      - "extensions"
    resources:
      # The cluster operator needs to access and manage deployments to run deployment based Strimzi components
      - deployments
      - deployments/scale
      # The cluster operator needs to access replica sets to manage Strimzi components and to determine error states
      - replicasets
      # The cluster operator needs to access and manage replication controllers to manage replicasets
      - replicationcontrollers
      # The cluster operator needs to access and manage network policies to lock down communication between Strimzi components
      - networkpolicies
      # The cluster operator needs to access and manage ingresses which allow external access to the services in a cluster
      - ingresses
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - "apps"
    resources:
      # The cluster operator needs to access and manage deployments to run deployment based Strimzi components
      - deployments
      - deployments/scale
      - deployments/status
      # The cluster operator needs to access and manage stateful sets to run stateful sets based Strimzi components
      - statefulsets
      # The cluster operator needs to access replica-sets to manage Strimzi components and to determine error states
      - replicasets
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - ""
    resources:
      # The cluster operator needs to be able to create events and delegate permissions to do so
      - events
    verbs:
      - create
  - apiGroups:
      # OpenShift S2I requirements
      - apps.openshift.io
    resources:
      - deploymentconfigs
      - deploymentconfigs/scale
      - deploymentconfigs/status
      - deploymentconfigs/finalizers
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      # OpenShift S2I requirements
      - build.openshift.io
    resources:
      - buildconfigs
      - buildconfigs/instantiate
      - builds
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      # OpenShift S2I requirements
      - image.openshift.io
    resources:
      - imagestreams
      - imagestreams/status
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - networking.k8s.io
    resources:
      # The cluster operator needs to access and manage network policies to lock down communication between Strimzi components
      - networkpolicies
      # The cluster operator needs to access and manage ingresses which allow external access to the services in a cluster
      - ingresses
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - route.openshift.io
    resources:
      # The cluster operator needs to access and manage routes to expose Strimzi components for external access
      - routes
      - routes/custom-host
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - policy
    resources:
      # The cluster operator needs to access and manage pod disruption budgets this limits the number of concurrent disruptions
      # that a Strimzi component experiences, allowing for higher availability
      - poddisruptionbudgets
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update

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

ClusterRole with cluster-scoped resources for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-global
  labels:
    app: strimzi
rules:
  - apiGroups:
      - "rbac.authorization.k8s.io"
    resources:
      # The cluster operator needs to create and manage cluster role bindings in the case of an install where a user
      # has specified they want their cluster role bindings generated
      - clusterrolebindings
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - storage.k8s.io
    resources:
      # The cluster operator requires "get" permissions to view storage class details
      # This is because only a persistent volume of a supported storage class type can be resized
      - storageclasses
    verbs:
      - get
  - apiGroups:
      - ""
    resources:
      # The cluster operator requires "list" permissions to view all nodes in a cluster
      # The listing is used to determine the node addresses when NodePort access is configured
      # These addresses are then exposed in the custom resource states
      - nodes
    verbs:
      - list

The strimzi-kafka-broker ClusterRole represents the access needed by the init container in Kafka pods that is used for the rack feature. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.

ClusterRole for the Cluster Operator allowing it to delegate access to OpenShift nodes to the Kafka broker pods

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-kafka-broker
  labels:
    app: strimzi
rules:
  - apiGroups:
      - ""
    resources:
      # The Kafka Brokers require "get" permissions to view the node they are on
      # This information is used to generate a Rack ID that is used for High Availability configurations
      - nodes
    verbs:
      - get

The strimzi-topic-operator ClusterRole represents the access needed by the Topic Operator. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.

ClusterRole for the Cluster Operator allowing it to delegate access to events to the Topic Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-entity-operator
  labels:
    app: strimzi
rules:
  - apiGroups:
      - "kafka.strimzi.io"
    resources:
      # The entity operator runs the KafkaTopic assembly operator, which needs to access and manage KafkaTopic resources
      - kafkatopics
      - kafkatopics/status
      # The entity operator runs the KafkaUser assembly operator, which needs to access and manage KafkaUser resources
      - kafkausers
      - kafkausers/status
    verbs:
      - get
      - list
      - watch
      - create
      - patch
      - update
      - delete
  - apiGroups:
      - ""
    resources:
      - events
    verbs:
      # The entity operator needs to be able to create events
      - create
  - apiGroups:
      - ""
    resources:
      # The entity operator user-operator needs to access and manage secrets to store generated credentials
      - secrets
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update

The strimzi-kafka-client ClusterRole represents the access needed by the components based on Kafka clients which use the client rack-awareness. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.

ClusterRole for the Cluster Operator allowing it to delegate access to OpenShift nodes to the Kafka client based pods

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-kafka-client
  labels:
    app: strimzi
rules:
  - apiGroups:
      - ""
    resources:
      # The Kafka clients (Connect, Mirror Maker, etc.) require "get" permissions to view the node they are on
      # This information is used to generate a Rack ID (client.rack option) that is used for consuming from the closest
      # replicas when enabled
      - nodes
    verbs:
      - get

5.1.2.4. `ClusterRoleBindings`

The operator needs ClusterRoleBindings and RoleBindings which associates its ClusterRole with its ServiceAccount: ClusterRoleBindings are needed for ClusterRoles containing cluster-scoped resources.

Example ClusterRoleBinding for the Cluster Operator

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-cluster-operator-global
  apiGroup: rbac.authorization.k8s.io

ClusterRoleBindings are also needed for the ClusterRoles needed for delegation:

Example ClusterRoleBinding for the Cluster Operator for the Kafka broker rack-awarness

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator-kafka-broker-delegation
  labels:
    app: strimzi
# The Kafka broker cluster role must be bound to the cluster operator service account so that it can delegate the cluster role to the Kafka brokers.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-kafka-broker
  apiGroup: rbac.authorization.k8s.io

and

Example ClusterRoleBinding for the Cluster Operator for the Kafka client rack-awarness

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator-kafka-client-delegation
  labels:
    app: strimzi
# The Kafka clients cluster role must be bound to the cluster operator service account so that it can delegate the
# cluster role to the Kafka clients using it for consuming from closest replica.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-kafka-client
  apiGroup: rbac.authorization.k8s.io

ClusterRoles containing only namespaced resources are bound using RoleBindings only.

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-cluster-operator-namespaced
  apiGroup: rbac.authorization.k8s.io

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator-entity-operator-delegation
  labels:
    app: strimzi
# The Entity Operator cluster role must be bound to the cluster operator service account so that it can delegate the cluster role to the Entity Operator.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
  - kind: ServiceAccount
    name: strimzi-cluster-operator
    namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-entity-operator
  apiGroup: rbac.authorization.k8s.io

5.2. Using the Topic Operator

When you create, modify or delete a topic using the KafkaTopic resource, the Topic Operator ensures those changes are reflected in the Kafka cluster.

The Deploying and Upgrading AMQ Streams on OpenShift guide provides instructions to deploy the Topic Operator:

5.2.1. Kafka topic resource

The KafkaTopic resource is used to configure topics, including the number of partitions and replicas.

The full schema for KafkaTopic is described in KafkaTopic schema reference.

5.2.1.1. Identifying a Kafka cluster for topic handling

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

For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: topic-name-1
  labels:
    strimzi.io/cluster: my-cluster

The label is used by the Topic Operator to identify the KafkaTopic resource and create a new topic, and also in subsequent handling of the topic.

If the label does not match the Kafka cluster, the Topic Operator cannot identify the KafkaTopic and the topic is not created.

5.2.1.2. Kafka topic usage recommendations

When working with topics, be consistent. Always operate on either KafkaTopic resources or topics directly in OpenShift. Avoid routinely switching between both methods for a given topic.

Use topic names that reflect the nature of the topic, and remember that names cannot be changed later.

If creating a topic in Kafka, use a name that is a valid OpenShift resource name, otherwise the Topic Operator will need to create the corresponding KafkaTopic with a name that conforms to the OpenShift rules.

Note

Recommendations for identifiers and names in OpenShift are outlined in Identifiers and Names in OpenShift community article.

5.2.1.3. Kafka topic naming conventions

Kafka and OpenShift impose their own validation rules for the naming of topics in Kafka and KafkaTopic.metadata.name respectively. There are valid names for each which are invalid in the other.

Using the spec.topicName property, it is possible to create a valid topic in Kafka with a name that would be invalid for the Kafka topic in OpenShift.

The spec.topicName property inherits Kafka naming validation rules:

The name must not be longer than 249 characters.
Valid characters for Kafka topics are ASCII alphanumerics, ., _, and -.
The name cannot be . or .., though . can be used in a name, such as exampleTopic. or .exampleTopic.

spec.topicName must not be changed.

For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: topic-name-1
spec:
  topicName: topicName-1 1
  # ...

1: Upper case is invalid in OpenShift.

cannot be changed to:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: topic-name-1
spec:
  topicName: name-2
  # ...

