1. Configuration overview

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

This guide describes how to configure and manage a Strimzi deployment.

1.1. Configuring custom resources

Use custom resources to configure your Strimzi deployment.

You can use custom resources to configure and create instances of the following components:

  • Kafka clusters

  • Kafka Connect clusters

  • Kafka MirrorMaker

  • Kafka Bridge

  • Cruise Control

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

  • Securing client access to Kafka brokers

  • Accessing Kafka brokers from outside the cluster

  • Creating topics

  • Creating users (clients)

  • Controlling feature gates

  • Changing logging frequency

  • Allocating resource limits and requests

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

The Custom resource API reference describes the properties you can use in your configuration.

1.2. Configuring listeners to connect to Kafka brokers

Listeners are used to connect to Kafka brokers. Strimzi provides a generic GenericKafkaListener schema with properties to configure listeners through the Kafka resource.

The GenericKafkaListener provides a flexible approach to listener configuration. You can specify properties to configure internal listeners for connecting within the Kubernetes cluster, or external listeners for connecting outside the Kubernetes cluster.

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 Kubernetes network to an outside network. In which case, you can configure internal listeners (using the useServiceDnsDomain property) so that the Kubernetes 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 Kubernetes

You can configure external listeners for client access outside a Kubernetes 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 Kubernetes 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.

Note
 If you scale your Kafka cluster while using external listeners, it might trigger a rolling update of all Kafka brokers. This depends on the configuration.

1.3. Document Conventions

User-replaced values

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

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

1.4. Additional resources

2. Configuring a Strimzi deployment

Configure your Strimzi deployment using custom resources. Strimzi provides example configuration files, which can serve as a starting point when building your own Kafka component configuration for deployment.

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

  • A Kubernetes cluster

  • A running Cluster Operator

See the Deploying and Upgrading Strimzi 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: 3.2.0 (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
      default.replication.factor: 3
      min.insync.replicas: 2
      inter.broker.protocol.version: "3.2"
      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)
    # ...

    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 Kubernetes cluster.

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

    10. Port number used by the listener inside Kafka. The port number has to be unique within a given Kafka cluster. Allowed port numbers are 9092 and higher with the exception of ports 9404 and 9999, which are already used for Prometheus and JMX. Depending on the listener type, the port number might not be the same as the port number that connects Kafka clients.

    11. Listener type specified as internal, or for external listeners, as route, loadbalancer, nodeport or ingress.

    12. Enables TLS encryption for each listener. Default is false. TLS encryption is not required for route listeners.

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

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

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

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

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

    18. The config specifies the broker configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.

    19. SSL properties for listeners with TLS encryption enabled to enable a specific cipher suite or TLS version.

    20. Storage is configured as ephemeral, persistent-claim or jbod.

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

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

    23. Rack awareness is configured to spread replicas across different racks. A topologykey must match the label of a cluster node.

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

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

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

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

    28. Specified ZooKeeper loggers and log levels.

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

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

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

    32. Specified User Operator loggers and log levels.

    33. Kafka Exporter configuration. Kafka Exporter is an optional component for extracting metrics data from Kafka brokers, in particular consumer lag data.

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

  2. Create or update the resource:

    kubectl apply -f <kafka_configuration_file>

2.1.2. Configuring the Entity Operator

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

The Entity Operator comprises the:

  • Topic Operator to manage Kafka topics

  • User Operator to manage Kafka users

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

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

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

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 Customizing Kubernetes resources.

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

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

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

Topic Operator configuration properties

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

watchedNamespace

The Kubernetes 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 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 resources.

logging

The logging property configures the logging of the Topic Operator. For more details, see 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
    # ...
User Operator configuration properties

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

watchedNamespace

The Kubernetes 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 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 resources.

logging

The logging property configures the logging of the User Operator. For more details, see logging.

secretPrefix

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

Example User Operator configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60
    # ...

2.1.3. Kafka and ZooKeeper storage types

As stateful applications, Kafka and ZooKeeper need to store data on disk. Strimzi 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.
Data storage considerations

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

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
 Strimzi does not require Kubernetes raw block volumes.
File systems

Kafka uses a file system for storing messages. Strimzi is compatible with the XFS and ext4 file systems, which are commonly used with Kafka. Consider the underlying architecture and requirements of your deployment when choosing and setting up your file system.

For more information, refer to Filesystem Selection in the Kafka documentation.

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.
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
    # ...
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.

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 Kubernetes 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 Strimzi cluster is only supported in Kubernetes versions that support persistent volume resizing. The persistent volume to be resized must use a storage class that supports volume expansion. For other versions of Kubernetes and storage classes 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
# ...
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 Strimzi 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.

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.

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.

Resizing persistent volumes

You can provision increased storage capacity by increasing the size of the persistent volumes used by an existing Strimzi 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 Kubernetes.
Prerequisites

  • A Kubernetes cluster with support for volume resizing.

  • The Cluster Operator is running.

  • A Kafka cluster using persistent volumes created using a storage class that supports volume expansion.

Procedure

  1. 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:

    kubectl apply -f <kafka_configuration_file>

    Kubernetes increases the capacity of the selected persistent volumes in response to a request from the Cluster Operator. When the resizing is complete, the Cluster Operator restarts all pods that use the resized persistent volumes. This happens automatically.

Additional resources
JBOD storage overview

You can configure Strimzi 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.

JBOD configuration

To use JBOD with Strimzi, 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.

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.

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.

Adding volumes to JBOD storage

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

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

  • A Kubernetes cluster

  • A running Cluster Operator

  • A Kafka cluster with JBOD storage

Procedure

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

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

  2. Create or update the resource:

    kubectl apply -f <kafka_configuration_file>

  3. Create new topics or reassign existing partitions to the new disks.

Additional resources

For more information about reassigning topics, see Partition reassignment tool.

Removing volumes from JBOD storage

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

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

  • A Kubernetes cluster

  • A running Cluster Operator

  • A Kafka cluster with JBOD storage with two or more volumes

Procedure

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

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

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

  3. Create or update the resource:

    kubectl apply -f <kafka_configuration_file>
Additional resources

For more information about reassigning topics, see 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.

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

If you are using 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.

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 Strimzi.

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.

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.

    kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
    kubectl 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 Strimzi Kafka image to connect to a running Kafka broker.

    kubectl run --restart=Never --image=quay.io/strimzi/kafka:latest-kafka-3.2.0 <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.

    kubectl 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.

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

    kubectl 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.

    kubectl 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.

    kubectl cp topics.json <interactive_pod_name>:/tmp/topics.json

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

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

    Replace <namespace> with the Kubernetes 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
Additional resources
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.

    kubectl 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.

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

    Replace <namespace> with the Kubernetes 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.

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.

    kubectl 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.

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

    Replace <namespace> with the Kubernetes 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-zA-Z0-9.-]+\.[a-z0-9]+-delete$, the broker still has live partitions and should not be stopped.

    You can check this by executing the command:

    kubectl 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. Retrieving JMX metrics with JmxTrans

JmxTrans is a tool for retrieving JMX metrics data from Java processes and pushing that data, in various formats, to remote sinks inside or outside the cluster. JmxTrans can communicate with a secure JMX port.

Strimzi supports using JmxTrans to read JMX metrics from Kafka brokers.

JmxTrans reads JMX metrics data from secure or insecure Kafka brokers and pushes the data to remote sinks in various data formats. For example, JmxTrans can obtain JMX metrics about the request rate of each Kafka broker’s network and then push the data to a Logstash database outside the Kubernetes cluster.

For more information about JmxTrans, see the JmxTrans GitHub.

Configuring a JmxTrans deployment
Prerequisites

  • A running Kubernetes cluster

You can configure a JmxTrans deployment by using the Kafka.spec.jmxTrans property. A JmxTrans deployment can read from a secure or insecure Kafka broker. To configure a JmxTrans deployment, define the following properties:

  • Kafka.spec.jmxTrans.outputDefinitions

  • Kafka.spec.jmxTrans.kafkaQueries

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

Note
 To use JMXTrans, jmxOptions must be configured on the Kafka broker.
Configuring JmxTrans output definitions

Output definitions specify where JMX metrics are pushed to, and in which data format. For information about supported data formats, see Data formats. How many seconds JmxTrans agent waits for before pushing new data can be configured through the flushDelay property. The host and port properties define the target host address and target port the data is pushed to. The name property is a required property that is referenced by the Kafka.spec.jmxTrans.kafkaQueries property.

Here is an example configuration pushing JMX data in the Graphite format every 5 seconds to a Logstash database on http://myLogstash:9999, and another pushing to standardOut (standard output):

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  jmxTrans:
    outputDefinitions:
      - outputType: "com.googlecode.jmxtrans.model.output.GraphiteWriter"
        host: "http://myLogstash"
        port: 9999
        flushDelay: 5
        name: "logstash"
      - outputType: "com.googlecode.jmxtrans.model.output.StdOutWriter"
        name: "standardOut"
        # ...
    # ...
  zookeeper:
    # ...
Configuring JmxTrans queries

JmxTrans queries specify what JMX metrics are read from the Kafka brokers. Currently JmxTrans queries can only be sent to the Kafka Brokers. Configure the targetMBean property to specify which target MBean on the Kafka broker is addressed. Configuring the attributes property specifies which MBean attribute is read as JMX metrics from the target MBean. JmxTrans supports wildcards to read from target MBeans, and filter by specifying the typenames. The outputs property defines where the metrics are pushed to by specifying the name of the output definitions.

The following JmxTrans deployment reads from all MBeans that match the pattern kafka.server:type=BrokerTopicMetrics,name=* and have name in the target MBean’s name. From those Mbeans, it obtains JMX metrics about the Count attribute and pushes the metrics to standard output as defined by outputs.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  jmxTrans:
    kafkaQueries:
      - targetMBean: "kafka.server:type=BrokerTopicMetrics,*"
        typeNames: ["name"]
        attributes:  ["Count"]
        outputs: ["standardOut"]
  zookeeper:
    # ...

2.1.6. 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.

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.

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
 Strimzi 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
Configuring a maintenance time window

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

Prerequisites

  • A Kubernetes 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:

    kubectl apply -f <kafka_configuration_file>
Additional resources

Performing rolling updates:

2.1.7. 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 Strimzi.

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

  • A Kubernetes cluster is available.

  • A Kafka cluster is running.

  • The Cluster Operator is running.

Procedure

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

    For example:

    kubectl exec -ti my-cluster-zookeeper-0 -- bin/kafka-topics.sh --list --zookeeper localhost:12181

    Be sure to use localhost:12181.

    You can now run Kafka commands to ZooKeeper.

2.1.8. Deleting Kafka nodes manually

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

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

See the Deploying and Upgrading Strimzi 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 Kubernetes.

    Use kubectl annotate:

    kubectl 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.

2.1.9. Deleting ZooKeeper nodes manually

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

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

See the Deploying and Upgrading Strimzi 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 Kubernetes.

    Use kubectl annotate:

    kubectl 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.

2.1.10. List of Kafka cluster resources

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

Name given to the following ZooKeeper resources:

  • StatefulSet or StrimziPodSet (if the UseStrimziPodSets feature gate is enabled) for managing the ZooKeeper node pods.

  • Service account used by the ZooKeeper nodes.

  • PodDisruptionBudget configured for the ZooKeeper nodes.

cluster-name-zookeeper-idx

Pods created by the ZooKeeper StatefulSet or StrimziPodSet.

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-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

Name given to the following Kafka resources:

  • StatefulSet or StrimziPodSet (if the UseStrimziPodSets feature gate is enabled) for managing the Kafka broker pods.

  • Service account used by the Kafka pods.

  • PodDisruptionBudget configured for the Kafka brokers.

cluster-name-kafka-idx

Name given to the following Kafka resources:

  • Pods created by the Kafka StatefulSet or StrimziPodSet.

  • ConfigMap with Kafka broker configuration (if the UseStrimziPodSets feature gate is enabled).

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 Kubernetes cluster.

cluster-name-kafka-external-bootstrap

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

cluster-name-kafka-pod-id

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

cluster-name-kafka-external-bootstrap

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

cluster-name-kafka-pod-id

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

cluster-name-kafka-listener-name-bootstrap

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

cluster-name-kafka-listener-name-pod-id

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

cluster-name-kafka-listener-name-bootstrap

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

cluster-name-kafka-listener-name-pod-id

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

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-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

Name given to the following Entity Operator resources:

  • Deployment with Topic and User Operators.

  • Service account used by the Entity Operator.

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-topic-operator-certs

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

cluster-name-entity-user-operator-certs

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

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

Name given to the following Kafka Exporter resources:

  • Deployment with Kafka Exporter.

  • Service used to collect consumer lag metrics.

  • Service account used by the Kafka Exporter.

cluster-name-kafka-exporter-random-string

Pod created by the Kafka Exporter deployment.

Cruise Control

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

cluster-name-cruise-control

Name given to the following Cruise Control resources:

  • Deployment with Cruise Control.

  • Service used to communicate with Cruise Control.

  • Service account used by the Cruise Control.

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-network-policy-cruise-control

Network policy managing access to the Cruise Control service.

JMXTrans

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

cluster-name-jmxtrans

Name given to the following JMXTrans resources:

  • Deployment with JMXTrans.

  • Service account used by the JMXTrans.

cluster-name-jmxtrans-random-string

Pod created by the JMXTrans deployment.

cluster-name-jmxtrans-config

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

2.2. Kafka Connect cluster configuration

This section describes how to configure a Kafka Connect deployment in your Strimzi 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 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.

KafkaConnector configuration

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

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

To manage connectors, you can use 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.

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

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

See the Deploying and Upgrading Strimzi 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. The number of replica nodes for the workers that run tasks.

    4. Authentication for the Kafka Connect cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-256/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 Strimzi.

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

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

    10. (Required) List of connector plugins and their artifacts to add to the new container image. Each plugin must be configured with at least one artifact.

    11. External configuration for 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:

    kubectl 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 Strimzi, 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

  • A Kubernetes 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:

    Property Name

    offset.storage.topic

    connect-cluster-offsets

    status.storage.topic

    connect-cluster-status

    config.storage.topic

    connect-cluster-configs

    group

    connect-cluster

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

  2. Create or update the resource.

    kubectl apply -f KAFKA-USER-CONFIG-FILE

2.2.4. List of Kafka Connect cluster resources

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

2.3. Kafka MirrorMaker cluster configuration

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

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

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 Strimzi 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. The number of replica nodes.

    2. Bootstrap servers for consumer and producer.

    3. Group ID for the consumer.

    4. The number of consumer streams.

    5. The offset auto-commit interval in milliseconds.

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

    7. Authentication for consumer or producer, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-256/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. Distributed tracing is enabled for Jaeger.

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

  2. Create or update the resource:

    kubectl apply -f <your-file>

2.3.2. List of Kafka MirrorMaker cluster resources

The following resources are created by the Cluster Operator in the Kubernetes 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 Strimzi 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. The full schema of the KafkaMirrorMaker2 resource is described in the KafkaMirrorMaker2 schema reference.

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.

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.

MirrorMaker 2.0 replication
Figure 1. Replication across two clusters

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

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.

MirrorMaker 2.0 bidirectional architecture
Figure 2. Topic renaming

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

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

Unidirectional replication (active/passive)

The MirrorMaker 2.0 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.

Topic configuration synchronization

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

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.

Offset tracking

MirrorMaker 2.0 tracks offsets for consumer groups using internal topics.

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

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

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

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

The location of the offset-syncs topic is the source cluster by default. You can use the offset-syncs.topic.location connector configuration to change this to the target cluster. You need read/write access to the cluster that contains the topic. Using the target cluster as the location of the offset-syncs topic allows you to use MirrorMaker 2.0 even if you have only read access to the source cluster.

Synchronizing consumer group offsets

The __consumer_offsets topic stores information on committed offsets, for each consumer group. Offset synchronization periodically transfers the consumer offsets for the consumer groups of a source cluster into the consumer offsets topic of a target cluster.

Offset synchronization is particularly useful in an active/passive configuration. If the active cluster goes down, consumer applications can switch to the passive (standby) cluster and pick up from the last transferred offset position.

To use topic offset synchronization, enable the synchronization by adding sync.group.offsets.enabled to the checkpoint connector configuration, and setting the property to true. Synchronization is disabled by default.

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

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.

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. Connector configuration

Use Mirrormaker 2.0 connector configuration for the internal connectors that orchestrate the synchronization of data between Kafka clusters.

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

Table 1. MirrorMaker 2.0 connector configuration properties
Property sourceConnector checkpointConnector heartbeatConnector
admin.timeout.ms

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

✓

✓

✓
replication.policy.class

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

✓

✓

✓
replication.policy.separator

The separator used for topic naming in the target cluster. Default is . (dot). It is only used when the replication.policy.class is the DefaultReplicationPolicy.

✓

✓

✓
consumer.poll.timeout.ms

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

✓

✓
offset-syncs.topic.location

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

✓

✓
topic.filter.class

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

✓

✓
config.property.filter.class

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

✓
config.properties.exclude

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

✓
offset.lag.max

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

✓
offset-syncs.topic.replication.factor

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

✓
refresh.topics.enabled

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

✓
refresh.topics.interval.seconds

Frequency of topic refresh. Default is 600 (10 minutes).

✓
replication.factor

The replication factor for new topics. Default is 2.

✓
sync.topic.acls.enabled

Enables synchronization of ACLs from the source cluster. Default is true. Not compatible with the User Operator.

✓
sync.topic.acls.interval.seconds

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

✓
sync.topic.configs.enabled

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

✓
sync.topic.configs.interval.seconds

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

✓
checkpoints.topic.replication.factor

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

✓
emit.checkpoints.enabled

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

✓
emit.checkpoints.interval.seconds

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

✓
group.filter.class

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

✓
refresh.groups.enabled

Enables check for new consumer groups. Default is true.

✓
refresh.groups.interval.seconds

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

✓
sync.group.offsets.enabled

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

✓
sync.group.offsets.interval.seconds

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

✓
emit.heartbeats.enabled

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

✓
emit.heartbeats.interval.seconds

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

✓
heartbeats.topic.replication.factor

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

✓

2.4.4. Specifying a maximum number of tasks

Connectors create the tasks that are responsible for moving data in and out of Kafka. Each connector comprises one or more tasks that are distributed across a group of worker pods that run the tasks.

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

You can specify the maximum number of connector tasks in your MirrorMaker configuration using the tasksMax property. Without specifying a maximum number of tasks, the default setting is a single task. If the infrastructure supports the processing overhead, increasing the number of tasks can improve throughput.

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

For the source connector, the maximum number of tasks possible is one for each partition being replicated from the source cluster. For the checkpoint connector, the maximum number of tasks possible is one for each group being replicated from the source cluster. The number of tasks that are started for these connectors is the lower value between the maximum number of possible tasks and the value for tasksMax.

The heartbeat connector always uses a single task.

2.4.5. Handling high volumes of messages

If your MirrorMaker 2.0 deployment is going to be handling a high volume of messages, you might need to adjust its configuration to support it.

The flush pipeline for data replication is source topic → (Kafka Connect) source message queue → producer buffer → target topic. An offset flush timeout period (offset.flush.timeout.ms) is the time to wait for the producer buffer (producer.buffer.memory) to flush and offset data to be committed. Try to avoid a situation where a large producer buffer and an insufficient offset flush timeout period causes a failed to flush or failed to commit offsets type of error.

This type of error means that there are too many messages in the producer buffer, so they can’t all be flushed before the offset flush timeout is reached.

If you are getting this type of error, try the following configuration changes:

  • Decreasing the default value in bytes of the producer.buffer.memory

  • Increasing the default value in milliseconds of the offset.flush.timeout.ms

The changes should help to keep the underlying Kafka Connect queue of outstanding messages at a manageable size. You might need to adjust the values to have the desired effect.

If these configuration changes don’t resolve the error, you can try increasing the number of tasks that run in parallel by doing the following.

  • Increasing the number of tasks using the tasksMax property

  • Increasing the number of nodes for the workers that run tasks using the replicas property

Example MirrorMaker 2.0 configuration for handling high volumes of messages
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 3.2.0
  replicas: 5
  connectCluster: "my-cluster-target"
  clusters:
  - alias: "my-cluster-source"
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092
  - alias: "my-cluster-target"
    config:
      offset.flush.timeout.ms: 10000
      producer.buffer.memory: 8388608
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector:
      tasksMax: 10
Checking the message flow

If you are using Prometheus and Grafana to monitor your deployment, you can check the MirrorMaker 2.0 message flow. The example MirrorMaker 2.0 Grafana dashboard provided with Strimzi shows the following metrics related to the flush pipeline.

  • The number of messages in Kafka Connect’s outstanding messages queue

  • The available bytes of the producer buffer

  • The offset commit timeout in milliseconds

You can use these metrics to gauge whether or not you need to tune your configuration based on the volume of messages.

Additional resources

2.4.6. 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.

2.4.7. Configuring Kafka MirrorMaker 2.0

Use the properties of the KafkaMirrorMaker2 resource to configure your Kafka MirrorMaker 2.0 deployment. Use MirrorMaker 2.0 to synchronize data between Kafka clusters

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

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:

Minimal configuration for MirrorMaker 2.0
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 3.2.0
  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.

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

Prerequisites

  • Strimzi is running

  • Source and target Kafka clusters are 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: 3.2.0 (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: "org.apache.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: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy"
    topicsPattern: "topic1|topic2|topic3" (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. The number of replica nodes for the workers that run tasks.

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

    4. Specification for the Kafka clusters being synchronized.

    5. Cluster alias for the source Kafka cluster.

    6. Authentication for the source cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-256/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 Strimzi.

    13. SSL properties for external listeners to run with a specific cipher suite for a TLS version.

    14. Hostname verification is enabled by setting to HTTPS. An empty string disables the verification.

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

    16. MirrorMaker 2.0 connectors.

    17. Cluster alias for the source cluster used by the MirrorMaker 2.0 connectors.

    18. Cluster alias for the target cluster used by the MirrorMaker 2.0 connectors.

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

    20. 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. This feature is not compatible with the User Operator. If you are using the User Operator, set this property to false.

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

    25. 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 a comma-separated list or regular expression pattern. The source connector replicates the specified topics. The checkpoint connector tracks offsets for the specified topics. Here we request three topics by name.

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

    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. Distributed tracing is enabled for Jaeger.

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

  2. Create or update the resource:

    kubectl apply -f MIRRORMAKER-CONFIGURATION-FILE

2.4.8. Securing a Kafka MirrorMaker 2.0 deployment

This procedure describes in outline the configuration required to secure a MirrorMaker 2.0 deployment.

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

For the Kafka clusters, you specify internal listeners for secure connections within a Kubernetes cluster and external listeners for connections outside the Kubernetes cluster.

You can configure authentication and authorization mechanisms. The security options implemented for the source and target Kafka clusters must be compatible with the security options implemented for MirrorMaker 2.0.

After you have created the cluster and user authentication credentials, you specify them in your MirrorMaker configuration for secure connections.

Note
 In this procedure, the certificates generated by the Cluster Operator are used, but you can replace them by installing your own certificates. You can also configure your listener to use a Kafka listener certificate managed by an external Certificate Authority.
Before you start

Before starting this procedure, take a look at the example configuration files provided by Strimzi. They include examples for securing a deployment of MirrorMaker 2.0 using TLS or SCRAM-SHA-512 authentication. The examples specify internal listeners for connecting within a Kubernetes cluster.

The examples also show the ACLs needed by MirrorMaker 2.0 to allow operations on the source and target Kafka clusters.

Prerequisites

  • Strimzi is running

  • Separate namespaces for source and target clusters

The procedure assumes that the source and target Kafka clusters are installed to separate namespaces If you want to use the Topic Operator, you’ll need to do this. The Topic Operator only watches a single cluster in a specified namespace.

By separating the clusters into namespaces, you will need to copy the cluster secrets so they can be accessed outside the namespace. You need to reference the secrets in the MirrorMaker configuration.

Procedure

  1. Configure two Kafka resources, one to secure the source Kafka cluster and one to secure the target Kafka cluster.

    You can add listener configuration for authentication and enable authorization.

    In this example, an internal listener is configured for a Kafka cluster with TLS encryption and authentication. Kafka simple authorization is enabled.

    Example source Kafka cluster configuration with TLS authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-source-cluster
spec:
  kafka:
    version: 3.2.0
    replicas: 1
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
    authorization:
      type: simple
    config:
      offsets.topic.replication.factor: 1
      transaction.state.log.replication.factor: 1
      transaction.state.log.min.isr: 1
      default.replication.factor: 1
      min.insync.replicas: 1
      inter.broker.protocol.version: "3.2"
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  zookeeper:
    replicas: 1
    storage:
      type: persistent-claim
      size: 100Gi
      deleteClaim: false
  entityOperator:
    topicOperator: {}
    userOperator: {}
    Example target Kafka cluster configuration with TLS authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-target-cluster
spec:
  kafka:
    version: 3.2.0
    replicas: 1
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
    authorization:
      type: simple
    config:
      offsets.topic.replication.factor: 1
      transaction.state.log.replication.factor: 1
      transaction.state.log.min.isr: 1
      default.replication.factor: 1
      min.insync.replicas: 1
      inter.broker.protocol.version: "3.2"
    storage:
      type: jbod
      volumes:
        - id: 0
          type: persistent-claim
          size: 100Gi
          deleteClaim: false
  zookeeper:
    replicas: 1
    storage:
      type: persistent-claim
      size: 100Gi
      deleteClaim: false
  entityOperator:
    topicOperator: {}
    userOperator: {}

  2. Create or update the Kafka resources in separate namespaces.

    kubectl apply -f <kafka_configuration_file> -n <namespace>

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

    The certificates are created in the secret <cluster_name>-cluster-ca-cert.

  3. Configure two KafkaUser resources, one for a user of the source Kafka cluster and one for a user of the target Kafka cluster.

    1. Configure the same authentication and authorization types as the corresponding source and target Kafka cluster. For example, if you used tls authentication and simple authorization type in the Kafka configuration for the source Kafka cluster, use the same in the KafkaUser configuration.

    2. Configure the ACLs needed by MirrorMaker 2.0 to allow operations on the source and target Kafka clusters.

      The ACLs are used by the internal MirrorMaker connectors, and by the underlying Kafka Connect framework.

    Example source user configuration for TLS client authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-source-user
  labels:
    strimzi.io/cluster: my-source-cluster
spec:
  authentication:
    type: tls
  authorization:
    type: simple
    acls:
      # MirrorSourceConnector
      - resource: # Not needed if offset-syncs.topic.location=target
          type: topic
          name: mm2-offset-syncs.my-target-cluster.internal
        operation: Create
      - resource: # Not needed if offset-syncs.topic.location=target
          type: topic
          name: mm2-offset-syncs.my-target-cluster.internal
        operation: DescribeConfigs
      - resource: # Not needed if offset-syncs.topic.location=target
          type: topic
          name: mm2-offset-syncs.my-target-cluster.internal
        operation: Write
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operation: Read
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operation: DescribeConfigs
      # MirrorCheckpointConnector
      - resource:
          type: cluster
        operation: Describe
      - resource: # Needed for every group for which offsets are synced
          type: group
          name: "*"
        operation: Describe
      - resource: # Not needed if offset-syncs.topic.location=target
          type: topic
          name: mm2-offset-syncs.my-target-cluster.internal
        operation: Read
    Example target user configuration for TLS client authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-target-user
  labels:
    strimzi.io/cluster: my-target-cluster
spec:
  authentication:
    type: tls
  authorization:
    type: simple
    acls:
      # Underlying Kafka Connect internal topics to store configuration, offsets, or status
      - resource:
          type: group
          name: mirrormaker2-cluster
        operation: Read
      - resource:
          type: topic
          name: mirrormaker2-cluster-configs
        operation: Read
      - resource:
          type: topic
          name: mirrormaker2-cluster-configs
        operation: Describe
      - resource:
          type: topic
          name: mirrormaker2-cluster-configs
        operation: DescribeConfigs
      - resource:
          type: topic
          name: mirrormaker2-cluster-configs
        operation: Write
      - resource:
          type: topic
          name: mirrormaker2-cluster-configs
        operation: Create
      - resource:
          type: topic
          name: mirrormaker2-cluster-status
        operation: Read
      - resource:
          type: topic
          name: mirrormaker2-cluster-status
        operation: Describe
      - resource:
          type: topic
          name: mirrormaker2-cluster-status
        operation: DescribeConfigs
      - resource:
          type: topic
          name: mirrormaker2-cluster-status
        operation: Write
      - resource:
          type: topic
          name: mirrormaker2-cluster-status
        operation: Create
      - resource:
          type: topic
          name: mirrormaker2-cluster-offsets
        operation: Read
      - resource:
          type: topic
          name: mirrormaker2-cluster-offsets
        operation: Write
      - resource:
          type: topic
          name: mirrormaker2-cluster-offsets
        operation: Describe
      - resource:
          type: topic
          name: mirrormaker2-cluster-offsets
        operation: DescribeConfigs
      - resource:
          type: topic
          name: mirrormaker2-cluster-offsets
        operation: Create
      # MirrorSourceConnector
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operation: Create
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operation: Alter
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operation: AlterConfigs
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operation: Write
      # MirrorCheckpointConnector
      - resource:
          type: cluster
        operation: Describe
      - resource:
          type: topic
          name: my-source-cluster.checkpoints.internal
        operation: Create
      - resource:
          type: topic
          name: my-source-cluster.checkpoints.internal
        operation: Describe
      - resource:
          type: topic
          name: my-source-cluster.checkpoints.internal
        operation: Write
      - resource: # Needed for every group for which the offset is synced
          type: group
          name: "*"
        operation: Read
      - resource: # Needed for every group for which the offset is synced
          type: group
          name: "*"
        operation: Describe
      - resource: # Needed for every topic which is mirrored
          type: topic
          name: "*"
        operation: Read
      # MirrorHeartbeatConnector
      - resource:
          type: topic
          name: heartbeats
        operation: Create
      - resource:
          type: topic
          name: heartbeats
        operation: Describe
      - resource:
          type: topic
          name: heartbeats
        operation: Write
    Note
    		 You can use a certificate issued outside the User Operator by setting type to tls-external. For more information, see User authentication.

  4. Create or update a KafkaUser resource in each of the namespaces you created for the source and target Kafka clusters.

    kubectl apply -f <kafka_user_configuration_file> -n <namespace>

    The User Operator creates the users representing the client (MirrorMaker), and the security credentials used for client authentication, based on the chosen authentication type.

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

  5. Configure a KafkaMirrorMaker2 resource with the authentication details to connect to the source and target Kafka clusters.

