Using AMQ Streams on OpenShift


Red Hat AMQ Streams 2.0

Configure and manage a deployment of AMQ Streams 2.0 on OpenShift Container Platform

Abstract

Configure the operators and Kafka components deployed with AMQ Streams to build a large-scale messaging network.

Making open source more inclusive

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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.

You can use the Cluster Operator with a deployment of AMQ Streams Drain Cleaner to help with pod evictions. By deploying the AMQ Streams Drain Cleaner, you can use the Cluster Operator to move Kafka pods instead of OpenShift. AMQ Streams Drain Cleaner annotates pods being evicted with a rolling update annotation. The annotation informs the Cluster Operator to perform the rolling update.

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.

1.4.4. Feature gates in AMQ Streams Operators

You can enable and disable some features of operators using feature gates.

Feature gates are set in the operator configuration and have three stages of maturity: alpha, beta, or General Availability (GA).

For more information, see Feature gates.

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.

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.

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.

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

For more information on the configuration options available for listeners, see the GenericKafkaListener schema reference.

Configuring listeners to secure access to Kafka brokers

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

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 Accessing Kafka from external clients outside of the OpenShift cluster.

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 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.

AMQ Streams provides example configuration files, which can serve as a starting point when building your own Kafka component configuration for deployment.

Monitoring a Strimzi deployment

You can use Prometheus and Grafana to monitor your Strimzi deployment. For more information, see Introducing metrics to Kafka.

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 14.2.1, “Kafka schema reference”. For more information about Apache Kafka, see the Apache Kafka documentation.

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, see Securing access to Kafka brokers.

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.

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 inter.broker.protocol.version property for the Kafka config must be the version supported by the specified Kafka version (spec.kafka.version). The property represents the version of Kafka protocol used in a Kafka cluster.

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

An update to the inter.broker.protocol.version is required when upgrading your Kafka version. For more information, see Upgrading Kafka.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

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

Procedure

  1. 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: 3.0.0 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
          inter.broker.protocol.version: "3.0"
          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
    15
    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
    19
    20
    Storage is configured as ephemeral, persistent-claim or jbod.
    21
    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
    29
    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
    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.
  2. 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:

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 120.
zookeeperSessionTimeoutSeconds
The ZooKeeper session timeout in seconds. Default 18.
topicMetadataMaxAttempts
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation might take more time due to the number of partitions or replicas. Default 6.
image
The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 14.1.6, “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 14.1.5, “resources.
logging
The logging property configures the logging of the Topic Operator. For more details, see Section 14.2.44.1, “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.
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 14.1.6, “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 14.1.5, “resources.
logging
The logging property configures the logging of the User Operator. For more details, see Section 14.2.44.1, “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.

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

Warning

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

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.

Choose from one of the following options for your block storage:

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

  1. 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:
          # ...
  2. 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

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

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

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

    oc apply -f KAFKA-CONFIG-FILE
  3. 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 tool”.

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

  1. Reassign all partitions from the disks which are you going to remove. Any data in partitions still assigned to the disks which are going to be removed might be lost.
  2. Edit the spec.kafka.storage.volumes property in the Kafka resource. Remove one or more volumes from the volumes array. For example, remove the volumes with ids 1 and 2:

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

    oc apply -f KAFKA-CONFIG-FILE

Additional resources

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

2.1.4. Scaling clusters

Scale Kafka clusters by adding or removing brokers. If a cluster already has topics defined, you also have to reassign partitions.

You use the kafka-reassign-partitions.sh tool to reassign partitions. The tool uses a reassignment JSON file that specifies the topics to reassign.

You can generate a reassignment JSON file or create a file manually if you want to move specific partitions.

2.1.4.1. Broker scaling configuration

You configure the Kafka.spec.kafka.replicas configuration to add or reduce the number of brokers.

Broker addition

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 reassign from the existing brokers to the new broker.

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

Broker removal

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 tool

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 tool 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 traffic throttles (--throttle) that are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.

It is only possible to have one reassignment running in a cluster at any given time, and it is not possible to cancel a running reassignment. If you 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.

Partition 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:

Example partition reassignment file

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

Partitions not included in the JSON are not changed.

Partition reassignment between JBOD volumes

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

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

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

Example partition reassignment file specifying log directories

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

Partition 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. Use the --throttle parameter with the kafka-reassign-partitions.sh tool to throttle a reassignment. You specify a maximum threshold in bytes per second for the movement of partitions between brokers. For example, --throttle 5000000 sets a maximum threshold for moving partitions of 50 MBps.

Throttling might cause the reassignment to take longer to complete.

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

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

2.1.4.3. Generating reassignment JSON files

This procedure describes how to generate a reassignment JSON file. Use the reassignment file with the kafka-reassign-partitions.sh tool to reassign partitions after scaling a Kafka cluster.

The steps describe a secure reassignment process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and authentication.

Prerequisites

  • You have a running Cluster Operator.
  • You have a running Kafka cluster based on a Kafka resource configured with internal TLS authentication and encryption.

    Kafka configuration with TLS

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        listeners:
          # ...
          - name: tls
            port: 9093
            type: internal
            tls: true 1
            authentication:
              type: tls 2
        # ...

    1
    Enables TLS encryption for the internal listener.
    2
    Listener authentication mechanism specified as mutual TLS.
  • The running Kafka cluster contains a set of topics and partitions to reassign.

    Example topic configuration for my-topic

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaTopic
    metadata:
      name: my-topic
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      partitions: 10
      replicas: 3
      config:
        retention.ms: 7200000
        segment.bytes: 1073741824
        # ...

  • You have a KafkaUser configured with ACL rules that specify permission to produce and consume topics from the Kafka brokers.

    Example Kafka user configuration with ACL rules to allow operations on my-topic and my-cluster

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaUser
    metadata:
      name: my-user
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      authentication: 1
        type: tls
      authorization:
        type: simple 2
        acls:
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Write
            host: "*"
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Create
            host: "*"
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            operation: Describe
            host: "*"
          - resource:
              type: cluster
              name: my-cluster
              patternType: literal
            operation: Alter
            host: "*"
          # ...
      # ...

    1
    User authentication mechanism defined as mutual tls.
    2
    Simple authorization and accompanying list of ACL rules.
    Note

    Permission for a Describe operation is required as a minimum for TLS access to a topic.

Procedure

  1. Extract the cluster CA certificate and password from the <cluster_name>-cluster-ca-cert Secret of the Kafka cluster.

    oc get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
    oc get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.password}' | base64 -d > ca.password

    Replace <cluster_name> with the name of the Kafka cluster. When you deploy Kafka using the Kafka resource, a Secret with the cluster CA certificate is created with the Kafka cluster name (<cluster_name>-cluster-ca-cert). For example, my-cluster-cluster-ca-cert.

  2. Run a new interactive pod container using the AMQ Streams Kafka image to connect to a running Kafka broker.

    oc run --restart=Never --image=registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.1 <interactive_pod_name> -- /bin/sh -c "sleep 3600"

    Replace <interactive_pod_name> with the name of the pod.

  3. Copy the cluster CA certificate to the interactive pod container.

    oc cp ca.p12 <interactive_pod_name>:/tmp
  4. Extract the user CA certificate and password from the Secret of the Kafka user that has permission to access the Kafka brokers.

    oc get secret <kafka_user> -o jsonpath='{.data.user\.p12}' | base64 -d > user.p12
    oc get secret <kafka_user> -o jsonpath='{.data.user\.password}' | base64 -d > user.password

    Replace <kafka_user> with the name of the Kafka user. When you create a Kafka user using the KafkaUser resource, a Secret with the user CA certificate is created with the Kafka user name. For example, my-user.

  5. Copy the user CA certificate to the interactive pod container.

    oc cp user.p12 <interactive_pod_name>:/tmp

    The CA certificates allow the interactive pod container to connect to the Kafka broker using TLS.

  6. Create a config.properties file to specify the truststore and keystore used to authenticate connection to the Kafka cluster.

    Use the certificates and passwords you extracted in the previous steps.

    bootstrap.servers=<kafka_cluster_name>-kafka-bootstrap:9093 1
    security.protocol=SSL 2
    ssl.truststore.location=/tmp/ca.p12 3
    ssl.truststore.password=<truststore_password> 4
    ssl.keystore.location=/tmp/user.p12 5
    ssl.keystore.password=<keystore_password> 6
    1
    The bootstrap server address to connect to the Kafka cluster. Use your own Kafka cluster name to replace <kafka_cluster_name>.
    2
    The security protocol option when using TLS for encryption.
    3
    The truststore location contains the public key certificate (ca.p12) for the Kafka cluster.
    4
    The password (ca.password) for accessing the truststore.
    5
    The keystore location contains the public key certificate (user.p12) for the Kafka user.
    6
    The password (user.password) for accessing the keystore.
  7. Copy the config.properties file to the interactive pod container.

    oc cp config.properties <interactive_pod_name>:/tmp/config.properties
  8. Prepare a JSON file named topics.json that specifies the topics to move.

    Specify topic names as a comma-separated list.

    Example JSON file to reassign all the partitions of topic-a and topic-b

    {
      "version": 1,
      "topics": [
        { "topic": "topic-a"},
        { "topic": "topic-b"}
      ]
    }

  9. Copy the topics.json file to the interactive pod container.

    oc cp topics.json <interactive_pod_name>:/tmp/topics.json
  10. Start a shell process in the interactive pod container.

    oc exec -n <namespace> -ti <interactive_pod_name> /bin/bash

    Replace <namespace> with the OpenShift namespace where the pod is running.

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

    Example command to move all the partitions of topic-a and topic-b to brokers 0, 1 and 2

    bin/kafka-reassign-partitions.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --topics-to-move-json-file /tmp/topics.json \
      --broker-list 0,1,2 \
      --generate

2.1.4.4. Scaling up a Kafka cluster

Use a reassignment file to increase the number of brokers in a Kafka cluster.

The reassignment file should describe how partitions are reassigned to brokers in the enlarged Kafka cluster.

This procedure describes a secure scaling process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and authentication.

Prerequisites

  • You have a running Kafka cluster based on a Kafka resource configured with internal TLS authentication and encryption.
  • You have generated a reassignment JSON file named reassignment.json.
  • You are running an interactive pod container that is connected to the running Kafka broker.
  • You are connected as a KafkaUser configured with ACL rules that specify permission to manage the Kafka cluster and its topics.

See Generating reassignment JSON files.

Procedure

  1. Add as many new brokers as you need by increasing the Kafka.spec.kafka.replicas configuration option.
  2. Verify that the new broker pods have started.
  3. If you haven’t done so, run an interactive pod container to generate a reassignment JSON file named reassignment.json.
  4. Copy the reassignment.json file to the interactive pod container.

    oc cp reassignment.json <interactive_pod_name>:/tmp/reassignment.json

    Replace <interactive_pod_name> with the name of the pod.

  5. Start a shell process in the interactive pod container.

    oc exec -n <namespace> -ti <interactive_pod_name> /bin/bash

    Replace <namespace> with the OpenShift namespace where the pod is running.

  6. Run the partition reassignment using the kafka-reassign-partitions.sh script from the interactive pod container.

    bin/kafka-reassign-partitions.sh --bootstrap-server
     <cluster_name>-kafka-bootstrap:9093 \
     --command-config /tmp/config.properties \
     --reassignment-json-file /tmp/reassignment.json \
     --execute

    Replace <cluster_name> with the name of your Kafka cluster. For example, my-cluster-kafka-bootstrap:9093

    If you are going to throttle replication, you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 5000000 \
      --execute

    This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

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

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 10000000 \
      --execute
  7. Verify that the reassignment has completed using the kafka-reassign-partitions.sh command line tool from any of the broker pods. This is the same command as the previous step, but with the --verify option instead of the --execute option.