Note

Some Kafka client applications, such as Kafka Streams, can create topics in Kafka programmatically. If those topics have names that are invalid OpenShift resource names, the Topic Operator gives them a valid metadata.name based on the Kafka name. Invalid characters are replaced and a hash is appended to the name. For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: mytopic---c55e57fe2546a33f9e603caf57165db4072e827e
spec:
  topicName: myTopic
  # ...

5.2.2. Topic Operator topic store

The Topic Operator uses Kafka to store topic metadata describing topic configuration as key-value pairs. The topic store is based on the Kafka Streams key-value mechanism, which uses Kafka topics to persist the state.

Topic metadata is cached in-memory and accessed locally within the Topic Operator. Updates from operations applied to the local in-memory cache are persisted to a backup topic store on disk. The topic store is continually synchronized with updates from Kafka topics or OpenShift KafkaTopic custom resources. Operations are handled rapidly with the topic store set up this way, but should the in-memory cache crash it is automatically repopulated from the persistent storage.

5.2.2.1. Internal topic store topics

Internal topics support the handling of topic metadata in the topic store.

__strimzi_store_topic: Input topic for storing the topic metadata
__strimzi-topic-operator-kstreams-topic-store-changelog: Retains a log of compacted topic store values

Warning

Do not delete these topics, as they are essential to the running of the Topic Operator.

5.2.2.2. Migrating topic metadata from ZooKeeper

In previous releases of AMQ Streams, topic metadata was stored in ZooKeeper. The new process removes this requirement, bringing the metadata into the Kafka cluster, and under the control of the Topic Operator.

When upgrading to AMQ Streams 1.7, the transition to Topic Operator control of the topic store is seamless. Metadata is found and migrated from ZooKeeper, and the old store is deleted.

5.2.2.3. Downgrading to an AMQ Streams version that uses ZooKeeper to store topic metadata

If you are reverting back to a version of AMQ Streams earlier than 0.22, which uses ZooKeeper for the storage of topic metadata, you still downgrade your Cluster Operator to the previous version, then downgrade Kafka brokers and client applications to the previous Kafka version as standard.

However, you must also delete the topics that were created for the topic store using a kafka-admin command, specifying the bootstrap address of the Kafka cluster. For example:

oc run kafka-admin -ti --image=registry.redhat.io/amq7/amq-streams-kafka-27-rhel7:1.7.0 --rm=true --restart=Never -- ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi-topic-operator-kstreams-topic-store-changelog --delete && ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi_store_topic --delete

The command must correspond to the type of listener and authentication used to access the Kafka cluster.

The Topic Operator will reconstruct the ZooKeeper topic metadata from the state of the topics in Kafka.

5.2.2.4. Topic Operator topic replication and scaling

The recommended configuration for topics managed by the Topic Operator is a topic replication factor of 3, and a minimum of 2 in-sync replicas.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 1 1
  replicas: 3 2
  config:
    min.insync.replicas=2 3
  #...

1: The number of partitions for the topic. Generally, 1 partition is sufficient.
2: The number of replica topic partitions. Currently, this cannot be changed in the KafkaTopic resource, but it can be changed using the kafka-reassign-partitions.sh tool.
3: The minimum number of replica partitions that a message must be successfully written to, or an exception is raised.

Note

In-sync replicas are used in conjunction with the acks configuration for producer applications. The acks configuration determines the number of follower partitions a message must be replicated to before the message is acknowledged as successfully received. The Topic Operator runs with acks=all, whereby messages must be acknowledged by all in-sync replicas.

When scaling Kafka clusters by adding or removing brokers, replication factor configuration is not changed and replicas are not reassigned automatically. However, you can use the kafka-reassign-partitions.sh tool to change the replication factor, and manually reassign replicas to brokers.

Alternatively, though the integration of Cruise Control for AMQ Streams cannot change the replication factor for topics, the optimization proposals it generates for rebalancing Kafka include commands that transfer partition replicas and change partition leadership.

5.2.2.5. Handling changes to topics

A fundamental problem that the Topic Operator needs to solve is that there is no single source of truth: both the KafkaTopic resource and the Kafka topic can be modified independently of the Topic Operator. Complicating this, the Topic Operator might not always be able to observe changes at each end in real time. For example, when the Topic Operator is down.

To resolve this, the Topic Operator maintains information about each topic in the topic store. When a change happens in the Kafka cluster or OpenShift, it looks at both the state of the other system and the topic store in order to determine what needs to change to keep everything in sync. The same thing happens whenever the Topic Operator starts, and periodically while it is running.

For example, suppose the Topic Operator is not running, and a KafkaTopic called my-topic is created. When the Topic Operator starts, the topic store does not contain information on my-topic, so it can infer that the KafkaTopic was created after it was last running. The Topic Operator creates the topic corresponding to my-topic, and also stores metadata for my-topic in the topic store.

If you update Kafka topic configuration or apply a change through the KafkaTopic custom resource, the topic store is updated after the Kafka cluster is reconciled.

The topic store also allows the Topic Operator to manage scenarios where the topic configuration is changed in Kafka topics and updated through OpenShift KafkaTopic custom resources, as long as the changes are not incompatible. For example, it is possible to make changes to the same topic config key, but to different values. For incompatible changes, the Kafka configuration takes priority, and the KafkaTopic is updated accordingly.

Note

You can also use the KafkaTopic resource to delete topics using a oc delete -f KAFKA-TOPIC-CONFIG-FILE command. To be able to do this, delete.topic.enable must be set to true (default) in the spec.kafka.config of the Kafka resource.

Additional resources

Downgrading AMQ Streams
Producer configuration tuning and data durability
Scaling cluster and partition reassignment
Cruise Control for cluster rebalancing

5.2.3. Configuring a Kafka topic

Use the properties of the KafkaTopic resource to configure a Kafka topic.

You can use oc apply to create or modify topics, and oc delete to delete existing topics.

For example:

oc apply -f <topic-config-file>
oc delete KafkaTopic <topic-name>

This procedure shows how to create a topic with 10 partitions and 2 replicas.

Before you start

It is important that you consider the following before making your changes:

Kafka does not support making the following changes through the KafkaTopic resource:
- Changing topic names using spec.topicName
- Decreasing partition size using spec.partitions
You cannot use spec.replicas to change the number of replicas that were initially specified.
Increasing spec.partitions for topics with keys will change how records are partitioned, which can be particularly problematic when the topic uses semantic partitioning.

Prerequisites

A running Kafka cluster configured with a Kafka broker listener using TLS authentication and encryption.
A running Topic Operator (typically deployed with the Entity Operator).
For deleting a topic, delete.topic.enable=true (default) in the spec.kafka.config of the Kafka resource.

Procedure

Prepare a file containing the KafkaTopic to be created.
An example KafkaTopic
```
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: orders
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10
  replicas: 2
```
Tip
When modifying a topic, you can get the current version of the resource using oc get kafkatopic orders -o yaml.
Create the KafkaTopic resource in OpenShift.
```
oc apply -f TOPIC-CONFIG-FILE
```

5.2.4. Configuring the Topic Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the Topic Operator and set a limit on the amount of resources it can consume.

Prerequisites

The Cluster Operator is running.

Procedure

Update the Kafka cluster configuration in an editor, as required:
```
oc edit kafka MY-CLUSTER
```

In the spec.entityOperator.topicOperator.resources property in the Kafka resource, set the resource requests and limits for the Topic Operator.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  # Kafka and ZooKeeper sections...
  entityOperator:
    topicOperator:
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi

Apply the new configuration to create or update the resource.
```
oc apply -f KAFKA-CONFIG-FILE
```

5.3. Using the User Operator

When you create, modify or delete a user using the KafkaUser resource, the User Operator ensures those changes are reflected in the Kafka cluster.

The Deploying and Upgrading AMQ Streams on OpenShift guide provides instructions to deploy the User Operator:

For more information about the schema, see KafkaUser schema reference.

Authenticating and authorizing access to Kafka

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

For more information on using KafkUser to manage users and secure access to Kafka brokers, see Securing access to Kafka brokers.

5.3.1. Configuring the User Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the User Operator and set a limit on the amount of resources it can consume.

Prerequisites

The Cluster Operator is running.

Procedure

Update the Kafka cluster configuration in an editor, as required:
```
oc edit kafka MY-CLUSTER
```

In the spec.entityOperator.userOperator.resources property in the Kafka resource, set the resource requests and limits for the User Operator.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  # Kafka and ZooKeeper sections...
  entityOperator:
    userOperator:
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi

Save the file and exit the editor. The Cluster Operator applies the changes automatically.

5.4. Monitoring operators using Prometheus metrics

AMQ Streams operators expose Prometheus metrics. The metrics are automatically enabled and contain information about:

Number of reconciliations
Number of Custom Resources the operator is processing
Duration of reconciliations
JVM metrics from the operators

Additionally, we provide example Grafana dashboards.