    Example MirrorMaker 2.0 configuration with TLS authentication
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker-2
spec:
  version: 3.2.0
  replicas: 1
  connectCluster: "my-target-cluster"
  clusters:
    - alias: "my-source-cluster"
      bootstrapServers: my-source-cluster-kafka-bootstrap:9093
      tls: # (1)
        trustedCertificates:
          - secretName: my-source-cluster-cluster-ca-cert
            certificate: ca.crt
      authentication: # (2)
        type: tls
        certificateAndKey:
          secretName: my-source-user
          certificate: user.crt
          key: user.key
    - alias: "my-target-cluster"
      bootstrapServers: my-target-cluster-kafka-bootstrap:9093
      tls: # (3)
        trustedCertificates:
          - secretName: my-target-cluster-cluster-ca-cert
            certificate: ca.crt
      authentication: # (4)
        type: tls
        certificateAndKey:
          secretName: my-target-user
          certificate: user.crt
          key: user.key
      config:
        # -1 means it will use the default replication factor configured in the broker
        config.storage.replication.factor: -1
        offset.storage.replication.factor: -1
        status.storage.replication.factor: -1
  mirrors:
    - sourceCluster: "my-source-cluster"
      targetCluster: "my-target-cluster"
      sourceConnector:
        config:
          replication.factor: 1
          offset-syncs.topic.replication.factor: 1
          sync.topic.acls.enabled: "false"
      heartbeatConnector:
        config:
          heartbeats.topic.replication.factor: 1
      checkpointConnector:
        config:
          checkpoints.topic.replication.factor: 1
          sync.group.offsets.enabled: "true"
      topicsPattern: "topic1|topic2|topic3"
      groupsPattern: "group1|group2|group3"

    1. The TLS certificates for the source Kafka cluster. If they are in a separate namespace, copy the cluster secrets from the namespace of the Kafka cluster.

    2. The user authentication for accessing the source Kafka cluster using the TLS mechanism.

    3. The TLS certificates for the target Kafka cluster.

    4. The user authentication for accessing the target Kafka cluster.

  6. Create or update the KafkaMirrorMaker2 resource in the same namespace as the target Kafka cluster.

    kubectl apply -f <mirrormaker2_configuration_file> -n <namespace_of_target_cluster>
Additional resources

2.4.9. 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 a Kubernetes 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:

    kubectl get KafkaMirrorMaker2

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

    kubectl describe KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME

  3. To restart the connector, annotate the KafkaMirrorMaker2 resource in Kubernetes. In this example, kubectl annotate restarts a connector named my-source->my-target.MirrorSourceConnector:

    kubectl 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.

Additional resources

2.4.10. 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 a Kubernetes 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:

    kubectl 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.

    kubectl describe KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME

  3. To restart the connector task, annotate the KafkaMirrorMaker2 resource in Kubernetes. In this example, kubectl annotate restarts task 0 of a connector named my-source->my-target.MirrorSourceConnector:

    kubectl 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.

Additional resources

2.5. Kafka Bridge cluster configuration

This section describes how to configure a Kafka Bridge deployment in your Strimzi 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 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

  • A Kubernetes cluster

  • A running Cluster Operator

See the Deploying and Upgrading Strimzi 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: <cluster_name>-cluster-kafka-bootstrap:9092 (2)
  tls: (3)
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        certificate: ca.crt
      - secretName: my-cluster-cluster-cert
        certificate: ca2.crt
  authentication: (4)
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  http: (5)
    port: 8080
    cors: (6)
      allowedOrigins: "https://strimzi.io"
      allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  consumer: (7)
    config:
      auto.offset.reset: earliest
  producer: (8)
    config:
      delivery.timeout.ms: 300000
  resources: (9)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: (10)
    type: inline
    loggers:
      logger.bridge.level: "INFO"
      # enabling DEBUG just for send operation
      logger.send.name: "http.openapi.operation.send"
      logger.send.level: "DEBUG"
  jvmOptions: (11)
    "-Xmx": "1g"
    "-Xms": "1g"
  readinessProbe: (12)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  image: my-org/my-image:latest (13)
  template: (14)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    bridgeContainer: (15)
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"

    1. The number of replica nodes.

    2. Bootstrap server for connection to the target Kafka cluster. Use the name of the Kafka cluster as the <cluster_name>.

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

    4. Authentication for the Kafka Bridge cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-256/SCRAM-SHA-512 or PLAIN mechanism. By default, the Kafka Bridge connects to Kafka brokers without authentication.

    5. HTTP access to Kafka brokers.

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

    7. Consumer configuration options.

    8. Producer configuration options.

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

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

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

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

    13. 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:

    kubectl apply -f KAFKA-BRIDGE-CONFIG-FILE
Additional resources

2.5.2. List of Kafka Bridge cluster resources

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

bridge-cluster-name-bridge

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

bridge-cluster-name-bridge-service

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

bridge-cluster-name-bridge-config

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

bridge-cluster-name-bridge

Pod Disruption Budget configured for the Kafka Bridge worker nodes.

2.6. Customizing Kubernetes resources

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

Changing an operator-managed Kubernetes resource can be useful if you want to perform certain tasks, such as:

  • 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 the changes using the template property in the Strimzi custom resources. The template property is supported in the following resources. The API reference provides more details about the customizable fields.

Kafka.spec.kafka

See KafkaClusterTemplate schema reference

Kafka.spec.zookeeper

See ZookeeperClusterTemplate schema reference

Kafka.spec.entityOperator

See EntityOperatorTemplate schema reference

Kafka.spec.kafkaExporter

See KafkaExporterTemplate schema reference

Kafka.spec.cruiseControl

See CruiseControlTemplate schema reference

Kafka.spec.jmxTrans

See JmxTransTemplate schema reference

KafkaConnect.spec

See KafkaConnectTemplate schema reference

KafkaMirrorMaker.spec

See KafkaMirrorMakerTemplate schema reference

KafkaMirrorMaker2.spec

See KafkaConnectTemplate schema reference

KafkaBridge.spec

See KafkaBridgeTemplate schema reference

KafkaUser.spec

See KafkaUserTemplate schema reference

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

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

2.6.1. Customizing the image pull policy

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

Always

Container images are pulled from the registry every time the pod is started or restarted.

IfNotPresent

Container images are pulled from the registry only when they were not pulled before.

Never

Container images are never pulled from the registry.

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

2.6.2. Applying a termination grace period

Apply a termination grace period to give a Kafka cluster enough time to shut down cleanly.

Specify the time using the terminationGracePeriodSeconds property. Add the property to the template.pod configuration of the Kafka custom resource.

The time you add will depend on the size of your Kafka cluster. The Kubernetes default for the termination grace period is 30 seconds. If you observe that your clusters are not shutting down cleanly, you can increase the termination grace period.

A termination grace period is applied every time a pod is restarted. The period begins when Kubernetes sends a term (termination) signal to the processes running in the pod. The period should reflect the amount of time required to transfer the processes of the terminating pod to another pod before they are stopped. After the period ends, a kill signal stops any processes still running in the pod.

The following example adds a termination grace period of 120 seconds to the Kafka custom resource. You can also specify the configuration in the custom resources of other Kafka components.

Example termination grace period configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    template:
      pod:
        terminationGracePeriodSeconds: 120
        # ...
    # ...

2.7. Configuring pod scheduling

When two applications are scheduled to the same Kubernetes 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 Kubernetes specification. The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

Note
 On Kubernetes 1.16 and 1.17, the support for topologySpreadConstraint is disabled by default. In order to use topologySpreadConstraint, you have to enable the EvenPodsSpread feature gate in Kubernetes API server and scheduler.
Additional resources
Use pod anti-affinity to avoid critical applications sharing nodes

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

Use node affinity to schedule workloads onto specific nodes

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

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

Use node affinity and tolerations for dedicated nodes

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

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

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

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

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

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

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

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

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

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

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

  3. Create or update the resource.

    kubectl apply -f <kafka_configuration_file>

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, Kubernetes will not schedule Kafka brokers on the same nodes as other workloads. Typically, you want to avoid Kafka running on the same worker node as other network or storage intensive applications such as databases, storage or other messaging platforms.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

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

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

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <kafka_configuration_file>

2.7.4. Configuring node affinity in Kafka components

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Label the nodes where Strimzi components should be scheduled.

    This can be done using kubectl label:

    kubectl label node NAME-OF-NODE node-type=fast-network

    Alternatively, some of the existing labels might be reused.

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

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

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <kafka_configuration_file>

2.7.5. Setting up dedicated nodes and scheduling pods on them

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.

  2. Make sure there are no workloads scheduled on these nodes.

  3. Set the taints on the selected nodes:

    This can be done using kubectl taint:

    kubectl taint node NAME-OF-NODE dedicated=Kafka:NoSchedule

  4. Additionally, add a label to the selected nodes as well.

    This can be done using kubectl label:

    kubectl label node NAME-OF-NODE dedicated=Kafka

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

    For example:

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

  6. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <kafka_configuration_file>

2.8. Logging configuration

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

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

You specify a logging type in your logging specification:

  • inline when specifying logging levels directly

  • external when referencing a ConfigMap

Example inline logging configuration
spec:
  # ...
  logging:
    type: inline
    loggers:
      kafka.root.logger.level: "INFO"
Example external logging configuration
spec:
  # ...
  logging:
    type: external
    valueFrom:
      configMapKeyRef:
        name: my-config-map
        key: my-config-map-key

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

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.

Kafka component logging
Operator logging

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:

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

    The properties file defines the logging configuration:

    # Define the logger
kafka.root.logger.level="INFO"
# ...

  2. Define external logging in the spec of the resource, setting the logging.valueFrom.configMapKeyRef.name to the name of the ConfigMap and logging.valueFrom.configMapKeyRef.key to the key in this ConfigMap.

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

  3. Create or update the resource.

    kubectl apply -f <kafka_configuration_file>

2.8.3. Adding logging filters to Operators

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

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

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

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

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

  1. The MarkerFilter type compares a specified marker for filtering.

  2. The onMatch property accepts the log if the marker matches.

  3. The onMismatch property rejects the log if the marker does not match.

  4. The marker used for filtering is in the format KIND(NAMESPACE/NAME-OF-RESOURCE).

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

Multiple logging filter configuration
appender.console.filter.filter1.type=MarkerFilter
appender.console.filter.filter1.onMatch=ACCEPT
appender.console.filter.filter1.onMismatch=DENY
appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster-1)
appender.console.filter.filter2.type=MarkerFilter
appender.console.filter.filter2.onMatch=ACCEPT
appender.console.filter.filter2.onMismatch=DENY
appender.console.filter.filter2.marker=Kafka(my-namespace/my-kafka-cluster-2)
Adding filters to the Cluster Operator

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

Procedure

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

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

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

    Alternatively, edit the ConfigMap directly:

    kubectl edit configmap strimzi-cluster-operator

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

    kubectl create -f install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml
Adding filters to the Topic Operator or User Operator

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

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

Procedure

  1. Create the ConfigMap.

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

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

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

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

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

    The properties file defines the logging configuration:

    # Define the logger
rootLogger.level="INFO"
# Set the filters
appender.console.filter.filter1.type=MarkerFilter
appender.console.filter.filter1.onMatch=ACCEPT
appender.console.filter.filter1.onMismatch=DENY
appender.console.filter.filter1.marker=KafkaTopic(my-namespace/my-topic)
# ...

  2. Define external logging in the spec of the resource, setting the logging.valueFrom.configMapKeyRef.name to the name of the ConfigMap and logging.valueFrom.configMapKeyRef.key to the key in this ConfigMap.

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

    spec:
  # ...
  entityOperator:
    topicOperator:
      logging:
        type: external
        valueFrom:
          configMapKeyRef:
            name: logging-configmap
            key: log4j2.properties

  3. Apply the changes by deploying the Cluster Operator:

create -f install/cluster-operator -n my-cluster-operator-namespace
Additional resources

3. Loading configuration values from external sources

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

Kubernetes Configuration Provider

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

Suppose you have a Secret object that’s managed outside the Kafka namespace, or outside the Kafka cluster. The Kubernetes 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 Kubernetes secrets.

Note
 Kubernetes 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 Kubernetes Configuration Provider plugin.

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

Prerequisites

  • A Kubernetes 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 Kubernetes 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 Kubernetes 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

  • A Kubernetes 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}
    # ...
# ...

4. Accessing Kafka outside of the Kubernetes cluster

Use an external listener to expose your Strimzi Kafka cluster to a client outside a Kubernetes 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 Strimzi 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

  • A Kubernetes 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.

    kubectl apply -f <kafka_configuration_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 in the secret <cluster_name>-cluster-ca-cert.

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

    kubectl get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'

    For example:

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

  4. If TLS encryption is enabled, extract the public certificate of the broker certification authority.

    kubectl 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 Strimzi 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

  • A Kubernetes 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.

    kubectl apply -f <kafka_configuration_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 in the secret <cluster_name>-cluster-ca-cert.

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

    kubectl get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'

    For example:

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

  4. If TLS encryption is enabled, extract the public certificate of the broker certification authority.

    kubectl 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 Strimzi Kafka cluster from an external client outside of Kubernetes 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, Strimzi 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.

    kubectl apply -f <kafka_configuration_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 in the secret <cluster_name>-cluster-ca-cert.

    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.

    kubectl 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 Strimzi 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.

    kubectl apply -f <kafka_configuration_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 in the secret <cluster_name>-cluster-ca-cert.

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

    kubectl get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'

    For example:

    kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="listener1")].bootstrapServers}{"\n"}'

  4. Extract the public certificate of the broker certification authority.

    kubectl 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.

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

  4. Custom authentication

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

Note
 Try exploring the standard authentication options before using custom authentication. Custom authentication allows for any type of kafka-supported authentication. It can provide more flexibility, but also adds complexity.
options for listener authentication configuration
Figure 3. Kafka listener authentication options

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

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

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

The following example shows:

  • A plain listener configured for SCRAM-SHA-512 authentication

  • A tls listener with 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
# ...
Mutual TLS authentication

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

Strimzi can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. For mutual, or two-way, authentication, both the server and the client present certificates. When you configure 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.
SCRAM-SHA-512 authentication

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

When SCRAM-SHA-512 authentication is used with a TLS 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.

Network policies

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

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

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

Note
 Your configuration of Kubernetes must support ingress NetworkPolicies in order to use network policies in Strimzi.
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:

options for kafks authorization configuration
Figure 4. Kafka cluster authorization options
Super users

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

To designate super users for a Kafka cluster, add a list of user principals to the superUsers property. If a user uses 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.

Additional resources
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 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
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
  # ...
SCRAM-SHA-512 authentication

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

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

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

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

  1. The generated password, base64 encoded.

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

Decoding the generated password:

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

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

Example user with a password set for SCRAM-SHA-512 authentication
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: scram-sha-512
    password:
      valueFrom:
        secretKeyRef:
          name: my-secret (1)
          key: my-password (2)
  # ...

  1. The name of the secret containing the predefined password.

  2. The key for the password stored inside the secret.

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 Keycloak authorization, you can manage the ACL rules directly in Keycloak. You can also delegate authorization to the simple authorizer as a fallback option in the configuration. When delegation to the simple authorizer is enabled, the User Operator will enable management of ACL rules as well.

  • For custom authorization using a custom authorization plugin, use the supportsAdminApi property in the .spec.kafka.authorization configuration of the Kafka custom resource to enable or disable the support.

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

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

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

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.

Super user access to Kafka brokers

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

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

User quotas

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

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

  1. Byte-per-second quota on the amount of data the user can push to a Kafka broker

  2. Byte-per-second quota on the amount of data the user can fetch from a Kafka broker

  3. CPU utilization limit as a percentage of time for a client group

  4. Number of concurrent partition creation and deletion operations (mutations) allowed per second

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

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 Strimzi.

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

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

  • Kafka.spec.kafka.authorization matches KafkaUser.spec.authorization

The steps show the configuration for simple authorization and a listener using 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. Authorization enables simple authorization on the Kafka broker using the AclAuthorizer Kafka plugin.

      2. List of user principals with unlimited access to Kafka. CN is the common name from the client certificate when TLS authentication is used.

      3. Listener authentication mechanisms may be configured for each listener, and specified as mutual TLS, SCRAM-SHA-512 or token-based OAuth 2.0.

      If you are configuring an external listener, the configuration is dependent on the chosen connection mechanism.

  2. Create or update the Kafka resource.

    kubectl apply -f <kafka_configuration_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 in the secret <cluster_name>-cluster-ca-cert.

5.3.2. Securing user access to Kafka

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

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

For example:

  • kubectl apply -f USER-CONFIG-FILE

  • kubectl delete KafkaUser USER-NAME

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.

    kubectl apply -f USER-CONFIG-FILE

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

  • A Kubernetes cluster with support for Ingress NetworkPolicies.

  • The Cluster Operator is running.

Procedure

  1. Open the Kafka resource.

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

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

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

  3. Create or update the resource.

    Use kubectl apply:

    kubectl apply -f your-file
Additional resources

5.4. Using OAuth 2.0 token-based authentication

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

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

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

Note

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

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

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

In the context of Strimzi:

  • Kafka brokers act as OAuth 2.0 resource servers

  • Kafka clients act as OAuth 2.0 application clients

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

For a deployment of Strimzi, OAuth 2.0 integration provides:

  • Server-side OAuth 2.0 support for Kafka brokers

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

5.4.1. OAuth 2.0 authentication mechanisms

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

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

You configure Kafka broker listeners to use OAuth 2.0 authentication for connecting clients. If necessary, you can use the OAUTHBEARER and PLAIN mechanisms on the same oauth listener. The properties to support each mechanism must be explicitly specified in the oauth listener configuration.

OAUTHBEARER overview

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

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

Many Kafka client tools use libraries that provide basic support for OAUTHBEARER at the protocol level. To support application development, Strimzi provides an OAuth callback handler for the upstream Kafka Client Java libraries (but not for other libraries). Therefore, you do not need to write your own callback handlers. An application client can use the callback handler to provide the access token. Clients written in other languages, such as Go, must use custom code to connect to the authorization server and obtain the access token.

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

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

  • A long-lived access token, obtained manually at configuration time

  • A long-lived refresh token, obtained manually at configuration time

Note

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

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

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

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

PLAIN is a simple authentication mechanism used by all Kafka client tools. To enable PLAIN to be used with OAuth 2.0 authentication, Strimzi provides OAuth 2.0 over PLAIN server-side callbacks.

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

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

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

  • A long-lived access token, obtained manually at configuration time

For both methods, the client must provide the PLAIN username and password properties to pass credentials to the Kafka broker. The client uses these properties to pass a client ID and secret or username and access token.

Client IDs and secrets are used to obtain access tokens.

Access tokens are passed as password property values. You pass the access token with or without an $accessToken: prefix.

  • If you configure a token endpoint (tokenEndpointUri) in the listener configuration, you need the prefix.

  • If you don’t configure a token endpoint (tokenEndpointUri) in the listener configuration, you don’t need the prefix. The Kafka broker interprets the password as a raw access token.

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

Additional resources

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.
OAuth 2.0 client configuration on an authorization server

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

  • Client ID of kafka (for example)

  • Client ID and Secret as the authentication mechanism

Note
 You only need to use a client ID and secret when using a non-public introspection endpoint of the authorization server. The credentials are not typically required when using public authorization server endpoints, as with fast local JWT token validation.
OAuth 2.0 authentication configuration in the Kafka cluster

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

Assigining the authentication method type for OAuth 2.0
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
      #...

You can configure 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.

Fast local JWT token validation configuration

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

The local check ensures that a token:

  • Conforms to type by containing a (typ) claim value of Bearer for an access token

  • Is valid (not expired)

  • Has an issuer that matches a validIssuerURI

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

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

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

You can configure a certificate truststore as a Kubernetes Secret in your Strimzi project namespace, and use a tlsTrustedCertificates attribute to point to the Kubernetes Secret containing the truststore file.

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

Example configuration for fast local JWT token validation
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  kafka:
    #...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
          validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
          jwksEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/certs>
          userNameClaim: preferred_username
          maxSecondsWithoutReauthentication: 3600
          tlsTrustedCertificates:
          - secretName: oauth-server-cert
            certificate: ca.crt
    #...
OAuth 2.0 introspection endpoint configuration

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

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

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

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 Configuring OAuth 2.0 support for Kafka brokers.

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

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

Client re-authentication

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

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

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

Session expiry for OAUTHBEARER and PLAIN

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

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

  • The broker’s authenticated session will expire at the configured maxSecondsWithoutReauthentication.

  • The session will expire earlier if the access token expires before the configured time.

For PLAIN using the long-lived access token method:

  • The broker’s authenticated session will expire at the configured maxSecondsWithoutReauthentication.

  • Re-authentication will fail if the access token expires before the configured time. Although session re-authentication is attempted, PLAIN has no mechanism for refreshing tokens.

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

Additional resources

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 flows

OAuth 2.0 authentication flows depend on the underlying Kafka client and Kafka broker configuration. The flows must also be supported by the authorization server used.

The Kafka broker listener configuration determines how clients authenticate using an access token. The client can pass a client ID and secret to request an access token.

If a listener is configured to use PLAIN authentication, the client can authenticate with a client ID and secret or username and access token. These values are passed as the username and password properties of the PLAIN mechanism.

Listener configuration supports the following token validation options:

  • You can use fast local token validation based on JWT signature checking and local token introspection, without contacting an authorization server. The authorization server provides a JWKS endpoint with public certificates that are used to validate signatures on the tokens.

  • You can use a call to a token introspection endpoint provided by an authorization server. Each time a new Kafka broker connection is established, the broker passes the access token received from the client to the authorization server. The Kafka broker checks the response to confirm whether or not the token is valid.

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

Kafka client credentials can also be configured for the following types of authentication:

  • Direct local access using a previously generated long-lived access token

  • Contact with the authorization server for a new access token to be issued (using a client ID and a secret, or a refresh token)

Example client authentication flows using the SASL OAUTHBEARER mechanism

You can use the following communication flows for Kafka authentication using the SASL OAUTHBEARER mechanism.

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

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

  1. The Kafka client requests an access token from the authorization server using a client ID and secret, and optionally a refresh token.

  2. The authorization server generates a new access token.

  3. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.

  4. The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server using its own client ID and secret.

  5. A Kafka client session is established if the token is valid.

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

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

  1. The Kafka client authenticates with the authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token.

  2. The authorization server generates a new access token.

  3. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.

  4. The Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.

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

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

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

  2. The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server, using its own client ID and secret.

  3. A Kafka client session is established if the token is valid.

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

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

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

  2. The Kafka broker validates the access token locally using a JWT token signature check and local token introspection.

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

You can use the following communication flows for Kafka authentication using the OAuth PLAIN mechanism.

Client using a client ID and secret, with the broker obtaining the access token for the client

Client using a client ID and secret with the broker obtaining the access token for the client

  1. The Kafka client passes a clientId as a username and a secret as a password.

  2. The Kafka broker uses a token endpoint to pass the clientId and secret to the authorization server.

  3. The authorization server returns a fresh access token or an error if the client credentials are not valid.

  4. The Kafka broker validates the token in one of the following ways:

    1. If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if the token validation is successful.

    2. If local token introspection is used, a request is not made to the authorization server. The Kafka broker validates the access token locally using a JWT token signature check.

Client using a long-lived access token without a client ID and secret

Client using a long-lived access token without a client ID and secret

  1. The Kafka client passes a username and password. The password provides the value of an access token that was obtained manually and configured before running the client.

  2. The password is passed with or without an $accessToken: string prefix depending on whether or not the Kafka broker listener is configured with a token endpoint for authentication.

    1. If the token endpoint is configured, the password should be prefixed by $accessToken: to let the broker know that the password parameter contains an access token rather than a client secret. The Kafka broker interprets the username as the account username.

    2. If the token endpoint is not configured on the Kafka broker listener (enforcing a no-client-credentials mode), the password should provide the access token without the prefix. The Kafka broker interprets the username as the account username. In this mode, the client doesn’t use a client ID and secret, and the password parameter is always interpreted as a raw access token.

  3. The Kafka broker validates the token in one of the following ways:

    1. If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if token validation is successful.

    2. If local token introspection is used, there is no request made to the authorization server. Kafka broker validates the access token locally using a JWT token signature check.

5.4.6. Configuring OAuth 2.0 authentication

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

In order to use OAuth 2.0 for Strimzi, you must:

  1. Configure an OAuth 2.0 authorization server for the Strimzi cluster and Kafka clients

  2. Deploy or update the Kafka cluster with Kafka broker listeners configured to use OAuth 2.0

  3. Update your Java-based Kafka clients to use OAuth 2.0

  4. Update Kafka component clients to use OAuth 2.0

Configuring an OAuth 2.0 authorization server

This procedure describes in general what you need to do to configure an authorization server for integration with Strimzi.

These instructions are not product specific.

The steps are dependent on the chosen authorization server. Consult the product documentation for the authorization server for information on how to set up OAuth 2.0 access.

Note
 If you already have an authorization server deployed, you can skip the deployment step and use your current deployment.
Procedure

  1. Deploy the authorization server to your cluster.

  2. Access the CLI or admin console for the authorization server to configure OAuth 2.0 for Strimzi.

    Now prepare the authorization server to work with Strimzi.

  3. Configure a kafka-broker client.

  4. Configure clients for each Kafka client component of your application.

What to do next

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

Configuring OAuth 2.0 support for Kafka brokers

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

We advise use of OAuth 2.0 over an encrypted interface through 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

  • Strimzi and Kafka are running

  • An OAuth 2.0 authorization server is deployed

Procedure

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

    kubectl edit kafka my-cluster

  2. Configure the Kafka broker listeners configuration.

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

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

    Example 1: Configuring fast local JWT token validation
    #...
- name: 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)
    connectTimeoutSeconds: 60 (13)
    readTimeoutSeconds: 60 (14)
    groupsClaim: "$.groups" (15)
    groupsClaimDelimiter: "," (16)

    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 for clients on all platforms.

    9. Additional configuration for the PLAIN mechanism. If specified, clients can authenticate over PLAIN by passing an access token as the password using an $accessToken: prefix. For production, always use https:// urls.

    10. Additional custom rules can be imposed on the JWT access token during validation by setting this to a JsonPath filter query. If the access token does not contain the necessary data, it is rejected. When using the introspectionEndpointUri, the custom check is applied to the introspection endpoint response JSON.

    11. An audience parameter passed to the token endpoint. An audience is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId and secret. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.

    12. A scope parameter passed to the token endpoint. A scope is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId and secret. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.

    13. The connect timeout in seconds when connecting to the authorization server. The default value is 60.

    14. The read timeout in seconds when connecting to the authorization server. The default value is 60.

    15. A JsonPath query used to extract groups information from JWT token or introspection endpoint response. Not set by default. This can be used by a custom authorizer to make authorization decisions based on user groups.

    16. A delimiter used to parse groups information when returned as a single delimited string. The default value is ',' (comma).

  3. Save and exit the editor, then wait for rolling updates to complete.

  4. Check the update in the logs or by watching the pod state transitions:

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

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

What to do next
Configuring Kafka Java clients to use OAuth 2.0

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

  • Strimzi and Kafka are running

  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers

  • Kafka brokers are configured for OAuth 2.0

Procedure

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

    <dependency>
 <groupId>io.strimzi</groupId>
 <artifactId>kafka-oauth-client</artifactId>
 <version>0.10.0</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)
System.setProperty(ClientConfig.OAUTH_SCOPE, "<scope_value>") (4)
System.setProperty(ClientConfig.OAUTH_AUDIENCE, "<audience_value") (5)

    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.

    4. (Optional) The scope for requesting the token from the token endpoint. An authorization server may require a client to specify the scope.

    5. (Optional) The audience for requesting the token from the token endpoint. An authorization server may require a client to specify the audience.

  3. Enable the OAUTHBEARER or PLAIN mechanism on a TLS encrypted connection in the Kafka client configuration.

    For example:

    Enabling OAUTHBEARER for a Kafka client
    props.put("sasl.jaas.config", "org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required;");
props.put("security.protocol", "SASL_SSL");
props.put("sasl.mechanism", "OAUTHBEARER");
props.put("sasl.login.callback.handler.class", "io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler");
    Enabling PLAIN for a Kafka client
    props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username=\"$CLIENT_ID_OR_ACCOUNT_NAME\" password=\"$SECRET_OR_ACCESS_TOKEN\" ;");
props.put("security.protocol", "SASL_SSL"); (1)
props.put("sasl.mechanism", "PLAIN");

    1. Here we use SASL_SSL for use over TLS connections. Use SASL_PLAINTEXT over unencrypted connections for local development only.

  4. Verify that the Kafka client can access the Kafka brokers.

Configuring OAuth 2.0 for Kafka components

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

You can configure authentication for:

  • Kafka Connect

  • Kafka MirrorMaker

  • Kafka Bridge

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

Before you start

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

Prerequisites

  • Strimzi and Kafka are running

  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers

  • Kafka brokers are configured for OAuth 2.0

Procedure

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

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

    apiVersion: kafka.strimzi.io/v1beta2
kind: Secret
metadata:
 name: my-bridge-oauth
type: Opaque
data:
 clientSecret: MGQ1OTRmMzYtZTllZS00MDY2LWI5OGEtMTM5MzM2NjdlZjQw (1)

    1. The clientSecret key must be in base64 format.

  2. Create or edit the resource for the Kafka component so that OAuth 2.0 authentication is configured for the authentication property.

    For OAuth 2.0 authentication, you can use:

    • 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)
    connectTimeoutSeconds: 60 (6)
    readTimeoutSeconds: 60 (7)

    1. (Optional) Disable TLS hostname verification. Default is false.

    2. If the authorization server does not return a typ (type) claim inside the JWT token, you can apply checkAccessTokenType: false to skip the token type check. Default is true.

    3. If you are using opaque tokens, you can apply accessTokenIsJwt: false so that access tokens are not treated as JWT tokens.

    4. (Optional) The scope for requesting the token from the token endpoint. An authorization server may require a client to specify the scope. In this case it is any.

    5. (Optional) The audience for requesting the token from the token endpoint. An authorization server may require a client to specify the audience. In this case it is kafka.

    6. (Optional) The connect timeout in seconds when connecting to the authorization server. The default value is 60.

    7. (Optional) The read timeout in seconds when connecting to the authorization server. The default value is 60.

  3. Apply the changes to the deployment of your Kafka resource.

    kubectl apply -f your-file

  4. Check the update in the logs or by watching the pod state transitions:

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

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

5.4.7. Authorization server examples

When choosing an authorization server, consider the features that best support configuration of your chosen authentication flow.

For the purposes of testing OAuth 2.0 with Strimzi, Keycloak and ORY Hydra were implemented as the OAuth 2.0 authorization server.

For more information, see:

5.5. Using OAuth 2.0 token-based authorization

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

Strimzi supports the use of OAuth 2.0 token-based authorization through Keycloak Keycloak Authorization Services, which allows you to manage security policies and permissions centrally.

Security policies and permissions defined in Keycloak are used to grant access to resources on Kafka brokers. Users and clients are matched against policies that permit access to perform specific actions on Kafka brokers.