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --verify

    The reassignment has finished when the --verify command reports that each of the partitions being moved has completed successfully. This final --verify will also have the effect of removing any reassignment throttles.

  8. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.
2.1.4.5. Scaling down a Kafka cluster

Use a reassignment file to decrease the number of brokers in a Kafka cluster.

The reassignment file must describe how partitions are reassigned to the remaining brokers in the Kafka cluster. Brokers in the highest numbered pods are removed first.

This procedure describes a secure scaling process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and authentication.

Prerequisites

  • You have a running Kafka cluster based on a Kafka resource configured with internal TLS authentication and encryption.
  • You have generated a reassignment JSON file named reassignment.json.
  • You are running an interactive pod container that is connected to the running Kafka broker.
  • You are connected as a KafkaUser configured with ACL rules that specify permission to manage the Kafka cluster and its topics.

See Generating reassignment JSON files.

Procedure

  1. If you haven’t done so, run an interactive pod container to generate a reassignment JSON file named reassignment.json.
  2. Copy the reassignment.json file to the interactive pod container.

    oc cp reassignment.json <interactive_pod_name>:/tmp/reassignment.json

    Replace <interactive_pod_name> with the name of the pod.

  3. Start a shell process in the interactive pod container.

    oc exec -n <namespace> -ti <interactive_pod_name> /bin/bash

    Replace <namespace> with the OpenShift namespace where the pod is running.

  4. Run the partition reassignment using the kafka-reassign-partitions.sh script from the interactive pod container.

    bin/kafka-reassign-partitions.sh --bootstrap-server
     <cluster_name>-kafka-bootstrap:9093 \
     --command-config /tmp/config.properties \
     --reassignment-json-file /tmp/reassignment.json \
     --execute

    Replace <cluster_name> with the name of your Kafka cluster. For example, my-cluster-kafka-bootstrap:9093

    If you are going to throttle replication, you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 5000000 \
      --execute

    This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

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

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --throttle 10000000 \
      --execute
  5. Verify that the reassignment has completed using the kafka-reassign-partitions.sh command line tool from any of the broker pods. This is the same command as the previous step, but with the --verify option instead of the --execute option.

    bin/kafka-reassign-partitions.sh --bootstrap-server
      <cluster_name>-kafka-bootstrap:9093 \
      --command-config /tmp/config.properties \
      --reassignment-json-file /tmp/reassignment.json \
      --verify

    The reassignment has finished when the --verify command reports that each of the partitions being moved has completed successfully. This final --verify will also have the effect of removing any reassignment throttles.

  6. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.
  7. When all the partition reassignments have finished, the brokers being removed should not have responsibility for any of the partitions in the cluster. You can verify this by checking that the broker’s data log directory does not contain any live partition logs. If the log directory on the broker contains a directory that does not match the extended regular expression \.[a-z0-9]-delete$, the broker still has live partitions and should not be stopped.

    You can check this by executing the command:

    oc exec my-cluster-kafka-0 -c kafka -it -- \
      /bin/bash -c \
      "ls -l /var/lib/kafka/kafka-log_<n>_ | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'"

    where n is the number of the pods being deleted.

    If the above command prints any output then the broker still has live partitions. In this case, either the reassignment has not finished or the reassignment JSON file was incorrect.

  8. When you have confirmed that the broker has no live partitions, you can edit the Kafka.spec.kafka.replicas property of your Kafka resource to reduce the number of brokers.

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

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

  1. 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 * * ?"
  2. Create or update the resource:

    oc apply -f KAFKA-CONFIG-FILE

Additional resources

Performing rolling updates:

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

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

    For example:

    oc exec -ti my-cluster-zookeeper-0 -- bin/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

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

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

  2. Annotate the Pod resource in OpenShift.

    Use oc annotate:

    oc annotate pod cluster-name-kafka-index strimzi.io/delete-pod-and-pvc=true
  3. Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.

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

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

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

  2. Annotate the Pod resource in OpenShift.

    Use oc annotate:

    oc annotate pod cluster-name-zookeeper-index strimzi.io/delete-pod-and-pvc=true
  3. Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.

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 cluster configuration

This section describes how to configure a Kafka Connect 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. The full schema of the KafkaConnect resource is described in Section 14.2.58, “KafkaConnect schema reference”.

For more information on deploying connector plugins, see Extending Kafka Connect with connector plug-ins.

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 resource to configure your Kafka Connect deployment.

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

  1. Edit the spec properties of the KafkaConnect 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.
    2
    Enables KafkaConnectors for the Kafka Connect cluster.
    3
    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. You can also use configuration provider plugins to load configuration values from external sources.
    12
    Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
    13
    Specified Kafka Connect loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom 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.
  2. Create or update the resource:

    oc apply -f KAFKA-CONNECT-CONFIG-FILE
  3. 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
The Kafka Connect cluster ID within Kafka.
2
Kafka topic that stores connector offsets.
3
Kafka topic that stores connector and task status configurations.
4
Kafka topic that stores connector and task status updates.
Note

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

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

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

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

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 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
The Kafka Connect cluster ID within Kafka.
2
Kafka topic that stores connector offsets.
3
Kafka topic that stores connector and task status configurations.
4
Kafka topic that stores connector and task status updates.

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

  1. 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:

    PropertyName

    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: "*"
  2. 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

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

    oc get KafkaConnector
  2. To restart the connector, annotate the KafkaConnector resource in OpenShift. For example, using oc annotate:

    oc annotate KafkaConnector KAFKACONNECTOR-NAME strimzi.io/restart=true
  3. Wait for the next reconciliation to occur (every two minutes by default).

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

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

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

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

    oc describe KafkaConnector KAFKACONNECTOR-NAME
  3. 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
  4. Wait for the next reconciliation to occur (every two minutes by default).

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

2.2.6. Exposing the Kafka Connect API

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

Note

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

You can add the connector configuration as a JSON object.

Example curl request to add connector configuration

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

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

  • LoadBalancer or NodePort type services
  • Ingress resources
  • OpenShift routes
Note

The connection is insecure, so allow external access advisedly.

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

Selector configuration for the service

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

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

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

Example NetworkPolicy to allow requests to the Kafka Connect API

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

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

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

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. 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.

Important

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

The following procedure shows how the resource is configured:

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:

  • Source and target Kafka clusters must be available

Procedure

  1. 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
      include: "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
    2
    Bootstrap servers for consumer and producer.
    3
    4
    5
    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 included 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
    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.

  2. 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:

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

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

MirrorMaker 2.0 replication

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. 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. 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, and setting the property to true. Synchronization is disabled by default.

When using the IdentityReplicationPolicy in the source connector, it also has to be configured in the checkpoint connector configuration. This ensures that the mirrored consumer offsets will be applied for the correct topics.

The consumer offsets are only synchronized for consumer groups that are not active in the target 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: 3.0.0
  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:

  • Source and target Kafka clusters must be available

Procedure

  1. 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: 3.0.0 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
          tasksMax: 10 20
          config:
            replication.factor: 1 21
            offset-syncs.topic.replication.factor: 1 22
            sync.topic.acls.enabled: "false" 23
            refresh.topics.interval.seconds: 60 24
            replication.policy.separator: "" 25
            replication.policy.class: "io.strimzi.kafka.connect.mirror.IdentityReplicationPolicy" 26
        heartbeatConnector: 27
          config:
            heartbeats.topic.replication.factor: 1 28
        checkpointConnector: 29
          config:
            checkpoints.topic.replication.factor: 1 30
            refresh.groups.interval.seconds: 600 31
            sync.group.offsets.enabled: true 32
            sync.group.offsets.interval.seconds: 60 33
            emit.checkpoints.interval.seconds: 60 34
            replication.policy.class: "io.strimzi.kafka.connect.mirror.IdentityReplicationPolicy"
        topicsPattern: ".*" 35
        groupsPattern: "group1|group2|group3" 36
      resources: 37
        requests:
          cpu: "1"
          memory: 2Gi
        limits:
          cpu: "2"
          memory: 2Gi
      logging: 38
        type: inline
        loggers:
          connect.root.logger.level: "INFO"
      readinessProbe: 39
        initialDelaySeconds: 15
        timeoutSeconds: 5
      livenessProbe:
        initialDelaySeconds: 15
        timeoutSeconds: 5
      jvmOptions: 40
        "-Xmx": "1g"
        "-Xms": "1g"
      image: my-org/my-image:latest 41
      template: 42
        pod:
          affinity:
            podAntiAffinity:
              requiredDuringSchedulingIgnoredDuringExecution:
                - labelSelector:
                    matchExpressions:
                      - key: application
                        operator: In
                        values:
                          - postgresql
                          - mongodb
                  topologyKey: "kubernetes.io/hostname"
        connectContainer: 43
          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 44
      externalConfiguration: 45
        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
    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
    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
    The maximum number of tasks that the connector may create. Tasks handle the data replication and run in parallel. If the infrastructure supports the processing overhead, increasing this value can improve throughput. Kafka Connect distributes the tasks between members of the cluster. If there are more tasks than workers, workers are assigned multiple tasks. For sink connectors, aim to have one task for each topic partition consumed. For source connectors, the number of tasks that can run in parallel may also depend on the external system. The connector creates fewer than the maximum number of tasks if it cannot achieve the parallelism.
    21
    Replication factor for mirrored topics created at the target cluster.
    22
    Replication factor for the MirrorSourceConnector offset-syncs internal topic that maps the offsets of the source and target clusters.
    23
    When ACL rules synchronization is enabled, ACLs are applied to synchronized topics. The default is true.
    24
    Optional setting to change the frequency of checks for new topics. The default is for a check every 10 minutes.
    25
    Defines the separator used for the renaming of remote topics.
    26
    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.
    27
    Configuration for the MirrorHeartbeatConnector that performs connectivity checks. The config overrides the default configuration options.
    28
    Replication factor for the heartbeat topic created at the target cluster.
    29
    Configuration for the MirrorCheckpointConnector that tracks offsets. The config overrides the default configuration options.
    30
    Replication factor for the checkpoints topic created at the target cluster.
    31
    Optional setting to change the frequency of checks for new consumer groups. The default is for a check every 10 minutes.
    32
    Optional setting to synchronize consumer group offsets, which is useful for recovery in an active/passive configuration. Synchronization is not enabled by default.
    33
    If the synchronization of consumer group offsets is enabled, you can adjust the frequency of the synchronization.
    34
    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.
    35
    Topic replication from the source cluster defined as regular expression patterns. Here we request all topics.
    36
    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.
    37
    Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.
    38
    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.
    39
    Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
    40
    JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
    41
    ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
    42
    Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
    43
    Environment variables are also set for distributed tracing using Jaeger.
    44
    45
    External configuration for an OpenShift Secret mounted to Kafka MirrorMaker as an environment variable. You can also use configuration provider plugins to load configuration values from external sources.
  2. 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

  1. Find the name of the KafkaMirrorMaker2 custom resource that controls the Kafka MirrorMaker 2.0 connector you want to restart:

    oc get KafkaMirrorMaker2
  2. Find the name of the Kafka MirrorMaker 2.0 connector to be restarted from the KafkaMirrorMaker2 custom resource.

    oc describe KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME
  3. 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"
  4. 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.

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

  1. Find the name of the KafkaMirrorMaker2 custom resource that controls the Kafka MirrorMaker 2.0 connector you want to restart:

    oc get KafkaMirrorMaker2
  2. 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
  3. 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"
  4. 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.