For more information, see Setting up metrics and dashboards for AMQ Streams in the Deploying and upgrading AMQ Streams on OpenShift guide.

Chapter 6. Kafka Bridge

This chapter provides an overview of the AMQ Streams Kafka Bridge and helps you get started using its REST API to interact with AMQ Streams.

To try out the Kafka Bridge in your local environment, see the Section 6.2, “Kafka Bridge quickstart” later in this chapter.
For detailed configuration steps, see Section 2.5, “Kafka Bridge cluster configuration”.
To view the API documentation, see the Kafka Bridge API reference.

6.1. Kafka Bridge overview

You can use the AMQ Streams Kafka Bridge as an interface to make specific types of HTTP requests to the Kafka cluster.

6.1.1. Kafka Bridge interface

The Kafka Bridge provides a RESTful interface that allows HTTP-based clients to interact with a Kafka cluster. It offers the advantages of a web API connection to AMQ Streams, without the need for client applications to interpret the Kafka protocol.

The API has two main resources — consumers and topics — that are exposed and made accessible through endpoints to interact with consumers and producers in your Kafka cluster. The resources relate only to the Kafka Bridge, not the consumers and producers connected directly to Kafka.

6.1.1.1. HTTP requests

The Kafka Bridge supports HTTP requests to a Kafka cluster, with methods to:

Send messages to a topic.
Retrieve messages from topics.
Retrieve a list of partitions for a topic.
Create and delete consumers.
Subscribe consumers to topics, so that they start receiving messages from those topics.
Retrieve a list of topics that a consumer is subscribed to.
Unsubscribe consumers from topics.
Assign partitions to consumers.
Commit a list of consumer offsets.
Seek on a partition, so that a consumer starts receiving messages from the first or last offset position, or a given offset position.

The methods provide JSON responses and HTTP response code error handling. Messages can be sent in JSON or binary formats.

Clients can produce and consume messages without the requirement to use the native Kafka protocol.

Additional resources

To view the API documentation, including example requests and responses, see the Kafka Bridge API reference.

6.1.2. Supported clients for the Kafka Bridge

You can use the Kafka Bridge to integrate both internal and external HTTP client applications with your Kafka cluster.

Internal clients: Internal clients are container-based HTTP clients running in the same OpenShift cluster as the Kafka Bridge itself. Internal clients can access the Kafka Bridge on the host and port defined in the KafkaBridge custom resource.
External clients: External clients are HTTP clients running outside the OpenShift cluster in which the Kafka Bridge is deployed and running. External clients can access the Kafka Bridge through an OpenShift Route, a loadbalancer service, or using an Ingress.

HTTP internal and external client integration

Internal and external HTTP producers and consumers exchange data with the Kafka brokers through the Kafka Bridge

6.1.3. Securing the Kafka Bridge

AMQ Streams does not currently provide any encryption, authentication, or authorization for the Kafka Bridge. This means that requests sent from external clients to the Kafka Bridge are:

Not encrypted, and must use HTTP rather than HTTPS
Sent without authentication

However, you can secure the Kafka Bridge using other methods, such as:

OpenShift Network Policies that define which pods can access the Kafka Bridge.
Reverse proxies with authentication or authorization, for example, OAuth2 proxies.
API Gateways.
Ingress or OpenShift Routes with TLS termination.

The Kafka Bridge supports TLS encryption and TLS and SASL authentication when connecting to the Kafka Brokers. Within your OpenShift cluster, you can configure:

TLS or SASL-based authentication between the Kafka Bridge and your Kafka cluster
A TLS-encrypted connection between the Kafka Bridge and your Kafka cluster.

For more information, see Section 2.5.1, “Configuring the Kafka Bridge”.

You can use ACLs in Kafka brokers to restrict the topics that can be consumed and produced using the Kafka Bridge.

6.1.4. Accessing the Kafka Bridge outside of OpenShift

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

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

Services of types LoadBalancer or NodePort
Ingress resources
OpenShift Routes

If you decide to create Services, use the following labels in the selector 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.

6.1.5. Requests to the Kafka Bridge

Specify data formats and HTTP headers to ensure valid requests are submitted to the Kafka Bridge.

6.1.5.1. Content Type headers

API request and response bodies are always encoded as JSON.

When performing consumer operations, POST requests must provide the following Content-Type header if there is a non-empty body:
```
Content-Type: application/vnd.kafka.v2+json
```

When performing producer operations, POST requests must provide Content-Type headers specifying the embedded data format of the messages produced. This can be either json or binary.

Embedded data format	Content-Type header
JSON	`Content-Type: application/vnd.kafka.json.v2+json`
Binary	`Content-Type: application/vnd.kafka.binary.v2+json`

The embedded data format is set per consumer, as described in the next section.

The Content-Type must not be set if the POST request has an empty body. An empty body can be used to create a consumer with the default values.

6.1.5.2. Embedded data format

The embedded data format is the format of the Kafka messages that are transmitted, over HTTP, from a producer to a consumer using the Kafka Bridge. Two embedded data formats are supported: JSON and binary.

When creating a consumer using the /consumers/groupid endpoint, the POST request body must specify an embedded data format of either JSON or binary. This is specified in the format field, for example:

{
  "name": "my-consumer",
  "format": "binary", 1
...
}

1: A binary embedded data format.

The embedded data format specified when creating a consumer must match the data format of the Kafka messages it will consume.

If you choose to specify a binary embedded data format, subsequent producer requests must provide the binary data in the request body as Base64-encoded strings. For example, when sending messages using the /topics/topicname endpoint, records.value must be encoded in Base64:

{
  "records": [
    {
      "key": "my-key",
      "value": "ZWR3YXJkdGhldGhyZWVsZWdnZWRjYXQ="
    },
  ]
}

Producer requests must also provide a Content-Type header that corresponds to the embedded data format, for example, Content-Type: application/vnd.kafka.binary.v2+json.

6.1.5.3. Message format

When sending messages using the /topics endpoint, you enter the message payload in the request body, in the records parameter.

The records parameter can contain any of these optional fields:

Message headers
Message key
Message value
Destination partition

Example POST request to /topics

curl -X POST \
  http://localhost:8080/topics/my-topic \
  -H 'content-type: application/vnd.kafka.json.v2+json' \
  -d '{
    "records": [
        {
            "key": "my-key",
            "value": "sales-lead-0001"
            "partition": 2
            "headers": [
              {
                "key": "key1",
                "value": "QXBhY2hlIEthZmthIGlzIHRoZSBib21iIQ==" 1
              }
            ]
        },
    ]
}'

1: The header value in binary format and encoded as Base64.

6.1.5.4. Accept headers

After creating a consumer, all subsequent GET requests must provide an Accept header in the following format:

Accept: application/vnd.kafka.EMBEDDED-DATA-FORMAT.v2+json

The EMBEDDED-DATA-FORMAT is either json or binary.

For example, when retrieving records for a subscribed consumer using an embedded data format of JSON, include this Accept header:

Accept: application/vnd.kafka.json.v2+json

6.1.6. CORS

Cross-Origin Resource Sharing (CORS) allows you to specify allowed methods and originating URLs for accessing the Kafka cluster in your Kafka Bridge HTTP configuration.

Example CORS configuration for Kafka Bridge

# ...
cors:
  allowedOrigins: "https://strimzi.io"
  allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  # ...

CORS allows for simple and preflighted requests between origin sources on different domains.

Simple requests are suitable for standard requests using GET, HEAD, POST methods.

A preflighted request sends a HTTP OPTIONS request as an initial check that the actual request is safe to send. On confirmation, the actual request is sent. Preflight requests are suitable for methods that require greater safeguards, such as PUT and DELETE, and use non-standard headers.

All requests require an Origin value in their header, which is the source of the HTTP request.

6.1.6.1. Simple request

For example, this simple request header specifies the origin as https://strimzi.io.

Origin: https://strimzi.io

The header information is added to the request.

curl -v -X GET HTTP-ADDRESS/bridge-consumer/records \
-H 'Origin: https://strimzi.io'\
-H 'content-type: application/vnd.kafka.v2+json'

In the response from the Kafka Bridge, an Access-Control-Allow-Origin header is returned.

HTTP/1.1 200 OK
Access-Control-Allow-Origin: * 1

1: Returning an asterisk (*) shows the resource can be accessed by any domain.

6.1.6.2. Preflighted request

An initial preflight request is sent to Kafka Bridge using an OPTIONS method. The HTTP OPTIONS request sends header information to check that Kafka Bridge will allow the actual request.

Here the preflight request checks that a POST request is valid from https://strimzi.io.

OPTIONS /my-group/instances/my-user/subscription HTTP/1.1
Origin: https://strimzi.io
Access-Control-Request-Method: POST 1
Access-Control-Request-Headers: Content-Type 2

1: Kafka Bridge is alerted that the actual request is a POST request.
2: The actual request will be sent with a Content-Type header.