Kafka allows all users full access to brokers by default, and also provides the AclAuthorizer 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 Keycloak offers far greater flexibility on how you wish to implement access control to Kafka brokers. In addition, you can configure your Kafka brokers to use OAuth 2.0 authorization and ACLs.

Additional resources

5.5.1. OAuth 2.0 authorization mechanism

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

Kafka broker custom authorizer

A Keycloak authorizer (KeycloakRBACAuthorizer) is provided with Strimzi. To be able to use the Keycloak REST endpoints for Authorization Services provided by Keycloak, you configure a custom authorizer on the Kafka broker.

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

5.5.2. Configuring OAuth 2.0 authorization support

This procedure describes how to configure Kafka brokers to use OAuth 2.0 authorization using Keycloak Authorization Services.

Before you begin

Consider the access you require or want to limit for certain users. You can use a combination of Keycloak groups, roles, clients, and users to configure access in Keycloak.

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

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

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

  • Strimzi must be configured to use OAuth 2.0 with Keycloak for token-based authentication. You use the same Keycloak server endpoint when you set up authorization.

  • OAuth 2.0 authentication must be configured with the maxSecondsWithoutReauthentication option to enable re-authentication.

Procedure

  1. Access the Keycloak Admin Console or use the Keycloak Admin CLI to enable Authorization Services for the Kafka broker client you created when setting up OAuth 2.0 authentication.

  2. Use Authorization Services to define resources, authorization scopes, policies, and permissions for the client.

  3. Bind the permissions to users and clients by assigning them roles and groups.

  4. Configure the Kafka brokers to use Keycloak authorization by updating the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    kubectl edit kafka my-cluster

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

    For example:

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    authorization:
      type: keycloak (1)
      tokenEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token> (2)
      clientId: kafka (3)
      delegateToKafkaAcls: false (4)
      disableTlsHostnameVerification: false (5)
      superUsers: (6)
      - CN=fred
      - sam
      - CN=edward
      tlsTrustedCertificates: (7)
      - secretName: oauth-server-cert
        certificate: ca.crt
      grantsRefreshPeriodSeconds: 60 (8)
      grantsRefreshPoolSize: 5 (9)
      connectTimeoutSeconds: 60 (10)
      readTimeoutSeconds: 60 (11)
    #...

    1. Type keycloak enables Keycloak authorization.

    2. URI of the Keycloak token endpoint. For production, always use https:// urls. When you configure token-based oauth authentication, you specify a jwksEndpointUri as the URI for local JWT validation. The hostname for the tokenEndpointUri URI must be the same.

    3. The client ID of the OAuth 2.0 client definition in Keycloak that has Authorization Services enabled. Typically, kafka is used as the ID.

    4. (Optional) Delegate authorization to Kafka AclAuthorizer if access is denied by Keycloak Authorization Services policies. Default is false.

    5. (Optional) Disable TLS hostname verification. Default is false.

    6. (Optional) Designated super users.

    7. (Optional) Trusted certificates for TLS connection to the authorization server.

    8. (Optional) The time between two consecutive grants refresh runs. That is the maximum time for active sessions to detect any permissions changes for the user on Keycloak. The default value is 60.

    9. (Optional) The number of threads to use to refresh (in parallel) the grants for the active sessions. The default value is 5.

    10. (Optional) The connect timeout in seconds when connecting to the Keycloak token endpoint. The default value is 60.

    11. (Optional) The read timeout in seconds when connecting to the Keycloak token endpoint. The default value is 60.

  6. Save and exit the editor, then wait for rolling updates to complete.

  7. Check the update in the logs or by watching the pod state transitions:

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

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

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

5.5.3. Managing policies and permissions in Keycloak Authorization Services

This section describes the authorization models used by Keycloak Authorization Services and Kafka, and defines the important concepts in each model.

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

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

Kafka and Keycloak authorization models overview

Kafka and Keycloak Authorization Services use different authorization models.

Kafka authorization model

Kafka’s authorization model uses resource types. When a Kafka client performs an action on a broker, the broker uses the configured 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

Keycloak Authorization Services model

The Keycloak Authorization Services model has four concepts for defining and granting permissions: resources, authorization scopes, policies, and permissions.

Resources

A resource is a set of resource definitions that are used to match resources with permitted actions. A resource might be an individual topic, for example, or all topics with names starting with the same prefix. A resource definition is associated with a set of available authorization scopes, which represent a set of all actions available on the resource. Often, only a subset of these actions is actually permitted.

Authorization scopes

An authorization scope is a set of all the available actions on a specific resource definition. When you define a new resource, you add scopes from the set of all scopes.

Policies

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

  • Service accounts based on client ID or roles

  • User accounts based on username, groups, or roles.

Permissions

A permission grants a subset of authorization scopes on a specific resource definition to a set of users.

Additional resources
Map Keycloak Authorization Services to the Kafka authorization model

The Kafka authorization model is used as a basis for defining the Keycloak roles and resources that will control access to Kafka.

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

Note

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

The kafka client definition must have the Authorization Enabled option enabled in the Keycloak Admin Console.

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

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

Authorization scopes for Kafka permissions

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

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

  • Create

  • Write

  • Read

  • Delete

  • Describe

  • Alter

  • DescribeConfig

  • AlterConfig

  • ClusterAction

  • IdempotentWrite

Note

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

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

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

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

  • Prefix matching (when the pattern ends with *)

Example patterns for resources
Topic:my-topic
Topic:orders-*
Group:orders-*
Cluster:*

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

Example patterns for resources with cluster prefix
kafka-cluster:my-cluster,Topic:*
kafka-cluster:*,Group:b_*

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

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

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

Policies for applying access permission

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

  • Specific user or service accounts

  • Realm roles or client roles

  • User groups

  • JavaScript rules to match a client IP address

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

Permissions to grant access

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

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

Additional resources
Example permissions required for Kafka operations

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

Create a topic

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

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

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

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

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

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

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

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

bin/kafka-console-producer.sh  --topic my-topic \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties
Consume messages from a topic

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

Two resources are needed for matching. For example:

Topic:my-topic
Group:my-group-*
bin/kafka-console-consumer.sh --topic my-topic --group my-group-1 --from-beginning \
  --bootstrap-server my-cluster-kafka-bootstrap:9092 --consumer.config /tmp/config.properties
Produce messages to a topic using an idempotent producer

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

Two resources are needed for matching. For example:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5.5.4. Trying Keycloak Authorization Services

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

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

Strimzi provides example configuration files. These include the following example files for setting up Keycloak:

kafka-ephemeral-oauth-single-keycloak-authz.yaml

An example Kafka custom resource configured for OAuth 2.0 token-based authorization using Keycloak. You can use the custom resource to deploy a Kafka cluster that uses keycloak authorization and token-based oauth authentication.

kafka-authz-realm.json

An example Keycloak realm configured with sample groups, users, roles and clients. You can import the realm into a Keycloak instance to set up fine-grained permissions to access Kafka.

If you want to try the example with Keycloak, use these files to perform the tasks outlined in this section in the order shown.

  1. Accessing the Keycloak Admin Console

  2. Deploying a Kafka cluster with Keycloak authorization

  3. Preparing TLS connectivity for a CLI Kafka client session

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

Authentication

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

Targeted permissions with group or role policies

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

Accessing the Keycloak Admin Console

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

Prerequisites

  • A running Kubernetes cluster.

  • The Strimzi examples/security/keycloak-authorization/kafka-authz-realm.json file that contains the preconfigured realm.

Procedure

  1. Install the Keycloak server using the Keycloak Operator as described in Installing the Keycloak Operator in the Keycloak documentation.

  2. Wait until the Keycloak instance is running.

  3. Get the external hostname to be able to access the Admin Console.

    NS=sso
kubectl get ingress keycloak -n $NS

    In this example, we assume the Keycloak server is running in the sso namespace.

  4. Get the password for the admin user.

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

    The password is stored as a secret, so get the configuration YAML file for the Keycloak instance to identify the name of the secret (secretKeyRef.name).

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

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

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

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

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

    You can now upload the example realm to Keycloak using the Admin Console.

  7. Click Add Realm to import the example realm.

  8. Add the examples/security/keycloak-authorization/kafka-authz-realm.json file, and then click Create.

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

    The default view displays the Master realm.

  9. In the Keycloak Admin Console, go to Clients > kafka > Authorization > Settings and check that Decision Strategy is set to Affirmative.

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

  10. In the Keycloak Admin Console, go to Groups, Users, Roles and Clients to view the realm configuration.

    Groups

    Groups are used to create user groups and set user permissions. Groups are sets of users with a name assigned. They are used to compartmentalize users into geographical, organizational or departmental units. Groups can be linked to an LDAP identity provider. You can make a user a member of a group through a custom LDAP server admin user interface, for example, to grant permissions on Kafka resources.

    Users

    Users are used to create users. For this example, alice and bob are defined. alice is a member of the ClusterManager group and bob is a member of ClusterManager-my-cluster group. Users can be stored in an LDAP identity provider.

    Roles

    Roles mark users or clients as having certain permissions. Roles are a concept analogous to groups. They are usually used to tag users with organizational roles and have the requisite permissions. Roles cannot be stored in an LDAP identity provider. If LDAP is a requirement, you can use groups instead, and add Keycloak roles to the groups so that when users are assigned a group they also get a corresponding role.

    Clients

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

    • The kafka client is used by Kafka brokers to perform the necessary OAuth 2.0 communication for access token validation. This client also contains the authorization services resource definitions, policies, and authorization scopes used to perform authorization on the Kafka brokers. The authorization configuration is defined in the kafka client from the Authorization tab, which becomes visible when Authorization Enabled is switched on from the Settings tab.

    • The kafka-cli client is a public client that is used by the Kafka command line tools when authenticating with username and password to obtain an access token or a refresh token.

    • The team-a-client and team-b-client clients are confidential clients representing services with partial access to certain Kafka topics.

  11. In the Keycloak Admin Console, go to Authorization > Permissions to see the granted permissions that use the resources and policies defined for the realm.

    For example, the kafka client has the following permissions:

    Dev Team A can write to topics that start with x_ on any cluster
Dev Team B can read from topics that start with x_ on any cluster
Dev Team B can update consumer group offsets that start with x_ on any cluster
ClusterManager of my-cluster Group has full access to cluster config on my-cluster
ClusterManager of my-cluster Group has full access to consumer groups on my-cluster
ClusterManager of my-cluster Group has full access to topics on my-cluster
    Dev Team A

    The Dev Team A realm role can write to topics that start with x_ on any cluster. This combines a resource called Topic:x_*, Describe and Write scopes, and the Dev Team A policy. The Dev Team A policy matches all users that have a realm role called Dev Team A.

    Dev Team B

    The Dev Team B realm role can read from topics that start with x_ on any cluster. This combines Topic:x_*, Group:x_* resources, Describe and Read scopes, and the Dev Team B policy. The Dev Team B policy matches all users that have a realm role called Dev Team B. Matching users and clients have the ability to read from topics, and update the consumed offsets for topics and consumer groups that have names starting with x_.

Deploying a Kafka cluster with Keycloak authorization

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

Prerequisites

  • The Keycloak authorization server is deployed to your Kubernetes cluster and loaded with the example realm.

  • The Cluster Operator is deployed to your Kubernetes cluster.

  • The Strimzi examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml custom resource.

Procedure

  1. Use the hostname of the Keycloak instance you deployed to prepare a truststore certificate for Kafka brokers to communicate with the Keycloak server.

    SSO_HOST=SSO-HOSTNAME
SSO_HOST_PORT=$SSO_HOST:443
STOREPASS=storepass

echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.crt

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

  2. Deploy the certificate to Kubernetes as a secret.

    kubectl 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#" | kubectl create -n $NS -f -
Preparing TLS connectivity for a CLI Kafka client session

Create a new pod for an interactive CLI session. Set up a truststore with a Keycloak certificate for TLS connectivity. The truststore is to connect to Keycloak and the Kafka broker.

Prerequisites

  • The Keycloak authorization server is deployed to your Kubernetes cluster and loaded with the example realm.

    In the Keycloak Admin Console, check the roles assigned to the clients are displayed in Clients > Service Account Roles.

  • The Kafka cluster configured to connect with Keycloak is deployed to your Kubernetes cluster.

Procedure

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

    NS=sso
kubectl run -ti --restart=Never --image=quay.io/strimzi/kafka:latest-kafka-3.2.0 kafka-cli -n $NS -- /bin/sh
    Note
    		 If kubectl times out waiting on the image download, subsequent attempts may result in an AlreadyExists error.

  2. Attach to the pod container.

    kubectl attach -ti kafka-cli -n $NS

  3. Use the hostname of the Keycloak instance to prepare a certificate for client connection using TLS.

    SSO_HOST=SSO-HOSTNAME
SSO_HOST_PORT=$SSO_HOST:443
STOREPASS=storepass

echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.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.

Checking authorized access to Kafka using a CLI Kafka client session

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

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

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

Prerequisites
Setting up client and admin user configuration

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

    SSO_HOST=SSO-HOSTNAME

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

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

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

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

  3. Authenticate admin user alice by using curl and performing a password grant authentication to obtain a refresh token.

    USERNAME=alice
PASSWORD=alice-password

GRANT_RESPONSE=$(curl -X POST "https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" -H 'Content-Type: application/x-www-form-urlencoded' -d "grant_type=password&username=$USERNAME&password=$PASSWORD&client_id=kafka-cli&scope=offline_access" -s -k)

REFRESH_TOKEN=$(echo $GRANT_RESPONSE | awk -F "refresh_token\":\"" '{printf $2}' | awk -F "\"" '{printf $1}')

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

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

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

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

Producing messages with authorized access

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

  1. Write to topic my-topic.

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

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

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

  2. Write to topic a_messages.

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

    Messages are produced to Kafka successfully.

  3. Press CTRL+C to exit the CLI application.

  4. Check the Kafka container log for a debug log of Authorization GRANTED for the request.

    kubectl logs my-cluster-kafka-0 -f -n $NS
Consuming messages with authorized access

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

  1. Fetch messages from topic a_messages.

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

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

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

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

    The consumer receives all the messages from the a_messages topic.

Administering Kafka with authorized access

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

  1. List topics.

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

    The a_messages topic is returned.

  2. List consumer groups.

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

    The a_consumer_group_1 consumer group is returned.

    Fetch details on the cluster configuration.

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

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

Using clients with different permissions

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

  1. Write to topic a_messages.

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

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

  2. Write to topic b_messages.

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

    Messages are produced to Kafka successfully.

  3. Write to topic x_messages.

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

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

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

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

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

Managing Kafka with an authorized admin user

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

  1. Create the x_messages topic as alice.

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

    The topic is created successfully.

  2. List all topics as alice.

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

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

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

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

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

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

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

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

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

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

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

    The consumer receives all the messages from the x_messages topic.

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

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

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

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

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

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

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

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

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

    Messages are produced to Kafka successfully.

    alice can read from or write to any topic.

  9. Use alice to read the cluster configuration.

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

    The cluster configuration for this example is empty.

Additional resources

6. Using Strimzi Operators

Use the Strimzi 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, 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 Kubernetes 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 Strimzi on clusters where regular Kubernetes operations take longer than usual (because of slow downloading of Docker images, for example).

STRIMZI_ZOOKEEPER_ADMIN_SESSION_TIMEOUT_MS

Optional, default 10000 ms.

The session timeout (in milliseconds) for the Cluster Operator’s ZooKeeper admin client. This value should be increased if ZooKeeper requests from the Cluster Operator are regularly failing due to timeout issues. There is a maximum allowed session time set on the ZooKeeper server side via the maxSessionTimeout config. By default this session max value is 20 times the default tickTime (whose default is 2000) therefore 40000 ms. If you require a higher timeout, you will need to change the maxSessionTimeout ZooKeeper server config value.

STRIMZI_OPERATIONS_THREAD_POOL_SIZE

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

STRIMZI_OPERATOR_NAMESPACE

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

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

Optional. The labels of the namespace where the Strimzi Cluster Operator is running. Namespace labels are used to configure the namespace selector in network policies to allow the Strimzi 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 Strimzi Cluster Operator from any namespace in the Kubernetes cluster.

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

Optional, default regex pattern is ^app.kubernetes.io/(?!part-of).*. 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

  • JMX_TRANS

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 3.1.0=quay.io/strimzi/kafka:latest-kafka-3.1.0, 3.2.0=quay.io/strimzi/kafka:latest-kafka-3.2.0. This is used when a Kafka.spec.kafka.version property is specified but not the Kafka.spec.kafka.image in the Kafka resource.

STRIMZI_DEFAULT_KAFKA_INIT_IMAGE

Optional, default quay.io/strimzi/operator:latest. 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 3.1.0=quay.io/strimzi/kafka:latest-kafka-3.1.0, 3.2.0=quay.io/strimzi/kafka:latest-kafka-3.2.0. 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 3.1.0=quay.io/strimzi/kafka:latest-kafka-3.1.0, 3.2.0=quay.io/strimzi/kafka:latest-kafka-3.2.0. This is used when a KafkaMirrorMaker.spec.version property is specified but not the KafkaMirrorMaker.spec.image.

STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE

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

STRIMZI_DEFAULT_USER_OPERATOR_IMAGE

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

STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE

Optional, default quay.io/strimzi/kafka:latest-kafka-3.2.0. The image name to use as the default when deploying the sidecar container which provides TLS support for the Entity Operator, if no image is specified as the Kafka.spec.entityOperator.tlsSidecar.image in the Kafka resource.

STRIMZI_IMAGE_PULL_POLICY

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

STRIMZI_IMAGE_PULL_SECRETS

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

STRIMZI_KUBERNETES_VERSION

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

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

Optional. Overrides the default Kubernetes DNS domain name suffix.

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

For example, for broker kafka-0:

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

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

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

STRIMZI_CONNECT_BUILD_TIMEOUT_MS

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

STRIMZI_NETWORK_POLICY_GENERATION

Optional, default true. Controls whether Strimzi 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_DNS_CACHE_TTL

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

STRIMZI_POD_SET_RECONCILIATION_ONLY

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

STRIMZI_FEATURE_GATES

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

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:

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

Alternatively, edit the ConfigMap directly:

kubectl 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.
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 Kubernetes 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 Strimzi.

If the Cluster Operator is running in a separate namespace to the resources it manages, any namespace in the Kubernetes cluster is allowed access to the Cluster Operator unless network policy is configured. 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
  #...
Periodic reconciliation

Although the Cluster Operator reacts to all notifications about the desired cluster resources received from the Kubernetes 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 Kubernetes cluster.

In order to handle failovers properly, a periodic reconciliation process is executed by the Cluster Operator so that it can compare the state of the 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.

Provisioning Role-Based Access Control (RBAC)

For the Cluster Operator to function it needs permission within the Kubernetes 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 Kubernetes 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 Kubernetes resources.

Kubernetes 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.

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 Kubernetes 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

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.

ClusterRoles

The Cluster Operator needs to operate using ClusterRoles that gives access to the necessary resources. Depending on the Kubernetes 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 Kubernetes 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:
      - "core.strimzi.io"
    resources:
      # The cluster operator uses StrimziPodSets to manage the Kafka and ZooKeeper pods
      - strimzipodsets
      - strimzipodsets/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 Kubernetes nodes to the Kafka broker pods
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-kafka-broker
  labels:
    app: strimzi
rules:
  - apiGroups:
      - ""
    resources:
      # The Kafka Brokers require "get" permissions to view the node they are on
      # This information is used to generate a Rack ID that is used for High Availability configurations
      - nodes
    verbs:
      - get

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

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

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

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

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

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

ClusterRoleBindings are also needed for the ClusterRoles needed for delegation:

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

and

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

ClusterRoles containing only namespaced resources are bound using RoleBindings only.

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

6.1.2. Configuring the Cluster Operator with default proxy settings

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

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

The format for a proxy address is http://IP-ADDRESS:PORT-NUMBER. To set up a proxy with a name and password, the format is http://USERNAME:PASSWORD@IP-ADDRESS:PORT-NUMBER.

Prerequisites

This procedure requires use of a Kubernetes user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the Kubernetes cluster usually means that permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators, such as system:admin.

Procedure

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

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

    1. Address of the proxy server.

    2. Secure address of the proxy server.

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

    Alternatively, edit the Deployment directly:

    kubectl edit deployment strimzi-cluster-operator

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

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

6.1.3. Configuring FIPS mode in the Cluster Operator

Federal Information Processing Standards (FIPS) are standards for computer security and interoperability. When running Strimzi on a FIPS-enabled Kubernetes cluster, the OpenJDK used in Strimzi container images will automatically switch to FIPS mode. This prevents Strimzi from running on the cluster. When you deploy Strimzi to the cluster, you will see errors similar to the following:

Exception in thread "main" io.fabric8.kubernetes.client.KubernetesClientException: An error has occurred.
	...
Caused by: java.security.KeyStoreException: sun.security.pkcs11.wrapper.PKCS11Exception: CKR_SESSION_READ_ONLY
	...
Caused by: sun.security.pkcs11.wrapper.PKCS11Exception: CKR_SESSION_READ_ONLY
	...

If you want to run Strimzi on your FIPS-enabled cluster, you can disable the OpenJDK FIPS mode by setting the FIPS_MODE environment variable to disabled in the deployment configuration for the Cluster Operator. The Strimzi deployment won’t be FIPS compliant, but the Strimzi operators as well as all of its operands will be able to run on the FIPS-enabled Kubernetes cluster.

Procedure

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

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

    1. Disables the FIPS mode.

    Alternatively, edit the Deployment directly:

    kubectl edit deployment strimzi-cluster-operator

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

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

6.2. Using the Topic Operator

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

The Deploying and Upgrading Strimzi guide provides instructions to deploy the Topic Operator:

6.2.1. Kafka topic resource

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

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

Identifying a Kafka cluster for topic handling

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

For example:

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

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

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

Kafka topic usage recommendations

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

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

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

Note
 For information on the requirements for identifiers and names in Kubernetes, refer to Object Names and IDs.
Kafka topic naming conventions

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

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

The spec.topicName property inherits Kafka naming validation rules:

  • The name must not be longer than 249 characters.

  • Valid characters for Kafka topics are ASCII alphanumerics, ., _, and -.

  • The name cannot be . or .., though . can be used in a name, such as exampleTopic. or .exampleTopic.

spec.topicName must not be changed.

For example:

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

  1. Upper case is invalid in Kubernetes.

cannot be changed to:

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

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

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

6.2.2. Topic Operator topic store

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

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

Internal topic store topics

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

__strimzi_store_topic

Input topic for storing the topic metadata

__strimzi-topic-operator-kstreams-topic-store-changelog

Retains a log of compacted topic store values

Warning
 Do not delete these topics, as they are essential to the running of the Topic Operator.
Migrating topic metadata from ZooKeeper

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

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

Downgrading to a Strimzi version that uses ZooKeeper to store topic metadata

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

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

kubectl run kafka-admin -ti --image=quay.io/strimzi/kafka:latest-kafka-3.2.0 --rm=true --restart=Never -- ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi-topic-operator-kstreams-topic-store-changelog --delete && ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi_store_topic --delete

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

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

Topic Operator topic replication and scaling

The recommended configuration for topics managed by the Topic Operator is a topic replication factor of 3, and a minimum of 2 in-sync replicas.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10 (1)
  replicas: 3 (2)
  config:
    min.insync.replicas: 2 (3)
  #...

  1. The number of partitions for the topic.

  2. The number of replica topic partitions. Currently, this cannot be changed in the KafkaTopic resource, but it can be changed using the kafka-reassign-partitions.sh tool.

  3. The minimum number of replica partitions that a message must be successfully written to, or an exception is raised.

Note
 In-sync replicas are used in conjunction with the acks configuration for producer applications. The acks configuration determines the number of follower partitions a message must be replicated to before the message is acknowledged as successfully received. The Topic Operator runs with acks=all, whereby messages must be acknowledged by all in-sync replicas.

When scaling Kafka clusters by adding or removing brokers, replication factor configuration is not changed and replicas are not reassigned automatically. However, you can use the kafka-reassign-partitions.sh tool to change the replication factor, and manually reassign replicas to brokers.

Alternatively, though the integration of Cruise Control for Strimzi cannot change the replication factor for topics, the optimization proposals it generates for rebalancing Kafka include commands that transfer partition replicas and change partition leadership.

Handling changes to topics

A fundamental problem that the Topic Operator needs to solve is that there is no single source of truth: both the KafkaTopic resource and the Kafka topic can be modified independently of the Topic Operator. Complicating this, the Topic Operator might not always be able to observe changes at each end in real time. For example, when the Topic Operator is down.

To resolve this, the Topic Operator maintains information about each topic in the topic store. When a change happens in the Kafka cluster or Kubernetes, it looks at both the state of the other system and the topic store in order to determine what needs to change to keep everything in sync. The same thing happens whenever the Topic Operator starts, and periodically while it is running.

For example, suppose the Topic Operator is not running, and a KafkaTopic called my-topic is created. When the Topic Operator starts, the topic store does not contain information on my-topic, so it can infer that the KafkaTopic was created after it was last running. The Topic Operator creates the topic corresponding to my-topic, and also stores metadata for my-topic in the topic store.

If you update Kafka topic configuration or apply a change through the KafkaTopic custom resource, the topic store is updated after the Kafka cluster is reconciled.

The topic store also allows the Topic Operator to manage scenarios where the topic configuration is changed in Kafka topics and updated through Kubernetes KafkaTopic custom resources, as long as the changes are not incompatible. For example, it is possible to make changes to the same topic config key, but to different values. For incompatible changes, the Kafka configuration takes priority, and the KafkaTopic is updated accordingly.

Note
 You can also use the KafkaTopic resource to delete topics using a kubectl delete -f KAFKA-TOPIC-CONFIG-FILE command. To be able to do this, delete.topic.enable must be set to true (default) in the spec.kafka.config of the Kafka resource.
Additional resources

6.2.3. Configuring a Kafka topic

Use the properties of the KafkaTopic resource to configure a Kafka topic.

You can use kubectl apply to create or modify topics, and kubectl delete to delete existing topics.

For example:

  • kubectl apply -f <topic-config-file>

  • kubectl delete KafkaTopic <topic-name>

This procedure shows how to create a topic with 10 partitions and 2 replicas.

Before you start

It is important that you consider the following before making your changes:

  • Kafka does not support making the following changes through the KafkaTopic resource:

    • Changing topic names using spec.topicName

    • Decreasing partition size using spec.partitions

  • You cannot use spec.replicas to change the number of replicas that were initially specified.

  • Increasing spec.partitions for topics with keys will change how records are partitioned, which can be particularly problematic when the topic uses semantic partitioning.

Prerequisites
Procedure

  1. Prepare a file containing the KafkaTopic to be created.

    An example KafkaTopic
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: orders
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10
  replicas: 2
    Tip
    		 When modifying a topic, you can get the current version of the resource using kubectl get kafkatopic orders -o yaml.

  2. Create the KafkaTopic resource in Kubernetes.

    kubectl apply -f TOPIC-CONFIG-FILE

6.2.4. Configuring the Topic Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the Topic Operator and set a limit on the amount of resources it can consume.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Update the Kafka cluster configuration in an editor, as required:

    kubectl edit kafka MY-CLUSTER

  2. In the spec.entityOperator.topicOperator.resources property in the Kafka resource, set the resource requests and limits for the Topic Operator.

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  # Kafka and ZooKeeper sections...
  entityOperator:
    topicOperator:
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi

  3. Apply the new configuration to create or update the resource.

    kubectl apply -f <kafka_configuration_file>

6.3. Using the User Operator

When you create, modify or delete a user using the KafkaUser resource, the User Operator ensures those changes are reflected in the Kafka cluster.

The Deploying and Upgrading Strimzi guide provides instructions to deploy the User Operator:

For more information about the schema, see KafkaUser schema reference.

Authenticating and authorizing access to Kafka

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

For more information on using KafkUser to manage users and secure access to Kafka brokers, see Securing access to Kafka brokers.

6.3.1. Configuring the User Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the User Operator and set a limit on the amount of resources it can consume.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Update the Kafka cluster configuration in an editor, as required:

    kubectl edit kafka MY-CLUSTER

  2. In the spec.entityOperator.userOperator.resources property in the Kafka resource, set the resource requests and limits for the User Operator.

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  # Kafka and ZooKeeper sections...
  entityOperator:
    userOperator:
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi

    Save the file and exit the editor. The Cluster Operator applies the changes automatically.

6.4. Configuring feature gates

Strimzi 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 Strimzi 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. 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

6.4.1. ControlPlaneListener feature gate

The ControlPlaneListener feature gate has a default state of enabled.

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

Disabling the ControlPlaneListener feature gate

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

Important
 The ControlPlaneListener feature gate must be disabled when upgrading from or downgrading to Strimzi 0.22 and earlier versions.

6.4.2. ServiceAccountPatching feature gate

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

6.4.3. UseStrimziPodSets feature gate

The UseStrimziPodSets feature gate has a default state of disabled.

Currently, Strimzi relies on StatefulSets to create and manage pods for the ZooKeeper and Kafka clusters. Strimzi creates the StatefulSet and Kubernetes creates the pods according to the StatefulSet definition. When a pod is deleted, Kubernetes is responsible for recreating it. The use of StatefulSets has the following limitations:

  • Pods are always created or removed based on their index numbers

  • All pods in the StatefulSet need to have a similar configuration

  • Changing storage configuration for the Pods in the StatefulSet is complicated

The UseStrimziPodSets feature gate introduces a resource for managing pods called StrimziPodSet. When the feature gate is enabled, this resource is used instead of the StatefulSets. Strimzi handles the creation and management of pods instead of Kubernetes. Using StrimziPodSets instead of StatefulSets provides more control over the functionality.

Enabling the UseStrimziPodSets feature gate

To enable the UseStrimziPodSets feature gate, specify +UseStrimziPodSets in the STRIMZI_FEATURE_GATES environment variable in the Cluster Operator configuration.

Important
 The UseStrimziPodSets feature gate must be disabled when downgrading to Strimzi 0.27 and earlier versions.

6.4.4. (Preview) UseKRaft feature gate

The UseKRaft feature gate has a default state of disabled.

The UseKRaft feature gate deploys the Kafka cluster in the KRaft (Kafka Raft metadata) mode without ZooKeeper. This feature gate is currently intended only for development and testing.

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

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

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

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

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

  • The Topic Operator is not supported. The spec.entityOperator.topicOperator property must be removed from the Kafka custom resource.

  • SCRAM-SHA-512 authentication is not supported.

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

  • Liveness and readiness probes are disabled.

  • All Kafka nodes have both the controller and broker KRaft roles. Kafka clusters with separate controller and broker nodes are not supported.