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 14.2.110, “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

  1. 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
    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
    8
    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
    Optional: Container image configuration, which is recommended only in special situations.
    14
    Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
    15
    Environment variables are also set for distributed tracing using Jaeger.
  2. 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.

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

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
  • 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.

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 to schedule each Kafka broker on a different worker node

Many Kafka brokers or ZooKeeper nodes can run on the same OpenShift worker node. If the worker node fails, they will all become unavailable at the same time. To improve reliability, you can use podAntiAffinity configuration to schedule each Kafka broker or ZooKeeper node on a different OpenShift worker node.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. To make sure that no worker nodes are shared by Kafka brokers or ZooKeeper nodes, use the strimzi.io/name label. Set the topologyKey to kubernetes.io/hostname to specify that the selected pods are not scheduled on nodes with the same hostname. This will still allow the same worker node to be shared by a single Kafka broker and a single ZooKeeper node. For example:

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

    Where CLUSTER-NAME is the name of your Kafka custom resource.

  2. If you even want to make sure that a Kafka broker and ZooKeeper node do not share the same worker node, use the strimzi.io/cluster label. For example:

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

    Where CLUSTER-NAME is the name of your Kafka custom resource.

  3. Create or update the resource.

    oc apply -f KAFKA-CONFIG-FILE

2.7.3. Configuring pod anti-affinity in Kafka components

Pod anti-affinity configuration helps with the stability and performance of Kafka brokers. By using podAntiAffinity, OpenShift will not schedule Kafka brokers on the same nodes as other workloads. Typically, you want to avoid Kafka running on the same worker node as other network or storage intensive applications such as databases, storage or other messaging platforms.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

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

    This can be done using oc apply:

    oc apply -f KAFKA-CONFIG-FILE

2.7.4. Configuring node affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. 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.

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

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

    This can be done using oc apply:

    oc apply -f KAFKA-CONFIG-FILE

2.7.5. Setting up dedicated nodes and scheduling pods on them

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.
  2. Make sure there are no workloads scheduled on these nodes.
  3. Set the taints on the selected nodes:

    This can be done using oc adm taint:

    oc adm taint node NAME-OF-NODE dedicated=Kafka:NoSchedule
  4. 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
  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment.

    For example:

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

    This can be done using oc apply:

    oc apply -f KAFKA-CONFIG-FILE

2.8. Logging configuration

Configure logging levels in the custom resources of Kafka components and AMQ Streams Operators. You can specify the logging levels directly in the spec.logging property of the custom resource. Or you can define the logging properties in a ConfigMap that’s referenced in the custom resource using the configMapKeyRef property.

The advantages of using a ConfigMap are that the logging properties are maintained in one place and are accessible to more than one resource. You can also reuse the ConfigMap for more than one resource. If you are using a ConfigMap to specify loggers for AMQ Streams Operators, you can also append the logging specification to add filters.

You specify a logging type in your logging specification:

  • inline when specifying logging levels directly
  • external when referencing a ConfigMap

Example inline logging configuration

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

Example external logging configuration

spec:
  # ...
  logging:
    type: external
    valueFrom:
      configMapKeyRef:
        name: my-config-map
        key: my-config-map-key

Values for the name and key of the ConfigMap are mandatory. Default logging is used if the name or key is not set.

2.8.1. Logging options for Kafka components and operators

For more information on configuring logging for specific Kafka components or operators, see the following sections.

2.8.2. Creating a ConfigMap for logging

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

The ConfigMap must contain the appropriate logging configuration.

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

The configuration must be placed under these properties.

In this procedure a ConfigMap defines a root logger for a Kafka resource.

Procedure

  1. Create the ConfigMap.

    You can create the ConfigMap as a YAML file or from a properties file.

    ConfigMap example with a root logger definition for Kafka:

    kind: ConfigMap
    apiVersion: v1
    metadata:
      name: logging-configmap
    data:
      log4j.properties:
        kafka.root.logger.level="INFO"

    If you are using a properties file, specify the file at the command line:

    oc create configmap logging-configmap --from-file=log4j.properties

    The properties file defines the logging configuration:

    # Define the logger
    kafka.root.logger.level="INFO"
    # ...
  2. Define external logging in the spec of the resource, setting the logging.valueFrom.configMapKeyRef.name to the name of the ConfigMap and logging.valueFrom.configMapKeyRef.key to the key in this ConfigMap.

    spec:
      # ...
      logging:
        type: external
        valueFrom:
          configMapKeyRef:
            name: logging-configmap
            key: log4j.properties
  3. Create or update the resource.

    oc apply -f KAFKA-CONFIG-FILE

2.8.3. Adding logging filters to Operators

If you are using a ConfigMap to configure the (log4j2) logging levels for AMQ Streams Operators, you can also define logging filters to limit what’s returned in the log.

Logging filters are useful when you have a large number of logging messages. Suppose you set the log level for the logger as DEBUG (rootLogger.level="DEBUG"). Logging filters reduce the number of logs returned for the logger at that level, so you can focus on a specific resource. When the filter is set, only log messages matching the filter are logged.

Filters use markers to specify what to include in the log. You specify a kind, namespace and name for the marker. For example, if a Kafka cluster is failing, you can isolate the logs by specifying the kind as Kafka, and use the namespace and name of the failing cluster.

This example shows a marker filter for a Kafka cluster named my-kafka-cluster.

Basic logging filter configuration

rootLogger.level="INFO"
appender.console.filter.filter1.type=MarkerFilter 1
appender.console.filter.filter1.onMatch=ACCEPT 2
appender.console.filter.filter1.onMismatch=DENY 3
appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster) 4

1
The MarkerFilter type compares a specified marker for filtering.
2
The onMatch property accepts the log if the marker matches.
3
The onMismatch property rejects the log if the marker does not match.
4
The marker used for filtering is in the format KIND(NAMESPACE/NAME-OF-RESOURCE).

You can create one or more filters. Here, the log is filtered for two Kafka clusters.

Multiple logging filter configuration

appender.console.filter.filter1.type=MarkerFilter
appender.console.filter.filter1.onMatch=ACCEPT
appender.console.filter.filter1.onMismatch=DENY
appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster-1)
appender.console.filter.filter2.type=MarkerFilter
appender.console.filter.filter2.onMatch=ACCEPT
appender.console.filter.filter2.onMismatch=DENY
appender.console.filter.filter2.marker=Kafka(my-namespace/my-kafka-cluster-2)

Adding filters to the Cluster Operator

To add filters to the Cluster Operator, update its logging ConfigMap YAML file (install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml).

Procedure

  1. Update the 050-ConfigMap-strimzi-cluster-operator.yaml file to add the filter properties to the ConfigMap.

    In this example, the filter properties return logs only for the my-kafka-cluster Kafka cluster:

    kind: ConfigMap
    apiVersion: v1
    metadata:
      name: strimzi-cluster-operator
    data:
      log4j2.properties:
        #...
        appender.console.filter.filter1.type=MarkerFilter
        appender.console.filter.filter1.onMatch=ACCEPT
        appender.console.filter.filter1.onMismatch=DENY
        appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster)

    Alternatively, edit the ConfigMap directly:

    oc edit configmap strimzi-cluster-operator
  2. If you updated the YAML file instead of editing the ConfigMap directly, apply the changes by deploying the ConfigMap:

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

Adding filters to the Topic Operator or User Operator

To add filters to the Topic Operator or User Operator, create or edit a logging ConfigMap.

In this procedure a logging ConfigMap is created with filters for the Topic Operator. The same approach is used for the User Operator.

Procedure

  1. Create the ConfigMap.

    You can create the ConfigMap as a YAML file or from a properties file.

    In this example, the filter properties return logs only for the my-topic topic:

    kind: ConfigMap
    apiVersion: v1
    metadata:
      name: logging-configmap
    data:
      log4j2.properties:
        rootLogger.level="INFO"
        appender.console.filter.filter1.type=MarkerFilter
        appender.console.filter.filter1.onMatch=ACCEPT
        appender.console.filter.filter1.onMismatch=DENY
        appender.console.filter.filter1.marker=KafkaTopic(my-namespace/my-topic)

    If you are using a properties file, specify the file at the command line:

    oc create configmap logging-configmap --from-file=log4j2.properties

    The properties file defines the logging configuration:

    # Define the logger
    rootLogger.level="INFO"
    # Set the filters
    appender.console.filter.filter1.type=MarkerFilter
    appender.console.filter.filter1.onMatch=ACCEPT
    appender.console.filter.filter1.onMismatch=DENY
    appender.console.filter.filter1.marker=KafkaTopic(my-namespace/my-topic)
    # ...
  2. Define external logging in the spec of the resource, setting the logging.valueFrom.configMapKeyRef.name to the name of the ConfigMap and logging.valueFrom.configMapKeyRef.key to the key in this ConfigMap.

    For the Topic Operator, logging is specified in the topicOperator configuration of the Kafka resource.

    spec:
      # ...
      entityOperator:
        topicOperator:
          logging:
            type: external
            valueFrom:
              configMapKeyRef:
                name: logging-configmap
                key: log4j2.properties
  3. Apply the changes by deploying the Cluster Operator:
create -f install/cluster-operator -n my-cluster-operator-namespace

Chapter 3. Loading configuration values from external sources

Use configuration provider plugins to load configuration data from external sources. The providers operate independently of AMQ Streams. You can use them to load configuration data for all Kafka components, including producers and consumers. Use them, for example, to provide the credentials for Kafka Connect connector configuration.

OpenShift Configuration Provider

The OpenShift Configuration Provider plugin loads configuration data from OpenShift secrets or config maps.

Suppose you have a Secret object that’s managed outside the Kafka namespace, or outside the Kafka cluster. The OpenShift Configuration Provider allows you to reference the values of the secret in your configuration without extracting the files. You just need to tell the provider what secret to use and provide access rights. The provider loads the data without needing to restart the Kafka component, even when using a new Secret or ConfigMap object. This capability avoids disruption when a Kafka Connect instance hosts multiple connectors.

Environment Variables Configuration Provider

The Environment Variables Configuration Provider plugin loads configuration data from environment variables.

The values for the environment variables can be mapped from secrets or config maps. You can use the Environment Variables Configuration Provider, for example, to load certificates or JAAS configuration from environment variables mapped from OpenShift secrets.

Note

OpenShift Configuration Provider can’t use mounted files. For example, it can’t load values that need the location of a truststore or keystore. Instead, you can mount config maps or secrets into a Kafka Connect pod as environment variables or volumes. You can use the Environment Variables Configuration Provider to load values for environment variables. You add configuration using the externalConfiguration property in KafkaConnect.spec. You don’t need to set up access rights with this approach. However, Kafka Connect will need a restart when using a new Secret or ConfigMap for a connector. This will cause disruption to all the Kafka Connect instance’s connectors.

3.1. Loading configuration values from a config map

This procedure shows how to use the OpenShift Configuration Provider plugin.

In the procedure, an external ConfigMap object provides configuration properties for a connector.

Prerequisites

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

Procedure

  1. Create a ConfigMap or Secret that contains the configuration properties.

    In this example, a ConfigMap object named my-connector-configuration contains connector properties:

    Example ConfigMap with connector properties

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: my-connector-configuration
    data:
      option1: value1
      option2: value2

  2. Specify the OpenShift Configuration Provider in the Kafka Connect configuration.

    The specification shown here can support loading values from secrets and config maps.