OPTIONS is added to the header information of the preflight request.

curl -v -X OPTIONS -H 'Origin: https://strimzi.io' \
-H 'Access-Control-Request-Method: POST' \
-H 'content-type: application/vnd.kafka.v2+json'

Kafka Bridge responds to the initial request to confirm that the request will be accepted. The response header returns allowed origins, methods and headers.

HTTP/1.1 200 OK
Access-Control-Allow-Origin: https://strimzi.io
Access-Control-Allow-Methods: GET,POST,PUT,DELETE,OPTIONS,PATCH
Access-Control-Allow-Headers: content-type

If the origin or method is rejected, an error message is returned.

The actual request does not require Access-Control-Request-Method header, as it was confirmed in the preflight request, but it does require the origin header.

curl -v -X POST HTTP-ADDRESS/topics/bridge-topic \
-H 'Origin: https://strimzi.io' \
-H 'content-type: application/vnd.kafka.v2+json'

The response shows the originating URL is allowed.

HTTP/1.1 200 OK
Access-Control-Allow-Origin: https://strimzi.io

Additional resources

Fetch CORS specification

6.1.7. Kafka Bridge API resources

For the full list of REST API endpoints and descriptions, including example requests and responses, see the Kafka Bridge API reference.

6.1.8. Kafka Bridge deployment

You deploy the Kafka Bridge into your OpenShift cluster by using the Cluster Operator.

After the Kafka Bridge is deployed, the Cluster Operator creates Kafka Bridge objects in your OpenShift cluster. Objects include the deployment, service, and pod, each named after the name given in the custom resource for the Kafka Bridge.

Additional resources

For deployment instructions, see Deploying Kafka Bridge to your OpenShift cluster in the Deploying and Upgrading AMQ Streams on OpenShift guide.
For detailed information on configuring the Kafka Bridge, see Section 2.5, “Kafka Bridge cluster configuration”
For information on configuring the host and port for the KafkaBridge resource, see Section 2.5.1, “Configuring the Kafka Bridge”.
For information on integrating external clients, see Section 6.1.4, “Accessing the Kafka Bridge outside of OpenShift”.

6.2. Kafka Bridge quickstart

Use this quickstart to try out the AMQ Streams Kafka Bridge in your local development environment. You will learn how to:

Deploy the Kafka Bridge to your OpenShift cluster
Expose the Kafka Bridge service to your local machine by using port-forwarding
Produce messages to topics and partitions in your Kafka cluster
Create a Kafka Bridge consumer
Perform basic consumer operations, such as subscribing the consumer to topics and retrieving the messages that you produced

In this quickstart, HTTP requests are formatted as curl commands that you can copy and paste to your terminal. Access to an OpenShift cluster is required; to run and manage a local OpenShift cluster, use a tool such as Minikube, CodeReady Containers, or MiniShift.

Ensure you have the prerequisites and then follow the tasks in the order provided in this chapter.

About data formats

In this quickstart, you will produce and consume messages in JSON format, not binary. For more information on the data formats and HTTP headers used in the example requests, see Section 6.1.5, “Requests to the Kafka Bridge”.

Prerequisites for the quickstart

Cluster administrator access to a local or remote OpenShift cluster.
AMQ Streams is installed.
A running Kafka cluster, deployed by the Cluster Operator, in an OpenShift namespace.
The Entity Operator is deployed and running as part of the Kafka cluster.

6.2.1. Deploying the Kafka Bridge to your OpenShift cluster

AMQ Streams includes a YAML example that specifies the configuration of the AMQ Streams Kafka Bridge. Make some minimal changes to this file and then deploy an instance of the Kafka Bridge to your OpenShift cluster.

Procedure

Edit the examples/bridge/kafka-bridge.yaml file.
```
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: quickstart 1
spec:
  replicas: 1
  bootstrapServers: <cluster-name>-kafka-bootstrap:9092 2
  http:
    port: 8080
```
1
When the Kafka Bridge is deployed, -bridge is appended to the name of the deployment and other related resources. In this example, the Kafka Bridge deployment is named quickstart-bridge and the accompanying Kafka Bridge service is named quickstart-bridge-service.
2
In the bootstrapServers property, enter the name of the Kafka cluster as the <cluster-name>.
Deploy the Kafka Bridge to your OpenShift cluster:
```
oc apply -f examples/bridge/kafka-bridge.yaml
```
A quickstart-bridge deployment, service, and other related resources are created in your OpenShift cluster.

Verify that the Kafka Bridge was successfully deployed:

oc get deployments

NAME                             READY   UP-TO-DATE   AVAILABLE   AGE
quickstart-bridge                  1/1     1            1          34m
my-cluster-connect                 1/1     1            1          24h
my-cluster-entity-operator         1/1     1            1          24h
#...

What to do next

After deploying the Kafka Bridge to your OpenShift cluster, expose the Kafka Bridge service to your local machine.

Additional resources

For more detailed information about configuring the Kafka Bridge, see Section 2.5, “Kafka Bridge cluster configuration”.

6.2.2. Exposing the Kafka Bridge service to your local machine

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

Note

Port forwarding is only suitable for development and testing purposes.

Procedure

List the names of the pods in your OpenShift cluster:

oc get pods -o name

pod/kafka-consumer
# ...
pod/quickstart-bridge-589d78784d-9jcnr
pod/strimzi-cluster-operator-76bcf9bc76-8dnfm