Enabling the UseStrimziPodSets feature gate

To enable the UseKRaft feature gate, specify +UseKRaft in the STRIMZI_FEATURE_GATES environment variable in the Cluster Operator configuration.

Important
 The UseKRaft feature gate depends on the UseStrimziPodSets feature gate. When enabling the UseKRaft feature gate, make sure that the USeStrimziPodSets feature gate is enabled as well.

6.4.5. Feature gate releases

Feature gates have three stages of maturity:

  • Alpha — typically disabled by default

  • Beta — typically enabled by default

  • General Availability (GA) — typically always enabled

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

  • The ControlPlaneListener feature gate moved to beta stage in Strimzi 0.27 and is expected to remain in the beta stage until Strimzi 0.31.

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

  • The UseStrimziPodSets feature gate is currently planned to move to the beta stage in Strimzi 0.30.

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

Note
 Feature gates might be removed when they reach GA. This means that the feature was incorporated into the Strimzi core features and can no longer be disabled.
Table 2. Feature gates and the Strimzi versions when they moved to alpha, beta, or GA
Feature gate Alpha Beta GA

ControlPlaneListener

0.23

0.27

-

ServiceAccountPatching

0.24

0.27

0.30

UseStrimziPodSets

0.28

-

-

UseKRaft

0.29

-

-

If a feature gate is enabled, you may need to disable it before upgrading or downgrading from a specific Strimzi version. The following table shows which feature gates you need to disable when upgrading or downgrading Strimzi versions.

Table 3. Feature gates to disable when upgrading or downgrading Strimzi
Disable Feature gate Upgrading from Strimzi version Downgrading to Strimzi version

ControlPlaneListener

0.22 and earlier

0.22 and earlier

UseStrimziPodSets

-

0.27 and earlier

6.5. Monitoring operators using Prometheus metrics

Strimzi operators expose Prometheus metrics. The metrics are automatically enabled and contain information about:

  • Number of reconciliations

  • Number of Custom Resources the operator is processing

  • Duration of reconciliations

  • JVM metrics from the operators

Additionally, we provide an example Grafana dashboard.

For more information about Prometheus, see the Introducing Metrics to Kafka in the Deploying and Upgrading Strimzi guide.

7. Cruise Control for cluster rebalancing

You can deploy Cruise Control to your Strimzi cluster and use it to rebalance the Kafka cluster.

Cruise Control is an open source system for automating Kafka operations, such as monitoring cluster workload, rebalancing a cluster based on predefined constraints, and detecting and fixing anomalies. It consists of four main components—​the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor—​and a REST API for client interactions. Strimzi utilizes the REST API to support the following Cruise Control features:

  • Generating optimization proposals from multiple optimization goals.

  • Rebalancing a Kafka cluster based on an optimization proposal.

Other Cruise Control features are not currently supported, including anomaly detection, notifications, write-your-own goals, and changing the topic replication factor.

Strimzi provides example configuration files. Example YAML configuration files for Cruise Control are provided in examples/cruise-control/.

7.1. Why use Cruise Control?

Cruise Control reduces the time and effort involved in running an efficient and balanced Kafka cluster.

A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might be unevenly distributed across the available brokers. To rebalance the cluster, administrators must monitor the load on brokers and manually reassign busy partitions to brokers with spare capacity.

Cruise Control automates the cluster rebalancing process. It constructs a workload model of resource utilization for the cluster—​based on CPU, disk, and network load—​and generates optimization proposals (that you can approve or reject) for more balanced partition assignments. A set of configurable optimization goals is used to calculate these proposals.

When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster. When the cluster rebalancing operation is complete, the broker pods are used more effectively and the Kafka cluster is more evenly balanced.

Additional resources

7.2. Optimization goals overview

To rebalance a Kafka cluster, Cruise Control uses optimization goals to generate optimization proposals, which you can approve or reject.

Optimization goals are constraints on workload redistribution and resource utilization across a Kafka cluster. Strimzi supports most of the optimization goals developed in the Cruise Control project. The supported goals, in the default descending order of priority, are as follows:

  1. Rack-awareness

  2. Minimum number of leader replicas per broker for a set of topics

  3. Replica capacity

  4. Capacity: Disk capacity, Network inbound capacity, Network outbound capacity, CPU capacity

  5. Replica distribution

  6. Potential network output

  7. Resource distribution: Disk utilization distribution, Network inbound utilization distribution, Network outbound utilization distribution, CPU utilization distribution

    Note

    The resource distribution goals are controlled using capacity limits on broker resources.

  8. Leader bytes-in rate distribution

  9. Topic replica distribution

  10. Leader replica distribution

  11. Preferred leader election

  12. Intra-broker disk capacity

  13. Intra-broker disk usage distribution

For more information on each optimization goal, see Goals in the Cruise Control Wiki.

Note
 "Write your own" goals and Kafka assigner goals are not yet supported.

Goals configuration in Strimzi custom resources

You configure optimization goals in Kafka and KafkaRebalance custom resources. Cruise Control has configurations for hard optimization goals that must be satisfied, as well as main, default, and user-provided optimization goals. Optimization goals for resource distribution (disk, network inbound, network outbound, and CPU) are subject to capacity limits on broker resources.

The following sections describe each goal configuration in more detail.

Hard goals and soft goals

Hard goals are goals that must be satisfied in optimization proposals. Goals that are not configured as hard goals are known as soft goals. You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations. An optimization proposal that violates one or more soft goals, but satisfies all hard goals, is valid.

Cruise Control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by Cruise Control and not sent to the user for approval.

Note
 For example, you might have a soft goal to distribute a topic’s replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met.

In Cruise Control, the following main optimization goals are preset as hard goals:

RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal

You configure hard goals in the Cruise Control deployment configuration, by editing the hard.goals property in Kafka.spec.cruiseControl.config.

  • To inherit the preset hard goals from Cruise Control, do not specify the hard.goals property in Kafka.spec.cruiseControl.config

  • To change the preset hard goals, specify the desired goals in the hard.goals property, using their fully-qualified domain names.

Example Kafka configuration for hard optimization goals
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl:
    brokerCapacity:
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    config:
      hard.goals: >
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal
      # ...

Increasing the number of configured hard goals will reduce the likelihood of Cruise Control generating valid optimization proposals.

If skipHardGoalCheck: true is specified in the KafkaRebalance custom resource, Cruise Control does not check that the list of user-provided optimization goals (in KafkaRebalance.spec.goals) contains all the configured hard goals (hard.goals). Therefore, if some, but not all, of the user-provided optimization goals are in the hard.goals list, Cruise Control will still treat them as hard goals even if skipHardGoalCheck: true is specified.

Main optimization goals

The main optimization goals are available to all users. Goals that are not listed in the main optimization goals are not available for use in Cruise Control operations.

Unless you change the Cruise Control deployment configuration, Strimzi will inherit the following main optimization goals from Cruise Control, in descending priority order:

RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal

Six of these goals are preset as hard goals.

To reduce complexity, we recommend that you use the inherited main optimization goals, unless you need to completely exclude one or more goals from use in KafkaRebalance resources. The priority order of the main optimization goals can be modified, if desired, in the configuration for default optimization goals.

You configure main optimization goals, if necessary, in the Cruise Control deployment configuration: Kafka.spec.cruiseControl.config.goals

  • To accept the inherited main optimization goals, do not specify the goals property in Kafka.spec.cruiseControl.config.

  • If you need to modify the inherited main optimization goals, specify a list of goals, in descending priority order, in the goals configuration option.

Note
 If you change the inherited main optimization goals, you must ensure that the hard goals, if configured in the hard.goals property in Kafka.spec.cruiseControl.config, are a subset of the main optimization goals that you configured. Otherwise, errors will occur when generating optimization proposals.
Default optimization goals

Cruise Control uses the default optimization goals to generate the cached optimization proposal. For more information about the cached optimization proposal, see Optimization proposals overview.

You can override the default optimization goals by setting user-provided optimization goals in a KafkaRebalance custom resource.

Unless you specify default.goals in the Cruise Control deployment configuration, the main optimization goals are used as the default optimization goals. In this case, the cached optimization proposal is generated using the main optimization goals.

  • To use the main optimization goals as the default goals, do not specify the default.goals property in Kafka.spec.cruiseControl.config.

  • To modify the default optimization goals, edit the default.goals property in Kafka.spec.cruiseControl.config. You must use a subset of the main optimization goals.

Example Kafka configuration for default optimization goals
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl:
    brokerCapacity:
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    config:
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal
      # ...

If no default optimization goals are specified, the cached proposal is generated using the main optimization goals.

User-provided optimization goals

User-provided optimization goals narrow down the configured default goals for a particular optimization proposal. You can set them, as required, in spec.goals in a KafkaRebalance custom resource:

KafkaRebalance.spec.goals

User-provided optimization goals can generate optimization proposals for different scenarios. For example, you might want to optimize leader replica distribution across the Kafka cluster without considering disk capacity or disk utilization. So, you create a KafkaRebalance custom resource containing a single user-provided goal for leader replica distribution.

User-provided optimization goals must:

  • Include all configured hard goals, or an error occurs

  • Be a subset of the main optimization goals

To ignore the configured hard goals when generating an optimization proposal, add the skipHardGoalCheck: true property to the KafkaRebalance custom resource. See Generating optimization proposals.

Additional resources

7.3. Optimization proposals overview

An optimization proposal is a summary of proposed changes that would produce a more balanced Kafka cluster, with partition workloads distributed more evenly among the brokers. Each optimization proposal is based on the set of optimization goals that was used to generate it, subject to any configured capacity limits on broker resources.

All optimization proposals are estimates of the impact of a proposed rebalance. You can approve or reject a proposal. You cannot approve a cluster rebalance without first generating the optimization proposal.

Contents of optimization proposals

An optimization proposal comprises a summary and broker load. The summary is contained in the KafkaRebalance resource.

Summary

The summary provides an overview of the proposed cluster rebalance and indicates the scale of the changes involved. A summary of a successfully generated optimization proposal is contained in the Status.OptimizationResult property of the KafkaRebalance resource. The information provided is a summary of the full optimization proposal.

Broker load

The broker load shows before and after values for the proposed rebalance, so you can see the impact on each of the brokers in the cluster. A broker load is stored in a ConfigMap that contains data as a JSON string.

7.3.1. Approving or rejecting an optimization proposal

An optimization proposal summary shows the proposed scope of changes.

You can use the name of the KafkaRebalance resource to return a summary from the command line.

Returning an optimization proposal summary
kubectl describe kafkarebalance <kafka_rebalance_resource_name> -n <namespace>

You can also use the jq command line JSON parser tool.

Returning an optimization proposal summary using jq
kubectl get kafkarebalance -o json | jq <jq_query>.

Use the summary to decide whether to approve or reject an optimization proposal.

Approving an optimization proposal

You approve the optimization proposal by setting the strimzi.io/rebalance annotation of the KafkaRebalance resource to approve. Cruise Control applies the proposal to the Kafka cluster and starts a cluster rebalance operation.

Rejecting an optimization proposal

If you choose not to approve an optimization proposal, you can change the optimization goals or update any of the rebalance performance tuning options, and then generate another proposal. You can use the strimzi.io/refresh annotation to generate a new optimization proposal for a KafkaRebalance resource.

Use optimization proposals to assess the movements required for a rebalance. For example, a summary describes inter-broker and intra-broker movements. Inter-broker rebalancing moves data between separate brokers. Intra-broker rebalancing moves data between disks on the same broker when you are using a JBOD storage configuration. Such information can be useful even if you don’t go ahead and approve the proposal.

You might reject an optimization proposal, or delay its approval, because of the additional load on a Kafka cluster when rebalancing.

In the following example, the proposal suggests the rebalancing of data between separate brokers. The rebalance involves the movement of 55 partition replicas, totaling 12MB of data, across the brokers. Though the inter-broker movement of partition replicas has a high impact on performance, the total amount of data is not large. If the total data was much larger, you could reject the proposal, or time when to approve the rebalance to limit the impact on the performance of the Kafka cluster.

Rebalance performance tuning options can help reduce the impact of data movement. If you can extend the rebalance period, you can divide the rebalance into smaller batches. Fewer data movements at a single time reduces the load on the cluster.

Example optimization proposal summary
Name:         my-rebalance
Namespace:    myproject
Labels:       strimzi.io/cluster=my-cluster
Annotations:  API Version:  kafka.strimzi.io/v1alpha1
Kind:         KafkaRebalance
Metadata:
# ...
Status:
  Conditions:
    Last Transition Time:  2022-04-05T14:36:11.900Z
    Status:                ProposalReady
    Type:                  State
  Observed Generation:     1
  Optimization Result:
    Data To Move MB:  0
    Excluded Brokers For Leadership:
    Excluded Brokers For Replica Move:
    Excluded Topics:
    Intra Broker Data To Move MB:         12
    Monitored Partitions Percentage:      100
    Num Intra Broker Replica Movements:   0
    Num Leader Movements:                 24
    Num Replica Movements:                55
    On Demand Balancedness Score After:   82.91290759174306
    On Demand Balancedness Score Before:  78.01176356230222
    Recent Windows:                       5
  Session Id:                             a4f833bd-2055-4213-bfdd-ad21f95bf184

The proposal will also move 24 partition leaders to different brokers. This requires a change to the ZooKeeper configuration, which has a low impact on performance.

The balancedness scores are measurements of the overall balance of the Kafka cluster before and after the optimization proposal is approved. A balancedness score is based on optimization goals. If all goals are satisfied, the score is 100. The score is reduced for each goal that will not be met. Compare the balancedness scores to see whether the Kafka cluster is less balanced than it could be following a rebalance.

Optimization proposal summary properties

The following table explains the properties contained in the optimization proposal’s summary section.

Table 4. Properties contained in an optimization proposal summary
JSON property Description

numIntraBrokerReplicaMovements

The total number of partition replicas that will be transferred between the disks of the cluster’s brokers.

Performance impact during rebalance operation: Relatively high, but lower than numReplicaMovements.

excludedBrokersForLeadership

Not yet supported. An empty list is returned.

numReplicaMovements

The number of partition replicas that will be moved between separate brokers.

Performance impact during rebalance operation: Relatively high.

onDemandBalancednessScoreBefore, onDemandBalancednessScoreAfter

A measurement of the overall balancedness of a Kafka Cluster, before and after the optimization proposal was generated.

The score is calculated by subtracting the sum of the BalancednessScore of each violated soft goal from 100. Cruise Control assigns a BalancednessScore to every optimization goal based on several factors, including priority—​the goal’s position in the list of default.goals or user-provided goals.

The Before score is based on the current configuration of the Kafka cluster. The After score is based on the generated optimization proposal.

intraBrokerDataToMoveMB

The sum of the size of each partition replica that will be moved between disks on the same broker (see also numIntraBrokerReplicaMovements).

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see dataToMoveMB).

recentWindows

The number of metrics windows upon which the optimization proposal is based.

dataToMoveMB

The sum of the size of each partition replica that will be moved to a separate broker (see also numReplicaMovements).

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete.

monitoredPartitionsPercentage

The percentage of partitions in the Kafka cluster covered by the optimization proposal. Affected by the number of excludedTopics.

excludedTopics

If you specified a regular expression in the spec.excludedTopicsRegex property in the KafkaRebalance resource, all topic names matching that expression are listed here. These topics are excluded from the calculation of partition replica/leader movements in the optimization proposal.

numLeaderMovements

The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration.

Performance impact during rebalance operation: Relatively low.

excludedBrokersForReplicaMove

Not yet supported. An empty list is returned.
Broker load properties

The broker load is stored in a ConfigMap (with the same name as the KafkaRebalance custom resource) as a JSON formatted string. This JSON string consists of a JSON object with keys for each broker IDs linking to a number of metrics for each broker. Each metric consist of three values. The first is the metric value before the optimization proposal is applied, the second is the expected value of the metric after the proposal is applied, and the third is the difference between the first two values (after minus before).

Note
 The ConfigMap appears when the KafkaRebalance resource is in the ProposalReady state and remains after the rebalance is complete.

You can use the name of the ConfigMap to view its data from the command line.

Returning ConfigMap data
kubectl describe configmaps <my_rebalance_configmap_name> -n <namespace>

You can also use the jq command line JSON parser tool to extract the JSON string from the ConfigMap.

Extracting the JSON string from the ConfigMap using jq
kubectl get configmaps <my_rebalance_configmap_name> -o json | jq '.["data"]["brokerLoad.json"]|fromjson|.'

The following table explains the properties contained in the optimization proposal’s broker load ConfigMap:

JSON property Description

leaders

The number of replicas on this broker that are partition leaders.

replicas

The number of replicas on this broker.

cpuPercentage

The CPU utilization as a percentage of the defined capacity.

diskUsedPercentage

The disk utilization as a percentage of the defined capacity.

diskUsedMB

The absolute disk usage in MB.

networkOutRate

The total network output rate for the broker.

leaderNetworkInRate

The network input rate for all partition leader replicas on this broker.

followerNetworkInRate

The network input rate for all follower replicas on this broker.

potentialMaxNetworkOutRate

The hypothetical maximum network output rate that would be realized if this broker became the leader of all the replicas it currently hosts.

Cached optimization proposal

Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster. If you generate an optimization proposal using the default optimization goals, Cruise Control returns the most recent cached proposal.

To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms setting in the Cruise Control deployment configuration. Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server.

7.3.2. Rebalancing modes

When using the KafkaRebalance custom resource for running a rebalancing operation, you have three available modes:

  • full

  • add-brokers

  • remove-brokers

You specify a mode using the spec.mode property of the KafkaRebalance custom resource.

full

The full mode runs a full rebalance by moving replicas across all the brokers in the cluster. This is the default mode if the spec.mode property is not defined in the KafkaRebalance custom resource.

add-brokers

The add-brokers mode is used after scaling up a Kafka cluster by adding one or more brokers. Normally, after scaling up a Kafka cluster, new brokers are used to host only the partitions of newly created topics. If no new topics are created, the newly added brokers are not used and the existing brokers remain under the same load. By using the add-brokers mode immediately after adding brokers to the cluster, the rebalancing operation moves replicas from existing brokers to the newly added brokers. You specify the new brokers as a list using the spec.brokers property of the KafkaRebalance custom resource.

remove-brokers

The remove-brokers mode is used before scaling down a Kafka cluster by removing one or more brokers. If you scale down a Kafka cluster, brokers are shut down even if they host replicas. This can lead to under-replicated partitions and possibly result in some partitions being under their minimum ISR (in-sync replicas). To avoid this potential problem, the remove-brokers mode moves replicas off the brokers that are going to be removed. When these brokers are not hosting replicas anymore, you can safely run the scaling down operation. You specify the brokers you’re removing as a list in the spec.brokers property in the KafkaRebalance custom resource.

In general, use the full rebalance mode to rebalance a Kafka cluster by spreading the load across brokers. Use the add-brokers and remove-brokers modes only if you want to scale your cluster up or down and rebalance the replicas accordingly.

The procedure to run a rebalance is actually the same across the three different modes. The only difference is with specifying a mode through the spec.mode property and, if needed, listing brokers that have been added or will be removed through the spec.brokers property.

Additional resources

7.4. Rebalance performance tuning overview

You can adjust several performance tuning options for cluster rebalances. These options control how partition replica and leadership movements in a rebalance are executed, as well as the bandwidth that is allocated to a rebalance operation.

7.4.1. Partition reassignment commands

Optimization proposals are comprised of separate partition reassignment commands. When you approve a proposal, the Cruise Control server applies these commands to the Kafka cluster.

A partition reassignment command consists of either of the following types of operations:

  • Partition movement: Involves transferring the partition replica and its data to a new location. Partition movements can take one of two forms:

    • Inter-broker movement: The partition replica is moved to a log directory on a different broker.

    • Intra-broker movement: The partition replica is moved to a different log directory on the same broker.

  • Leadership movement: This involves switching the leader of the partition’s replicas.

Cruise Control issues partition reassignment commands to the Kafka cluster in batches. The performance of the cluster during the rebalance is affected by the number of each type of movement contained in each batch.

7.4.2. Replica movement strategies

Cluster rebalance performance is also influenced by the replica movement strategy that is applied to the batches of partition reassignment commands. By default, Cruise Control uses the BaseReplicaMovementStrategy, which simply applies the commands in the order they were generated. However, if there are some very large partition reassignments early in the proposal, this strategy can slow down the application of the other reassignments.

Cruise Control provides four alternative replica movement strategies that can be applied to optimization proposals:

  • PrioritizeSmallReplicaMovementStrategy: Order reassignments in order of ascending size.

  • PrioritizeLargeReplicaMovementStrategy: Order reassignments in order of descending size.

  • PostponeUrpReplicaMovementStrategy: Prioritize reassignments for replicas of partitions which have no out-of-sync replicas.

  • PrioritizeMinIsrWithOfflineReplicasStrategy: Prioritize reassignments with (At/Under)MinISR partitions with offline replicas. This strategy will only work if cruiseControl.config.concurrency.adjuster.min.isr.check.enabled is set to true in the Kafka custom resource’s spec.

These strategies can be configured as a sequence. The first strategy attempts to compare two partition reassignments using its internal logic. If the reassignments are equivalent, then it passes them to the next strategy in the sequence to decide the order, and so on.

7.4.3. Intra-broker disk balancing

Moving a large amount of data between disks on the same broker has less impact than between separate brokers. If you are running a Kafka deployment that uses JBOD storage with multiple disks on the same broker, Cruise Control can balance partitions between the disks.

Note
 If you are using JBOD storage with a single disk, intra-broker disk balancing will result in a proposal with 0 partition movements since there are no disks to balance between.

To perform an intra-broker disk balance, set rebalanceDisk to true under the KafkaRebalance.spec. When setting rebalanceDisk to true, do not set a goals field in the KafkaRebalance.spec, as Cruise Control will automatically set the intra-broker goals and ignore the inter-broker goals. Cruise Control does not perform inter-broker and intra-broker balancing at the same time.

7.4.4. Rebalance tuning options

Cruise Control provides several configuration options for tuning the rebalance parameters discussed above. You can set these tuning options at either the Cruise Control server or optimization proposal levels:

  • The Cruise Control server setting can be set in the Kafka custom resource under Kafka.spec.cruiseControl.config.

  • The individual rebalance performance configurations can be set under KafkaRebalance.spec.

The relevant configurations are summarized in the following table.

Table 5. Rebalance performance tuning configuration
Cruise Control properties KafkaRebalance properties Default Description

num.concurrent.partition.movements.per.broker

concurrentPartitionMovementsPerBroker

5

The maximum number of inter-broker partition movements in each partition reassignment batch

num.concurrent.intra.broker.partition.movements

concurrentIntraBrokerPartitionMovements

2

The maximum number of intra-broker partition movements in each partition reassignment batch

num.concurrent.leader.movements

concurrentLeaderMovements

1000

The maximum number of partition leadership changes in each partition reassignment batch

default.replication.throttle

replicationThrottle

Null (no limit)

The bandwidth (in bytes per second) to assign to partition reassignment

default.replica.movement.strategies

replicaMovementStrategies

BaseReplicaMovementStrategy

The list of strategies (in priority order) used to determine the order in which partition reassignment commands are executed for generated proposals. For the server setting, use a comma separated string with the fully qualified names of the strategy class (add com.linkedin.kafka.cruisecontrol.executor.strategy. to the start of each class name). For the KafkaRebalance resource setting use a YAML array of strategy class names.

-

rebalanceDisk

false

Enables intra-broker disk balancing, which balances disk space utilization between disks on the same broker. Only applies to Kafka deployments that use JBOD storage with multiple disks.

Changing the default settings affects the length of time that the rebalance takes to complete, as well as the load placed on the Kafka cluster during the rebalance. Using lower values reduces the load but increases the amount of time taken, and vice versa.

Additional resources

7.5. Cruise Control configuration

The config property in Kafka.spec.cruiseControl contains configuration options as keys with values as one of the following JSON types:

  • String

  • Number

  • Boolean

You can specify and configure all the options listed in the "Configurations" section of the Cruise Control documentation, apart from those managed directly by Strimzi. Specifically, you cannot modify configuration options with keys equal to or starting with one of the keys mentioned here.

If restricted options are specified, they are ignored and a warning message is printed to the Cluster Operator log file. All the supported options are passed to Cruise Control.

An example Cruise Control configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    config:
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal
      cpu.balance.threshold: 1.1
      metadata.max.age.ms: 300000
      send.buffer.bytes: 131072
    # ...

Cross-Origin Resource Sharing configuration

Cross-Origin Resource Sharing (CORS) allows you to specify allowed methods and originating URLs for accessing REST APIs.

By default, CORS is disabled for the Cruise Control REST API. When enabled, only GET requests for read-only access to the Kafka cluster state are allowed. This means that external applications, which are running in different origins than the Strimzi components, cannot make POST requests to the Cruise Control API. However, those applications can make GET requests to access read-only information about the Kafka cluster, such as the current cluster load or the most recent optimization proposal.

Enabling CORS for Cruise Control

You enable and configure CORS in Kafka.spec.cruiseControl.config.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    config:
      webserver.http.cors.enabled: true
      webserver.http.cors.origin: "*"
      webserver.http.cors.exposeheaders: "User-Task-ID,Content-Type"
    # ...

For more information, see REST APIs in the Cruise Control Wiki.

Capacity configuration

Cruise Control uses capacity limits to determine if optimization goals for resource distribution are being broken. There are four goals of this type:

  • DiskUsageDistributionGoal - Disk utilization distribution

  • CpuUsageDistributionGoal - CPU utilization distribution

  • NetworkInboundUsageDistributionGoal - Network inbound utilization distribution

  • NetworkOutboundUsageDistributionGoal - Network outbound utilization distribution

You specify capacity limits for Kafka broker resources in the brokerCapacity property in Kafka.spec.cruiseControl . They are enabled by default and you can change their default values. Capacity limits can be set for the following broker resources:

  • inboundNetwork - Inbound network throughput in byte units per second (Default: 10000KiB/s)

  • outboundNetwork - Outbound network throughput in byte units per second (Default: 10000KiB/s)

For network throughput, use an integer value with standard Kubernetes byte units (K, M, G) or their bibyte (power of two) equivalents (Ki, Mi, Gi) per second.

Note
 Disk and CPU capacity limits are automatically generated by Strimzi, so you do not need to set them.
Note

In order to guarantee accurate rebalance proposal when using CPU goals, you can set CPU requests equal to CPU limits in Kafka.spec.kafka.resources. That way, all CPU resources are reserved upfront and are always available. This configuration allows Cruise Control to properly evaluate the CPU utilization when preparing the rebalance proposals based on CPU goals.
An example Cruise Control brokerCapacity configuration using bibyte units
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    brokerCapacity:
      inboundNetwork: 10000KiB/s
      outboundNetwork: 10000KiB/s
    # ...

7.5.1. Capacity overrides

Brokers might be running on nodes with heterogeneous network resources. If that’s the case, specify overrides that set the network capacity limits for each broker. The overrides ensure an accurate rebalance between the brokers. Override capacity limits can be set for the following broker resources:

  • inboundNetwork - Inbound network throughput in byte units per second (Default: 10000KiB/s)

  • outboundNetwork - Outbound network throughput in byte units per second (Default: 10000KiB/s)

An example of Cruise Control capacity overrides configuration using bibyte units
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    brokerCapacity:
      inboundNetwork: 10000KiB/s
      outboundNetwork: 10000KiB/s
      overrides:
      - brokers: [0]
        inboundNetwork: 20000KiB/s
        outboundNetwork: 20000KiB/s
      - brokers: [1, 2]
        inboundNetwork: 30000KiB/s
        outboundNetwork: 30000KiB/s
Additional resources

For more information, refer to the BrokerCapacity schema reference.

Logging configuration

Cruise Control has its own configurable logger:

  • rootLogger.level

Cruise Control uses the Apache log4j 2 logger implementation.

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.valueFrom.configMapKeyRef.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties. Both logging.valueFrom.configMapKeyRef.name and logging.valueFrom.configMapKeyRef.key properties are mandatory. A ConfigMap using the exact logging configuration specified is created with the custom resource when the Cluster Operator is running, then recreated after each reconciliation. If you do not specify a custom ConfigMap, default logging settings are used. If a specific logger value is not set, upper-level logger settings are inherited for that logger. Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
  cruiseControl:
    # ...
    logging:
      type: inline
      loggers:
        rootLogger.level: "INFO"
    # ...
External logging
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
  cruiseControl:
    # ...
    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: customConfigMap
          key: cruise-control-log4j.properties
    # ...

Cruise Control REST API security

The Cruise Control REST API is secured with HTTP Basic authentication and SSL to protect the cluster against potentially destructive Cruise Control operations, such as decommissioning Kafka brokers.

We recommend that Cruise Control in Strimzi is only used with these settings enabled. You should not disable the built-in HTTP Basic authentication or SSL settings described below.

  • To disable the built-in HTTP Basic authentication, set webserver.security.enable to false.

  • To disable the built-in SSL, set webserver.ssl.enable to false.

Example Cruise Control configuration to disable API authorization, authentication, and SSL
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    config:
      webserver.security.enable: false
      webserver.ssl.enable: false
# ...

7.6. Deploying Cruise Control

To deploy Cruise Control to your Strimzi cluster, define the configuration using the cruiseControl property in the Kafka resource, and then create or update the resource.

Deploy one instance of Cruise Control per Kafka cluster.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the Kafka resource and add the cruiseControl property.

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

    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    brokerCapacity: # (1)
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
      # ...
    config: # (2)
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal
         # ...
      cpu.balance.threshold: 1.1
      metadata.max.age.ms: 300000
      send.buffer.bytes: 131072
      # ...
    resources: # (3)
      requests:
        cpu: 1
        memory: 512Mi
      limits:
        cpu: 2
        memory: 2Gi
    logging: # (4)
        type: inline
        loggers:
          rootLogger.level: "INFO"
    template: # (5)
      pod:
        metadata:
          labels:
            label1: value1
        securityContext:
          runAsUser: 1000001
          fsGroup: 0
        terminationGracePeriodSeconds: 120
    readinessProbe: # (6)
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe: # (7)
      initialDelaySeconds: 15
      timeoutSeconds: 5
# ...

    1. Specifies capacity limits for broker resources. For more information, see Capacity configuration.

    2. Defines the Cruise Control configuration, including the default optimization goals (in default.goals) and any customizations to the main optimization goals (in goals) or the hard goals (in hard.goals). You can provide any standard Cruise Control configuration option apart from those managed directly by Strimzi. For more information on configuring optimization goals, see Optimization goals overview.

    3. CPU and memory resources reserved for Cruise Control. For more information, see resources.

    4. Defined loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties key. Cruise Control has a single logger named rootLogger.level. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information, see Logging configuration.