    Example Kafka Connect configuration to enable the OpenShift Configuration Provider

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: my-connect
      annotations:
        strimzi.io/use-connector-resources: "true"
    spec:
      # ...
      config:
        # ...
        config.providers: secrets,configmaps 1
        config.providers.secrets.class: io.strimzi.kafka.KubernetesSecretConfigProvider 2
        config.providers.configmaps.class: io.strimzi.kafka.KubernetesConfigMapConfigProvider 3
      # ...

    1
    The alias for the configuration provider is used to define other configuration parameters. The provider parameters use the alias from config.providers, taking the form config.providers.${alias}.class.
    2
    KubernetesSecretConfigProvider provides values from secrets.
    3
    KubernetesConfigMapConfigProvider provides values from config maps.
  3. Create or update the resource to enable the provider.

    kubectl apply -f <kafka_connect_configuration_file>
  4. Create a role that permits access to the values in the external config map.

    Example role to access values from a config map

    apiVersion: rbac.authorization.k8s.io/v1
    kind: Role
    metadata:
      name: connector-configuration-role
    rules:
    - apiGroups: [""]
      resources: ["configmaps"]
      resourceNames: ["my-connector-configuration"]
      verbs: ["get"]
    # ...

    The rule gives the role permission to access the my-connector-configuration config map.

  5. Create a role binding to permit access to the namespace that contains the config map.

    Example role binding to access the namespace that contains the config map

    apiVersion: rbac.authorization.k8s.io/v1
    kind: RoleBinding
    metadata:
      name: connector-configuration-role-binding
    subjects:
    - kind: ServiceAccount
      name: my-connect-connect
      namespace: my-project
    roleRef:
      kind: Role
      name: connector-configuration-role
      apiGroup: rbac.authorization.k8s.io
    # ...

    The role binding gives the role permission to access the my-project namespace.

    The service account must be the same one used by the Kafka Connect deployment. The service account name format is <cluster_name>-connect, where <cluster_name> is the name of the KafkaConnect custom resource.

  6. Reference the config map in the connector configuration.

    Example connector configuration referencing the config map

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-connector
      labels:
        strimzi.io/cluster: my-connect
    spec:
      # ...
      config:
        option: ${configmaps:my-project/my-connector-configuration:option1}
        # ...
    # ...

    Placeholders for the property values in the config map are referenced in the connector configuration. The placeholder structure is configmaps:<path_and_file_name>:<property>. KubernetesConfigMapConfigProvider reads and extracts the option1 property value from the external config map.

3.2. Loading configuration values from environment variables

This procedure shows how to use the Environment Variables Configuration Provider plugin.

In the procedure, environment variables provide configuration properties for a connector. A database password is specified as an environment variable.

Prerequisites

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

Procedure

  1. Specify the Environment Variables Configuration Provider in the Kafka Connect configuration.

    Define environment variables using the externalConfiguration property.

    Example Kafka Connect configuration to enable the Environment Variables Configuration Provider

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: my-connect
      annotations:
        strimzi.io/use-connector-resources: "true"
    spec:
      # ...
      config:
        # ...
        config.providers: env 1
        config.providers.env.class: io.strimzi.kafka.EnvVarConfigProvider 2
      # ...
      externalConfiguration:
        env:
          - name: DB_PASSWORD 3
            valueFrom:
              secretKeyRef:
                name: db-creds 4
                key: dbPassword 5
      # ...

    1
    The alias for the configuration provider is used to define other configuration parameters. The provider parameters use the alias from config.providers, taking the form config.providers.${alias}.class.
    2
    EnvVarConfigProvider provides values from environment variables.
    3
    The DB_PASSWORD environment variable takes a password value from a secret.
    4
    The name of the secret containing the predefined password.
    5
    The key for the password stored inside the secret.
  2. Create or update the resource to enable the provider.

    kubectl apply -f <kafka_connect_configuration_file>
  3. Reference the environment variable in the connector configuration.

    Example connector configuration referencing the environment variable

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-connector
      labels:
        strimzi.io/cluster: my-connect
    spec:
      # ...
      config:
        option: ${env:DB_PASSWORD}
        # ...
    # ...

Chapter 4. Accessing Kafka outside of the OpenShift cluster

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.

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

Note

route is only supported on OpenShift

4.1. Accessing Kafka using node ports

This procedure describes how to access a 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

  1. 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:
        # ...
  2. 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.

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

    oc get kafka KAFKA-CLUSTER-NAME -o=jsonpath='{.status.listeners[?(@.type=="external")].bootstrapServers}{"\n"}'
  4. 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.

4.2. Accessing Kafka using loadbalancers

This procedure describes how to access a 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

  1. 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:
        # ...
  2. 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.

  3. 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"}'
  4. 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.

4.3. Accessing Kafka using ingress

This procedure shows how to access a 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

Procedure

  1. 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.
  2. 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.

  3. 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.

4.4. Accessing Kafka using OpenShift routes

This procedure describes how to access a 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

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

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      labels:
        app: my-cluster
      name: my-cluster
      namespace: myproject
    spec:
      kafka:
        # ...
        listeners:
          - name: 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.

  2. 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.

  3. 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"}'
  4. 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 5. 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

5.1. Security options for Kafka

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

5.1.1. 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 Accessing Kafka outside of the OpenShift cluster.

Supported authentication options:

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

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

Figure 5.1. 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 reserved for inter-broker communication (9091 or 9090) 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
# ...

5.1.1.1. 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.

5.1.1.2. 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.

5.1.1.3. Network policies

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

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

If you want to use custom network policies, you can set the STRIMZI_NETWORK_POLICY_GENERATION environment variable to false in the Cluster Operator configuration. For more information, see Cluster Operator configuration.

Note

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

5.1.1.4. Additional listener configuration options

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

5.1.2. 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:

Figure 5.2. Kafka cluster authorization options

options for kafks authorization configuration
5.1.2.1. 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
    # ...

5.2. 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.

Configuring users for secure access to Kafka brokers

For more information on configuring a KafkaUser resource to access Kafka brokers securely, see the following sections:

5.2.1. 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.

5.2.2. 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 types:

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

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

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

5.2.2.1. TLS client authentication

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

Example user 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 12, Managing TLS certificates.

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

5.2.2.2. TLS client authentication using a certificate issued outside the User Operator

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

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

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

5.2.2.3. SCRAM-SHA-512 authentication

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

Example user with SCRAM-SHA-512 authentication enabled

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

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

Example secret with user credentials

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

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

Decoding the generated password:

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

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

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

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

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

5.2.3. 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. The simple authorization will use the Kafka Admin API to manage the ACL rules inside your Kafka cluster. Whether ACL management in the User Operator is enabled or not depends on your authorization configuration in the Kafka cluster.

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

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

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

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

5.2.3.1. 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.

5.2.3.2. 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.

5.2.3.3. User quotas

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

An example KafkaUser with user quotas

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

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

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

5.3. 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.

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

5.3.1. 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

  1. Configure the Kafka resource.

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

      For example:

      apiVersion: kafka.strimzi.io/v1beta2
      kind: Kafka
      spec:
        kafka:
          # ...
          authorization: 1
            type: simple
            superUsers: 2
              - CN=client_1
              - user_2
              - CN=client_3
          listeners:
            - name: tls
              port: 9093
              type: internal
              tls: true
              authentication:
                type: tls 3
          # ...
        zookeeper:
          # ...
      1
      2
      List of user principals with unlimited access to Kafka. CN is the common name from the client certificate when 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.

  2. 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.

5.3.2. 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

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

Procedure

  1. Configure the KafkaUser resource.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaUser
    metadata:
      name: my-user
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      authentication: 1
        type: tls
      authorization:
        type: simple 2
        acls:
          - resource:
              type: topic
              name: my-topic
              patternType: literal
            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.
  2. Create or update the KafkaUser resource.

    oc apply -f USER-CONFIG-FILE

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

5.3.3. Restricting access to Kafka listeners using network policies

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

Prerequisites

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

Procedure

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

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

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

    Use oc apply:

    oc apply -f your-file

5.4. Using OAuth 2.0 token-based authentication

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

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

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

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

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

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

In the context of AMQ Streams:

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

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

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

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

5.4.1. 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. 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

5.4.2. 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.

5.4.2.1. 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.

5.4.2.2. 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.

5.4.2.3. 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
    #...

5.4.2.4. 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

5.4.3. 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 5.4.6.2, “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.

5.4.4. 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

5.4.5. 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.

5.4.5.1. 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 delegating validation to authorization server

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

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

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

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

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

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

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

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

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

  1. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. 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.

5.4.6. 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:

5.4.6.1. 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:

Procedure

  1. Deploy Red Hat Single Sign-On to your OpenShift cluster.

    Check the progress of the deployment in your OpenShift web console.

  2. 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.

  3. Create and enable a realm.

    You can use an existing master realm.

  4. Adjust the session and token timeouts for the realm, if required.
  5. Create a client called kafka-broker.
  6. 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
  7. Click Save before continuing.
  8. From the Credentials tab, take a note of the secret for using in your AMQ Streams Kafka cluster configuration.
  9. 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.

5.4.6.2. 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:

Before you start

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

Prerequisites

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

Procedure

  1. Update the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    oc edit kafka my-cluster
  2. Configure the Kafka broker listeners configuration.

    The configuration for each type of listener does not have to be the same, as they are independent.

    The examples here show the configuration options as configured for external listeners.

    Example 1: Configuring fast local JWT token validation

    #...
    - name: external
      port: 9094
      type: loadbalancer
      tls: true
      authentication:
        type: oauth 1
        validIssuerUri: <https://<auth-server-address>/auth/realms/external> 2
        jwksEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/certs> 3
        userNameClaim: preferred_username 4
        maxSecondsWithoutReauthentication: 3600 5
        tlsTrustedCertificates: 6
        - secretName: oauth-server-cert
          certificate: ca.crt
        disableTlsHostnameVerification: true 7
        jwksExpirySeconds: 360 8
        jwksRefreshSeconds: 300 9
        jwksMinRefreshPauseSeconds: 1 10

    1
    Listener type set to oauth.
    2
    URI of the token issuer used for authentication.
    3
    URI of the JWKS certificate endpoint used for local JWT validation.
    4
    The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authentication flow and the authorization server used.
    5
    (Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
    6
    (Optional) Trusted certificates for TLS connection to the authorization server.
    7
    (Optional) Disable TLS hostname verification. Default is false.
    8
    The duration the JWKS certificates are considered valid before they expire. Default is 360 seconds. If you specify a longer time, consider the risk of allowing access to revoked certificates.
    9
    The period between refreshes of JWKS certificates. The interval must be at least 60 seconds shorter than the expiry interval. Default is 300 seconds.
    10
    The minimum pause in seconds between consecutive attempts to refresh JWKS public keys. When an unknown signing key is encountered, the JWKS keys refresh is scheduled outside the regular periodic schedule with at least the specified pause since the last refresh attempt. The refreshing of keys follows the rule of exponential backoff, retrying on unsuccessful refreshes with ever increasing pause, until it reaches jwksRefreshSeconds. The default value is 1.

    Example 2: Configuring token validation using an introspection endpoint

    - name: external
      port: 9094
      type: loadbalancer
      tls: true
      authentication:
        type: oauth
        validIssuerUri: <https://<auth-server-address>/auth/realms/external>
        introspectionEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token/introspect> 1
        clientId: kafka-broker 2
        clientSecret: 3
          secretName: my-cluster-oauth
          key: clientSecret
        userNameClaim: preferred_username 4
        maxSecondsWithoutReauthentication: 3600 5

    1
    URI of the token introspection endpoint.
    2
    Client ID to identify the client.
    3
    Client Secret and client ID is used for authentication.
    4
    The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authorization server used.
    5
    (Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.

    Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional (optional) configuration settings you can use:

      # ...
      authentication:
        type: oauth
        # ...
        checkIssuer: false 1
        checkAudience: true 2
        fallbackUserNameClaim: client_id 3
        fallbackUserNamePrefix: client-account- 4
        validTokenType: bearer 5
        userInfoEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/userinfo 6
        enableOauthBearer: false 7
        enablePlain: true 8
        tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token 9
        customClaimCheck: "@.custom == 'custom-value'" 10
        clientAudience: AUDIENCE 11
        clientScope: SCOPE 12
    1
    If your authorization server does not provide an iss claim, it is not possible to perform an issuer check. In this situation, set checkIssuer to false and do not specify a validIssuerUri. Default is true.
    2
    If your authorization server provides an aud (audience) claim, and you want to enforce an audience check, set checkAudience to true. Audience checks identify the intended recipients of tokens. As a result, the Kafka broker will reject tokens that do not have its clientId in their aud claim. Default is false.
    3
    An authorization server may not provide a single attribute to identify both regular users and clients. When a client authenticates in its own name, the server might provide a client ID. When a user authenticates using a username and password, to obtain a refresh token or an access token, the server might provide a username attribute in addition to a client ID. Use this fallback option to specify the username claim (attribute) to use if a primary user ID attribute is not available.
    4
    In situations where fallbackUserNameClaim is applicable, it may also be necessary to prevent name collisions between the values of the username claim, and those of the fallback username claim. Consider a situation where a client called producer exists, but also a regular user called producer exists. In order to differentiate between the two, you can use this property to add a prefix to the user ID of the client.
    5
    (Only applicable when using introspectionEndpointUri) Depending on the authorization server you are using, the introspection endpoint may or may not return the token type attribute, or it may contain different values. You can specify a valid token type value that the response from the introspection endpoint has to contain.
    6
    (Only applicable when using introspectionEndpointUri) The authorization server may be configured or implemented in such a way to not provide any identifiable information in an Introspection Endpoint response. In order to obtain the user ID, you can configure the URI of the userinfo endpoint as a fallback. The userNameClaim, fallbackUserNameClaim, and fallbackUserNamePrefix settings are applied to the response of userinfo endpoint.
    7
    Set this to false`to disable the OAUTHBEARER mechanism on the listener. At least one of PLAIN or OAUTHBEARER has to be enabled. Default is `true.
    8
    Set to true to enable PLAIN authentication on the listener, which is supported by all clients on all platforms. The Kafka client must enable the PLAIN mechanism and set the username and password. PLAIN can be used to authenticate either by using the OAuth access token, or the OAuth clientId and secret (the client credentials). The behavior is additionally controlled by whether tokenEndpointUri is specified or not. Default is false. If tokenEndpointUri is specified and the client sets password to start with the string $accessToken:, the server interprets the password as the access token and the username as the account username. Otherwise, the username is interpreted as the clientId and the password as the client secret, which the broker uses to obtain the access token in the client’s name. If tokenEndpointUri is not specified, the password is always interpreted as an access token and the username is always interpreted as the account username, which must match the principal id extracted from the token. This is known as 'no-client-credentials' mode because the client must always obtain the access token by itself, and can’t use clientId and secret.
    9
    Additional configuration for PLAIN mechanism to allow clients to authenticate by passing clientId and secret as username and password as described in the previous point. If not specified the clients can authenticate over PLAIN only by passing an access token as password parameter.
    10
    Additional custom rules can be imposed on the JWT access token during validation by setting this to a JsonPath filter query. If the access token does not contain the necessary data, it is rejected. When using the introspectionEndpointUri, the custom check is applied to the introspection endpoint response JSON.
    11
    (Optional) An audience parameter passed to the token endpoint. An audience is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId and secret. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.
    12
    (Optional) A scope parameter passed to the token endpoint. A scope is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId and secret. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.
  3. Save and exit the editor, then wait for rolling updates to complete.
  4. Check the update in the logs or by watching the pod state transitions:

    oc logs -f ${POD_NAME} -c ${CONTAINER_NAME}
    oc get pod -w

    The rolling update configures the brokers to use OAuth 2.0 authentication.

5.4.6.3. 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

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

    <dependency>
     <groupId>io.strimzi</groupId>
     <artifactId>kafka-oauth-client</artifactId>
     <version>{oauth-version}</version>
    </dependency>
  2. 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.
  3. 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.
  4. Verify that the Kafka client can access the Kafka brokers.
5.4.6.4. 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:

Prerequisites

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

Procedure

  1. Create a client secret and mount it to the component as an environment variable.

    For example, here we are creating a client Secret for the Kafka Bridge:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Secret
    metadata:
     name: my-bridge-oauth
    type: Opaque
    data:
     clientSecret: MGQ1OTRmMzYtZTllZS00MDY2LWI5OGEtMTM5MzM2NjdlZjQw 1
    1
    The clientSecret key must be in base64 format.
  2. Create or edit the resource for the Kafka component so that OAuth 2.0 authentication is configured for the authentication property.

    For OAuth 2.0 authentication, you can use:

    • 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
        audience: kafka 5
    1
    (Optional) Disable TLS hostname verification. Default is false.
    2
    If the authorization server does not return a typ (type) claim inside the JWT token, you can apply checkAccessTokenType: false to skip the token type check. Default is true.
    3
    If you are using opaque tokens, you can apply accessTokenIsJwt: false so that access tokens are not treated as JWT tokens.
    4
    (Optional) The scope for requesting the token from the token endpoint. An authorization server may require a client to specify the scope. In this case it is any.
    5
    (Optional) The audience for requesting the token from the token endpoint. An authorization server may require a client to specify the audience. In this case it is kafka.
  3. Apply the changes to the deployment of your Kafka resource.

    oc apply -f your-file
  4. Check the update in the logs or by watching the pod state transitions:

    oc logs -f ${POD_NAME} -c ${CONTAINER_NAME}
    oc get pod -w

    The rolling updates configure the component for interaction with Kafka brokers using OAuth 2.0 authentication.

5.5. 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.

5.5.1. 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.

5.5.1.1. 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.

5.5.2. Configuring OAuth 2.0 authorization support

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

Before you begin

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

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

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

Note

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

Prerequisites

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

Procedure

  1. Access the Red Hat Single Sign-On Admin Console or use the Red Hat Single Sign-On Admin CLI to enable Authorization Services for the Kafka broker client you created when setting up OAuth 2.0 authentication.
  2. Use Authorization Services to define resources, authorization scopes, policies, and permissions for the client.
  3. Bind the permissions to users and clients by assigning them roles and groups.
  4. Configure the Kafka brokers to use Red Hat Single Sign-On authorization by updating the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    oc edit kafka my-cluster
  5. Configure the Kafka broker kafka configuration to use keycloak authorization, and to be able to access the authorization server and Authorization Services.

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        authorization:
          type: keycloak 1
          tokenEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token> 2
          clientId: kafka 3
          delegateToKafkaAcls: false 4
          disableTlsHostnameVerification: false 5
          superUsers: 6
          - CN=fred
          - sam
          - CN=edward
          tlsTrustedCertificates: 7
          - secretName: oauth-server-cert
            certificate: ca.crt
          grantsRefreshPeriodSeconds: 60 8
          grantsRefreshPoolSize: 5 9
        #...
    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. When you configure token-based oauth authentication, you specify a jwksEndpointUri as the URI for local JWT validation. The hostname for the tokenEndpointUri URI must be the same.
    3
    The client ID of the OAuth 2.0 client definition in Red Hat Single Sign-On that has Authorization Services enabled. Typically, kafka is used as the ID.
    4
    (Optional) Delegate authorization to Kafka AclAuthorizer if access is denied by Red Hat Single Sign-On Authorization Services policies. Default is false.
    5
    (Optional) Disable TLS hostname verification. Default is false.
    6
    (Optional) Designated super users.
    7
    (Optional) Trusted certificates for TLS connection to the authorization server.
    8
    (Optional) The time between two consecutive grants refresh runs. That is the maximum time for active sessions to detect any permissions changes for the user on Red Hat Single Sign-On. The default value is 60.
    9
    (Optional) The number of threads to use to refresh (in parallel) the grants for the active sessions. The default value is 5.
  6. Save and exit the editor, then wait for rolling updates to complete.
  7. Check the update in the logs or by watching the pod state transitions:

    oc logs -f ${POD_NAME} -c kafka
    oc get pod -w

    The rolling update configures the brokers to use OAuth 2.0 authorization.

  8. Verify the configured permissions by accessing Kafka brokers as clients or users with specific roles, making sure they have the necessary access, or do not have the access they are not supposed to have.

5.5.3. Managing policies and permissions in Red Hat Single Sign-On Authorization Services

This section describes the authorization models used by Red Hat Single Sign-On Authorization Services and Kafka, and defines the important concepts in each model.

To grant permissions to access Kafka, you can map Red Hat Single Sign-On Authorization Services objects to Kafka resources by creating an OAuth client specification in Red Hat Single Sign-On. Kafka permissions are granted to user accounts or service accounts using Red Hat Single Sign-On Authorization Services rules.

Examples are shown of the different user permissions required for common Kafka operations, such as creating and listing topics.

5.5.3.1. Kafka and Red Hat Single Sign-On authorization models overview

Kafka and Red Hat Single Sign-On Authorization Services use different authorization models.

Kafka authorization model

Kafka’s authorization model uses resource types. When a Kafka client performs an action on a broker, the broker uses the configured KeycloakRBACAuthorizer to check the client’s permissions, based on the action and resource type.

Kafka uses five resource types to control access: Topic, Group, Cluster, TransactionalId, and DelegationToken. Each resource type has a set of available permissions.

Topic

  • Create
  • Write
  • Read
  • Delete
  • Describe
  • DescribeConfigs
  • Alter
  • AlterConfigs

Group

  • Read
  • Describe
  • Delete

Cluster

  • Create
  • Describe
  • Alter
  • DescribeConfigs
  • AlterConfigs
  • IdempotentWrite
  • ClusterAction

TransactionalId

  • Describe
  • Write

DelegationToken

  • Describe
Red Hat Single Sign-On Authorization Services model

The Red Hat Single Sign-On Authorization Services model has four concepts for defining and granting permissions: resources, authorization scopes, policies, and permissions.

Resources
A resource is a set of resource definitions that are used to match resources with permitted actions. A resource might be an individual topic, for example, or all topics with names starting with the same prefix. A resource definition is associated with a set of available authorization scopes, which represent a set of all actions available on the resource. Often, only a subset of these actions is actually permitted.
Authorization scopes
An authorization scope is a set of all the available actions on a specific resource definition. When you define a new resource, you add scopes from the set of all scopes.
Policies

A policy is an authorization rule that uses criteria to match against a list of accounts. Policies can match:

  • Service accounts based on client ID or roles
  • User accounts based on username, groups, or roles.
Permissions
A permission grants a subset of authorization scopes on a specific resource definition to a set of users.

Additional resources

5.5.3.2. Map Red Hat Single Sign-On Authorization Services to the Kafka authorization model

The Kafka authorization model is used as a basis for defining the Red Hat Single Sign-On roles and resources that will control access to Kafka.

To grant Kafka permissions to user accounts or service accounts, you first create an OAuth client specification in Red Hat Single Sign-On for the Kafka broker. You then specify Red Hat Single Sign-On Authorization Services rules on the client. Typically, the client id of the OAuth client that represents the broker is kafka. The example configuration files provided with AMQ Streams use kafka as the OAuth client id.