Connect to the quickstart-bridge pod on port 8080:
```
oc port-forward pod/quickstart-bridge-589d78784d-9jcnr 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.

6.2.3. Producing messages to topics and partitions

Next, produce messages to topics in JSON format by using the topics endpoint. You can specify destination partitions for messages in the request body, as shown here. The partitions endpoint provides an alternative method for specifying a single destination partition for all messages as a path parameter.

Procedure

In a text editor, create a YAML definition for a Kafka topic with three partitions.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: bridge-quickstart-topic
  labels:
    strimzi.io/cluster: <kafka-cluster-name> 1
spec:
  partitions: 3 2
  replicas: 1
  config:
    retention.ms: 7200000
    segment.bytes: 1073741824

1: The name of the Kafka cluster in which the Kafka Bridge is deployed.
2: The number of partitions for the topic.

Save the file to the examples/topic directory as bridge-quickstart-topic.yaml.

Create the topic in your OpenShift cluster:

oc apply -f examples/topic/bridge-quickstart-topic.yaml

Using the Kafka Bridge, produce three messages to the topic you created:

curl -X POST \
  http://localhost:8080/topics/bridge-quickstart-topic \
  -H 'content-type: application/vnd.kafka.json.v2+json' \
  -d '{
    "records": [
        {
            "key": "my-key",
            "value": "sales-lead-0001"
        },
        {
            "value": "sales-lead-0002",
            "partition": 2
        },
        {
            "value": "sales-lead-0003"
        }
    ]
}'

sales-lead-0001 is sent to a partition based on the hash of the key.
sales-lead-0002 is sent directly to partition 2.
sales-lead-0003 is sent to a partition in the bridge-quickstart-topic topic using a round-robin method.

If the request is successful, the Kafka Bridge returns an offsets array, along with a 200 code and a content-type header of application/vnd.kafka.v2+json. For each message, the offsets array describes:
- The partition that the message was sent to
- The current message offset of the partition
  Example response
```
#...
{
  "offsets":[
    {
      "partition":0,
      "offset":0
    },
    {
      "partition":2,
      "offset":0
    },
    {
      "partition":0,
      "offset":1
    }
  ]
}
```

What to do next

After producing messages to topics and partitions, create a Kafka Bridge consumer.

Additional resources

POST /topics/{topicname} in the API reference documentation.
POST /topics/{topicname}/partitions/{partitionid} in the API reference documentation.

6.2.4. Creating a Kafka Bridge consumer

Before you can perform any consumer operations in the Kafka cluster, you must first create a consumer by using the consumers endpoint. The consumer is referred to as a Kafka Bridge consumer.

Procedure

Create a Kafka Bridge consumer in a new consumer group named bridge-quickstart-consumer-group:
```
curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "name": "bridge-quickstart-consumer",
    "auto.offset.reset": "earliest",
    "format": "json",
    "enable.auto.commit": false,
    "fetch.min.bytes": 512,
    "consumer.request.timeout.ms": 30000
  }'
```
- The consumer is named bridge-quickstart-consumer and the embedded data format is set as json.
- Some basic configuration settings are defined.
- The consumer will not commit offsets to the log automatically because the enable.auto.commit setting is false. You will commit the offsets manually later in this quickstart.
  If the request is successful, the Kafka Bridge returns the consumer ID (instance_id) and base URL (base_uri) in the response body, along with a 200 code.
  Example response
```
#...
{
  "instance_id": "bridge-quickstart-consumer",
  "base_uri":"http://<bridge-name>-bridge-service:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer"
}
```
Copy the base URL (base_uri) to use in the other consumer operations in this quickstart.

What to do next

Now that you have created a Kafka Bridge consumer, you can subscribe it to topics.

Additional resources

POST /consumers/{groupid} in the API reference documentation.

6.2.5. Subscribing a Kafka Bridge consumer to topics

After you have created a Kafka Bridge consumer, subscribe it to one or more topics by using the subscription endpoint. Once subscribed, the consumer starts receiving all messages that are produced to the topic.

Procedure

Subscribe the consumer to the bridge-quickstart-topic topic that you created earlier, in Producing messages to topics and partitions:
```
curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/subscription \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "topics": [
        "bridge-quickstart-topic"
    ]
}'
```
The topics array can contain a single topic (as shown here) or multiple topics. If you want to subscribe the consumer to multiple topics that match a regular expression, you can use the topic_pattern string instead of the topics array.
If the request is successful, the Kafka Bridge returns a 204 (No Content) code only.

What to do next

After subscribing a Kafka Bridge consumer to topics, you can retrieve messages from the consumer.

Additional resources

POST /consumers/{groupid}/instances/{name}/subscription in the API reference documentation.

6.2.6. Retrieving the latest messages from a Kafka Bridge consumer

Next, retrieve the latest messages from the Kafka Bridge consumer by requesting data from the records endpoint. In production, HTTP clients can call this endpoint repeatedly (in a loop).

Procedure

Produce additional messages to the Kafka Bridge consumer, as described in Producing messages to topics and partitions.
Submit a GET request to the records endpoint:
```
curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \
  -H 'accept: application/vnd.kafka.json.v2+json'
```
After creating and subscribing to a Kafka Bridge consumer, a first GET request will return an empty response because the poll operation starts a rebalancing process to assign partitions.
Repeat step two to retrieve messages from the Kafka Bridge consumer.
The Kafka Bridge returns an array of messages — describing the topic name, key, value, partition, and offset — in the response body, along with a 200 code. Messages are retrieved from the latest offset by default.
```
HTTP/1.1 200 OK
content-type: application/vnd.kafka.json.v2+json
#...
[
  {
    "topic":"bridge-quickstart-topic",
    "key":"my-key",
    "value":"sales-lead-0001",
    "partition":0,
    "offset":0
  },
  {
    "topic":"bridge-quickstart-topic",
    "key":null,
    "value":"sales-lead-0003",
    "partition":0,
    "offset":1
  },
#...
```
Note
If an empty response is returned, produce more records to the consumer as described in Producing messages to topics and partitions, and then try retrieving messages again.

What to do next

After retrieving messages from a Kafka Bridge consumer, try committing offsets to the log.

Additional resources

GET /consumers/{groupid}/instances/{name}/records in the API reference documentation.

6.2.7. Commiting offsets to the log

Next, use the offsets endpoint to manually commit offsets to the log for all messages received by the Kafka Bridge consumer. This is required because the Kafka Bridge consumer that you created earlier, in Creating a Kafka Bridge consumer, was configured with the enable.auto.commit setting as false.

Procedure

Commit offsets to the log for the bridge-quickstart-consumer:
```
curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/offsets
```
Because no request body is submitted, offsets are committed for all the records that have been received by the consumer. Alternatively, the request body can contain an array (OffsetCommitSeekList) that specifies the topics and partitions that you want to commit offsets for.
If the request is successful, the Kafka Bridge returns a 204 code only.

What to do next

After committing offsets to the log, try out the endpoints for seeking to offsets.

Additional resources

POST /consumers/{groupid}/instances/{name}/offsets in the API reference documentation.

6.2.8. Seeking to offsets for a partition

Next, use the positions endpoints to configure the Kafka Bridge consumer to retrieve messages for a partition from a specific offset, and then from the latest offset. This is referred to in Apache Kafka as a seek operation.

Procedure

Seek to a specific offset for partition 0 of the quickstart-bridge-topic topic:

curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "offsets": [
        {
            "topic": "bridge-quickstart-topic",
            "partition": 0,
            "offset": 2
        }
    ]
}'

If the request is successful, the Kafka Bridge returns a 204 code only.

Submit a GET request to the records endpoint:

curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \
  -H 'accept: application/vnd.kafka.json.v2+json'

The Kafka Bridge returns messages from the offset that you seeked to.

Restore the default message retrieval behavior by seeking to the last offset for the same partition. This time, use the positions/end endpoint.

curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions/end \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "partitions": [
        {
            "topic": "bridge-quickstart-topic",
            "partition": 0
        }
    ]
}'

If the request is successful, the Kafka Bridge returns another 204 code.

Note

You can also use the positions/beginning endpoint to seek to the first offset for one or more partitions.

What to do next

In this quickstart, you have used the AMQ Streams Kafka Bridge to perform several common operations on a Kafka cluster. You can now delete the Kafka Bridge consumer that you created earlier.

Additional resources

POST /consumers/{groupid}/instances/{name}/positions in the API reference documentation.
POST /consumers/{groupid}/instances/{name}/positions/beginning in the API reference documentation.
POST /consumers/{groupid}/instances/{name}/positions/end in the API reference documentation.

6.2.9. Deleting a Kafka Bridge consumer

Finally, delete the Kafa Bridge consumer that you used throughout this quickstart.

Procedure

Delete the Kafka Bridge consumer by sending a DELETE request to the instances endpoint.
```
curl -X DELETE http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer
```
If the request is successful, the Kafka Bridge returns a 204 code only.

Additional resources

DELETE /consumers/{groupid}/instances/{name} in the API reference documentation.

Chapter 7. Using the Kafka Bridge with 3scale

You can deploy and integrate Red Hat 3scale API Management with the AMQ Streams Kafka Bridge.

7.1. Using the Kafka Bridge with 3scale

With a plain deployment of the Kafka Bridge, there is no provision for authentication or authorization, and no support for a TLS encrypted connection to external clients.

3scale can secure the Kafka Bridge with TLS, and provide authentication and authorization. Integration with 3scale also means that additional features like metrics, rate limiting and billing are available.

With 3scale, you can use different types of authentication for requests from external clients wishing to access AMQ Streams. 3scale supports the following types of authentication:

Standard API Keys: Single randomized strings or hashes acting as an identifier and a secret token.
Application Identifier and Key pairs: Immutable identifier and mutable secret key strings.
OpenID Connect: Protocol for delegated authentication.

Using an existing 3scale deployment?

If you already have 3scale deployed to OpenShift and you wish to use it with the Kafka Bridge, ensure that you have the correct setup.