    5. Customization of deployment templates and pods.

    6. Healthcheck readiness probes.

    7. Healthcheck liveness probes.

  2. Create or update the resource:

    kubectl apply -f kafka.yaml

  3. Check the status of the deployment:

    kubectl get deployments -n <my_cluster_operator_namespace>
    Output shows the deployment name and readiness
    NAME                      READY  UP-TO-DATE  AVAILABLE
my-cluster-cruise-control 1/1    1           1

    my-cluster is the name of the Kafka cluster.

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

Auto-created topics

The following table shows the three topics that are automatically created when Cruise Control is deployed. These topics are required for Cruise Control to work properly and must not be deleted or changed. You can change the name of the topic using the specified configuration option.

Table 6. Auto-created topics
Auto-created topic configuration Default topic name Created by Function

metric.reporter.topic

strimzi.cruisecontrol.metrics

Strimzi Metrics Reporter

Stores the raw metrics from the Metrics Reporter in each Kafka broker.

partition.metric.sample.store.topic

strimzi.cruisecontrol.partitionmetricsamples

Cruise Control

Stores the derived metrics for each partition. These are created by the Metric Sample Aggregator.

broker.metric.sample.store.topic

strimzi.cruisecontrol.modeltrainingsamples

Cruise Control

Stores the metrics samples used to create the Cluster Workload Model.

To prevent the removal of records that are needed by Cruise Control, log compaction is disabled in the auto-created topics.

Note
 If the names of the auto-created topics are changed in a Kafka cluster that already has Cruise Control enabled, the old topics will not be deleted and should be manually removed.
What to do next

After configuring and deploying Cruise Control, you can generate optimization proposals.

Additional resources

7.7. Generating optimization proposals

When you create or update a KafkaRebalance resource, Cruise Control generates an optimization proposal for the Kafka cluster based on the configured optimization goals. Analyze the information in the optimization proposal and decide whether to approve it. You can use the results of the optimization proposal to rebalance your Kafka cluster.

You can run the optimization proposal in one of the following modes:

  • full

  • add-brokers

  • remove-brokers.

The mode you use depends on whether you are rebalancing across all the brokers already running in the Kafka cluster; or you want to rebalance after scaling up or before scaling down your Kafka cluster. For more information, see Rebalancing modes with broker scaling.

Prerequisites
Procedure

  1. Create a KafkaRebalance resource:

    1. To use the default optimization goals defined in the Kafka resource, leave the spec property empty. Cruise Control rebalances a Kafka cluster in full mode by default.

      Example configuration with full rebalancing by default
      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec: {}

      You can also run a full rebalance by specifying the full mode through the spec.mode property.

      Example configuration specifying full mode
      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec:
  mode: full

      If you want to rebalance a Kafka cluster after scaling up, specify the add-brokers mode.

      In this mode, existing replicas are moved to the newly added brokers. You need to specify the brokers as a list.

      Example configuration specifying add-brokers mode
      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec:
  mode: add-brokers
  brokers: [3, 4] (1)

      1. List of newly added brokers added by the scale up operation. This property is mandatory.

      If you want to rebalance a Kafka cluster before scaling down, specify the remove-brokers mode.

      In this mode, replicas are moved off the brokers that are going to be removed. You need to specify the brokers that are being removed as a list.

      Example of rebalancing to run before a scale down by using the remove-brokers mode
      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec:
  mode: remove-brokers
  brokers: [3, 4] (1)

      1. List of brokers to be removed by the scale down operation. This property is mandatory.

      Note
      		 The following steps and the steps to approve or stop a rebalance are the same regardless of the rebalance mode you are using.

    2. To configure user-provided optimization goals instead of using the default goals, add the goals property and enter one or more goals.

      In the following example, rack awareness and replica capacity are configured as user-provided optimization goals:

      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec:
  goals:
    - RackAwareGoal
    - ReplicaCapacityGoal

    3. To ignore the configured hard goals, add the skipHardGoalCheck: true property:

      apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec:
  goals:
    - RackAwareGoal
    - ReplicaCapacityGoal
  skipHardGoalCheck: true

  2. Create or update the resource:

    kubectl apply -f your-file

    The Cluster Operator requests the optimization proposal from Cruise Control. This might take a few minutes depending on the size of the Kafka cluster.

  3. Check the status of the KafkaRebalance resource:

    kubectl describe kafkarebalance rebalance-cr-name

    Cruise Control returns one of two statuses:

    • PendingProposal: The rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready.

    • ProposalReady: The optimization proposal is ready for review and, if desired, approval. The optimization proposal is contained in the Status.Optimization Result property of the KafkaRebalance resource.

  4. Review the optimization proposal.

    kubectl describe kafkarebalance rebalance-cr-name

    Here is an example proposal:

    Status:
  Conditions:
    Last Transition Time:  2020-05-19T13:50:12.533Z
    Status:                ProposalReady
    Type:                  State
  Observed Generation:     1
  Optimization Result:
    Data To Move MB:  0
    Excluded Brokers For Leadership:
    Excluded Brokers For Replica Move:
    Excluded Topics:
    Intra Broker Data To Move MB:         0
    Monitored Partitions Percentage:      100
    Num Intra Broker Replica Movements:   0
    Num Leader Movements:                 0
    Num Replica Movements:                26
    On Demand Balancedness Score After:   81.8666802863978
    On Demand Balancedness Score Before:  78.01176356230222
    Recent Windows:                       1
  Session Id:                             05539377-ca7b-45ef-b359-e13564f1458c

    The properties in the Optimization Result section describe the pending cluster rebalance operation. For descriptions of each property, see Contents of optimization proposals.

Insufficient CPU capacity

If a Kafka cluster is overloaded in terms of CPU utilization, you might see an insufficient CPU capacity error in the KafkaRebalance status. It’s worth noting that this utilization value is unaffected by the excludedTopics configuration. Although optimization proposals will not reassign replicas of excluded topics, their load is still considered in the utilization calculation.

Example CPU utilization error
com.linkedin.kafka.cruisecontrol.exception.OptimizationFailureException:
        [CpuCapacityGoal] Insufficient capacity for cpu (Utilization 615.21,
        Allowed Capacity 420.00, Threshold: 0.70). Add at least 3 brokers with
        the same cpu capacity (100.00) as broker-0. Add at least 3 brokers with
        the same cpu capacity (100.00) as broker-0.
Note

The error shows CPU capacity as a percentage rather than CPU cores. For that reason, it does not directly map to the number of CPUs configured in Kafka CR. It is like having a single virtual CPU per broker, which has the cycles of Kafka.spec.kafka.resources.limits.cpu CPUs. This has no effect on the rebalance behavior, since the ratio between CPU utilization and capacity remains the same.
What to do next

Approving an optimization proposal

Additional resources

7.8. Approving an optimization proposal

You can approve an optimization proposal generated by Cruise Control, if its status is ProposalReady. Cruise Control will then apply the optimization proposal to the Kafka cluster, reassigning partitions to brokers and changing partition leadership.

Caution

This is not a dry run. Before you approve an optimization proposal, you must:
Prerequisites
Procedure

Perform these steps for the optimization proposal that you want to approve:

  1. Unless the optimization proposal is newly generated, check that it is based on current information about the state of the Kafka cluster. To do so, refresh the optimization proposal to make sure it uses the latest cluster metrics:

    1. Annotate the KafkaRebalance resource in Kubernetes with refresh:

      kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh

    2. Check the status of the KafkaRebalance resource:

      kubectl describe kafkarebalance rebalance-cr-name

    3. Wait until the status changes to ProposalReady.

  2. Approve the optimization proposal that you want Cruise Control to apply.

    Annotate the KafkaRebalance resource in Kubernetes:

    kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=approve

  3. The Cluster Operator detects the annotated resource and instructs Cruise Control to rebalance the Kafka cluster.

  4. Check the status of the KafkaRebalance resource:

    kubectl describe kafkarebalance rebalance-cr-name

  5. Cruise Control returns one of three statuses:

    • Rebalancing: The cluster rebalance operation is in progress.

    • Ready: The cluster rebalancing operation completed successfully. To use the same KafkaRebalance custom resource to generate another optimization proposal, apply the refresh annotation to the custom resource. This moves the custom resource to the PendingProposal or ProposalReady state. You can then review the optimization proposal and approve it, if desired.

    • NotReady: An error occurred—​see Fixing problems with a KafkaRebalance resource.

Additional resources

7.9. Stopping a cluster rebalance

Once started, a cluster rebalance operation might take some time to complete and affect the overall performance of the Kafka cluster.

If you want to stop a cluster rebalance operation that is in progress, apply the stop annotation to the KafkaRebalance custom resource. This instructs Cruise Control to finish the current batch of partition reassignments and then stop the rebalance. When the rebalance has stopped, completed partition reassignments have already been applied; therefore, the state of the Kafka cluster is different when compared to prior to the start of the rebalance operation. If further rebalancing is required, you should generate a new optimization proposal.

Note
 The performance of the Kafka cluster in the intermediate (stopped) state might be worse than in the initial state.
Prerequisites

  • You have approved the optimization proposal by annotating the KafkaRebalance custom resource with approve.

  • The status of the KafkaRebalance custom resource is Rebalancing.

Procedure

  1. Annotate the KafkaRebalance resource in Kubernetes:

    kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=stop

  2. Check the status of the KafkaRebalance resource:

    kubectl describe kafkarebalance rebalance-cr-name

  3. Wait until the status changes to Stopped.

Additional resources

7.10. Fixing problems with a KafkaRebalance resource

If an issue occurs when creating a KafkaRebalance resource or interacting with Cruise Control, the error is reported in the resource status, along with details of how to fix it. The resource also moves to the NotReady state.

To continue with the cluster rebalance operation, you must fix the problem in the KafkaRebalance resource itself or with the overall Cruise Control deployment. Problems might include the following:

  • A misconfigured parameter in the KafkaRebalance resource.

  • The strimzi.io/cluster label for specifying the Kafka cluster in the KafkaRebalance resource is missing.

  • The Cruise Control server is not deployed as the cruiseControl property in the Kafka resource is missing.

  • The Cruise Control server is not reachable.

After fixing the issue, you need to add the refresh annotation to the KafkaRebalance resource. During a “refresh”, a new optimization proposal is requested from the Cruise Control server.

Prerequisites
Procedure

  1. Get information about the error from the KafkaRebalance status:

    kubectl describe kafkarebalance rebalance-cr-name

  2. Attempt to resolve the issue in the KafkaRebalance resource.

  3. Annotate the KafkaRebalance resource in Kubernetes:

    kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh

  4. Check the status of the KafkaRebalance resource:

    kubectl describe kafkarebalance rebalance-cr-name

  5. Wait until the status changes to PendingProposal, or directly to ProposalReady.

Additional resources

8. Distributed tracing

Distributed tracing allows you to track the progress of transactions between applications in a distributed system. In a microservices architecture, tracing tracks the progress of transactions between services. Trace data is useful for monitoring application performance and investigating issues with target systems and end-user applications.

In Strimzi, tracing facilitates the end-to-end tracking of messages: from source systems to Kafka, and then from Kafka to target systems and applications. It complements the metrics that are available to view in Grafana dashboards, as well as the component loggers.

How Strimzi supports tracing

Support for tracing is built in to the following components:

  • Kafka Connect

  • MirrorMaker

  • MirrorMaker 2.0

  • Strimzi Kafka Bridge

You enable and configure tracing for these components using template configuration properties in their custom resources.

To enable tracing in Kafka producers, consumers, and Kafka Streams API applications, you instrument application code using the OpenTracing Apache Kafka Client Instrumentation library (included with Strimzi). When instrumented, clients generate trace data; for example, when producing messages or writing offsets to the log.

Traces are sampled according to a sampling strategy and then visualized in the Jaeger user interface.

Note

Tracing is not supported for Kafka brokers.

Setting up tracing for applications and systems beyond Strimzi is outside the scope of this chapter. To learn more about this subject, search for "inject and extract" in the OpenTracing documentation.

Outline of procedures

To set up tracing for Strimzi, follow these procedures in order:

Prerequisites

  • The Jaeger backend components are deployed to your Kubernetes cluster. For deployment instructions, see the Jaeger documentation.

8.1. Overview of OpenTracing and Jaeger

Strimzi uses the OpenTracing and Jaeger projects.

OpenTracing is an API specification that is independent from the tracing or monitoring system.

  • The OpenTracing APIs are used to instrument application code

  • Instrumented applications generate traces for individual transactions across the distributed system

  • Traces are composed of spans that define specific units of work over time

Jaeger is a tracing system for microservices-based distributed systems.

  • Jaeger implements the OpenTracing APIs and provides client libraries for instrumentation

  • The Jaeger user interface allows you to query, filter, and analyze trace data

The Jaeger user interface showing a simple query

Additional resources

8.2. Setting up tracing for Kafka clients

Initialize a Jaeger tracer to instrument your client applications for distributed tracing.

8.2.1. Initializing a Jaeger tracer for Kafka clients

Configure and initialize a Jaeger tracer using a set of tracing environment variables.

Procedure

In each client application:

  1. Add Maven dependencies for Jaeger to the pom.xml file for the client application:

    <dependency>
    <groupId>io.jaegertracing</groupId>
    <artifactId>jaeger-client</artifactId>
    <version>1.3.2</version>
</dependency>

  2. Define the configuration of the Jaeger tracer using the tracing environment variables.

  3. Create the Jaeger tracer from the environment variables that you defined in step two:

    Tracer tracer = Configuration.fromEnv().getTracer();
    Note
    		 For alternative ways to initialize a Jaeger tracer, see the Java OpenTracing library documentation.

  4. Register the Jaeger tracer as a global tracer:

    GlobalTracer.register(tracer);

A Jaeger tracer is now initialized for the client application to use.

8.2.2. Environment variables for tracing

Use these environment variables when configuring a Jaeger tracer for Kafka clients.

Note
 The tracing environment variables are part of the Jaeger project and are subject to change. For the latest environment variables, see the Jaeger documentation.
Property Required Description

JAEGER_SERVICE_NAME

Yes

The name of the Jaeger tracer service.

JAEGER_AGENT_HOST

No

The hostname for communicating with the jaeger-agent through the User Datagram Protocol (UDP).

JAEGER_AGENT_PORT

No

The port used for communicating with the jaeger-agent through UDP.

JAEGER_ENDPOINT

No

The traces endpoint. Only define this variable if the client application will bypass the jaeger-agent and connect directly to the jaeger-collector.

JAEGER_AUTH_TOKEN

No

The authentication token to send to the endpoint as a bearer token.

JAEGER_USER

No

The username to send to the endpoint if using basic authentication.

JAEGER_PASSWORD

No

The password to send to the endpoint if using basic authentication.

JAEGER_PROPAGATION

No

A comma-separated list of formats to use for propagating the trace context. Defaults to the standard Jaeger format. Valid values are jaeger, b3, and w3c.

JAEGER_REPORTER_LOG_SPANS

No

Indicates whether the reporter should also log the spans.

JAEGER_REPORTER_MAX_QUEUE_SIZE

No

The reporter’s maximum queue size.

JAEGER_REPORTER_FLUSH_INTERVAL

No

The reporter’s flush interval, in ms. Defines how frequently the Jaeger reporter flushes span batches.

JAEGER_SAMPLER_TYPE

No

The sampling strategy to use for client traces:

  • Constant

  • Probabilistic

  • Rate Limiting

  • Remote (the default)

To sample all traces, use the Constant sampling strategy with a parameter of 1.

For an overview of the Jaeger architecture and client sampling configuration parameters, see the Jaeger documentation.

JAEGER_SAMPLER_PARAM

No

The sampler parameter (number).

JAEGER_SAMPLER_MANAGER_HOST_PORT

No

The hostname and port to use if a Remote sampling strategy is selected.

JAEGER_TAGS

No

A comma-separated list of tracer-level tags that are added to all reported spans.

The value can also refer to an environment variable using the format ${envVarName:default}. :default is optional and identifies a value to use if the environment variable cannot be found.
Additional resources

8.3. Instrumenting Kafka clients with tracers

Instrument Kafka producer and consumer clients, and Kafka Streams API applications for distributed tracing.

8.3.1. Instrumenting producers and consumers for tracing

Use a Decorator pattern or Interceptors to instrument your Java producer and consumer application code for tracing.

Procedure

In the application code of each producer and consumer application:

  1. Add the Maven dependency for OpenTracing to the producer or consumer’s pom.xml file.

    <dependency>
    <groupId>io.opentracing.contrib</groupId>
    <artifactId>opentracing-kafka-client</artifactId>
    <version>0.1.15</version>
</dependency>

  2. Instrument your client application code using either a Decorator pattern or Interceptors.

    • To use a Decorator pattern:

      // Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);

// Create an instance of the TracingKafkaProducer:
TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer,
        tracer);

// Send:
tracingProducer.send(...);

// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);

// Create an instance of the TracingKafkaConsumer:
TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer,
        tracer);

// Subscribe:
tracingConsumer.subscribe(Collections.singletonList("messages"));

// Get messages:
ConsumerRecords<Integer, String> records = tracingConsumer.poll(1000);

// Retrieve SpanContext from polled record (consumer side):
ConsumerRecord<Integer, String> record = ...
SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);

    • To use Interceptors:

      // Register the tracer with GlobalTracer:
GlobalTracer.register(tracer);

// Add the TracingProducerInterceptor to the sender properties:
senderProps.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
          TracingProducerInterceptor.class.getName());

// Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);

// Send:
producer.send(...);

// Add the TracingConsumerInterceptor to the consumer properties:
consumerProps.put(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG,
          TracingConsumerInterceptor.class.getName());

// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);

// Subscribe:
consumer.subscribe(Collections.singletonList("messages"));

// Get messages:
ConsumerRecords<Integer, String> records = consumer.poll(1000);

// Retrieve the SpanContext from a polled message (consumer side):
ConsumerRecord<Integer, String> record = ...
SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
Custom span names in a Decorator pattern

A span is a logical unit of work in Jaeger, with an operation name, start time, and duration.

To use a Decorator pattern to instrument your producer and consumer applications, define custom span names by passing a BiFunction object as an additional argument when creating the TracingKafkaProducer and TracingKafkaConsumer objects. The OpenTracing Apache Kafka Client Instrumentation library includes several built-in span names.

Example: Using custom span names to instrument client application code in a Decorator pattern
// Create a BiFunction for the KafkaProducer that operates on (String operationName, ProducerRecord consumerRecord) and returns a String to be used as the name:

BiFunction<String, ProducerRecord, String> producerSpanNameProvider =
    (operationName, producerRecord) -> "CUSTOM_PRODUCER_NAME";

// Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);

// Create an instance of the TracingKafkaProducer
TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer,
        tracer,
        producerSpanNameProvider);

// Spans created by the tracingProducer will now have "CUSTOM_PRODUCER_NAME" as the span name.

// Create a BiFunction for the KafkaConsumer that operates on (String operationName, ConsumerRecord consumerRecord) and returns a String to be used as the name:

BiFunction<String, ConsumerRecord, String> consumerSpanNameProvider =
    (operationName, consumerRecord) -> operationName.toUpperCase();

// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);

// Create an instance of the TracingKafkaConsumer, passing in the consumerSpanNameProvider BiFunction:

TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer,
        tracer,
        consumerSpanNameProvider);

// Spans created by the tracingConsumer will have the operation name as the span name, in upper-case.
// "receive" -> "RECEIVE"
Built-in span names

When defining custom span names, you can use the following BiFunctions in the ClientSpanNameProvider class. If no spanNameProvider is specified, CONSUMER_OPERATION_NAME and PRODUCER_OPERATION_NAME are used.

BiFunction Description

CONSUMER_OPERATION_NAME, PRODUCER_OPERATION_NAME

Returns the operationName as the span name: "receive" for consumers and "send" for producers.

CONSUMER_PREFIXED_OPERATION_NAME(String prefix), PRODUCER_PREFIXED_OPERATION_NAME(String prefix)

Returns a String concatenation of prefix and operationName.

CONSUMER_TOPIC, PRODUCER_TOPIC

Returns the name of the topic that the message was sent to or retrieved from in the format (record.topic()).

PREFIXED_CONSUMER_TOPIC(String prefix), PREFIXED_PRODUCER_TOPIC(String prefix)

Returns a String concatenation of prefix and the topic name in the format (record.topic()).

CONSUMER_OPERATION_NAME_TOPIC, PRODUCER_OPERATION_NAME_TOPIC

Returns the operation name and the topic name: "operationName - record.topic()".

CONSUMER_PREFIXED_OPERATION_NAME_TOPIC(String prefix), PRODUCER_PREFIXED_OPERATION_NAME_TOPIC(String prefix)

Returns a String concatenation of prefix and "operationName - record.topic()".

8.3.2. Instrumenting Kafka Streams applications for tracing

This section describes how to instrument Kafka Streams API applications for distributed tracing.

Procedure

In each Kafka Streams API application:

  1. Add the opentracing-kafka-streams dependency to the pom.xml file for your Kafka Streams API application:

    <dependency>
    <groupId>io.opentracing.contrib</groupId>
    <artifactId>opentracing-kafka-streams</artifactId>
    <version>0.1.15</version>
</dependency>

  2. Create an instance of the TracingKafkaClientSupplier supplier interface:

    KafkaClientSupplier supplier = new TracingKafkaClientSupplier(tracer);

  3. Provide the supplier interface to KafkaStreams:

    KafkaStreams streams = new KafkaStreams(builder.build(), new StreamsConfig(config), supplier);
streams.start();

8.4. Setting up tracing for MirrorMaker, Kafka Connect, and the Kafka Bridge

Distributed tracing is supported for MirrorMaker, MirrorMaker 2.0, Kafka Connect, and the Strimzi Kafka Bridge.

Tracing in MirrorMaker and MirrorMaker 2.0

For MirrorMaker and MirrorMaker 2.0, messages are traced from the source cluster to the target cluster. The trace data records messages entering and leaving the MirrorMaker or MirrorMaker 2.0 component.

Tracing in Kafka Connect

Only messages produced and consumed by Kafka Connect itself are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems. For more information, see Configuring Kafka Connect.

Tracing in the Kafka Bridge

Messages produced and consumed by the Kafka Bridge are traced. Incoming HTTP requests from client applications to send and receive messages through the Kafka Bridge are also traced. To have end-to-end tracing, you must configure tracing in your HTTP clients.

8.4.1. Enabling tracing in MirrorMaker, Kafka Connect, and Kafka Bridge resources

Update the configuration of KafkaMirrorMaker, KafkaMirrorMaker2, KafkaConnect, and KafkaBridge custom resources to specify and configure a Jaeger tracer service for each resource. Updating a tracing-enabled resource in your Kubernetes cluster triggers two events:

  • Interceptor classes are updated in the integrated consumers and producers in MirrorMaker, MirrorMaker 2.0, Kafka Connect, or the Strimzi Kafka Bridge.

  • For MirrorMaker, MirrorMaker 2.0, and Kafka Connect, the tracing agent initializes a Jaeger tracer based on the tracing configuration defined in the resource.

  • For the Kafka Bridge, a Jaeger tracer based on the tracing configuration defined in the resource is initialized by the Kafka Bridge itself.

Procedure

Perform these steps for each KafkaMirrorMaker, KafkaMirrorMaker2, KafkaConnect, and KafkaBridge resource.

  1. In the spec.template property, configure the Jaeger tracer service. For example:

    Jaeger tracer configuration for Kafka Connect
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect-cluster
spec:
  #...
  template:
    connectContainer: (1)
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"
  tracing: (2)
    type: jaeger
  #...
    Jaeger tracer configuration for MirrorMaker
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  #...
  template:
    mirrorMakerContainer:
      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
#...
    Jaeger tracer configuration for MirrorMaker 2.0
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mm2-cluster
spec:
  #...
  template:
    connectContainer:
      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
#...
    Jaeger tracer configuration for the Kafka Bridge
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  #...
  template:
    bridgeContainer:
      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
#...

    1. Use the tracing environment variables as template configuration properties.

    2. Set the spec.tracing.type property to jaeger.

  2. Create or update the resource:

    kubectl apply -f your-file
Additional resources

9. Managing TLS certificates

Strimzi supports TLS for encrypted communication between Kafka and Strimzi components.

Communication is always encrypted between the following components:

  • Communication between Kafka and ZooKeeper

  • Interbroker communication between Kafka brokers

  • Internodal communication between ZooKeeper nodes

  • Strimzi operator communication with Kafka brokers and ZooKeeper nodes

Communication between Kafka clients and Kafka brokers is encrypted according to how the cluster is configured. For the Kafka and Strimzi components, TLS certificates are also used for authentication.

The Cluster Operator automatically sets up and renews TLS certificates to enable encryption and authentication within your cluster. It also sets up other TLS certificates if you want to enable encryption or TLS authentication between Kafka brokers and clients.

Certificate Authority (CA) certificates are generated by the Cluster Operator to verify the identities of components and clients. If you don’t want to use the CAs generated by the Cluster Operator, you can install your own cluster and client CA certificates.

You can also provide Kafka listener certificates for TLS listeners or external listeners that have TLS encryption enabled. Use Kafka listener certificates to incorporate the security infrastructure you already have in place.

Note
 Any certificates you provide are not renewed by the Cluster Operator.
Secure Communication
Figure 5. Example architecture of the communication secured by TLS

9.1. Certificate Authorities

To support encryption, each Strimzi component needs its own private keys and public key certificates. All component certificates are signed by an internal Certificate Authority (CA) called the cluster CA.

Similarly, each Kafka client application connecting to Strimzi using TLS client authentication needs to provide private keys and certificates. A second internal CA, named the clients CA, is used to sign certificates for the Kafka clients.

9.1.1. CA certificates

Both the cluster CA and clients CA have a self-signed public key certificate.

Kafka brokers are configured to trust certificates signed by either the cluster CA or clients CA. Components that clients do not need to connect to, such as ZooKeeper, only trust certificates signed by the cluster CA. Unless TLS encryption for external listeners is disabled, client applications must trust certificates signed by the cluster CA. This is also true for client applications that perform mutual TLS authentication.

By default, Strimzi automatically generates and renews CA certificates issued by the cluster CA or clients CA. You can configure the management of these CA certificates in the Kafka.spec.clusterCa and Kafka.spec.clientsCa objects. Certificates provided by users are not renewed.

You can provide your own CA certificates for the cluster CA or clients CA. For more information, see Installing your own CA certificates. If you provide your own certificates, you must manually renew them when needed.

9.1.2. Installing your own CA certificates

This procedure describes how to install your own CA certificates and keys instead of using the CA certificates and private keys generated by the Cluster Operator.

The Cluster Operator automatically generates and renews the following secrets:

CLUSTER-NAME-cluster-ca

The cluster secret that contains the private key for the cluster CA.

CLUSTER-NAME-cluster-ca-cert

The cluster secret that contains a cluster CA certificate. The certificate contains a public key to validate the identity of Kafka brokers.

CLUSTER-NAME-clients-ca

The client secret that contains the private key for the client CA.

CLUSTER-NAME-clients-ca-cert

The client secret that contains a client CA certificate. The certificate contains a public key to validate the identity of clients accessing the Kafka brokers.

Strimzi uses these secrets by default.

This procedure describes the steps to replace the secrets to use your own cluster or client CA certificates.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster is not yet deployed.

  • Your own X.509 certificates and keys in PEM format for the cluster CA or clients CA.

    • If you want to use a cluster or clients CA which is not a Root CA, you have to include the whole chain in the certificate file. The chain should be in the following order:

      1. The cluster or clients CA

      2. One or more intermediate CAs

      3. The root CA

    • All CAs in the chain should be configured using the X509v3 Basic Constraints extension. Basic Constraints limit the path length of a certificate chain.

  • The OpenSSL TLS management tool for converting certificates.

Before you begin

The Cluster Operator generates the following files for the CLUSTER-NAME-cluster-ca-cert secret:

  • ca.crt cluster certificate in PEM format

  • ca.p12 cluster certificate in PKCS #12 format

  • ca.password to access the PKCS #12 file

Some applications cannot use PEM certificates and support only PKCS #12 certificates. You can also add your own cluster certificate in PKCS #12 format.

If you don’t have a cluster certificate in PKCS #12 format, use the OpenSSL TLS management tool to generate one from your ca.crt file.

Example certificate generation command
openssl pkcs12 -export -in ca.crt --nokeys -out ca.p12 -password pass:P12-PASSWORD -caname ca.crt

Replace P12-PASSWORD with your own password.

You can do the same for the CLUSTER-NAME-clients-ca-cert secret, which also contains certificates in PEM and PKCS #12 format by default.

Procedure

  1. Replace the CA certificate generated by the Cluster Operator.

    1. Delete the existing secret.

      kubectl delete secret CA-CERTIFICATE-SECRET

      CA-CERTIFICATE-SECRET is the name of the Secret:

      • CLUSTER-NAME-cluster-ca-cert for the cluster CA certificate

      • CLUSTER-NAME-clients-ca-cert for the clients CA certificate

      Replace CLUSTER-NAME with the name of your Kafka cluster.

      Ignore any "Not Exists" errors.

    2. Create the new secret.

      Client secret creation with a certificate in PEM format only
      kubectl create secret generic CLUSTER-NAME-clients-ca-cert --from-file=ca.crt=ca.crt
      Cluster secret creation with certificates in PEM and PKCS #12 format
      kubectl create secret generic CLUSTER-NAME-cluster-ca-cert \
  --from-file=ca.crt=ca.crt \
  --from-file=ca.p12=ca.p12 \
  --from-literal=ca.password=P12-PASSWORD

  2. Replace the private key generated by the Cluster Operator.

    1. Delete the existing secret.

      kubectl delete secret CA-KEY-SECRET

      CA-KEY-SECRET is the name of CA key:

      • CLUSTER-NAME-cluster-ca for the cluster CA key

      • CLUSTER-NAME-clients-ca for the clients CA key

    2. Create the new secret.

      kubectl create secret generic CA-KEY-SECRET --from-file=ca.key=ca.key

  3. Label the secrets.

    kubectl label secret CA-CERTIFICATE-SECRET strimzi.io/kind=Kafka strimzi.io/cluster=CLUSTER-NAME
    kubectl label secret CA-KEY-SECRET strimzi.io/kind=Kafka strimzi.io/cluster=CLUSTER-NAME

    • Label strimzi.io/kind=Kafka identifies the Kafka custom resource.

    • Label strimzi.io/cluster=CLUSTER-NAME identifies the Kafka cluster.

  4. Annotate the secrets

    kubectl annotate secret CA-CERTIFICATE-SECRET strimzi.io/ca-cert-generation=CA-CERTIFICATE-GENERATION
    kubectl annotate secret CA-KEY-SECRET strimzi.io/ca-key-generation=CA-KEY-GENERATION

    • Annotation strimzi.io/ca-cert-generation=CA-CERTIFICATE-GENERATION defines the generation of a new CA certificate.

    • Annotation strimzi.io/ca-key-generation=CA-KEY-GENERATION defines the generation of a new CA key.