Note

If you have multiple Kafka clusters, you can use a single OAuth client (kafka) for all of them. This gives you a single, unified space in which to define and manage authorization rules. However, you can also use different OAuth client ids (for example, my-cluster-kafka or cluster-dev-kafka) and define authorization rules for each cluster within each client configuration.

The kafka client definition must have the Authorization Enabled option enabled in the Red Hat Single Sign-On Admin Console.

All permissions exist within the scope of the kafka client. If you have different Kafka clusters configured with different OAuth client IDs, they each need a separate set of permissions even though they’re part of the same Red Hat Single Sign-On realm.

When the Kafka client uses OAUTHBEARER authentication, the Red Hat Single Sign-On authorizer (KeycloakRBACAuthorizer) uses the access token of the current session to retrieve a list of grants from the Red Hat Single Sign-On server. To retrieve the grants, the authorizer evaluates the Red Hat Single Sign-On Authorization Services policies and permissions.

Authorization scopes for Kafka permissions

An initial Red Hat Single Sign-On configuration usually involves uploading authorization scopes to create a list of all possible actions that can be performed on each Kafka resource type. This step is performed once only, before defining any permissions. You can add authorization scopes manually instead of uploading them.

Authorization scopes must contain all the possible Kafka permissions regardless of the resource type:

  • Create
  • Write
  • Read
  • Delete
  • Describe
  • Alter
  • DescribeConfig
  • AlterConfig
  • ClusterAction
  • IdempotentWrite
Note

If you’re certain you won’t need a permission (for example, IdempotentWrite), you can omit it from the list of authorization scopes. However, that permission won’t be available to target on Kafka resources.

Resource patterns for permissions checks

Resource patterns are used for pattern matching against the targeted resources when performing permission checks. The general pattern format is RESOURCE-TYPE:PATTERN-NAME.

The resource types mirror the Kafka authorization model. The pattern allows for two matching options:

  • Exact matching (when the pattern does not end with *)
  • Prefix matching (when the pattern ends with *)

Example patterns for resources

Topic:my-topic
Topic:orders-*
Group:orders-*
Cluster:*

Additionally, the general pattern format can be prefixed by kafka-cluster:CLUSTER-NAME followed by a comma, where CLUSTER-NAME refers to the metadata.name in the Kafka custom resource.

Example patterns for resources with cluster prefix

kafka-cluster:my-cluster,Topic:*
kafka-cluster:*,Group:b_*

When the kafka-cluster prefix is missing, it is assumed to be kafka-cluster:*.

When defining a resource, you can associate it with a list of possible authorization scopes which are relevant to the resource. Set whatever actions make sense for the targeted resource type.

Though you may add any authorization scope to any resource, only the scopes supported by the resource type are considered for access control.

Policies for applying access permission

Policies are used to target permissions to one or more user accounts or service accounts. Targeting can refer to:

  • Specific user or service accounts
  • Realm roles or client roles
  • User groups
  • JavaScript rules to match a client IP address

A policy is given a unique name and can be reused to target multiple permissions to multiple resources.

Permissions to grant access

Use fine-grained permissions to pull together the policies, resources, and authorization scopes that grant access to users.

The name of each permission should clearly define which permissions it grants to which users. For example, Dev Team B can read from topics starting with x.

Additional resources

5.5.3.3. Example permissions required for Kafka operations

The following examples demonstrate the user permissions required for performing common operations on Kafka.

Create a topic

To create a topic, the Create permission is required for the specific topic, or for Cluster:kafka-cluster.

bin/kafka-topics.sh --create --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

List topics

If a user has the Describe permission on a specified topic, the topic is listed.

bin/kafka-topics.sh --list \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Display topic details

To display a topic’s details, Describe and DescribeConfigs permissions are required on the topic.

bin/kafka-topics.sh --describe --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Produce messages to a topic

To produce messages to a topic, Describe and Write permissions are required on the topic.

If the topic hasn’t been created yet, and topic auto-creation is enabled, the permissions to create a topic are required.

bin/kafka-console-producer.sh  --topic my-topic \
  --broker-list my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties

Consume messages from a topic

To consume messages from a topic, Describe and Read permissions are required on the topic. Consuming from the topic normally relies on storing the consumer offsets in a consumer group, which requires additional Describe and Read permissions on the consumer group.

Two resources are needed for matching. For example:

Topic:my-topic
Group:my-group-*
bin/kafka-console-consumer.sh --topic my-topic --group my-group-1 --from-beginning \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --consumer.config /tmp/config.properties

Produce messages to a topic using an idempotent producer

As well as the permissions for producing to a topic, an additional IdempotentWrite permission is required on the Cluster resource.

Two resources are needed for matching. For example:

Topic:my-topic
Cluster:kafka-cluster
bin/kafka-console-producer.sh  --topic my-topic \
  --broker-list my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties --producer-property enable.idempotence=true --request-required-acks -1

List consumer groups

When listing consumer groups, only the groups on which the user has the Describe permissions are returned. Alternatively, if the user has the Describe permission on the Cluster:kafka-cluster, all the consumer groups are returned.

bin/kafka-consumer-groups.sh --list \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Display consumer group details

To display a consumer group’s details, the Describe permission is required on the group and the topics associated with the group.

bin/kafka-consumer-groups.sh --describe --group my-group-1 \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Change topic configuration

To change a topic’s configuration, the Describe and Alter permissions are required on the topic.

bin/kafka-topics.sh --alter --topic my-topic --partitions 2 \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Display Kafka broker configuration

In order to use kafka-configs.sh to get a broker’s configuration, the DescribeConfigs permission is required on the Cluster:kafka-cluster.

bin/kafka-configs.sh --entity-type brokers --entity-name 0 --describe --all \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Change Kafka broker configuration

To change a Kafka broker’s configuration, DescribeConfigs and AlterConfigs permissions are required on Cluster:kafka-cluster.

bin/kafka-configs --entity-type brokers --entity-name 0 --alter --add-config log.cleaner.threads=2 \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Delete a topic

To delete a topic, the Describe and Delete permissions are required on the topic.

bin/kafka-topics.sh --delete --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties

Select a lead partition

To run leader selection for topic partitions, the Alter permission is required on the Cluster:kafka-cluster.

bin/kafka-leader-election.sh --topic my-topic --partition 0 --election-type PREFERRED  /
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --admin.config /tmp/config.properties

Reassign partitions

To generate a partition reassignment file, Describe permissions are required on the topics involved.

bin/kafka-reassign-partitions.sh --topics-to-move-json-file /tmp/topics-to-move.json --broker-list "0,1" --generate \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties > /tmp/partition-reassignment.json

To execute the partition reassignment, Describe and Alter permissions are required on Cluster:kafka-cluster. Also, Describe permissions are required on the topics involved.

bin/kafka-reassign-partitions.sh --reassignment-json-file /tmp/partition-reassignment.json --execute \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties

To verify partition reassignment, Describe, and AlterConfigs permissions are required on Cluster:kafka-cluster, and on each of the topics involved.

bin/kafka-reassign-partitions.sh --reassignment-json-file /tmp/partition-reassignment.json --verify \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties

5.5.4. Trying Red Hat Single Sign-On Authorization Services

This example explains how to use Red Hat Single Sign-On Authorization Services with keycloak authorization. Use Red Hat Single Sign-On Authorization Services to enforce access restrictions on Kafka clients. Red Hat Single Sign-On Authorization Services use authorization scopes, policies and permissions to define and apply access control to resources.

Red Hat Single Sign-On Authorization Services REST endpoints provide a list of granted permissions on resources for authenticated users. The list of grants (permissions) is fetched from the Red Hat Single Sign-On server as the first action after an authenticated session is established by the Kafka client. The list is refreshed in the background so that changes to the grants are detected. Grants are cached and enforced locally on the Kafka broker for each user session to provide fast authorization decisions.

AMQ Streams provides example configuration files. These include the following example files for setting up Red Hat Single Sign-On:

kafka-ephemeral-oauth-single-keycloak-authz.yaml
An example Kafka custom resource configured for OAuth 2.0 token-based authorization using Red Hat Single Sign-On. You can use the custom resource to deploy a Kafka cluster that uses keycloak authorization and token-based oauth authentication.
kafka-authz-realm.json
An example Red Hat Single Sign-On realm configured with sample groups, users, roles and clients. You can import the realm into a Red Hat Single Sign-On instance to set up fine-grained permissions to access Kafka.

If you want to try the example with Red Hat Single Sign-On, use these files to perform the tasks outlined in this section in the order shown.

Authentication

When you configure token-based oauth authentication, you specify a jwksEndpointUri as the URI for local JWT validation. When you configure keycloak authorization, you specify a tokenEndpointUri as the URI of the Red Hat Single Sign-On token endpoint. The hostname for both URIs must be the same.

Targeted permissions with group or role policies

In Red Hat Single Sign-On, confidential clients with service accounts enabled can authenticate to the server in their own name using a client ID and a secret. This is convenient for microservices that typically act in their own name, and not as agents of a particular user (like a web site). Service accounts can have roles assigned like regular users. They cannot, however, have groups assigned. As a consequence, if you want to target permissions to microservices using service accounts, you cannot use group policies, and should instead use role policies. Conversely, if you want to limit certain permissions only to regular user accounts where authentication with a username and password is required, you can achieve that as a side effect of using the group policies rather than the role policies. This is what is used in this example for permissions that start with ClusterManager. Performing cluster management is usually done interactively using CLI tools. It makes sense to require the user to log in before using the resulting access token to authenticate to the Kafka broker. In this case, the access token represents the specific user, rather than the client application.

5.5.4.1. Accessing the Red Hat Single Sign-On Admin Console

Set up Red Hat Single Sign-On, then connect to its Admin Console and add the preconfigured realm. Use the example kafka-authz-realm.json file to import the realm. You can check the authorization rules defined for the realm in the Admin Console. The rules grant access to the resources on the Kafka cluster configured to use the example Red Hat Single Sign-On realm.

Prerequisites

  • A running OpenShift cluster.
  • The AMQ Streams examples/security/keycloak-authorization/kafka-authz-realm.json file that contains the preconfigured realm.

Procedure

  1. Install the Red Hat Single Sign-On server using the Red Hat Single Sign-On Operator as described in Server Installation and Configuration in the Red Hat Single Sign-On documentation.
  2. Wait until the Red Hat Single Sign-On instance is running.
  3. Get the external hostname to be able to access the Admin Console.

    NS=sso
    oc get ingress keycloak -n $NS

    In this example, we assume the Red Hat Single Sign-On server is running in the sso namespace.

  4. Get the password for the admin user.

    oc get -n $NS pod keycloak-0 -o yaml | less

    The password is stored as a secret, so get the configuration YAML file for the Red Hat Single Sign-On instance to identify the name of the secret (secretKeyRef.name).

  5. Use the name of the secret to obtain the clear text password.

    SECRET_NAME=credential-keycloak
    oc get -n $NS secret $SECRET_NAME -o yaml | grep PASSWORD | awk '{print $2}' | base64 -D

    In this example, we assume the name of the secret is credential-keycloak.

  6. Log in to the Admin Console with the username admin and the password you obtained.

    Use https://HOSTNAME to access the OpenShift ingress.

    You can now upload the example realm to Red Hat Single Sign-On using the Admin Console.

  7. Click Add Realm to import the example realm.
  8. Add the examples/security/keycloak-authorization/kafka-authz-realm.json file, and then click Create.

    You now have kafka-authz as your current realm in the Admin Console.

    The default view displays the Master realm.

  9. In the Red Hat Single Sign-On Admin Console, go to Clients > kafka > Authorization > Settings and check that Decision Strategy is set to Affirmative.