Setup is described in Section 7.2, “Deploying 3scale for the Kafka Bridge”.

7.1.1. Kafka Bridge service discovery

3scale is integrated using service discovery, which requires that 3scale is deployed to the same OpenShift cluster as AMQ Streams and the Kafka Bridge.

Your AMQ Streams Cluster Operator deployment must have the following environment variables set:

STRIMZI_CUSTOM_KAFKA_BRIDGE_SERVICE_LABELS
STRIMZI_CUSTOM_KAFKA_BRIDGE_SERVICE_ANNOTATIONS

When the Kafka Bridge is deployed, the service that exposes the REST interface of the Kafka Bridge uses the annotations and labels for discovery by 3scale.

A discovery.3scale.net=true label is used by 3scale to find a service.
Annotations provide information about the service.

You can check your configuration in the OpenShift console by navigating to Services for the Kafka Bridge instance. Under Annotations you will see the endpoint to the OpenAPI specification for the Kafka Bridge.

7.1.2. 3scale APIcast gateway policies

3scale is used in conjunction with 3scale APIcast, an API gateway deployed with 3scale that provides a single point of entry for the Kafka Bridge.

APIcast policies provide a mechanism to customize how the gateway operates. 3scale provides a set of standard policies for gateway configuration. You can also create your own policies.

For more information on APIcast policies, see Administering the API Gateway in the 3scale documentation.

APIcast policies for the Kafka Bridge

A sample policy configuration for 3scale integration with the Kafka Bridge is provided with the policies_config.json file, which defines:

Anonymous access
Header modification
Routing
URL rewriting

Gateway policies are enabled or disabled through this file.

You can use this sample as a starting point for defining your own policies.

Anonymous access

The anonymous access policy exposes a service without authentication, providing default credentials (for anonymous access) when a HTTP client does not provide them. The policy is not mandatory and can be disabled or removed if authentication is always needed.

Header modification

The header modification policy allows existing HTTP headers to be modified, or new headers added to requests or responses passing through the gateway. For 3scale integration, the policy adds headers to every request passing through the gateway from a HTTP client to the Kafka Bridge.

When the Kafka Bridge receives a request for creating a new consumer, it returns a JSON payload containing a base_uri field with the URI that the consumer must use for all the subsequent requests. For example:

{
  "instance_id": "consumer-1",
  "base_uri":"http://my-bridge:8080/consumers/my-group/instances/consumer1"
}

When using APIcast, clients send all subsequent requests to the gateway and not to the Kafka Bridge directly. So the URI requires the gateway hostname, not the address of the Kafka Bridge behind the gateway.

Using header modification policies, headers are added to requests from the HTTP client so that the Kafka Bridge uses the gateway hostname.

For example, by applying a Forwarded: host=my-gateway:80;proto=http header, the Kafka Bridge delivers the following to the consumer.

{
    "instance_id": "consumer-1",
    "base_uri":"http://my-gateway:80/consumers/my-group/instances/consumer1"
}

An X-Forwarded-Path header carries the original path contained in a request from the client to the gateway. This header is strictly related to the routing policy applied when a gateway supports more than one Kafka Bridge instance.

Routing

A routing policy is applied when there is more than one Kafka Bridge instance. Requests must be sent to the same Kafka Bridge instance where the consumer was initially created, so a request must specify a route for the gateway to forward a request to the appropriate Kafka Bridge instance.

A routing policy names each bridge instance, and routing is performed using the name. You specify the name in the KafkaBridge custom resource when you deploy the Kafka Bridge.

For example, each request (using X-Forwarded-Path) from a consumer to:

http://my-gateway:80/my-bridge-1/consumers/my-group/instances/consumer1

is forwarded to:

http://my-bridge-1-bridge-service:8080/consumers/my-group/instances/consumer1

URL rewriting policy removes the bridge name, as it is not used when forwarding the request from the gateway to the Kafka Bridge.

URL rewriting

The URL rewiring policy ensures that a request to a specific Kafka Bridge instance from a client does not contain the bridge name when forwarding the request from the gateway to the Kafka Bridge.

The bridge name is not used in the endpoints exposed by the bridge.

7.1.3. TLS validation

You can set up APIcast for TLS validation, which requires a self-managed deployment of APIcast using a template. The apicast service is exposed as a route.

You can also apply a TLS policy to the Kafka Bridge API.

For more information on TLS configuration, see Administering the API Gateway in the 3scale documentation.

7.1.4. 3scale documentation

The procedure to deploy 3scale for use with the Kafka Bridge assumes some understanding of 3scale.

For more information, refer to the 3scale product documentation:

Product Documentation for Red Hat 3scale API Management

7.2. Deploying 3scale for the Kafka Bridge

In order to use 3scale with the Kafka Bridge, you first deploy it and then configure it to discover the Kafka Bridge API.

You will also use 3scale APIcast and 3scale toolbox.

APIcast is provided by 3scale as an NGINX-based API gateway for HTTP clients to connect to the Kafka Bridge API service.
3scale toolbox is a configuration tool that is used to import the OpenAPI specification for the Kafka Bridge service to 3scale.

In this scenario, you run AMQ Streams, Kafka, the Kafka Bridge and 3scale/APIcast in the same OpenShift cluster.

Note

If you already have 3scale deployed in the same cluster as the Kafka Bridge, you can skip the deployment steps and use your current deployment.

Prerequisites

For the 3scale deployment:

Check the Red Hat 3scale API Management supported configurations.
Installation requires a user with cluster-admin role, such as system:admin.
You need access to the JSON files describing the:
- Kafka Bridge OpenAPI specification (openapiv2.json)
- Header modification and routing policies for the Kafka Bridge (policies_config.json)
  Find the JSON files on GitHub.

Procedure

Deploy 3scale API Management to the OpenShift cluster.
1. Create a new project or use an existing project.
```
oc new-project my-project \
    --description="description" --display-name="display_name"
```
2. Deploy 3scale.
  Use the information provided in the Installing 3scale guide to deploy 3scale on OpenShift using a template or operator.
  Whichever approach you use, make sure that you set the WILDCARD_DOMAIN parameter to the domain of your OpenShift cluster.
  Make a note of the URLS and credentials presented for accessing the 3scale Admin Portal.

Grant authorization for 3scale to discover the Kafka Bridge service:

oc adm policy add-cluster-role-to-user view system:serviceaccount:my-project:amp

Verify that 3scale was successfully deployed to the Openshift cluster from the OpenShift console or CLI.
For example:
```
oc get deployment 3scale-operator
```
Set up 3scale toolbox.
1. Use the information provided in the Operating 3scale guide to install 3scale toolbox.
2. Set environment variables to be able to interact with 3scale:
```
export REMOTE_NAME=strimzi-kafka-bridge 1
export SYSTEM_NAME=strimzi_http_bridge_for_apache_kafka 2
export TENANT=strimzi-kafka-bridge-admin 3
export PORTAL_ENDPOINT=$TENANT.3scale.net 4
export TOKEN=3scale access token 5
```
  1
  REMOTE_NAME is the name assigned to the remote address of the 3scale Admin Portal.
  2
  SYSTEM_NAME is the name of the 3scale service/API created by importing the OpenAPI specification through the 3scale toolbox.
  3
  TENANT is the tenant name of the 3scale Admin Portal (that is, https://$TENANT.3scale.net).
  4
  PORTAL_ENDPOINT is the endpoint running the 3scale Admin Portal.
  5
  TOKEN is the access token provided by the 3scale Admin Portal for interaction through the 3scale toolbox or HTTP requests.
3. Configure the remote web address of the 3scale toolbox:
```
3scale remote add $REMOTE_NAME https://$TOKEN@$PORTAL_ENDPOINT/
```
  Now the endpoint address of the 3scale Admin portal does not need to be specified every time you run the toolbox.

Check that your Cluster Operator deployment has the labels and annotations properties required for the Kafka Bridge service to be discovered by 3scale.

#...
env:
- name: STRIMZI_CUSTOM_KAFKA_BRIDGE_SERVICE_LABELS
    value: |
    discovery.3scale.net=true
- name: STRIMZI_CUSTOM_KAFKA_BRIDGE_SERVICE_ANNOTATIONS
    value: |
    discovery.3scale.net/scheme=http
    discovery.3scale.net/port=8080
    discovery.3scale.net/path=/
    discovery.3scale.net/description-path=/openapi
#...

If not, add the properties through the OpenShift console or try redeploying the Cluster Operator and the Kafka Bridge.

Discover the Kafka Bridge API service through 3scale.
1. Log in to the 3scale Admin portal using the credentials provided when 3scale was deployed.
2. From the 3scale Admin Portal, navigate to New API → Import from OpenShift where you will see the Kafka Bridge service.
3. Click Create Service.
  You may need to refresh the page to see the Kafka Bridge service.
  Now you need to import the configuration for the service. You do this from an editor, but keep the portal open to check the imports are successful.
Edit the Host field in the OpenAPI specification (JSON file) to use the base URL of the Kafka Bridge service:
For example:
```
"host": "my-bridge-bridge-service.my-project.svc.cluster.local:8080"
```
Check the host URL includes the correct:
- Kafka Bridge name (my-bridge)
- Project name (my-project)
- Port for the Kafka Bridge (8080)

Import the updated OpenAPI specification using the 3scale toolbox:

3scale import openapi -k -d $REMOTE_NAME openapiv2.json -t myproject-my-bridge-bridge-service

Import the header modification and routing policies for the service (JSON file).

Locate the ID for the service you created in 3scale.

Here we use the `jq` utility:

export SERVICE_ID=$(curl -k -s -X GET "https://$PORTAL_ENDPOINT/admin/api/services.json?access_token=$TOKEN" | jq ".services[] | select(.service.system_name | contains(\"$SYSTEM_NAME\")) | .service.id")

You need the ID when importing the policies.

Import the policies:

curl -k -X PUT "https://$PORTAL_ENDPOINT/admin/api/services/$SERVICE_ID/proxy/policies.json" --data "access_token=$TOKEN" --data-urlencode policies_config@policies_config.json

From the 3scale Admin Portal, navigate to Integration → Configuration to check that the endpoints and policies for the Kafka Bridge service have loaded.
Navigate to Applications → Create Application Plan to create an application plan.
Navigate to Audience → Developer → Applications → Create Application to create an application.
The application is required in order to obtain a user key for authentication.
(Production environment step) To make the API available to the production gateway, promote the configuration:
```
3scale proxy-config promote $REMOTE_NAME $SERVICE_ID
```
Use an API testing tool to verify you can access the Kafka Bridge through the APIcast gateway using a call to create a consumer, and the user key created for the application.
For example:
```
https//my-project-my-bridge-bridge-service-3scale-apicast-staging.example.com:443/consumers/my-group?user_key=3dfc188650101010ecd7fdc56098ce95
```
If a payload is returned from the Kafka Bridge, the consumer was created successfully.
```
{
  "instance_id": "consumer1",