      If you are replacing CA certificates automatically generated by the Cluster Operator, use the next higher incremental value from the existing annotation and follow the replacing CA keys procedure. If there are no CA certificates automatically generated by the Cluster Operator, start from 0 (zero) as the incremental value (strimzi.io/ca-cert-generation=0) for your own CA certificate. Set a higher incremental value when you renew the certificates.

  5. Create the Kafka resource for your cluster, configuring either the Kafka.spec.clusterCa or the Kafka.spec.clientsCa object to not use generated CAs.

    Example fragment Kafka resource configuring the cluster CA to use certificates you supply for yourself
    kind: Kafka
version: kafka.strimzi.io/v1beta2
spec:
  # ...
  clusterCa:
    generateCertificateAuthority: false
Additional resources

9.2. Secrets

Strimzi uses secrets to store private and public key certificates for Kafka clusters, clients, and users. Secrets are used for establishing TLS encrypted connections between Kafka brokers, and between brokers and clients. They are also used for mutual TLS authentication.

Cluster and clients secrets are always pairs: one contains the public key and one contains the private key.

Cluster secret

A cluster secret contains the cluster CA to sign Kafka broker certificates. Connecting clients use the certificate to establish a TLS encrypted connection with a Kafka cluster. The certificate verifies broker identity.

Client secret

A client secret contains the clients CA for a user to sign its own client certificate. This allows mutual authentication against the Kafka cluster. The broker validates a client’s identity through the certificate.

User secret

A user secret contains a private key and certificate. The secret is created and signed by the clients CA when a new user is created. The key and certificate are used to authenticate and authorize the user when accessing the cluster.

9.2.1. Secrets in PEM and PKCS #12 formats

Secrets provide private keys and certificates in PEM and PKCS #12 formats. Use the format that’s suitable for your client. Using private keys and certificates in PEM format means that users have to get them from the secrets, and generate a corresponding truststore or keystore to use in their applications. PKCS #12 storage provides a truststore or keystore that can be used directly.

PKCS #12 defines an archive file format (.p12) for storing cryptography objects into a single file with password protection. You can use PKCS #12 to manage certificates and keys in one place.

Each secret contains fields specific to PKCS #12.

  • The .p12 field contains the certificates and keys.

  • The .password field is the password that protects the archive.

All keys are 2048 bits in size and are valid by default for 365 days from initial generation. You can change the validity period.

9.2.2. Secrets generated by the Cluster Operator

The Cluster Operator generates the following certificates, which are saved as secrets in the Kubernetes cluster. Strimzi uses these secrets by default.

The cluster CA and clients CA have separate secrets for the private key and public key.

<cluster_name>-cluster-ca

Contains the private key of the cluster CA. Strimzi and Kafka components use the private key to sign server certificates.

<cluster_name>-cluster-ca-cert

Contains the public key of the cluster CA. Kafka clients use the public key to verify the identity of the Kafka brokers they are connecting to with TLS server authentication.

<cluster_name>-clients-ca

Contains the private key of the clients CA. Kafka clients use the private key to sign new user certificates for TLS client authentication when connecting to Kafka brokers.

<cluster_name>-clients-ca-cert

Contains the public key of the client CA. Kafka brokers use the public key to verify the identity of clients accessing the Kafka brokers when TLS client authentication is used.

Secrets for communication between Strimzi components contain a private key and a public key certificate signed by the cluster CA.

<cluster_name>-kafka-brokers

Contains the private and public keys for Kafka brokers.

<cluster_name>-zookeeper-nodes

Contains the private and public keys for ZooKeeper nodes.

<cluster_name>-cluster-operator-certs

Contains the private and public keys for encrypting communication between the Cluster Operator and Kafka or ZooKeeper.

<cluster_name>-entity-topic-operator-certs

Contains the private and public keys for encrypting communication between the Topic Operator and Kafka or ZooKeeper.

<cluster_name>-entity-user-operator-certs

Contains the private and public keys for encrypting communication between the User Operator and Kafka or ZooKeeper.

<cluster_name>-cruise-control-certs

Contains the private and public keys for encrypting communication between Cruise Control and Kafka or ZooKeeper.

<cluster_name>-kafka-exporter-certs

Contains the private and public keys for encrypting communication between Kafka Exporter and Kafka or ZooKeeper.

Note
 You can provide your own server certificates and private keys to connect to to Kafka brokers using Kafka listener certificates rather than certificates signed by the cluster CA or clients CA.

9.2.3. Cluster CA secrets

Cluster CA secrets are managed by the Cluster Operator in a Kafka cluster.

Only the <cluster_name>-cluster-ca-cert secret is required by clients. All other cluster secrets are accessed by Strimzi components. You can enforce this using Kubernetes role-based access controls, if necessary.

Note
 The CA certificates in <cluster_name>-cluster-ca-cert must be trusted by Kafka client applications so that they validate the Kafka broker certificates when connecting to Kafka brokers over TLS.
Table 7. Fields in the <cluster_name>-cluster-ca secret
Field Description

ca.key

The current private key for the cluster CA.
Table 8. Fields in the <cluster_name>-cluster-ca-cert secret
Field Description

ca.p12

PKCS #12 archive file for storing certificates and keys.

ca.password

Password for protecting the PKCS #12 archive file.

ca.crt

The current certificate for the cluster CA.
Table 9. Fields in the <cluster_name>-kafka-brokers secret
Field Description

<cluster_name>-kafka-<num>.p12

PKCS #12 archive file for storing certificates and keys.

<cluster_name>-kafka-<num>.password

Password for protecting the PKCS #12 archive file.

<cluster_name>-kafka-<num>.crt

Certificate for a Kafka broker pod <num>. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

<cluster_name>-kafka-<num>.key

Private key for a Kafka broker pod <num>.
Table 10. Fields in the <cluster_name>-zookeeper-nodes secret
Field Description

<cluster_name>-zookeeper-<num>.p12

PKCS #12 archive file for storing certificates and keys.

<cluster_name>-zookeeper-<num>.password

Password for protecting the PKCS #12 archive file.

<cluster_name>-zookeeper-<num>.crt

Certificate for ZooKeeper node <num>. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

<cluster_name>-zookeeper-<num>.key

Private key for ZooKeeper pod <num>.
Table 11. Fields in the <cluster_name>-cluster-operator-certs secret
Field Description

cluster-operator.p12

PKCS #12 archive file for storing certificates and keys.

cluster-operator.password

Password for protecting the PKCS #12 archive file.

cluster-operator.crt

Certificate for TLS communication between the Cluster Operator and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

cluster-operator.key

Private key for TLS communication between the Cluster Operator and Kafka or ZooKeeper.
Table 12. Fields in the <cluster_name>-entity-topic-operator-certs secret
Field Description

entity-operator.p12

PKCS #12 archive file for storing certificates and keys.

entity-operator.password

Password for protecting the PKCS #12 archive file.

entity-operator.crt

Certificate for TLS communication between the Topic Operator and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

entity-operator.key

Private key for TLS communication between the Topic Operator and Kafka or ZooKeeper.
Table 13. Fields in the <cluster_name>-entity-user-operator-certs secret
Field Description

entity-operator.p12

PKCS #12 archive file for storing certificates and keys.

entity-operator.password

Password for protecting the PKCS #12 archive file.

entity-operator.crt

Certificate for TLS communication between the User Operator and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

entity-operator.key

Private key for TLS communication between the User Operator and Kafka or ZooKeeper.
Table 14. Fields in the <cluster_name>-cruise-control-certs secret
Field Description

cruise-control.p12

PKCS #12 archive file for storing certificates and keys.

cruise-control.password

Password for protecting the PKCS #12 archive file.

cruise-control.crt

Certificate for TLS communication between Cruise Control and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

cruise-control.key

Private key for TLS communication between the Cruise Control and Kafka or ZooKeeper.
Table 15. Fields in the <cluster_name>-kafka-exporter-certs secret
Field Description

kafka-exporter.p12

PKCS #12 archive file for storing certificates and keys.

kafka-exporter.password

Password for protecting the PKCS #12 archive file.

kafka-exporter.crt

Certificate for TLS communication between Kafka Exporter and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster_name>-cluster-ca.

kafka-exporter.key

Private key for TLS communication between the Kafka Exporter and Kafka or ZooKeeper.

9.2.4. Client CA secrets

Clients CA secrets are managed by the Cluster Operator in a Kafka cluster.

The certificates in <cluster_name>-clients-ca-cert are those which the Kafka brokers trust.

The <cluster_name>-clients-ca secret is used to sign the certificates of client applications. This secret must be accessible to the Strimzi components and for administrative access if you are intending to issue application certificates without using the User Operator. You can enforce this using Kubernetes role-based access controls, if necessary.

Table 16. Fields in the <cluster_name>-clients-ca secret
Field Description

ca.key

The current private key for the clients CA.
Table 17. Fields in the <cluster_name>-clients-ca-cert secret
Field Description

ca.p12

PKCS #12 archive file for storing certificates and keys.

ca.password

Password for protecting the PKCS #12 archive file.

ca.crt

The current certificate for the clients CA.

9.2.5. User secrets

User secrets are managed by the User Operator.

When a user is created using the User Operator, a secret is generated using the name of the user.

Table 18. Fields in the user_name secret
Secret name Field within secret Description

<user_name>

user.p12

PKCS #12 archive file for storing certificates and keys.

user.password

Password for protecting the PKCS #12 archive file.

user.crt

Certificate for the user, signed by the clients CA

user.key

Private key for the user

9.2.6. Adding labels and annotations to cluster CA secrets

By configuring the clusterCaCert template property in the Kafka custom resource, you can add custom labels and annotations to the Cluster CA secrets created by the Cluster Operator. Labels and annotations are useful for identifying objects and adding contextual information. You configure template properties in Strimzi custom resources.

Example template customization to add labels and annotations to secrets
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    template:
      clusterCaCert:
        metadata:
          labels:
            label1: value1
            label2: value2
          annotations:
            annotation1: value1
            annotation2: value2
    # ...

For more information on configuring template properties, see Customizing Kubernetes resources.

9.2.7. Disabling ownerReference in the CA secrets

By default, the Cluster and Client CA secrets are created with an ownerReference property that is set to the Kafka custom resource. This means that, when the Kafka custom resource is deleted, the CA secrets are also deleted (garbage collected) by Kubernetes.

If you want to reuse the CA for a new cluster, you can disable the ownerReference by setting the generateSecretOwnerReference property for the Cluster and Client CA secrets to false in the Kafka configuration. When the ownerReference is disabled, CA secrets are not deleted by Kubernetes when the corresponding Kafka custom resource is deleted.

Example Kafka configuration with disabled ownerReference for Cluster and Client CAs
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
# ...
  clusterCa:
    generateSecretOwnerReference: false
  clientsCa:
    generateSecretOwnerReference: false
# ...
Additional resources

9.3. Certificate renewal and validity periods

Cluster CA and clients CA certificates are only valid for a limited time period, known as the validity period. This is usually defined as a number of days since the certificate was generated.

For CA certificates automatically created by the Cluster Operator, you can configure the validity period of:

  • Cluster CA certificates in Kafka.spec.clusterCa.validityDays

  • Client CA certificates in Kafka.spec.clientsCa.validityDays

The default validity period for both certificates is 365 days. Manually-installed CA certificates should have their own validity periods defined.

When a CA certificate expires, components and clients that still trust that certificate will not accept TLS connections from peers whose certificates were signed by the CA private key. The components and clients need to trust the new CA certificate instead.

To allow the renewal of CA certificates without a loss of service, the Cluster Operator will initiate certificate renewal before the old CA certificates expire.

You can configure the renewal period of the certificates created by the Cluster Operator:

  • Cluster CA certificates in Kafka.spec.clusterCa.renewalDays

  • Client CA certificates in Kafka.spec.clientsCa.renewalDays

The default renewal period for both certificates is 30 days.

The renewal period is measured backwards, from the expiry date of the current certificate.

Validity period against renewal period
Not Before                                     Not After
    |                                              |
    |<--------------- validityDays --------------->|
                              <--- renewalDays --->|

To make a change to the validity and renewal periods after creating the Kafka cluster, you configure and apply the Kafka custom resource, and manually renew the CA certificates. If you do not manually renew the certificates, the new periods will be used the next time the certificate is renewed automatically.

Example Kafka configuration for certificate validity and renewal periods
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
# ...
  clusterCa:
    renewalDays: 30
    validityDays: 365
    generateCertificateAuthority: true
  clientsCa:
    renewalDays: 30
    validityDays: 365
    generateCertificateAuthority: true
# ...

The behavior of the Cluster Operator during the renewal period depends on the settings for the certificate generation properties, generateCertificateAuthority and generateCertificateAuthority.

true

If the properties are set to true, a CA certificate is generated automatically by the Cluster Operator, and renewed automatically within the renewal period.

false

If the properties are set to false, a CA certificate is not generated by the Cluster Operator. Use this option if you are installing your own certificates.

9.3.1. Renewal process with automatically generated CA certificates

The Cluster Operator performs the following process to renew CA certificates:

  1. Generate a new CA certificate, but retain the existing key. The new certificate replaces the old one with the name ca.crt within the corresponding Secret.

  2. Generate new client certificates (for ZooKeeper nodes, Kafka brokers, and the Entity Operator). This is not strictly necessary because the signing key has not changed, but it keeps the validity period of the client certificate in sync with the CA certificate.

  3. Restart ZooKeeper nodes so that they will trust the new CA certificate and use the new client certificates.

  4. Restart Kafka brokers so that they will trust the new CA certificate and use the new client certificates.

  5. Restart the Topic and User Operators so that they will trust the new CA certificate and use the new client certificates.

9.3.2. Client certificate renewal

The Cluster Operator is not aware of the client applications using the Kafka cluster.

When connecting to the cluster, and to ensure they operate correctly, client applications must:

  • Trust the cluster CA certificate published in the <cluster>-cluster-ca-cert Secret.

  • Use the credentials published in their <user-name> Secret to connect to the cluster.

    The User Secret provides credentials in PEM and PKCS #12 format, or it can provide a password when using SCRAM-SHA authentication. The User Operator creates the user credentials when a user is created.

You must ensure clients continue to work after certificate renewal. The renewal process depends on how the clients are configured.

If you are provisioning client certificates and keys manually, you must generate new client certificates and ensure the new certificates are used by clients within the renewal period. Failure to do this by the end of the renewal period could result in client applications being unable to connect to the cluster.

Note
 For workloads running inside the same Kubernetes cluster and namespace, Secrets can be mounted as a volume so the client Pods construct their keystores and truststores from the current state of the Secrets. For more details on this procedure, see Configuring internal clients to trust the cluster CA.

9.3.3. Manually renewing the CA certificates generated by the Cluster Operator

Cluster and clients CA certificates generated by the Cluster Operator auto-renew at the start of their respective certificate renewal periods. However, you can use the strimzi.io/force-renew annotation to manually renew one or both of these certificates before the certificate renewal period starts. You might do this for security reasons, or if you have changed the renewal or validity periods for the certificates.

A renewed certificate uses the same private key as the old certificate.

Note
 If you are using your own CA certificates, the force-renew annotation cannot be used. Instead, follow the procedure for renewing your own CA certificates.
Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster in which CA certificates and private keys are installed.

Procedure

  1. Apply the strimzi.io/force-renew annotation to the Secret that contains the CA certificate that you want to renew.

    Table 19. Annotation for the Secret that forces renewal of certificates
    Certificate Secret Annotate command

    Cluster CA

    KAFKA-CLUSTER-NAME-cluster-ca-cert

    kubectl annotate secret KAFKA-CLUSTER-NAME-cluster-ca-cert strimzi.io/force-renew=true

    Clients CA

    KAFKA-CLUSTER-NAME-clients-ca-cert

    kubectl annotate secret KAFKA-CLUSTER-NAME-clients-ca-cert strimzi.io/force-renew=true

    At the next reconciliation the Cluster Operator will generate a new CA certificate for the Secret that you annotated. If maintenance time windows are configured, the Cluster Operator will generate the new CA certificate at the first reconciliation within the next maintenance time window.

    Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.

  2. Check the period the CA certificate is valid:

    For example, using an openssl command:

    kubectl get secret CA-CERTIFICATE-SECRET -o 'jsonpath={.data.CA-CERTIFICATE}' | base64 -d | openssl x509 -subject -issuer -startdate -enddate -noout

    CA-CERTIFICATE-SECRET is the name of the Secret, which is KAFKA-CLUSTER-NAME-cluster-ca-cert for the cluster CA certificate and KAFKA-CLUSTER-NAME-clients-ca-cert for the clients CA certificate.

    CA-CERTIFICATE is the name of the CA certificate, such as jsonpath={.data.ca\.crt}.

    The command returns a notBefore and notAfter date, which is the validity period for the CA certificate.

    For example, for a cluster CA certificate:

    subject=O = io.strimzi, CN = cluster-ca v0
issuer=O = io.strimzi, CN = cluster-ca v0
notBefore=Jun 30 09:43:54 2020 GMT
notAfter=Jun 30 09:43:54 2021 GMT

  3. Delete old certificates from the Secret.

    When components are using the new certificates, older certificates might still be active. Delete the old certificates to remove any potential security risk.

Additional resources

9.3.4. Replacing private keys used by the CA certificates generated by the Cluster Operator

You can replace the private keys used by the cluster CA and clients CA certificates generated by the Cluster Operator. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.

Note
 If you are using your own CA certificates, the force-replace annotation cannot be used. Instead, follow the procedure for renewing your own CA certificates.
Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster in which CA certificates and private keys are installed.

Procedure

  • Apply the strimzi.io/force-replace annotation to the Secret that contains the private key that you want to renew.

    Table 20. Commands for replacing private keys
    Private key for Secret Annotate command

    Cluster CA

    CLUSTER-NAME-cluster-ca

    kubectl annotate secret CLUSTER-NAME-cluster-ca strimzi.io/force-replace=true

    Clients CA

    CLUSTER-NAME-clients-ca

    kubectl annotate secret CLUSTER-NAME-clients-ca strimzi.io/force-replace=true

At the next reconciliation the Cluster Operator will:

  • Generate a new private key for the Secret that you annotated

  • Generate a new CA certificate

If maintenance time windows are configured, the Cluster Operator will generate the new private key and CA certificate at the first reconciliation within the next maintenance time window.

Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.

Additional resources

9.3.5. Renewing your own CA certificates

This procedure describes how to renew CA certificates that you are using instead of the certificates generated by the Cluster Operator.

If you are not changing the corresponding CA keys, perform the steps in this procedure. Otherwise, perform the steps to replace private keys used by your own CA certificates.

If you are using your own certificates, the Cluster Operator will not renew them automatically. Therefore, it is important that you follow this procedure during the renewal period of the certificate in order to replace CA certificates that will soon expire.

The procedure describes the renewal of CA certificates in PEM format.

Prerequisites
Procedure

  1. Update the Secret for the CA certificate.

    Edit the existing secret to add the new CA certificate and update the certificate generation annotation value.

    kubectl edit secret <ca_certificate_secret_name>

    <ca_certificate_secret_name> is the name of the Secret, which is <kafka_cluster_name>-cluster-ca-cert for the cluster CA certificate and <kafka_cluster_name>-clients-ca-cert for the clients CA certificate.

    The following example shows a secret for a cluster CA certificate that’s associated with a Kafka cluster named my-cluster.

    Example secret configuration for a cluster CA certificate
    apiVersion: v1
kind: Secret
data:
  ca.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... (1)
metadata:
  annotations:
    strimzi.io/ca-cert-generation: "0" (2)
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/kind: Kafka
  name: my-cluster-cluster-ca-cert
  #...
type: Opaque

    1. Current base64-encoded CA certificate

    2. Current CA certificate generation annotation value

  2. Encode your new CA certificate into base64.

    cat <path_to_new_certificate> | base64

  3. Update the CA certificate.

    Copy the base64-encoded CA certificate from the previous step as the value for the ca.crt property under data.

  4. Increase the value of the CA certificate generation annotation.

    Update the strimzi.io/ca-cert-generation annotation with a higher incremental value. For example, change strimzi.io/ca-cert-generation=0 to strimzi.io/ca-cert-generation=1. If the Secret is missing the annotation, the value is treated as 0, so add the annotation with a value of 1.

    When Strimzi generates certificates, the certificate generation annotation is automatically incremented by the Cluster Operator. For manual renewal of your own CA certificates, set the annotations with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates. The strimzi.io/ca-cert-generation has to be incremented on each CA certificate renewal.

  5. Save the secret with the new CA certificate and certificate generation annotation value.

    Example secret configuration updated with a new CA certificate
    apiVersion: v1
kind: Secret
data:
  ca.crt: GCa6LS3RTHeKFiFDGBOUDYFAZ0F... (1)
metadata:
  annotations:
    strimzi.io/ca-cert-generation: "1" (2)
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/kind: Kafka
  name: my-cluster-cluster-ca-cert
  #...
type: Opaque

    1. New base64-encoded CA certificate

    2. New CA certificate generation annotation value

On the next reconciliation, the Cluster Operator performs a rolling update of ZooKeeper, Kafka, and other components to trust the new CA certificate.

If maintenance time windows are configured, the Cluster Operator will roll the pods at the first reconciliation within the next maintenance time window.

9.3.6. Replacing private keys used by your own CA certificates

This procedure describes how to renew CA certificates and private keys that you are using instead of the certificates and keys generated by the Cluster Operator.

Perform the steps in this procedure when you are also changing the corresponding CA keys. Otherwise, perform the steps to renew your own CA certificates.

If you are using your own certificates, the Cluster Operator will not renew them automatically. Therefore, it is important that you follow this procedure during the renewal period of the certificate in order to replace CA certificates that will soon expire.

The procedure describes the renewal of CA certificates in PEM format.

Before going through the following steps, make sure that the CN (Common Name) of the new CA certificate is different from the current one. For example, when the Cluster Operator renews certificates automatically it adds a v<version_number> suffix to identify a version. Do the same with your own CA certificate by adding a different suffix on each renewal. By using a different key to generate a new CA certificate, you retain the current CA certificate stored in the Secret.

Prerequisites
Procedure

  1. Pause the reconciliation of the Kafka custom resource.

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

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

      For example, for a Kafka custom resource named my-cluster:

      kubectl annotate Kafka my-cluster strimzi.io/pause-reconciliation="true"

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

      kubectl describe Kafka <name_of_custom_resource>

      The type condition changes to ReconciliationPaused at the lastTransitionTime.

  2. Update the Secret for the CA certificate.

    1. Edit the existing secret to add the new CA certificate and update the certificate generation annotation value.

      kubectl edit secret <ca_certificate_secret_name>

      <ca_certificate_secret_name> is the name of the Secret, which is KAFKA-CLUSTER-NAME-cluster-ca-cert for the cluster CA certificate and KAFKA-CLUSTER-NAME-clients-ca-cert for the clients CA certificate.

      The following example shows a secret for a cluster CA certificate that’s associated with a Kafka cluster named my-cluster.

      Example secret configuration for a cluster CA certificate
      apiVersion: v1
kind: Secret
data:
  ca.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... (1)
metadata:
  annotations:
    strimzi.io/ca-cert-generation: "0" (2)
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/kind: Kafka
  name: my-cluster-cluster-ca-cert
  #...
type: Opaque

      1. Current base64-encoded CA certificate

      2. Current CA certificate generation annotation value

    2. Rename the current CA certificate to retain it.

      Rename the current ca.crt property under data as ca-<date>.crt, where <date> is the certificate expiry date in the format YEAR-MONTH-DAYTHOUR-MINUTE-SECONDZ. For example ca-2022-01-26T17-32-00Z.crt:. Leave the value for the property as it is to retain the current CA certificate.

    3. Encode your new CA certificate into base64.

      cat <path_to_new_certificate> | base64

    4. Update the CA certificate.

      Create a new ca.crt property under data and copy the base64-encoded CA certificate from the previous step as the value for ca.crt property.

    5. Increase the value of the CA certificate generation annotation.

      Update the strimzi.io/ca-cert-generation annotation with a higher incremental value. For example, change strimzi.io/ca-cert-generation=0 to strimzi.io/ca-cert-generation=1. If the Secret is missing the annotation, the value is treated as 0, so add the annotation with a value of 1.

      When Strimzi generates certificates, the certificate generation annotation is automatically incremented by the Cluster Operator. For manual renewal of your own CA certificates, set the annotations with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates. The strimzi.io/ca-cert-generation has to be incremented on each CA certificate renewal.

    6. Save the secret with the new CA certificate and certificate generation annotation value.

      Example secret configuration updated with a new CA certificate
      apiVersion: v1
kind: Secret
data:
  ca.crt: GCa6LS3RTHeKFiFDGBOUDYFAZ0F... (1)
  ca-2022-01-26T17-32-00Z.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... (2)
metadata:
  annotations:
    strimzi.io/ca-cert-generation: "1" (3)
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/kind: Kafka
  name: my-cluster-cluster-ca-cert
  #...
type: Opaque

      1. New base64-encoded CA certificate

      2. Old base64-encoded CA certificate

      3. New CA certificate generation annotation value

  3. Update the Secret for the CA key used to sign your new CA certificate.

    1. Edit the existing secret to add the new CA key and update the key generation annotation value.

      kubectl edit secret <ca_key_name>

      <ca_key_name> is the name of CA key, which is <kafka_cluster_name>-cluster-ca for the cluster CA key and <kafka_cluster_name>-clients-ca for the clients CA key.

      The following example shows a secret for a cluster CA key that’s associated with a Kafka cluster named my-cluster.

      Example secret configuration for a cluster CA key
      apiVersion: v1
kind: Secret
data:
  ca.key: SA1cKF1GFDzOIiPOIUQBHDNFGDFS... (1)
metadata:
  annotations:
    strimzi.io/ca-key-generation: "0" (2)
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/kind: Kafka
  name: my-cluster-cluster-ca
  #...
type: Opaque

      1. Current base64-encoded CA key

      2. Current CA key generation annotation value

    2. Encode the CA key into base64.

      cat <path_to_new_key> | base64

    3. Update the CA key.

      Copy the base64-encoded CA key from the previous step as the value for the ca.key property under data.

    4. Increase the value of the CA key generation annotation.

      Update the strimzi.io/ca-key-generation annotation with a higher incremental value. For example, change strimzi.io/ca-key-generation=0 to strimzi.io/ca-key-generation=1. If the Secret is missing the annotation, it is treated as 0, so add the annotation with a value of 1.

      When Strimzi generates certificates, the key generation annotation is automatically incremented by the Cluster Operator. For manual renewal of your own CA certificates together with a new CA key, set the annotation with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates and keys. The strimzi.io/ca-key-generation has to be incremented on each CA certificate renewal.

  4. Save the secret with the new CA key and key generation annotation value.

    Example secret configuration updated with a new CA key
    apiVersion: v1
kind: Secret
data:
  ca.key: AB0cKF1GFDzOIiPOIUQWERZJQ0F... (1)
metadata:
  annotations:
    strimzi.io/ca-key-generation: "1" (2)
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/kind: Kafka
  name: my-cluster-cluster-ca
  #...
type: Opaque

    1. New base64-encoded CA key

    2. New CA key generation annotation value

  5. Resume from the pause.

    To resume the Kafka custom resource reconciliation, set the pause-reconciliation annotation to false.

    kubectl annotate --overwrite Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation="false"

    You can also do the same by removing the pause-reconciliation annotation.

    kubectl annotate Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation-

On the next reconciliation, the Cluster Operator performs a rolling update of ZooKeeper, Kafka, and other components to trust the new CA certificate. When the rolling update is complete, the Cluster Operator will start a new one to generate new server certificates signed by the new CA key.

If maintenance time windows are configured, the Cluster Operator will roll the pods at the first reconciliation within the next maintenance time window.

9.4. TLS connections

9.4.1. ZooKeeper communication

Communication between the ZooKeeper nodes on all ports, as well as between clients and ZooKeeper, is encrypted using TLS.

Communication between Kafka brokers and ZooKeeper nodes is also encrypted.

9.4.2. Kafka inter-broker communication

Communication between Kafka brokers is always encrypted using TLS.

Unless the ControlPlaneListener feature gate is enabled, all inter-broker communication goes through an internal listener on port 9091. If you enable the feature gate, traffic from the control plane goes through an internal control plane listener on port 9090. Traffic from the data plane continues to use the existing internal listener on port 9091.

These internal listeners are not available to Kafka clients.

9.4.3. Topic and User Operators

All Operators use encryption for communication with both Kafka and ZooKeeper. In Topic and User Operators, a TLS sidecar is used when communicating with ZooKeeper.

9.4.4. Cruise Control

Cruise Control uses encryption for communication with both Kafka and ZooKeeper. A TLS sidecar is used when communicating with ZooKeeper.

9.4.5. Kafka Client connections

Encrypted or unencrypted communication between Kafka brokers and clients is configured using the tls property for spec.kafka.listeners.

9.5. Configuring internal clients to trust the cluster CA

This procedure describes how to configure a Kafka client that resides inside the Kubernetes cluster — connecting to a TLS listener — to trust the cluster CA certificate.

The easiest way to achieve this for an internal client is to use a volume mount to access the Secrets containing the necessary certificates and keys.

Follow the steps to configure trust certificates that are signed by the cluster CA for Java-based Kafka Producer, Consumer, and Streams APIs.

Choose the steps to follow according to the certificate format of the cluster CA: PKCS #12 (.p12) or PEM (.crt).

The steps describe how to mount the Cluster Secret that verifies the identity of the Kafka cluster to the client pod.

Prerequisites

  • The Cluster Operator must be running.

  • There needs to be a Kafka resource within the Kubernetes cluster.

  • You need a Kafka client application inside the Kubernetes cluster that will connect using TLS, and needs to trust the cluster CA certificate.

  • The client application must be running in the same namespace as the Kafka resource.

Using PKCS #12 format (.p12)

  1. Mount the cluster Secret as a volume when defining the client pod.

    For example:

    kind: Pod
apiVersion: v1
metadata:
  name: client-pod
spec:
  containers:
  - name: client-name
    image: client-name
    volumeMounts:
    - name: secret-volume
      mountPath: /data/p12
    env:
    - name: SECRET_PASSWORD
      valueFrom:
        secretKeyRef:
          name: my-secret
          key: my-password
  volumes:
  - name: secret-volume
    secret:
      secretName: my-cluster-cluster-ca-cert

    Here we’re mounting:

    • The PKCS #12 file into an exact path, which can be configured

    • The password into an environment variable, where it can be used for Java configuration

  2. Configure the Kafka client with the following properties:

    • A security protocol option:

      • security.protocol: SSL when using TLS for encryption (with or without TLS authentication).

      • security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.

    • ssl.truststore.location with the truststore location where the certificates were imported.

    • ssl.truststore.password with the password for accessing the truststore.

    • ssl.truststore.type=PKCS12 to identify the truststore type.