    An affirmative policy means that at least one policy must be satisfied for a client to access the Kafka cluster.

  10. In the Red Hat Single Sign-On Admin Console, go to Groups, Users, Roles and Clients to view the realm configuration.

    Groups
    Groups are used to create user groups and set user permissions. Groups are sets of users with a name assigned. They are used to compartmentalize users into geographical, organizational or departmental units. Groups can be linked to an LDAP identity provider. You can make a user a member of a group through a custom LDAP server admin user interface, for example, to grant permissions on Kafka resources.
    Users
    Users are used to create users. For this example, alice and bob are defined. alice is a member of the ClusterManager group and bob is a member of ClusterManager-my-cluster group. Users can be stored in an LDAP identity provider.
    Roles
    Roles mark users or clients as having certain permissions. Roles are a concept analogous to groups. They are usually used to tag users with organizational roles and have the requisite permissions. Roles cannot be stored in an LDAP identity provider. If LDAP is a requirement, you can use groups instead, and add Red Hat Single Sign-On roles to the groups so that when users are assigned a group they also get a corresponding role.
    Clients

    Clients can have specific configurations. For this example, kafka, kafka-cli, team-a-client, and team-b-client clients are configured.

    • The kafka client is used by Kafka brokers to perform the necessary OAuth 2.0 communication for access token validation. This client also contains the authorization services resource definitions, policies, and authorization scopes used to perform authorization on the Kafka brokers. The authorization configuration is defined in the kafka client from the Authorization tab, which becomes visible when Authorization Enabled is switched on from the Settings tab.
    • The kafka-cli client is a public client that is used by the Kafka command line tools when authenticating with username and password to obtain an access token or a refresh token.
    • The team-a-client and team-b-client clients are confidential clients representing services with partial access to certain Kafka topics.
  11. In the Red Hat Single Sign-On Admin Console, go to Authorization > Permissions to see the granted permissions that use the resources and policies defined for the realm.

    For example, the kafka client has the following permissions:

    Dev Team A can write to topics that start with x_ on any cluster
    Dev Team B can read from topics that start with x_ on any cluster
    Dev Team B can update consumer group offsets that start with x_ on any cluster
    ClusterManager of my-cluster Group has full access to cluster config on my-cluster
    ClusterManager of my-cluster Group has full access to consumer groups on my-cluster
    ClusterManager of my-cluster Group has full access to topics on my-cluster
    Dev Team A
    The Dev Team A realm role can write to topics that start with x_ on any cluster. This combines a resource called Topic:x_*, Describe and Write scopes, and the Dev Team A policy. The Dev Team A policy matches all users that have a realm role called Dev Team A.
    Dev Team B
    The Dev Team B realm role can read from topics that start with x_ on any cluster. This combines Topic:x_*, Group:x_* resources, Describe and Read scopes, and the Dev Team B policy. The Dev Team B policy matches all users that have a realm role called Dev Team B. Matching users and clients have the ability to read from topics, and update the consumed offsets for topics and consumer groups that have names starting with x_.
5.5.4.2. Deploying a Kafka cluster with Red Hat Single Sign-On authorization

Deploy a Kafka cluster configured to connect to the Red Hat Single Sign-On server. Use the example kafka-ephemeral-oauth-single-keycloak-authz.yaml file to deploy the Kafka cluster as a Kafka custom resource. The example deploys a single-node Kafka cluster with keycloak authorization and oauth authentication.

Prerequisites

  • The Red Hat Single Sign-On authorization server is deployed to your OpenShift cluster and loaded with the example realm.
  • The Cluster Operator is deployed to your OpenShift cluster.
  • The AMQ Streams examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml custom resource.

Procedure

  1. Use the hostname of the Red Hat Single Sign-On instance you deployed to prepare a truststore certificate for Kafka brokers to communicate with the Red Hat Single Sign-On server.

    SSO_HOST=SSO-HOSTNAME
    SSO_HOST_PORT=$SSO_HOST:443
    STOREPASS=storepass
    
    echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.crt

    The certificate is required as OpenShift ingress is used to make a secure (HTTPS) connection.

  2. Deploy the certificate to OpenShift as a secret.

    oc create secret generic oauth-server-cert --from-file=/tmp/sso.crt -n $NS
  3. Set the hostname as an environment variable

    SSO_HOST=SSO-HOSTNAME
  4. Create and deploy the example Kafka cluster.

    cat examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml | sed -E 's#\${SSO_HOST}'"#$SSO_HOST#" | oc create -n $NS -f -
5.5.4.3. Preparing TLS connectivity for a CLI Kafka client session

Create a new pod for an interactive CLI session. Set up a truststore with a Red Hat Single Sign-On certificate for TLS connectivity. The truststore is to connect to Red Hat Single Sign-On and the Kafka broker.

Prerequisites

  • The Red Hat Single Sign-On authorization server is deployed to your OpenShift cluster and loaded with the example realm.

    In the Red Hat Single Sign-On Admin Console, check the roles assigned to the clients are displayed in Clients > Service Account Roles.

  • The Kafka cluster configured to connect with Red Hat Single Sign-On is deployed to your OpenShift cluster.

Procedure

  1. Run a new interactive pod container using the AMQ Streams Kafka image to connect to a running Kafka broker.

    NS=sso
    oc run -ti --restart=Never --image=registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.1 kafka-cli -n $NS -- /bin/sh
    Note

    If oc times out waiting on the image download, subsequent attempts may result in an AlreadyExists error.

  2. Attach to the pod container.

    oc attach -ti kafka-cli -n $NS
  3. Use the hostname of the Red Hat Single Sign-On instance to prepare a certificate for client connection using TLS.

    SSO_HOST=SSO-HOSTNAME
    SSO_HOST_PORT=$SSO_HOST:443
    STOREPASS=storepass
    
    echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.crt
  4. Create a truststore for TLS connection to the Kafka brokers.

    keytool -keystore /tmp/truststore.p12 -storetype pkcs12 -alias sso -storepass $STOREPASS -import -file /tmp/sso.crt -noprompt
  5. Use the Kafka bootstrap address as the hostname of the Kafka broker and the tls listener port (9093) to prepare a certificate for the Kafka broker.

    KAFKA_HOST_PORT=my-cluster-kafka-bootstrap:9093
    STOREPASS=storepass
    
    echo "Q" | openssl s_client -showcerts -connect $KAFKA_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/my-cluster-kafka.crt
  6. Add the certificate for the Kafka broker to the truststore.

    keytool -keystore /tmp/truststore.p12 -storetype pkcs12 -alias my-cluster-kafka -storepass $STOREPASS -import -file /tmp/my-cluster-kafka.crt -noprompt

    Keep the session open to check authorized access.

5.5.4.4. Checking authorized access to Kafka using a CLI Kafka client session

Check the authorization rules applied through the Red Hat Single Sign-On realm using an interactive CLI session. Apply the checks using Kafka’s example producer and consumer clients to create topics with user and service accounts that have different levels of access.

Use the team-a-client and team-b-client clients to check the authorization rules. Use the alice admin user to perform additional administrative tasks on Kafka.

The AMQ Streams Kafka image used in this example contains Kafka producer and consumer binaries.

Prerequisites

Setting up client and admin user configuration

  1. Prepare a Kafka configuration file with authentication properties for the team-a-client client.

    SSO_HOST=SSO-HOSTNAME
    
    cat > /tmp/team-a-client.properties << EOF
    security.protocol=SASL_SSL
    ssl.truststore.location=/tmp/truststore.p12
    ssl.truststore.password=$STOREPASS
    ssl.truststore.type=PKCS12
    sasl.mechanism=OAUTHBEARER
    sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      oauth.client.id="team-a-client" \
      oauth.client.secret="team-a-client-secret" \
      oauth.ssl.truststore.location="/tmp/truststore.p12" \
      oauth.ssl.truststore.password="$STOREPASS" \
      oauth.ssl.truststore.type="PKCS12" \
      oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
    sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
    EOF

    The SASL OAUTHBEARER mechanism is used. This mechanism requires a client ID and client secret, which means the client first connects to the Red Hat Single Sign-On server to obtain an access token. The client then connects to the Kafka broker and uses the access token to authenticate.

  2. Prepare a Kafka configuration file with authentication properties for the team-b-client client.

    cat > /tmp/team-b-client.properties << EOF
    security.protocol=SASL_SSL
    ssl.truststore.location=/tmp/truststore.p12
    ssl.truststore.password=$STOREPASS
    ssl.truststore.type=PKCS12
    sasl.mechanism=OAUTHBEARER
    sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      oauth.client.id="team-b-client" \
      oauth.client.secret="team-b-client-secret" \
      oauth.ssl.truststore.location="/tmp/truststore.p12" \
      oauth.ssl.truststore.password="$STOREPASS" \
      oauth.ssl.truststore.type="PKCS12" \
      oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
    sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
    EOF
  3. Authenticate admin user alice by using curl and performing a password grant authentication to obtain a refresh token.

    USERNAME=alice
    PASSWORD=alice-password
    
    GRANT_RESPONSE=$(curl -X POST "https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" -H 'Content-Type: application/x-www-form-urlencoded' -d "grant_type=password&username=$USERNAME&password=$PASSWORD&client_id=kafka-cli&scope=offline_access" -s -k)
    
    REFRESH_TOKEN=$(echo $GRANT_RESPONSE | awk -F "refresh_token\":\"" '{printf $2}' | awk -F "\"" '{printf $1}')

    The refresh token is an offline token that is long-lived and does not expire.

  4. Prepare a Kafka configuration file with authentication properties for the admin user alice.

    cat > /tmp/alice.properties << EOF
    security.protocol=SASL_SSL
    ssl.truststore.location=/tmp/truststore.p12
    ssl.truststore.password=$STOREPASS
    ssl.truststore.type=PKCS12
    sasl.mechanism=OAUTHBEARER
    sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      oauth.refresh.token="$REFRESH_TOKEN" \
      oauth.client.id="kafka-cli" \
      oauth.ssl.truststore.location="/tmp/truststore.p12" \
      oauth.ssl.truststore.password="$STOREPASS" \
      oauth.ssl.truststore.type="PKCS12" \
      oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
    sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
    EOF

    The kafka-cli public client is used for the oauth.client.id in the sasl.jaas.config. Since it’s a public client it does not require a secret. The client authenticates with the refresh token that was authenticated in the previous step. The refresh token requests an access token behind the scenes, which is then sent to the Kafka broker for authentication.

Producing messages with authorized access

Use the team-a-client configuration to check that you can produce messages to topics that start with a_ or x_.

  1. Write to topic my-topic.

    bin/kafka-console-producer.sh --broker-list my-cluster-kafka-bootstrap:9093 --topic my-topic \
      --producer.config=/tmp/team-a-client.properties
    First message

    This request returns a Not authorized to access topics: [my-topic] error.

    team-a-client has a Dev Team A role that gives it permission to perform any supported actions on topics that start with a_, but can only write to topics that start with x_. The topic named my-topic matches neither of those rules.

  2. Write to topic a_messages.

    bin/kafka-console-producer.sh --broker-list my-cluster-kafka-bootstrap:9093 --topic a_messages \
      --producer.config /tmp/team-a-client.properties
    First message
    Second message

    Messages are produced to Kafka successfully.

  3. Press CTRL+C to exit the CLI application.
  4. Check the Kafka container log for a debug log of Authorization GRANTED for the request.

    oc logs my-cluster-kafka-0 -f -n $NS

Consuming messages with authorized access

Use the team-a-client configuration to consume messages from topic a_messages.