  "base uri": "https//my-project-my-bridge-bridge-service-3scale-apicast-staging.example.com:443/consumers/my-group/instances/consumer1"
}
```
The base URI is the address that the client will use in subsequent requests.

Chapter 8. Cruise Control for cluster rebalancing

You can deploy Cruise Control to your AMQ Streams cluster and use it to rebalance the Kafka cluster.

Cruise Control is an open source system for automating Kafka operations, such as monitoring cluster workload, rebalancing a cluster based on predefined constraints, and detecting and fixing anomalies. It consists of four main components—the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor—and a REST API for client interactions. AMQ Streams utilizes the REST API to support the following Cruise Control features:

Generating optimization proposals from multiple optimization goals.
Rebalancing a Kafka cluster based on an optimization proposal.

Other Cruise Control features are not currently supported, including anomaly detection, notifications, write-your-own goals, and changing the topic replication factor.

Example YAML files for Cruise Control are provided in examples/cruise-control/.

8.1. Why use Cruise Control?

Cruise Control reduces the time and effort involved in running an efficient and balanced Kafka cluster.

A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might be unevenly distributed across the available brokers. To rebalance the cluster, administrators must monitor the load on brokers and manually reassign busy partitions to brokers with spare capacity.

Cruise Control automates the cluster rebalancing process. It constructs a workload model of resource utilization for the cluster—based on CPU, disk, and network load—and generates optimization proposals (that you can approve or reject) for more balanced partition assignments. A set of configurable optimization goals is used to calculate these proposals.

When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster. When the cluster rebalancing operation is complete, the broker pods are used more effectively and the Kafka cluster is more evenly balanced.

Additional resources

Cruise Control Wiki

8.2. Optimization goals overview

To rebalance a Kafka cluster, Cruise Control uses optimization goals to generate optimization proposals, which you can approve or reject.

Optimization goals are constraints on workload redistribution and resource utilization across a Kafka cluster. AMQ Streams supports most of the optimization goals developed in the Cruise Control project. The supported goals, in the default descending order of priority, are as follows:

Rack-awareness
Minimum number of leader replicas per broker for a set of topics
Replica capacity
Capacity: Disk capacity, Network inbound capacity, Network outbound capacity, CPU capacity
Replica distribution
Potential network output
Resource distribution: Disk utilization distribution, Network inbound utilization distribution, Network outbound utilization distribution, CPU utilization distribution
Note
The resource distribution goals are controlled using capacity limits on broker resources.
Leader bytes-in rate distribution
Topic replica distribution
Leader replica distribution
Preferred leader election

For more information on each optimization goal, see Goals in the Cruise Control Wiki.

Note

Intra-broker disk goals, "Write your own" goals, and Kafka assigner goals are not yet supported.

Goals configuration in AMQ Streams custom resources

You configure optimization goals in Kafka and KafkaRebalance custom resources. Cruise Control has configurations for hard optimization goals that must be satisfied, as well as master, default, and user-provided optimization goals. Optimization goals for resource distribution (disk, network inbound, network outbound, and CPU) are subject to capacity limits on broker resources.

The following sections describe each goal configuration in more detail.

Hard goals and soft goals

Hard goals are goals that must be satisfied in optimization proposals. Goals that are not configured as hard goals are known as soft goals. You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations. An optimization proposal that violates one or more soft goals, but satisfies all hard goals, is valid.

Cruise Control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by Cruise Control and not sent to the user for approval.

Note

For example, you might have a soft goal to distribute a topic’s replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met.

In Cruise Control, the following master optimization goals are preset as hard goals:

RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal

You configure hard goals in the Cruise Control deployment configuration, by editing the hard.goals property in Kafka.spec.cruiseControl.config.

To inherit the preset hard goals from Cruise Control, do not specify the hard.goals property in Kafka.spec.cruiseControl.config
To change the preset hard goals, specify the desired goals in the hard.goals property, using their fully-qualified domain names.

Example Kafka configuration for hard optimization goals

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl:
    brokerCapacity:
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    config:
      hard.goals: >
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal
      # ...

Increasing the number of configured hard goals will reduce the likelihood of Cruise Control generating valid optimization proposals.

If skipHardGoalCheck: true is specified in the KafkaRebalance custom resource, Cruise Control does not check that the list of user-provided optimization goals (in KafkaRebalance.spec.goals) contains all the configured hard goals (hard.goals). Therefore, if some, but not all, of the user-provided optimization goals are in the hard.goals list, Cruise Control will still treat them as hard goals even if skipHardGoalCheck: true is specified.

Master optimization goals

The master optimization goals are available to all users. Goals that are not listed in the master optimization goals are not available for use in Cruise Control operations.

Unless you change the Cruise Control deployment configuration, AMQ Streams will inherit the following master optimization goals from Cruise Control, in descending priority order:

RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal

Six of these goals are preset as hard goals.

To reduce complexity, we recommend that you use the inherited master optimization goals, unless you need to completely exclude one or more goals from use in KafkaRebalance resources. The priority order of the master optimization goals can be modified, if desired, in the configuration for default optimization goals.

You configure master optimization goals, if necessary, in the Cruise Control deployment configuration: Kafka.spec.cruiseControl.config.goals

To accept the inherited master optimization goals, do not specify the goals property in Kafka.spec.cruiseControl.config.
If you need to modify the inherited master optimization goals, specify a list of goals, in descending priority order, in the goals configuration option.

Note

If you change the inherited master optimization goals, you must ensure that the hard goals, if configured in the hard.goals property in Kafka.spec.cruiseControl.config, are a subset of the master optimization goals that you configured. Otherwise, errors will occur when generating optimization proposals.

Default optimization goals

Cruise Control uses the default optimization goals to generate the cached optimization proposal. For more information about the cached optimization proposal, see Section 8.3, “Optimization proposals overview”.

You can override the default optimization goals by setting user-provided optimization goals in a KafkaRebalance custom resource.

Unless you specify default.goals in the Cruise Control deployment configuration, the master optimization goals are used as the default optimization goals. In this case, the cached optimization proposal is generated using the master optimization goals.

To use the master optimization goals as the default goals, do not specify the default.goals property in Kafka.spec.cruiseControl.config.
To modify the default optimization goals, edit the default.goals property in Kafka.spec.cruiseControl.config. You must use a subset of the master optimization goals.

Example Kafka configuration for default optimization goals

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl:
    brokerCapacity:
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    config:
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal
      # ...

If no default optimization goals are specified, the cached proposal is generated using the master optimization goals.

User-provided optimization goals

User-provided optimization goals narrow down the configured default goals for a particular optimization proposal. You can set them, as required, in spec.goals in a KafkaRebalance custom resource:

KafkaRebalance.spec.goals

User-provided optimization goals can generate optimization proposals for different scenarios. For example, you might want to optimize leader replica distribution across the Kafka cluster without considering disk capacity or disk utilization. So, you create a KafkaRebalance custom resource containing a single user-provided goal for leader replica distribution.

User-provided optimization goals must:

Include all configured hard goals, or an error occurs
Be a subset of the master optimization goals

To ignore the configured hard goals when generating an optimization proposal, add the skipHardGoalCheck: true property to the KafkaRebalance custom resource. See Section 8.7, “Generating optimization proposals”.

Additional resources

Section 8.5, “Cruise Control configuration”
Configurations in the Cruise Control Wiki.

8.3. Optimization proposals overview

An optimization proposal is a summary of proposed changes that would produce a more balanced Kafka cluster, with partition workloads distributed more evenly among the brokers. Each optimization proposal is based on the set of optimization goals that was used to generate it, subject to any configured capacity limits on broker resources.

An optimization proposal is contained in the Status.Optimization Result property of a KafkaRebalance custom resource. The information provided is a summary of the full optimization proposal. Use the summary to decide whether to:

Approve the optimization proposal. This instructs Cruise Control to apply the proposal to the Kafka cluster and start a cluster rebalance operation.
Reject the optimization proposal. You can change the optimization goals and then generate another proposal.

All optimization proposals are dry runs: you cannot approve a cluster rebalance without first generating an optimization proposal. There is no limit to the number of optimization proposals that can be generated.

Cached optimization proposal

Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster. If you generate an optimization proposal using the default optimization goals, Cruise Control returns the most recent cached proposal.

To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms setting in the Cruise Control deployment configuration. Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server.

Contents of optimization proposals

The following table describes the properties contained in an optimization proposal:

Table 8.1. Properties contained in an optimization proposal
JSON property	Description
`numIntraBrokerReplicaMovements`	The total number of partition replicas that will be transferred between the disks of the cluster’s brokers. Performance impact during rebalance operation: Relatively high, but lower than `numReplicaMovements`.