Using PEM format (.crt)

  1. Mount the cluster Secret as a volume when defining the client pod.

    For example:

    kind: Pod
apiVersion: v1
metadata:
  name: client-pod
spec:
  containers:
  - name: client-name
    image: client-name
    volumeMounts:
    - name: secret-volume
      mountPath: /data/crt
  volumes:
  - name: secret-volume
    secret:
      secretName: my-cluster-cluster-ca-cert

  2. Use the certificate with clients that use certificates in X.509 format.

9.6. Configuring external clients to trust the cluster CA

This procedure describes how to configure a Kafka client that resides outside the Kubernetes cluster – connecting to an external listener – to trust the cluster CA certificate. Follow this procedure when setting up the client and during the renewal period, when the old clients CA certificate is replaced.

Follow the steps to configure trust certificates that are signed by the cluster CA for Java-based Kafka Producer, Consumer, and Streams APIs.

Choose the steps to follow according to the certificate format of the cluster CA: PKCS #12 (.p12) or PEM (.crt).

The steps describe how to obtain the certificate from the Cluster Secret that verifies the identity of the Kafka cluster.

Important
 The <cluster-name>-cluster-ca-cert Secret will contain more than one CA certificate during the CA certificate renewal period. Clients must add all of them to their truststores.
Prerequisites

  • The Cluster Operator must be running.

  • There needs to be a Kafka resource within the Kubernetes cluster.

  • You need a Kafka client application outside the Kubernetes cluster that will connect using TLS, and needs to trust the cluster CA certificate.

Using PKCS #12 format (.p12)

  1. Extract the cluster CA certificate and password from the CLUSTER-NAME-cluster-ca-cert Secret of the Kafka cluster.

    kubectl get secret CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
    kubectl 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.

  2. Configure the Kafka client with the following properties:

    • A security protocol option:

      • security.protocol: SSL when using TLS for encryption (with or without TLS authentication).

      • security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.

    • ssl.truststore.location with the truststore location where the certificates were imported.

    • ssl.truststore.password with the password for accessing the truststore. This property can be omitted if it is not needed by the truststore.

    • ssl.truststore.type=PKCS12 to identify the truststore type.

Using PEM format (.crt)

  1. Extract the cluster CA certificate from the CLUSTER-NAME-cluster-ca-cert Secret of the Kafka cluster.

    kubectl get secret CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

  2. Use the certificate with clients that use certificates in X.509 format.

9.7. Kafka listener certificates

You can provide your own server certificates and private keys for any listener with TLS encryption enabled. These user-provided certificates are called Kafka listener certificates.

Providing Kafka listener certificates allows you to leverage existing security infrastructure, such as your organization’s private CA or a public CA. Kafka clients will need to trust the CA which was used to sign the listener certificate.

You must manually renew Kafka listener certificates when needed.

9.7.1. Providing your own Kafka listener certificates

This procedure shows how to configure a listener to use your own private key and server certificate, called a Kafka listener certificate.

Your client applications should use the CA public key as a trusted certificate in order to verify the identity of the Kafka broker.

Prerequisites

  • A Kubernetes cluster.

  • The Cluster Operator is running.

  • For each listener, a compatible server certificate signed by an external CA.

Procedure

  1. Create a Secret containing your private key and server certificate:

    kubectl create secret generic my-secret --from-file=my-listener-key.key --from-file=my-listener-certificate.crt

  2. Edit the Kafka resource for your cluster. Configure the listener to use your Secret, certificate file, and private key file in the configuration.brokerCertChainAndKey property.

    Example configuration for a loadbalancer external listener with TLS encryption enabled
    # ...
listeners:
  - name: plain
    port: 9092
    type: internal
    tls: false
  - name: external
    port: 9094
    type: loadbalancer
    tls: true
    authentication:
      type: tls
    configuration:
      brokerCertChainAndKey:
        secretName: my-secret
        certificate: my-listener-certificate.crt
        key: my-listener-key.key
# ...
    Example configuration for a TLS listener
    # ...
listeners:
  - name: plain
    port: 9092
    type: internal
    tls: false
  - name: tls
    port: 9093
    type: internal
    tls: true
    authentication:
      type: tls
    configuration:
      brokerCertChainAndKey:
        secretName: my-secret
        certificate: my-listener-certificate.crt
        key: my-listener-key.key
# ...

  3. Apply the new configuration to create or update the resource:

    kubectl apply -f kafka.yaml

    The Cluster Operator starts a rolling update of the Kafka cluster, which updates the configuration of the listeners.

    Note
    		 A rolling update is also started if you update a Kafka listener certificate in a Secret that is already used by a TLS or external listener.
Additional resources

9.7.2. Alternative subjects in server certificates for Kafka listeners

In order to use TLS hostname verification with your own Kafka listener certificates, you must use the correct Subject Alternative Names (SANs) for each listener. The certificate SANs must specify hostnames for:

  • All of the Kafka brokers in your cluster

  • The Kafka cluster bootstrap service

You can use wildcard certificates if they are supported by your CA.

TLS listener SAN examples

Use the following examples to help you specify hostnames of the SANs in your certificates for TLS listeners.

Wildcards example
//Kafka brokers
*.<cluster-name>-kafka-brokers
*.<cluster-name>-kafka-brokers.<namespace>.svc

// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc
Non-wildcards example
// Kafka brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers.<namespace>.svc
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers.<namespace>.svc
# ...

// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc
External listener SAN examples

For external listeners which have TLS encryption enabled, the hostnames you need to specify in certificates depends on the external listener type.

Table 21. SANs for each type of external listener
External listener type In the SANs, specify…​

Route

Addresses of all Kafka broker Routes and the address of the bootstrap Route.

You can use a matching wildcard name.

loadbalancer

Addresses of all Kafka broker loadbalancers and the bootstrap loadbalancer address.

You can use a matching wildcard name.

NodePort

Addresses of all Kubernetes worker nodes that the Kafka broker pods might be scheduled to.

You can use a matching wildcard name.
Additional resources

10. Managing Strimzi

This chapter covers tasks to maintain a deployment of Strimzi.

10.1. Working with custom resources

You can use kubectl commands to retrieve information and perform other operations on Strimzi custom resources.

Using kubectl with the status subresource of a custom resource allows you to get the information about the resource.

10.1.1. Performing kubectl operations on custom resources

Use kubectl commands, such as get, describe, edit, or delete, to perform operations on resource types. For example, kubectl get kafkatopics retrieves a list of all Kafka topics and kubectl get kafkas retrieves all deployed Kafka clusters.

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

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

kubectl get k

NAME         DESIRED KAFKA REPLICAS   DESIRED ZK REPLICAS
my-cluster   3                        3
Table 22. Long and short names for each Strimzi resource
Strimzi resource Long name Short name

Kafka

kafka

k

Kafka Topic

kafkatopic

kt

Kafka User

kafkauser

ku

Kafka Connect

kafkaconnect

kc

Kafka Connector

kafkaconnector

kctr

Kafka Mirror Maker

kafkamirrormaker

kmm

Kafka Mirror Maker 2

kafkamirrormaker2

kmm2

Kafka Bridge

kafkabridge

kb

Kafka Rebalance

kafkarebalance

kr
Resource categories

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

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

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

kubectl get strimzi

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

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

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

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

kubectl get strimzi -o name

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

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

kubectl delete $(kubectl get strimzi -o name)

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

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

Querying the status of sub-resources

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

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

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

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

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

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

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

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

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

10.1.2. Strimzi custom resource status information

Several resources have a status property, as described in the following table.

Table 23. Custom resource status properties
Strimzi resource Schema reference Publishes status information on…​

Kafka

KafkaStatus schema reference

The Kafka cluster.

KafkaConnect

KafkaConnectStatus schema reference

The Kafka Connect cluster, if deployed.

KafkaConnector

KafkaConnectorStatus schema reference

KafkaConnector resources, if deployed.

KafkaMirrorMaker

KafkaMirrorMakerStatus schema reference

The Kafka MirrorMaker tool, if deployed.

KafkaTopic

KafkaTopicStatus schema reference

Kafka topics in your Kafka cluster.

KafkaUser

KafkaUserStatus schema reference

Kafka users in your Kafka cluster.

KafkaBridge

KafkaBridgeStatus schema reference

The Strimzi Kafka Bridge, if deployed.

The status property of a resource provides information on the resource’s:

  • Current state, in the status.conditions property

  • Last observed generation, in the status.observedGeneration property

The status property also provides resource-specific information. For example:

  • KafkaStatus provides information on listener addresses, and the id of the Kafka cluster.

  • KafkaConnectStatus provides the REST API endpoint for Kafka Connect connectors.

  • KafkaUserStatus provides the user name of the Kafka user and the Secret in which their credentials are stored.

  • KafkaBridgeStatus provides the HTTP address at which external client applications can access the Bridge service.

A resource’s current state is useful for tracking progress related to the resource achieving its desired state, as defined by the spec property. The status conditions provide the time and reason the state of the resource changed and details of events preventing or delaying the operator from realizing the resource’s desired state.

The last observed generation is the generation of the resource that was last reconciled by the Cluster Operator. If the value of observedGeneration is different from the value of metadata.generation, the operator has not yet processed the latest update to the resource. If these values are the same, the status information reflects the most recent changes to the resource.

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

Here we see the status property specified for a Kafka custom resource.

Kafka custom resource with status
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
spec:
  # ...
status:
  conditions: (1)
  - lastTransitionTime: 2021-07-23T23:46:57+0000
    status: "True"
    type: Ready (2)
  observedGeneration: 4 (3)
  listeners: (4)
  - addresses:
    - host: my-cluster-kafka-bootstrap.myproject.svc
      port: 9092
    type: plain
  - addresses:
    - host: my-cluster-kafka-bootstrap.myproject.svc
      port: 9093
    certificates:
    - |
      -----BEGIN CERTIFICATE-----
      ...
      -----END CERTIFICATE-----
    type: tls
  - addresses:
    - host: 172.29.49.180
      port: 9094
    certificates:
    - |
      -----BEGIN CERTIFICATE-----
      ...
      -----END CERTIFICATE-----
    type: external
  clusterId: CLUSTER-ID (5)
# ...

  1. Status conditions describe criteria related to the status that cannot be deduced from the existing resource information, or are specific to the instance of a resource.

  2. The Ready condition indicates whether the Cluster Operator currently considers the Kafka cluster able to handle traffic.

  3. The observedGeneration indicates the generation of the Kafka custom resource that was last reconciled by the Cluster Operator.

  4. The listeners describe the current Kafka bootstrap addresses by type.

  5. The Kafka cluster id.

    Important
    		 The address in the custom resource status for external listeners with type nodeport is currently not supported.
Note
 The Kafka bootstrap addresses listed in the status do not signify that those endpoints or the Kafka cluster is in a ready state.
Accessing status information

You can access status information for a resource from the command line. For more information, see Finding the status of a custom resource.

10.1.3. Finding the status of a custom resource

This procedure describes how to find the status of a custom resource.

Prerequisites

  • A Kubernetes cluster.

  • The Cluster Operator is running.

Procedure

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

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

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

Additional resources

10.2. Pausing reconciliation of custom resources

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

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

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

Prerequisites

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

Procedure

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

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

    For example, for the KafkaConnect custom resource:

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

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

    kubectl describe <kind_of_custom_resource> <name_of_custom_resource>

    The type condition changes to ReconciliationPaused at the lastTransitionTime.

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

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

Additional resources

10.3. Evicting pods with Strimzi Drain Cleaner

Kafka and ZooKeeper pods might be evicted during Kubernetes upgrades, maintenance or pod rescheduling. If your Kafka broker and ZooKeeper pods were deployed by Strimzi, you can use the Strimzi Drain Cleaner tool to handle the pod evictions. Since the Strimzi Drain Cleaner will handle the eviction instead of Kubernetes, you need to set the podDisruptionBudget for your Kafka deployment to 0 (zero). Kubernetes will then no longer be allowed to evict the pod automatically.

By deploying the Strimzi Drain Cleaner, you can use the Cluster Operator to move Kafka pods instead of Kubernetes. The Cluster Operator ensures that topics are never under-replicated. Kafka can remain operational during the eviction process. The Cluster Operator waits for topics to synchronize, as the Kubernetes worker nodes drain consecutively.

An admission webhook notifies the Strimzi Drain Cleaner of pod eviction requests to the Kubernetes API. The Strimzi Drain Cleaner then adds a rolling update annotation to the pods to be drained. This informs the Cluster Operator to perform a rolling update of an evicted pod.

Note
 If you are not using the Strimzi Drain Cleaner, you can add pod annotations to perform rolling updates manually.
Webhook configuration

The Strimzi Drain Cleaner deployment files include a ValidatingWebhookConfiguration resource file. The resource provides the configuration for registering the webhook with the Kubernetes API.

The configuration defines the rules for the Kubernetes API to follow in the event of a pod eviction request. The rules specify that only CREATE operations related to pods/eviction sub-resources are intercepted. If these rules are met, the API forwards the notification.

The clientConfig points to the Strimzi Drain Cleaner service and /drainer endpoint that exposes the webhook. The webhook uses a secure TLS connection, which requires authentication. The caBundle property specifies the certificate chain to validate HTTPS communication. Certificates are encoded in Base64.

Webhook configuration for pod eviction notifications
apiVersion: admissionregistration.k8s.io/v1
kind: ValidatingWebhookConfiguration
# ...
webhooks:
  - name: strimzi-drain-cleaner.strimzi.io
    rules:
      - apiGroups:   [""]
        apiVersions: ["v1"]
        operations:  ["CREATE"]
        resources:   ["pods/eviction"]
        scope:       "Namespaced"
    clientConfig:
      service:
        namespace: "strimzi-drain-cleaner"
        name: "strimzi-drain-cleaner"
        path: /drainer
        port: 443
        caBundle: Cg==
    # ...

10.3.1. Prerequisites

To deploy and use the Strimzi Drain Cleaner, you need to download the deployment files.

The Strimzi Drain Cleaner deployment files are available from the GitHub releases page.

10.3.2. Deploying the Strimzi Drain Cleaner

Deploy the Strimzi Drain Cleaner to the Kubernetes cluster where the Cluster Operator and Kafka cluster are running.

Prerequisites

  • You have downloaded the Strimzi Drain Cleaner deployment files.

  • You have a highly available Kafka cluster deployment running with Kubernetes worker nodes that you would like to update.

  • Topics are replicated for high availability.

    Topic configuration specifies a replication factor of at least 3 and a minimum number of in-sync replicas to 1 less than the replication factor.

    Kafka topic replicated for high availability
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 1
  replicas: 3
  config:
    # ...
    min.insync.replicas: 2
    # ...
Excluding ZooKeeper

If you don’t want to include ZooKeeper, you can remove the --zookeeper command option from the Strimzi Drain Cleaner Deployment configuration file.

apiVersion: apps/v1
kind: Deployment
spec:
  # ...
  template:
    spec:
      serviceAccountName: strimzi-drain-cleaner
      containers:
        - name: strimzi-drain-cleaner
          # ...
          command:
            - "/application"
            - "-Dquarkus.http.host=0.0.0.0"
            - "--kafka"
            - "--zookeeper" (1)
          # ...

  1. Remove this option to exclude ZooKeeper from Strimzi Drain Cleaner operations.

Procedure

  1. Configure a pod disruption budget of 0 (zero) for your Kafka deployment using template settings in the Kafka resource.

    Specifying a pod disruption budget
    apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
  namespace: myproject
spec:
  kafka:
    template:
      podDisruptionBudget:
        maxUnavailable: 0

  # ...
  zookeeper:
    template:
      podDisruptionBudget:
        maxUnavailable: 0
  # ...

    Reducing the maximum pod disruption budget to zero prevents Kubernetes from automatically evicting the pods in case of voluntary disruptions, leaving the Strimzi Drain Cleaner and Strimzi Cluster Operator to roll the pod which will be scheduled by Kubernetes on a different worker node.

    Add the same configuration for ZooKeeper if you want to use Strimzi Drain Cleaner to drain ZooKeeper nodes.

  2. Update the Kafka resource:

    kubectl apply -f <kafka-configuration-file>

  3. Deploy the Strimzi Drain Cleaner.

    You can use cert-manager in the deployment process.

    • If you are using cert-manager with Kubernetes, apply the resources in the /install/drain-cleaner/certmanager directory.

      kubectl apply -f ./install/drain-cleaner/certmanager

      The TLS certificates for the webhook are generated automatically and injected into the webhook configuration.

    • If you are using OpenShift, apply the resources in the /install/drain-cleaner/openshift directory.

      kubectl apply -f ./install/drain-cleaner/openshift

    • If you are not using cert-manager with Kubernetes, apply the resources in the /install/drain-cleaner/kubernetes directory.

      kubectl apply -f ./install/drain-cleaner/kubernetes

      The resources are configured with TLS certificates that have already been generated. Use these certificates if you are not changing any configuration for the Strimzi Drain Cleaner, such as namespaces, service names, or pod names.

      Note
      		 You can use the build script and files provided in webhook-certificates to generate your own certificates. The script uses the CFSSL and openSSL tools to generate the certificates. After you have generated the certificates, you need to add them to the 040-Secret.yaml and 070-ValidatingWebhookConfiguration.yaml files.

10.3.3. Using the Strimzi Drain Cleaner

Use the Strimzi Drain Cleaner in combination with the Cluster Operator to move Kafka broker or ZooKeeper pods from nodes that are being drained. When you run the Strimzi Drain Cleaner, it annotates pods with a rolling update pod annotation. The Cluster Operator performs rolling updates based on the annotation.

Prerequisites
Procedure

  1. Drain a specified Kubernetes node hosting the Kafka broker or ZooKeeper pods.

    kubectl get nodes
kubectl drain <name-of-node> --delete-emptydir-data --ignore-daemonsets --timeout=6000s --force

  2. Check the eviction events in the Strimzi Drain Cleaner log to verify that the pods have been annotated for restart.

    Strimzi Drain Cleaner log show annotations of pods
    INFO ... Received eviction webhook for Pod my-cluster-zookeeper-2 in namespace my-project
INFO ... Pod my-cluster-zookeeper-2 in namespace my-project will be annotated for restart
INFO ... Pod my-cluster-zookeeper-2 in namespace my-project found and annotated for restart

INFO ... Received eviction webhook for Pod my-cluster-kafka-0 in namespace my-project
INFO ... Pod my-cluster-kafka-0 in namespace my-project will be annotated for restart
INFO ... Pod my-cluster-kafka-0 in namespace my-project found and annotated for restart

  3. Check the reconciliation events in the Cluster Operator log to verify the rolling updates.

    Cluster Operator log shows rolling updates
    INFO  PodOperator:68 - Reconciliation #13(timer) Kafka(my-project/my-cluster): Rolling Pod my-cluster-zookeeper-2
INFO  PodOperator:68 - Reconciliation #13(timer) Kafka(my-project/my-cluster): Rolling Pod my-cluster-kafka-0
INFO  AbstractOperator:500 - Reconciliation #13(timer) Kafka(my-project/my-cluster): reconciled

10.4. Manually starting rolling updates of Kafka and ZooKeeper clusters

Strimzi supports the use of annotations on resources to manually trigger a rolling update of Kafka and ZooKeeper clusters through the Cluster Operator. Rolling updates restart the pods of the resource with new ones.

Manually performing a rolling update on a specific pod or set of pods is usually only required in exceptional circumstances. However, rather than deleting the pods directly, if you perform the rolling update through the Cluster Operator you ensure the following:

  • The manual deletion of the pod does not conflict with simultaneous Cluster Operator operations, such as deleting other pods in parallel.

  • The Cluster Operator logic handles the Kafka configuration specifications, such as the number of in-sync replicas.

10.4.1. Prerequisites

To perform a manual rolling update, you need a running Cluster Operator and Kafka cluster.

See the Deploying and Upgrading Strimzi guide for instructions on running a:

10.4.2. Performing a rolling update using a pod management annotation

This procedure describes how to trigger a rolling update of a Kafka cluster or ZooKeeper cluster.

To trigger the update, you add an annotation to the resource you are using to manage the pods running on the cluster. You annotate the StatefulSet or StrimziPodSet resource (if you enabled the UseStrimziPodSets feature gate).

Procedure

  1. Find the name of the resource that controls the Kafka or ZooKeeper pods you want to manually update.

    For example, if your Kafka cluster is named my-cluster, the corresponding names are my-cluster-kafka and my-cluster-zookeeper.

  2. Use kubectl annotate to annotate the appropriate resource in Kubernetes.

    Annotating a StatefulSet
    kubectl annotate statefulset <cluster_name>-kafka strimzi.io/manual-rolling-update=true

kubectl annotate statefulset <cluster_name>-zookeeper strimzi.io/manual-rolling-update=true
    Annotating a StrimziPodSet
    kubectl annotate strimzipodset <cluster_name>-kafka strimzi.io/manual-rolling-update=true

kubectl annotate strimzipodset <cluster_name>-zookeeper strimzi.io/manual-rolling-update=true

  3. Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated resource is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from the resource.

10.4.3. Performing a rolling update using a Pod annotation

This procedure describes how to manually trigger a rolling update of an existing Kafka cluster or ZooKeeper cluster using a Kubernetes Pod annotation. When multiple pods are annotated, consecutive rolling updates are performed within the same reconciliation run.

Prerequisites

You can perform a rolling update on a Kafka cluster regardless of the topic replication factor used. But for Kafka to stay operational during the update, you’ll need the following:

  • A highly available Kafka cluster deployment running with nodes that you wish to update.

  • Topics replicated for high availability.

    Topic configuration specifies a replication factor of at least 3 and a minimum number of in-sync replicas to 1 less than the replication factor.

    Kafka topic replicated for high availability
    apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 1
  replicas: 3
  config:
    # ...
    min.insync.replicas: 2
    # ...
Procedure

  1. Find the name of the Kafka or ZooKeeper Pod you want to manually update.

    For example, if your Kafka cluster is named my-cluster, the corresponding Pod names are my-cluster-kafka-index and my-cluster-zookeeper-index. The index starts at zero and ends at the total number of replicas minus one.

  2. Annotate the Pod resource in Kubernetes.

    Use kubectl annotate:

    kubectl annotate pod cluster-name-kafka-index strimzi.io/manual-rolling-update=true

kubectl annotate pod cluster-name-zookeeper-index strimzi.io/manual-rolling-update=true

  3. Wait for the next reconciliation to occur (every two minutes by default). A rolling update of the annotated Pod is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of a pod is complete, the annotation is removed from the Pod.

10.5. Discovering services using labels and annotations

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

A service discovery label and annotation is generated for services used to access the Kafka cluster:

  • Internal Kafka bootstrap service

  • HTTP Bridge service

The label helps to make the service discoverable, and the annotation provides connection details that a client application can use to make the connection.

The service discovery label, strimzi.io/discovery, is set as true for the Service resources. The service discovery annotation has the same key, providing connection details in JSON format for each service.

Example internal Kafka bootstrap service

apiVersion: v1
kind: Service
metadata:
  annotations:
    strimzi.io/discovery: |-
      [ {
        "port" : 9092,
        "tls" : false,
        "protocol" : "kafka",
        "auth" : "scram-sha-512"
      }, {
        "port" : 9093,
        "tls" : true,
        "protocol" : "kafka",
        "auth" : "tls"
      } ]
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/discovery: "true"
    strimzi.io/kind: Kafka
    strimzi.io/name: my-cluster-kafka-bootstrap
  name: my-cluster-kafka-bootstrap
spec:
  #...

Example HTTP Bridge service

apiVersion: v1
kind: Service
metadata:
  annotations:
    strimzi.io/discovery: |-
      [ {
        "port" : 8080,
        "tls" : false,
        "auth" : "none",
        "protocol" : "http"
      } ]
  labels:
    strimzi.io/cluster: my-bridge
    strimzi.io/discovery: "true"
    strimzi.io/kind: KafkaBridge
    strimzi.io/name: my-bridge-bridge-service

10.5.1. Returning connection details on services

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

kubectl get service -l strimzi.io/discovery=true

The connection details are returned when retrieving the service discovery label.

10.6. Recovering a cluster from persistent volumes

You can recover a Kafka cluster from persistent volumes (PVs) if they are still present.

You might want to do this, for example, after:

  • A namespace was deleted unintentionally

  • A whole Kubernetes cluster is lost, but the PVs remain in the infrastructure

10.6.1. Recovery from namespace deletion

Recovery from namespace deletion is possible because of the relationship between persistent volumes and namespaces. A PersistentVolume (PV) is a storage resource that lives outside of a namespace. A PV is mounted into a Kafka pod using a PersistentVolumeClaim (PVC), which lives inside a namespace.

The reclaim policy for a PV tells a cluster how to act when a namespace is deleted. If the reclaim policy is set as:

  • Delete (default), PVs are deleted when PVCs are deleted within a namespace

  • Retain, PVs are not deleted when a namespace is deleted

To ensure that you can recover from a PV if a namespace is deleted unintentionally, the policy must be reset from Delete to Retain in the PV specification using the persistentVolumeReclaimPolicy property:

apiVersion: v1
kind: PersistentVolume
# ...
spec:
  # ...
  persistentVolumeReclaimPolicy: Retain

Alternatively, PVs can inherit the reclaim policy of an associated storage class. Storage classes are used for dynamic volume allocation.

By configuring the reclaimPolicy property for the storage class, PVs that use the storage class are created with the appropriate reclaim policy. The storage class is configured for the PV using the storageClassName property.

apiVersion: v1
kind: StorageClass
metadata:
  name: gp2-retain
parameters:
  # ...
# ...
reclaimPolicy: Retain
apiVersion: v1
kind: PersistentVolume
# ...
spec:
  # ...
  storageClassName: gp2-retain
Note
 If you are using Retain as the reclaim policy, but you want to delete an entire cluster, you need to delete the PVs manually. Otherwise they will not be deleted, and may cause unnecessary expenditure on resources.

10.6.2. Recovery from loss of a Kubernetes cluster

When a cluster is lost, you can use the data from disks/volumes to recover the cluster if they were preserved within the infrastructure. The recovery procedure is the same as with namespace deletion, assuming PVs can be recovered and they were created manually.

10.6.3. Recovering a deleted cluster from persistent volumes

This procedure describes how to recover a deleted cluster from persistent volumes (PVs).

In this situation, the Topic Operator identifies that topics exist in Kafka, but the KafkaTopic resources do not exist.

When you get to the step to recreate your cluster, you have two options:

  1. Use Option 1 when you can recover all KafkaTopic resources.

    The KafkaTopic resources must therefore be recovered before the cluster is started so that the corresponding topics are not deleted by the Topic Operator.

  2. Use Option 2 when you are unable to recover all KafkaTopic resources.

    In this case, you deploy your cluster without the Topic Operator, delete the Topic Operator topic store metadata, and then redeploy the Kafka cluster with the Topic Operator so it can recreate the KafkaTopic resources from the corresponding topics.

Note
 If the Topic Operator is not deployed, you only need to recover the PersistentVolumeClaim (PVC) resources.
Before you begin

In this procedure, it is essential that PVs are mounted into the correct PVC to avoid data corruption. A volumeName is specified for the PVC and this must match the name of the PV.

For more information, see:

Note
 The procedure does not include recovery of KafkaUser resources, which must be recreated manually. If passwords and certificates need to be retained, secrets must be recreated before creating the KafkaUser resources.
Procedure

  1. Check information on the PVs in the cluster:

    kubectl get pv

    Information is presented for PVs with data.

    Example output showing columns important to this procedure:

    NAME                                         RECLAIMPOLICY CLAIM
pvc-5e9c5c7f-3317-11ea-a650-06e1eadd9a4c ... Retain ...    myproject/data-my-cluster-zookeeper-1
pvc-5e9cc72d-3317-11ea-97b0-0aef8816c7ea ... Retain ...    myproject/data-my-cluster-zookeeper-0
pvc-5ead43d1-3317-11ea-97b0-0aef8816c7ea ... Retain ...    myproject/data-my-cluster-zookeeper-2
pvc-7e1f67f9-3317-11ea-a650-06e1eadd9a4c ... Retain ...    myproject/data-0-my-cluster-kafka-0
pvc-7e21042e-3317-11ea-9786-02deaf9aa87e ... Retain ...    myproject/data-0-my-cluster-kafka-1
pvc-7e226978-3317-11ea-97b0-0aef8816c7ea ... Retain ...    myproject/data-0-my-cluster-kafka-2

    • NAME shows the name of each PV.

    • RECLAIM POLICY shows that PVs are retained.

    • CLAIM shows the link to the original PVCs.

  2. Recreate the original namespace:

    kubectl create namespace myproject

  3. Recreate the original PVC resource specifications, linking the PVCs to the appropriate PV:

    For example:

    apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: data-0-my-cluster-kafka-0
spec:
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 100Gi
  storageClassName: gp2-retain
  volumeMode: Filesystem
  volumeName: pvc-7e1f67f9-3317-11ea-a650-06e1eadd9a4c

  4. Edit the PV specifications to delete the claimRef properties that bound the original PVC.

    For example:

    apiVersion: v1
kind: PersistentVolume
metadata:
  annotations:
    kubernetes.io/createdby: aws-ebs-dynamic-provisioner
    pv.kubernetes.io/bound-by-controller: "yes"
    pv.kubernetes.io/provisioned-by: kubernetes.io/aws-ebs
  creationTimestamp: "<date>"
  finalizers:
  - kubernetes.io/pv-protection
  labels:
    failure-domain.beta.kubernetes.io/region: eu-west-1
    failure-domain.beta.kubernetes.io/zone: eu-west-1c
  name: pvc-7e226978-3317-11ea-97b0-0aef8816c7ea
  resourceVersion: "39431"
  selfLink: /api/v1/persistentvolumes/pvc-7e226978-3317-11ea-97b0-0aef8816c7ea
  uid: 7efe6b0d-3317-11ea-a650-06e1eadd9a4c
spec:
  accessModes:
  - ReadWriteOnce
  awsElasticBlockStore:
    fsType: xfs
    volumeID: aws://eu-west-1c/vol-09db3141656d1c258
  capacity:
    storage: 100Gi
  claimRef:
    apiVersion: v1
    kind: PersistentVolumeClaim
    name: data-0-my-cluster-kafka-2
    namespace: myproject
    resourceVersion: "39113"
    uid: 54be1c60-3319-11ea-97b0-0aef8816c7ea
  nodeAffinity:
    required:
      nodeSelectorTerms:
      - matchExpressions:
        - key: failure-domain.beta.kubernetes.io/zone
          operator: In
          values:
          - eu-west-1c
        - key: failure-domain.beta.kubernetes.io/region
          operator: In
          values:
          - eu-west-1
  persistentVolumeReclaimPolicy: Retain
  storageClassName: gp2-retain
  volumeMode: Filesystem

    In the example, the following properties are deleted:

    claimRef:
  apiVersion: v1
  kind: PersistentVolumeClaim
  name: data-0-my-cluster-kafka-2
  namespace: myproject
  resourceVersion: "39113"
  uid: 54be1c60-3319-11ea-97b0-0aef8816c7ea

  5. Deploy the Cluster Operator.

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

  6. Recreate your cluster.

    Follow the steps depending on whether or not you have all the KafkaTopic resources needed to recreate your cluster.