  1. Fetch messages from topic a_messages.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
      --from-beginning --consumer.config /tmp/team-a-client.properties

    The request returns an error because the Dev Team A role for team-a-client only has access to consumer groups that have names starting with a_.

  2. Update the team-a-client properties to specify the custom consumer group it is permitted to use.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
      --from-beginning --consumer.config /tmp/team-a-client.properties --group a_consumer_group_1

    The consumer receives all the messages from the a_messages topic.

Administering Kafka with authorized access

The team-a-client is an account without any cluster-level access, but it can be used with some administrative operations.

  1. List topics.

    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list

    The a_messages topic is returned.

  2. List consumer groups.

    bin/kafka-consumer-groups.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list

    The a_consumer_group_1 consumer group is returned.

    Fetch details on the cluster configuration.

    bin/kafka-configs.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties \
      --entity-type brokers --describe --entity-default

    The request returns an error because the operation requires cluster level permissions that team-a-client does not have.

Using clients with different permissions

Use the team-b-client configuration to produce messages to topics that start with b_.

  1. Write to topic a_messages.

    bin/kafka-console-producer.sh --broker-list my-cluster-kafka-bootstrap:9093 --topic a_messages \
      --producer.config /tmp/team-b-client.properties
    Message 1

    This request returns a Not authorized to access topics: [a_messages] error.

  2. Write to topic b_messages.

    bin/kafka-console-producer.sh --broker-list my-cluster-kafka-bootstrap:9093 --topic b_messages \
      --producer.config /tmp/team-b-client.properties
    Message 1
    Message 2
    Message 3

    Messages are produced to Kafka successfully.

  3. Write to topic x_messages.

    bin/kafka-console-producer.sh --broker-list my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --producer.config /tmp/team-b-client.properties
    Message 1

    A Not authorized to access topics: [x_messages] error is returned, The team-b-client can only read from topic x_messages.

  4. Write to topic x_messages using team-a-client.

    bin/kafka-console-producer.sh --broker-list my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --producer.config /tmp/team-a-client.properties
    Message 1

    This request returns a Not authorized to access topics: [x_messages] error. The team-a-client can write to the x_messages topic, but it does not have a permission to create a topic if it does not yet exist. Before team-a-client can write to the x_messages topic, an admin power user must create it with the correct configuration, such as the number of partitions and replicas.

Managing Kafka with an authorized admin user

Use admin user alice to manage Kafka. alice has full access to manage everything on any Kafka cluster.

  1. Create the x_messages topic as alice.

    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties \
      --topic x_messages --create --replication-factor 1 --partitions 1

    The topic is created successfully.

  2. List all topics as alice.

    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties --list
    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list
    bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-b-client.properties --list

    Admin user alice can list all the topics, whereas team-a-client and team-b-client can only list the topics they have access to.

    The Dev Team A and Dev Team B roles both have Describe permission on topics that start with x_, but they cannot see the other team’s topics because they do not have Describe permissions on them.

  3. Use the team-a-client to produce messages to the x_messages topic:

    bin/kafka-console-producer.sh --broker-list my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --producer.config /tmp/team-a-client.properties
    Message 1
    Message 2
    Message 3

    As alice created the x_messages topic, messages are produced to Kafka successfully.

  4. Use the team-b-client to produce messages to the x_messages topic.

    bin/kafka-console-producer.sh --broker-list my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --producer.config /tmp/team-b-client.properties
    Message 4
    Message 5

    This request returns a Not authorized to access topics: [x_messages] error.

  5. Use the team-b-client to consume messages from the x_messages topic:

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --from-beginning --consumer.config /tmp/team-b-client.properties --group x_consumer_group_b

    The consumer receives all the messages from the x_messages topic.

  6. Use the team-a-client to consume messages from the x_messages topic.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --from-beginning --consumer.config /tmp/team-a-client.properties --group x_consumer_group_a

    This request returns a Not authorized to access topics: [x_messages] error.

  7. Use the team-a-client to consume messages from a consumer group that begins with a_.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --from-beginning --consumer.config /tmp/team-a-client.properties --group a_consumer_group_a

    This request returns a Not authorized to access topics: [x_messages] error.

    Dev Team A has no Read access on topics that start with a x_.

  8. Use alice to produce messages to the x_messages topic.

    bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
      --from-beginning --consumer.config /tmp/alice.properties

    Messages are produced to Kafka successfully.

    alice can read from or write to any topic.

  9. Use alice to read the cluster configuration.

    bin/kafka-configs.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties \
      --entity-type brokers --describe --entity-default

    The cluster configuration for this example is empty.

Chapter 6. Using AMQ Streams Operators

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

6.1. Using the Cluster Operator

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

For information on deploying the Cluster Operator, see Deploying the Cluster Operator.

6.1.1. 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 14.1.6, “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_OPERATIONS_THREAD_POOL_SIZE
Optional, default 10 The worker thread pool size, which is used for various asynchronous and blocking operations that are run by the cluster operator.
STRIMZI_OPERATOR_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_LABELS_EXCLUSION_PATTERN

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

env:
  - name: STRIMZI_LABELS_EXCLUSION_PATTERN
    value: "^key1.*"
STRIMZI_CUSTOM_{COMPONENT_NAME}_LABELS

Optional. One or more custom labels to apply to all the pods created by the {COMPONENT_NAME} custom resource. The Cluster Operator labels the pods when the custom resource is created or is next reconciled.

Environment variables exist for the following components:

  • KAFKA
  • KAFKA_CONNECT
  • KAFKA_CONNECT_BUILD
  • ZOOKEEPER
  • ENTITY_OPERATOR
  • KAFKA_MIRROR_MAKER2
  • KAFKA_MIRROR_MAKER
  • CRUISE_CONTROL
  • KAFKA_BRIDGE
  • KAFKA_EXPORTER
STRIMZI_CUSTOM_RESOURCE_SELECTOR

Optional. 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, 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 2.8.0=registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:2.0.1, 3.0.0=registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.1. 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-rhel8-operator:2.0.1. 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 2.8.0=registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:2.0.1, 3.0.0=registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.1. This is used when a KafkaConnect.spec.version property is specified but not the KafkaConnect.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 2.8.0=registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:2.0.1, 3.0.0=registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.1. 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-rhel8-operator:2.0.1. 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-rhel8-operator:2.0.1. 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-30-rhel8:2.0.1. 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 OpenShift version information detected from the API server.

Example configuration for OpenShift version override

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

KUBERNETES_SERVICE_DNS_DOMAIN

Optional. Overrides the default OpenShift DNS domain name suffix.

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

For example, for broker kafka-0:

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

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

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

STRIMZI_CONNECT_BUILD_TIMEOUT_MS
Optional, default 300000 ms. The timeout for building new Kafka Connect images with additional 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.
STRIMZI_NETWORK_POLICY_GENERATION
Optional, default true. Controls whether AMQ Streams generates network policy resources. Network policies allow connections between Kafka components.

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

STRIMZI_FEATURE_GATES
Optional. Enables or disables features and functionality controlled by feature gates. For more information about each feature gate, see Section 6.1.1.1, “Feature gates”.
6.1.1.1. Feature gates

AMQ Streams operators support feature gates to enable or disable certain features and functionality. Enabling a feature gate changes the behavior of the relevant operator and introduces the feature to your AMQ Streams deployment.

Feature gates have a default state of either enabled or disabled. To modify a feature gate’s default state, use the STRIMZI_FEATURE_GATES environment variable in the operator’s configuration. You can modify multiple feature gates using this single environment variable.

Feature gates have three stages of maturity:

  • Alpha — typically disabled by default
  • Beta — typically enabled by default
  • General Availability (GA) — typically enabled by default

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

Note

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

Table 6.1. All feature gates and the AMQ Streams versions when they moved to alpha, beta, or GA
Feature gateAlphaBetaGA

ControlPlaneListener

1.8

-

-

ServiceAccountPatching

1.8

-

-

Configuring feature gates

You configure feature gates using the STRIMZI_FEATURE_GATES environment variable in the operator’s configuration. Specify a comma-separated list of feature gate names and prefixes. A + prefix enables the feature gate and a - prefix disables it.

Example feature gate configuration that enables FeatureGate1 and disables FeatureGate2

env:
  - name: STRIMZI_FEATURE_GATES
    value: +FeatureGate1,-FeatureGate2

6.1.1.1.1. Control plane listener feature gate

Use the ControlPlaneListener feature gate to change the communication paths used for inter-broker communications within your Kafka cluster.

The OpenShift control plane manages the workloads running on the worker nodes. Services such as the Kubernetes API server and the controller manager run on the control plane. The OpenShift data plane provides resources to containers, including CPU, memory, network, and storage.

In AMQ Streams, control plane traffic consists of controller connections that maintain the desired state of the Kafka cluster. Data plane traffic mainly consists of data replication between the leader broker and the follower brokers.

When the ControlPlaneListener feature gate is disabled, control plane and data plane traffic go through the same internal listener on port 9091. This was the default behavior before the feature gate was introduced.

When ControlPlaneListener is enabled, control plane traffic goes through a dedicated control plane listener on port 9090. Data plane traffic continues to use the internal listener on port 9091.

Using control plane listeners might improve performance because important controller connections, such as partition leadership changes, are not delayed by data replication across brokers.

Enabling the control plane listener feature gate

The ControlPlaneListener feature gate is in the alpha stage and has a default state of disabled. To enable it, specify +ControlPlaneListener in the STRIMZI_FEATURE_GATES environment variable in the Cluster Operator configuration.

This feature gate must be disabled when:

  • Upgrading from AMQ Streams 1.7 and earlier
  • Downgrading to AMQ Streams 1.7 and earlier
Note

The ControlPlaneListener feature gate was introduced in AMQ Streams 1.8 and is expected to remain in the alpha stage for a number of releases before moving to the beta stage.

6.1.1.1.2. Service Account patching feature gate

By default, the Cluster Operator does not update service accounts. To allow the Cluster Operator to apply updates, enable the ServiceAccountPatching feature gate.

Add +ServiceAccountPatching to the STRIMZI_FEATURE_GATES environment variable in the Cluster Operator configuration.

The feature gate is currently in the alpha phase and disabled by default. With the feature gate enabled, the Cluster Operator applies updates to service account configuration in every reconciliation. For example, you can change service account labels and annotations after the operands are already created.

Note

The ServiceAccountPatching feature gate was introduced in AMQ Streams 1.8 and is expected to remain in the alpha phase for a number of releases before it moves to the beta phase and is enabled by default.

6.1.1.2. 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 configmap strimzi-cluster-operator

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

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

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

Note

Do not remove the monitorInterval option from the ConfigMap.

6.1.1.3. 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
  #...

6.1.1.4. 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.

6.1.2. 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.

6.1.2.1. 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 and the cluster is not exposed via nodeport, 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 resource uses 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
6.1.2.2. 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.

6.1.2.3. 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 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:
      # Kafka Connect Build on OpenShift requirement
      - build.openshift.io
    resources:
      - buildconfigs
      - buildconfigs/instantiate
      - builds
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - networking.k8s.io
    resources:
      # The cluster operator needs to access and manage network policies to lock down communication between Strimzi components
      - networkpolicies
      # The cluster operator needs to access and manage ingresses which allow external access to the services in a cluster
      - ingresses
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - route.openshift.io
    resources:
      # The cluster operator needs to access and manage routes to expose Strimzi components for external access
      - routes
      - routes/custom-host
    verbs:
      - get
      - list
      - watch
      - create
      - delete
      - patch
      - update
  - apiGroups:
      - 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.authoriz