`excludedBrokersForLeadership`	Not yet supported. An empty list is returned.
`numReplicaMovements`	The number of partition replicas that will be moved between separate brokers. Performance impact during rebalance operation: Relatively high.
`onDemandBalancednessScoreBefore, onDemandBalancednessScoreAfter`	A measurement of the overall balancedness of a Kafka Cluster, before and after the optimization proposal was generated. The score is calculated by subtracting the sum of the `BalancednessScore` of each violated soft goal from 100. Cruise Control assigns a `BalancednessScore` to every optimization goal based on several factors, including priority—the goal’s position in the list of `default.goals` or user-provided goals. The `Before` score is based on the current configuration of the Kafka cluster. The `After` score is based on the generated optimization proposal.
`intraBrokerDataToMoveMB`	The sum of the size of each partition replica that will be moved between disks on the same broker (see also `numIntraBrokerReplicaMovements`). Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see `dataToMoveMB`).
`recentWindows`	The number of metrics windows upon which the optimization proposal is based.
`dataToMoveMB`	The sum of the size of each partition replica that will be moved to a separate broker (see also `numReplicaMovements`). Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete.
`monitoredPartitionsPercentage`	The percentage of partitions in the Kafka cluster covered by the optimization proposal. Affected by the number of `excludedTopics`.
`excludedTopics`	If you specified a regular expression in the `spec.excludedTopicsRegex` property in the `KafkaRebalance` resource, all topic names matching that expression are listed here. These topics are excluded from the calculation of partition replica/leader movements in the optimization proposal.
`numLeaderMovements`	The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration. Performance impact during rebalance operation: Relatively low.
`excludedBrokersForReplicaMove`	Not yet supported. An empty list is returned.

Additional resources

Section 8.2, “Optimization goals overview”
Section 8.7, “Generating optimization proposals”
Section 8.8, “Approving an optimization proposal”

8.4. Rebalance performance tuning overview

You can adjust several performance tuning options for cluster rebalances. These options control how partition replica and leadership movements in a rebalance are executed, as well as the bandwidth that is allocated to a rebalance operation.

Partition reassignment commands

Optimization proposals are comprised of separate partition reassignment commands. When you approve a proposal, the Cruise Control server applies these commands to the Kafka cluster.

A partition reassignment command consists of either of the following types of operations:

Partition movement: Involves transferring the partition replica and its data to a new location. Partition movements can take one of two forms:
- Inter-broker movement: The partition replica is moved to a log directory on a different broker.
- Intra-broker movement: The partition replica is moved to a different log directory on the same broker.
Leadership movement: This involves switching the leader of the partition’s replicas.

Cruise Control issues partition reassignment commands to the Kafka cluster in batches. The performance of the cluster during the rebalance is affected by the number of each type of movement contained in each batch.

Replica movement strategies

Cluster rebalance performance is also influenced by the replica movement strategy that is applied to the batches of partition reassignment commands. By default, Cruise Control uses the BaseReplicaMovementStrategy, which simply applies the commands in the order they were generated. However, if there are some very large partition reassignments early in the proposal, this strategy can slow down the application of the other reassignments.

Cruise Control provides three alternative replica movement strategies that can be applied to optimization proposals:

PrioritizeSmallReplicaMovementStrategy: Order reassignments in order of ascending size.
PrioritizeLargeReplicaMovementStrategy: Order reassignments in order of descending size.
PostponeUrpReplicaMovementStrategy: Prioritize reassignments for replicas of partitions which have no out-of-sync replicas.

These strategies can be configured as a sequence. The first strategy attempts to compare two partition reassignments using its internal logic. If the reassignments are equivalent, then it passes them to the next strategy in the sequence to decide the order, and so on.

Rebalance tuning options

Cruise Control provides several configuration options for tuning the rebalance parameters discussed above. You can set these tuning options at either the Cruise Control server or optimization proposal levels:

The Cruise Control server setting can be set in the Kafka custom resource under Kafka.spec.cruiseControl.config.
The individual rebalance performance configurations can be set under KafkaRebalance.spec.

The relevant configurations are summarized below:

Server and `KafkaRebalance` Configuration	Description	Default Value
`num.concurrent.partition.movements.per.broker`	The maximum number of inter-broker partition movements in each partition reassignment batch	5
`concurrentPartitionMovementsPerBroker`		5
`num.concurrent.intra.broker.partition.movements`	The maximum number of intra-broker partition movements in each partition reassignment batch	2
`concurrentIntraBrokerPartitionMovements`		2
`num.concurrent.leader.movements`	The maximum number of partition leadership changes in each partition reassignment batch	1000
`concurrentLeaderMovements`		1000
`default.replication.throttle`	The bandwidth (in bytes per second) to be assigned to the reassigning of partitions	No Limit
`replicationThrottle`		No Limit
`default.replica.movement.strategies`	The list of strategies (in priority order) used to determine the order in which partition reassignment commands are executed for generated proposals. For the server setting, use a comma separated string with the fully qualified names of the strategy class (add `com.linkedin.kafka.cruisecontrol.executor.strategy.` to the start of each class name). For the `KafkaRebalance` resource setting use a YAML array of strategy class names.	`BaseReplicaMovementStrategy`
`replicaMovementStrategies`		`BaseReplicaMovementStrategy`

Changing the default settings affects the length of time that the rebalance takes to complete, as well as the load placed on the Kafka cluster during the rebalance. Using lower values reduces the load but increases the amount of time taken, and vice versa.

Additional resources

Section 13.2.71, “CruiseControlSpec schema reference”.
Section 13.2.150, “KafkaRebalanceSpec schema reference”.

8.5. Cruise Control configuration

The config property in Kafka.spec.cruiseControl contains configuration options as keys with values as one of the following JSON types:

String
Number
Boolean

You can specify and configure all the options listed in the "Configurations" section of the Cruise Control documentation, apart from those managed directly by AMQ Streams. Specifically, you cannot modify configuration options with keys equal to or starting with one of the keys mentioned here.

If restricted options are specified, they are ignored and a warning message is printed to the Cluster Operator log file. All the supported options are passed to Cruise Control.

An example Cruise Control configuration

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    config:
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal
      cpu.balance.threshold: 1.1
      metadata.max.age.ms: 300000
      send.buffer.bytes: 131072
    # ...

Cross-Origin Resource Sharing configuration

Cross-Origin Resource Sharing (CORS) allows you to specify allowed methods and originating URLs for accessing REST APIs.

By default, CORS is disabled for the Cruise Control REST API. When enabled, only GET requests for read-only access to the Kafka cluster state are allowed. This means that external applications, which are running in different origins than the AMQ Streams components, cannot make POST requests to the Cruise Control API. However, those applications can make GET requests to access read-only information about the Kafka cluster, such as the current cluster load or the most recent optimization proposal.

Enabling CORS for Cruise Control

You enable and configure CORS in Kafka.spec.cruiseControl.config.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    config:
      webserver.http.cors.enabled: true
      webserver.http.cors.origin: "*"
      webserver.http.cors.exposeheaders: "User-Task-ID,Content-Type"
    # ...

For more information, see REST APIs in the Cruise Control Wiki.

Capacity configuration

Cruise Control uses capacity limits to determine if optimization goals for resource distribution are being broken. There are four goals of this type:

DiskUsageDistributionGoal - Disk utilization distribution
CpuUsageDistributionGoal - CPU utilization distribution
NetworkInboundUsageDistributionGoal - Network inbound utilization distribution
NetworkOutboundUsageDistributionGoal - Network outbound utilization distribution

You specify capacity limits for Kafka broker resources in the brokerCapacity property in Kafka.spec.cruiseControl . They are enabled by default and you can change their default values. Capacity limits can be set for the following broker resources, using the standard OpenShift byte units (K, M, G and T) or their bibyte (power of two) equivalents (Ki, Mi, Gi and Ti):

disk - Disk storage per broker (Default: 100000Mi)
cpuUtilization - CPU utilization as a percentage (Default: 100)
inboundNetwork - Inbound network throughput in byte units per second (Default: 10000KiB/s)
outboundNetwork - Outbound network throughput in byte units per second (Default: 10000KiB/s)

Because AMQ Streams Kafka brokers are homogeneous, Cruise Control applies the same capacity limits to every broker it is monitoring.

An example Cruise Control brokerCapacity configuration using bibyte units

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    brokerCapacity:
      disk: 100Gi
      cpuUtilization: 100
      inboundNetwork: 10000KiB/s
      outboundNetwork: 10000KiB/s
    # ...

Additional resources

For more information, refer to the Section 13.2.73, “BrokerCapacity schema reference”.

Logging configuration

Cruise Control has its own configurable logger:

rootLogger.level

Cruise Control uses the Apache log4j 2 logger implementation.

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.valueFrom.configMapKeyRef.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties. Both logging.valueFrom.configMapKeyRef.name and logging.valueFrom.configMapKeyRef.key properties are mandatory. A ConfigMap using the exact logging configuration specified is created with the custom resource when the Cluster Operator is running, then recreated after each reconciliation. If you do not specify a custom ConfigMap, default logging settings are used. If a specific logger value is not set, upper-level logger settings are inherited for that logger. Here we see examples of inline and external logging.

Inline logging

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
  cruiseControl:
    # ...
    logging:
      type: inline
      loggers:
        rootLogger.level: "INFO"
    # ...

External logging

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
  cruiseControl:
    # ...
    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: customConfigMap
          key: cruise-c