    Option 1: If you have all the KafkaTopic resources that existed before you lost your cluster, including internal topics such as committed offsets from __consumer_offsets:

    1. Recreate all KafkaTopic resources.

      It is essential that you recreate the resources before deploying the cluster, or the Topic Operator will delete the topics.

    2. Deploy the Kafka cluster.

      For example:

      kubectl apply -f kafka.yaml

    Option 2: If you do not have all the KafkaTopic resources that existed before you lost your cluster:

    1. Deploy the Kafka cluster, as with the first option, but without the Topic Operator by removing the topicOperator property from the Kafka resource before deploying.

      If you include the Topic Operator in the deployment, the Topic Operator will delete all the topics.

    2. Delete the internal topic store topics from the Kafka cluster:

      kubectl run kafka-admin -ti --image=quay.io/strimzi/kafka:latest-kafka-3.2.0 --rm=true --restart=Never -- ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi-topic-operator-kstreams-topic-store-changelog --delete && ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi_store_topic --delete

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

    3. Enable the Topic Operator by redeploying the Kafka cluster with the topicOperator property to recreate the KafkaTopic resources.

      For example:

      apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  #...
  entityOperator:
    topicOperator: {} (1)
    #...

    1. Here we show the default configuration, which has no additional properties. You specify the required configuration using the properties described in EntityTopicOperatorSpec schema reference.

  7. Verify the recovery by listing the KafkaTopic resources:

    kubectl get KafkaTopic

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

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

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

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

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

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

Procedure

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

    The plugin properties are shown in this example configuration.

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

    1. Loads the Kafka Static Quota plugin.

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

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

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

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

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

  2. Update the resource.

    kubectl apply -f <kafka_configuration_file>
Additional resources

10.8. Frequently asked questions

Why do I need cluster administrator privileges to install Strimzi?

To install Strimzi, you need to be able to create the following cluster-scoped resources:

  • Custom Resource Definitions (CRDs) to instruct Kubernetes about resources that are specific to Strimzi, such as Kafka and KafkaConnect

  • ClusterRoles and ClusterRoleBindings

Cluster-scoped resources, which are not scoped to a particular Kubernetes namespace, typically require cluster administrator privileges to install.

As a cluster administrator, you can inspect all the resources being installed (in the /install/ directory) to ensure that the ClusterRoles do not grant unnecessary privileges.

After installation, the Cluster Operator runs as a regular Deployment, so any standard (non-admin) Kubernetes user with privileges to access the Deployment can configure it. The cluster administrator can grant standard users the privileges necessary to manage Kafka custom resources.

See also:

Why does the Cluster Operator need to create ClusterRoleBindings?

Kubernetes has built-in privilege escalation prevention, which means that the Cluster Operator cannot grant privileges it does not have itself, specifically, it cannot grant such privileges in a namespace it cannot access. Therefore, the Cluster Operator must have the privileges necessary for all the components it orchestrates.

The Cluster Operator needs to be able to grant access so that:

  • The Topic Operator can manage KafkaTopics, by creating Roles and RoleBindings in the namespace that the operator runs in

  • The User Operator can manage KafkaUsers, by creating Roles and RoleBindings in the namespace that the operator runs in

  • The failure domain of a Node is discovered by Strimzi, by creating a ClusterRoleBinding

When using rack-aware partition assignment, the broker pod needs to be able to get information about the Node it is running on, for example, the Availability Zone in Amazon AWS. A Node is a cluster-scoped resource, so access to it can only be granted through a ClusterRoleBinding, not a namespace-scoped RoleBinding.

Can standard Kubernetes users create Kafka custom resources?

By default, standard Kubernetes users will not have the privileges necessary to manage the custom resources handled by the Cluster Operator. The cluster administrator can grant a user the necessary privileges using Kubernetes RBAC resources.

For more information, see Designating Strimzi administrators in the Deploying and Upgrading Strimzi guide.

What do the failed to acquire lock warnings in the log mean?

For each cluster, the Cluster Operator executes only one operation at a time. The Cluster Operator uses locks to make sure that there are never two parallel operations running for the same cluster. Other operations must wait until the current operation completes before the lock is released.

INFO

Examples of cluster operations include cluster creation, rolling update, scale down , and scale up.

If the waiting time for the lock takes too long, the operation times out and the following warning message is printed to the log:

2018-03-04 17:09:24 WARNING AbstractClusterOperations:290 - Failed to acquire lock for kafka cluster lock::kafka::myproject::my-cluster

Depending on the exact configuration of STRIMZI_FULL_RECONCILIATION_INTERVAL_MS and STRIMZI_OPERATION_TIMEOUT_MS, this warning message might appear occasionally without indicating any underlying issues. Operations that time out are picked up in the next periodic reconciliation, so that the operation can acquire the lock and execute again.

Should this message appear periodically, even in situations when there should be no other operations running for a given cluster, it might indicate that the lock was not properly released due to an error. If this is the case, try restarting the Cluster Operator.

Why is hostname verification failing when connecting to NodePorts using TLS?

Currently, off-cluster access using NodePorts with TLS encryption enabled does not support TLS hostname verification. As a result, the clients that verify the hostname will fail to connect. For example, the Java client will fail with the following exception:

Caused by: java.security.cert.CertificateException: No subject alternative names matching IP address 168.72.15.231 found
 at sun.security.util.HostnameChecker.matchIP(HostnameChecker.java:168)
 at sun.security.util.HostnameChecker.match(HostnameChecker.java:94)
 at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:455)
 at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:436)
 at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:252)
 at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:136)
 at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1501)
 ... 17 more

To connect, you must disable hostname verification. In the Java client, you can do this by setting the configuration option ssl.endpoint.identification.algorithm to an empty string.

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

ssl.endpoint.identification.algorithm=

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

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

11. Tuning Kafka configuration

Use configuration properties to optimize the performance of Kafka brokers, producers and consumers.

A minimum set of configuration properties is required, but you can add or adjust properties to change how producers and consumers interact with Kafka brokers. For example, you can tune latency and throughput of messages so that clients can respond to data in real time.

You might start by analyzing metrics to gauge where to make your initial configurations, then make incremental changes and further comparisons of metrics until you have the configuration you need.

For more information about Apache Kafka configuration properties, see the Apache Kafka documentation.

11.1. Tools that help with tuning

The following tools help with Kafka tuning:

11.2. Managed broker configurations

When you deploy Strimzi on Kubernetes, you can specify broker configuration through the config property of the Kafka custom resource. However, certain broker configuration options are managed directly by Strimzi.

As such, you cannot configure the following options:

  • broker.id to specify the ID of the Kafka broker

  • log.dirs directories for log data

  • zookeeper.connect configuration to connect Kafka with ZooKeeper

  • listeners to expose the Kafka cluster to clients

  • authorization mechanisms to allow or decline actions executed by users

  • authentication mechanisms to prove the identity of users requiring access to Kafka

Broker IDs start from 0 (zero) and correspond to the number of broker replicas. Log directories are mounted to /var/lib/kafka/data/kafka-logIDX based on the spec.kafka.storage configuration in the Kafka custom resource. IDX is the Kafka broker pod index.

For a list of exclusions, see the KafkaClusterSpec schema reference.

11.3. Kafka broker configuration tuning

Use configuration properties to optimize the performance of Kafka brokers. You can use standard Kafka broker configuration options, except for properties managed directly by Strimzi.

11.3.1. Basic broker configuration

A typical broker configuration will include settings for properties related to topics, threads and logs.

Basic broker configuration properties
# ...
num.partitions=1
default.replication.factor=3
offsets.topic.replication.factor=3
transaction.state.log.replication.factor=3
transaction.state.log.min.isr=2
log.retention.hours=168
log.segment.bytes=1073741824
log.retention.check.interval.ms=300000
num.network.threads=3
num.io.threads=8
num.recovery.threads.per.data.dir=1
socket.send.buffer.bytes=102400
socket.receive.buffer.bytes=102400
socket.request.max.bytes=104857600
group.initial.rebalance.delay.ms=0
zookeeper.connection.timeout.ms=6000
# ...

11.3.2. Replicating topics for high availability

Basic topic properties set the default number of partitions and replication factor for topics, which will apply to topics that are created without these properties being explicitly set, including when topics are created automatically.

# ...
num.partitions=1
auto.create.topics.enable=false
default.replication.factor=3
min.insync.replicas=2
replica.fetch.max.bytes=1048576
# ...

For high availability environments, it is advisable to increase the replication factor to at least 3 for topics and set the minimum number of in-sync replicas required to 1 less than the replication factor.

The auto.create.topics.enable property is enabled by default so that topics that do not already exist are created automatically when needed by producers and consumers. If you are using automatic topic creation, you can set the default number of partitions for topics using num.partitions. Generally, however, this property is disabled so that more control is provided over topics through explicit topic creation.

For data durability, you should also set min.insync.replicas in your topic configuration and message delivery acknowledgments using acks=all in your producer configuration.

Use replica.fetch.max.bytes to set the maximum size, in bytes, of messages fetched by each follower that replicates the leader partition. Change this value according to the average message size and throughput. When considering the total memory allocation required for read/write buffering, the memory available must also be able to accommodate the maximum replicated message size when multiplied by all followers.

The delete.topic.enable property is enabled by default to allow topics to be deleted. In a production environment, you should disable this property to avoid accidental topic deletion, resulting in data loss. You can, however, temporarily enable it and delete topics and then disable it again.

Note
 When running Strimzi on Kubernetes, the Topic Operator can provide operator-style topic management. You can use the KafkaTopic resource to create topics. For topics created using the KafkaTopic resource, the replication factor is set using spec.replicas. If delete.topic.enable is enabled, you can also delete topics using the KafkaTopic resource. 
# ...
auto.create.topics.enable=false
delete.topic.enable=true
# ...

11.3.3. Internal topic settings for transactions and commits

If you are using transactions to enable atomic writes to partitions from producers, the state of the transactions is stored in the internal __transaction_state topic. By default, the brokers are configured with a replication factor of 3 and a minimum of 2 in-sync replicas for this topic, which means that a minimum of three brokers are required in your Kafka cluster.

# ...
transaction.state.log.replication.factor=3
transaction.state.log.min.isr=2
# ...

Similarly, the internal __consumer_offsets topic, which stores consumer state, has default settings for the number of partitions and replication factor.

# ...
offsets.topic.num.partitions=50
offsets.topic.replication.factor=3
# ...

Do not reduce these settings in production. You can increase the settings in a production environment. As an exception, you might want to reduce the settings in a single-broker test environment.

11.3.4. Improving request handling throughput by increasing I/O threads

Network threads handle requests to the Kafka cluster, such as produce and fetch requests from client applications. Produce requests are placed in a request queue. Responses are placed in a response queue.

The number of network threads per listener should reflect the replication factor and the levels of activity from client producers and consumers interacting with the Kafka cluster. If you are going to have a lot of requests, you can increase the number of threads, using the amount of time threads are idle to determine when to add more threads.

To reduce congestion and regulate the request traffic, you can limit the number of requests allowed in the request queue. When the request queue is full, all incoming traffic is blocked.

I/O threads pick up requests from the request queue to process them. Adding more threads can improve throughput, but the number of CPU cores and disk bandwidth imposes a practical upper limit. At a minimum, the number of I/O threads should equal the number of storage volumes.

# ...
num.network.threads=3 (1)
queued.max.requests=500 (2)
num.io.threads=8 (3)
num.recovery.threads.per.data.dir=4 (4)
# ...

  1. The number of network threads for the Kafka cluster.

  2. The number of requests allowed in the request queue.

  3. The number of I/O threads for a Kafka broker.

  4. The number of threads used for log loading at startup and flushing at shutdown. Try setting to a value of at least the number of cores.

Configuration updates to the thread pools for all brokers might occur dynamically at the cluster level. These updates are restricted to between half the current size and twice the current size.

Tip

The following Kafka broker metrics can help with working out the number of threads required:

  • kafka.network:type=SocketServer,name=NetworkProcessorAvgIdlePercent provides metrics on the average time network threads are idle as a percentage.

  • kafka.server:type=KafkaRequestHandlerPool,name=RequestHandlerAvgIdlePercent provides metrics on the average time I/O threads are idle as a percentage.

If there is 0% idle time, all resources are in use, which means that adding more threads might be beneficial. When idle time goes below 30%, performance may start to suffer.

If threads are slow or limited due to the number of disks, you can try increasing the size of the buffers for network requests to improve throughput:

# ...
replica.socket.receive.buffer.bytes=65536
# ...

And also increase the maximum number of bytes Kafka can receive:

# ...
socket.request.max.bytes=104857600
# ...

11.3.5. Increasing bandwidth for high latency connections

Kafka batches data to achieve reasonable throughput over high-latency connections from Kafka to clients, such as connections between datacenters. However, if high latency is a problem, you can increase the size of the buffers for sending and receiving messages.

# ...
socket.send.buffer.bytes=1048576
socket.receive.buffer.bytes=1048576
# ...

You can estimate the optimal size of your buffers using a bandwidth-delay product calculation, which multiplies the maximum bandwidth of the link (in bytes/s) with the round-trip delay (in seconds) to give an estimate of how large a buffer is required to sustain maximum throughput.

11.3.6. Managing logs with data retention policies

Kafka uses logs to store message data. Logs are a series of segments associated with various indexes. New messages are written to an active segment, and never subsequently modified. Segments are read when serving fetch requests from consumers. Periodically, the active segment is rolled to become read-only and a new active segment is created to replace it. There is only a single segment active at a time. Older segments are retained until they are eligible for deletion.

Configuration at the broker level sets the maximum size in bytes of a log segment and the amount of time in milliseconds before an active segment is rolled:

# ...
log.segment.bytes=1073741824
log.roll.ms=604800000
# ...

You can override these settings at the topic level using segment.bytes and segment.ms. Whether you need to lower or raise these values depends on the policy for segment deletion. A larger size means the active segment contains more messages and is rolled less often. Segments also become eligible for deletion less often.

You can set time-based or size-based log retention and cleanup policies so that logs are kept manageable. Depending on your requirements, you can use log retention configuration to delete old segments. If log retention policies are used, non-active log segments are removed when retention limits are reached. Deleting old segments bounds the storage space required for the log so you do not exceed disk capacity.

For time-based log retention, you set a retention period based on hours, minutes and milliseconds. The retention period is based on the time messages were appended to the segment.

The milliseconds configuration has priority over minutes, which has priority over hours. The minutes and milliseconds configuration is null by default, but the three options provide a substantial level of control over the data you wish to retain. Preference should be given to the milliseconds configuration, as it is the only one of the three properties that is dynamically updateable.

# ...
log.retention.ms=1680000
# ...

If log.retention.ms is set to -1, no time limit is applied to log retention, so all logs are retained. Disk usage should always be monitored, but the -1 setting is not generally recommended as it can lead to issues with full disks, which can be hard to rectify.

For size-based log retention, you set a maximum log size (of all segments in the log) in bytes:

# ...
log.retention.bytes=1073741824
# ...

In other words, a log will typically have approximately log.retention.bytes/log.segment.bytes segments once it reaches a steady state. When the maximum log size is reached, older segments are removed.

A potential issue with using a maximum log size is that it does not take into account the time messages were appended to a segment. You can use time-based and size-based log retention for your cleanup policy to get the balance you need. Whichever threshold is reached first triggers the cleanup.

If you wish to add a time delay before a segment file is deleted from the system, you can add the delay using log.segment.delete.delay.ms for all topics at the broker level or file.delete.delay.ms for specific topics in the topic configuration.

# ...
log.segment.delete.delay.ms=60000
# ...

11.3.7. Removing log data with cleanup policies

The method of removing older log data is determined by the log cleaner configuration.

The log cleaner is enabled for the broker by default:

# ...
log.cleaner.enable=true
# ...

The log cleaner needs to be enabled if you are using log compaction cleanup policy. You can set the cleanup policy at the topic or broker level. Broker-level configuration is the default for topics that do not have policy set.

You can set policy to delete logs, compact logs, or do both:

# ...
log.cleanup.policy=compact,delete
# ...

The delete policy corresponds to managing logs with data retention policies. It is suitable when data does not need to be retained forever. The compact policy guarantees to keep the most recent message for each message key. Log compaction is suitable where message values are changeable, and you want to retain the latest update.

If cleanup policy is set to delete logs, older segments are deleted based on log retention limits. Otherwise, if the log cleaner is not enabled, and there are no log retention limits, the log will continue to grow.

If cleanup policy is set for log compaction, the head of the log operates as a standard Kafka log, with writes for new messages appended in order. In the tail of a compacted log, where the log cleaner operates, records will be deleted if another record with the same key occurs later in the log. Messages with null values are also deleted. If you’re not using keys, you can’t use compaction because keys are needed to identify related messages. While Kafka guarantees that the latest messages for each key will be retained, it does not guarantee that the whole compacted log will not contain duplicates.

Image of compaction showing key value writes
Figure 6. Log showing key value writes with offset positions before compaction

Using keys to identify messages, Kafka compaction keeps the latest message (with the highest offset) for a specific message key, eventually discarding earlier messages that have the same key. In other words, the message in its latest state is always available and any out-of-date records of that particular message are eventually removed when the log cleaner runs. You can restore a message back to a previous state.

Records retain their original offsets even when surrounding records get deleted. Consequently, the tail can have non-contiguous offsets. When consuming an offset that’s no longer available in the tail, the record with the next higher offset is found.

Image of compaction after log cleanup
Figure 7. Log after compaction

If you choose only a compact policy, your log can still become arbitrarily large. In which case, you can set policy to compact and delete logs. If you choose to compact and delete, first the log data is compacted, removing records with a key in the head of the log. After which, data that falls before the log retention threshold is deleted.

Image of compaction with retention point
Figure 8. Log retention point and compaction point

You set the frequency the log is checked for cleanup in milliseconds:

# ...
log.retention.check.interval.ms=300000
# ...

Adjust the log retention check interval in relation to the log retention settings. Smaller retention sizes might require more frequent checks.

The frequency of cleanup should be often enough to manage the disk space, but not so often it affects performance on a topic.

You can also set a time in milliseconds to put the cleaner on standby if there are no logs to clean:

# ...
log.cleaner.backoff.ms=15000
# ...

If you choose to delete older log data, you can set a period in milliseconds to retain the deleted data before it is purged:

# ...
log.cleaner.delete.retention.ms=86400000
# ...

The deleted data retention period gives time to notice the data is gone before it is irretrievably deleted.

To delete all messages related to a specific key, a producer can send a tombstone message. A tombstone has a null value and acts as a marker to tell a consumer the value is deleted. After compaction, only the tombstone is retained, which must be for a long enough period for the consumer to know that the message is deleted. When older messages are deleted, having no value, the tombstone key is also deleted from the partition.

11.3.8. Managing disk utilization

There are many other configuration settings related to log cleanup, but of particular importance is memory allocation.

The deduplication property specifies the total memory for cleanup across all log cleaner threads. You can set an upper limit on the percentage of memory used through the buffer load factor.

# ...
log.cleaner.dedupe.buffer.size=134217728
log.cleaner.io.buffer.load.factor=0.9
# ...

Each log entry uses exactly 24 bytes, so you can work out how many log entries the buffer can handle in a single run and adjust the setting accordingly.

If possible, consider increasing the number of log cleaner threads if you are looking to reduce the log cleaning time:

# ...
log.cleaner.threads=8
# ...

If you are experiencing issues with 100% disk bandwidth usage, you can throttle the log cleaner I/O so that the sum of the read/write operations is less than a specified double value based on the capabilities of the disks performing the operations:

# ...
log.cleaner.io.max.bytes.per.second=1.7976931348623157E308
# ...

11.3.9. Handling large message sizes

The default batch size for messages is 1MB, which is optimal for maximum throughput in most use cases. Kafka can accommodate larger batches at a reduced throughput, assuming adequate disk capacity.

Large message sizes are handled in four ways:

  1. Producer-side message compression writes compressed messages to the log.

  2. Reference-based messaging sends only a reference to data stored in some other system in the message’s value.

  3. Inline messaging splits messages into chunks that use the same key, which are then combined on output using a stream-processor like Kafka Streams.

  4. Broker and producer/consumer client application configuration built to handle larger message sizes.

The reference-based messaging and message compression options are recommended and cover most situations. With any of these options, care must be take to avoid introducing performance issues.

Producer-side compression

For producer configuration, you specify a compression.type, such as Gzip, which is then applied to batches of data generated by the producer. Using the broker configuration compression.type=producer, the broker retains whatever compression the producer used. Whenever producer and topic compression do not match, the broker has to compress batches again prior to appending them to the log, which impacts broker performance.

Compression also adds additional processing overhead on the producer and decompression overhead on the consumer, but includes more data in a batch, so is often beneficial to throughput when message data compresses well.

Combine producer-side compression with fine-tuning of the batch size to facilitate optimum throughput. Using metrics helps to gauge the average batch size needed.

Reference-based messaging

Reference-based messaging is useful for data replication when you do not know how big a message will be. The external data store must be fast, durable, and highly available for this configuration to work. Data is written to the data store and a reference to the data is returned. The producer sends a message containing the reference to Kafka. The consumer gets the reference from the message and uses it to fetch the data from the data store.

Image of reference-based messaging flow
Figure 9. Reference-based messaging flow

As the message passing requires more trips, end-to-end latency will increase. Another significant drawback of this approach is there is no automatic clean up of the data in the external system when the Kafka message gets cleaned up. A hybrid approach would be to only send large messages to the data store and process standard-sized messages directly.

Inline messaging

Inline messaging is complex, but it does not have the overhead of depending on external systems like reference-based messaging.

The producing client application has to serialize and then chunk the data if the message is too big. The producer then uses the Kafka ByteArraySerializer or similar to serialize each chunk again before sending it. The consumer tracks messages and buffers chunks until it has a complete message. The consuming client application receives the chunks, which are assembled before deserialization. Complete messages are delivered to the rest of the consuming application in order according to the offset of the first or last chunk for each set of chunked messages. Successful delivery of the complete message is checked against offset metadata to avoid duplicates during a rebalance.

Image of inline messaging flow
Figure 10. Inline messaging flow

Inline messaging has a performance overhead on the consumer side because of the buffering required, particularly when handling a series of large messages in parallel. The chunks of large messages can become interleaved, so that it is not always possible to commit when all the chunks of a message have been consumed if the chunks of another large message in the buffer are incomplete. For this reason, the buffering is usually supported by persisting message chunks or by implementing commit logic.

Configuration to handle larger messages

If larger messages cannot be avoided, and to avoid blocks at any point of the message flow, you can increase message limits. To do this, configure message.max.bytes at the topic level to set the maximum record batch size for individual topics. If you set message.max.bytes at the broker level, larger messages are allowed for all topics.

The broker will reject any message that is greater than the limit set with message.max.bytes. The buffer size for the producers (max.request.size) and consumers (message.max.bytes) must be able to accommodate the larger messages.

11.3.10. Controlling the log flush of message data

Generally, the recommendation is to not set explicit flush thresholds and let the operating system perform background flush using its default settings. Partition replication provides greater data durability than writes to any single disk, as a failed broker can recover from its in-sync replicas.

Log flush properties control the periodic writes of cached message data to disk. The scheduler specifies the frequency of checks on the log cache in milliseconds:

# ...
log.flush.scheduler.interval.ms=2000
# ...

You can control the frequency of the flush based on the maximum amount of time that a message is kept in-memory and the maximum number of messages in the log before writing to disk:

# ...
log.flush.interval.ms=50000
log.flush.interval.messages=100000
# ...

The wait between flushes includes the time to make the check and the specified interval before the flush is carried out. Increasing the frequency of flushes can affect throughput.

If you are using application flush management, setting lower flush thresholds might be appropriate if you are using faster disks.

11.3.11. Partition rebalancing for availability

Partitions can be replicated across brokers for fault tolerance. For a given partition, one broker is elected leader and handles all produce requests (writes to the log). Partition followers on other brokers replicate the partition data of the partition leader for data reliability in the event of the leader failing.

Followers do not normally serve clients, though rack configuration allows a consumer to consume messages from the closest replica when a Kafka cluster spans multiple datacenters. Followers operate only to replicate messages from the partition leader and allow recovery should the leader fail. Recovery requires an in-sync follower. Followers stay in sync by sending fetch requests to the leader, which returns messages to the follower in order. The follower is considered to be in sync if it has caught up with the most recently committed message on the leader. The leader checks this by looking at the last offset requested by the follower. An out-of-sync follower is usually not eligible as a leader should the current leader fail, unless unclean leader election is allowed.

You can adjust the lag time before a follower is considered out of sync:

# ...
replica.lag.time.max.ms=30000
# ...

Lag time puts an upper limit on the time to replicate a message to all in-sync replicas and how long a producer has to wait for an acknowledgment. If a follower fails to make a fetch request and catch up with the latest message within the specified lag time, it is removed from in-sync replicas. You can reduce the lag time to detect failed replicas sooner, but by doing so you might increase the number of followers that fall out of sync needlessly. The right lag time value depends on both network latency and broker disk bandwidth.

When a leader partition is no longer available, one of the in-sync replicas is chosen as the new leader. The first broker in a partition’s list of replicas is known as the preferred leader. By default, Kafka is enabled for automatic partition leader rebalancing based on a periodic check of leader distribution. That is, Kafka checks to see if the preferred leader is the current leader. A rebalance ensures that leaders are evenly distributed across brokers and brokers are not overloaded.

You can use Cruise Control for Strimzi to figure out replica assignments to brokers that balance load evenly across the cluster. Its calculation takes into account the differing load experienced by leaders and followers. A failed leader affects the balance of a Kafka cluster because the remaining brokers get the extra work of leading additional partitions.

For the assignment found by Cruise Control to actually be balanced it is necessary that partitions are lead by the preferred leader. Kafka can automatically ensure that the preferred leader is being used (where possible), changing the current leader if necessary. This ensures that the cluster remains in the balanced state found by Cruise Control.

You can control the frequency, in seconds, of the rebalance check and the maximum percentage of imbalance allowed for a broker before a rebalance is triggered.

#...
auto.leader.rebalance.enable=true
leader.imbalance.check.interval.seconds=300
leader.imbalance.per.broker.percentage=10
#...

The percentage leader imbalance for a broker is the ratio between the current number of partitions for which the broker is the current leader and the number of partitions for which it is the preferred leader. You can set the percentage to zero to ensure that preferred leaders are always elected, assuming they are in sync.

If the checks for rebalances need more control, you can disable automated rebalances. You can then choose when to trigger a rebalance using the kafka-leader-election.sh command line tool.

Note
 The Grafana dashboards provided with Strimzi show metrics for under-replicated partitions and partitions that do not have an active leader.

11.3.12. Unclean leader election

Leader election to an in-sync replica is considered clean because it guarantees no loss of data. And this is what happens by default. But what if there is no in-sync replica to take on leadership? Perhaps the ISR (in-sync replica) only contained the leader when the leader’s disk died. If a minimum number of in-sync replicas is not set, and there are no followers in sync with the partition leader when its hard drive fails irrevocably, data is already lost. Not only that, but a new leader cannot be elected because there are no in-sync followers.

You can configure how Kafka handles leader failure:

# ...
unclean.leader.election.enable=false
# ...

Unclean leader election is disabled by default, which means that out-of-sync replicas cannot become leaders. With clean leader election, if no other broker was in the ISR when the old leader was lost, Kafka waits until that leader is back online before messages can be written or read. Unclean leader election means out-of-sync replicas can become leaders, but you risk losing messages. The choice you make depends on whether your requirements favor availability or durability.

You can override the default configuration for specific topics at the topic level. If you cannot afford the risk of data loss, then leave the default configuration.

11.3.13. Avoiding unnecessary consumer group rebalances

For consumers joining a new consumer group, you can add a delay so that unnecessary rebalances to the broker are avoided:

# ...
group.initial.rebalance.delay.ms=3000
# ...

The delay is the amount of time that the coordinator waits for members to join. The longer the delay, the more likely it is that all the members will join in time and avoid a rebalance. But the delay also prevents the group from consuming until the period has ended.

11.4. Kafka producer configuration tuning

Use a basic producer configuration with optional properties that are tailored to specific use cases.

Adjusting your configuration to maximize throughput might increase latency or vice versa. You will need to experiment and tune your producer configuration to get the balance you need.

11.4.1. Basic producer configuration

Connection and serializer properties are required for every producer. Generally, it is good practice to add a client id for tracking, and use compression on the producer to reduce batch sizes in requests.

In a basic producer configuration:

  • The order of messages in a partition is not guaranteed.

  • The acknowledgment of messages reaching the broker does not guarantee durability.

Basic producer configuration properties
# ...
bootstrap.servers=localhost:9092 (1)
key.serializer=org.apache.kafka.common.serialization.StringSerializer (2)
value.serializer=org.apache.kafka.common.serialization.StringSerializer (3)
client.id=my-client (4)
compression.type=gzip (5)
# ...

  1. (Required) Tells the producer to connect to a Kafka cluster using a host:port bootstrap server address for a Kafka broker. The producer uses the address to discover and connect to all brokers in the cluster. Use a comma-separated list to specify two or three addresses in case a server is down, but it’s not necessary to provide a list of all the brokers in the cluster.

  2. (Required) Serializer to transform the key of each message to bytes prior to them being sent to a broker.

  3. (Required) Serializer to transform the value of each message to bytes prior to them being sent to a broker.

  4. (Optional) The logical name for the client, which is used in logs and metrics to identify the source of a request.

  5. (Optional) The codec for compressing messages, which are sent and might be stored in compressed format and then decompressed when reaching a consumer. Compression is useful for improving throughput and reducing the load on storage, but might not be suitable for low latency applications where the cost of compression or decompression could be prohibitive.

11.4.2. Data durability

Message delivery acknowledgments provide data durability to minimize the likelihood that messages are lost. Acknowledgments are enabled by default with the acks property set at acks=all.

# ...
acks=all (1)
# ...

  1. acks=all forces a partition leader to replicate messages to a certain number of followers before acknowledging that the message request was successfully received.

The acks=all setting offers the strongest guarantee of delivery, but it will increase the latency between the producer sending a message and receiving acknowledgment. If you don’t require such strong guarantees, a setting of acks=0 or acks=1 provides either no delivery guarantees or only acknowledgment that the leader partition has written the record to its log.

The number of brokers which need to have appended the messages to their logs before the acknowledgment is sent to the producer is determined by the topic’s min.insync.replicas configuration. A typical starting point is to have a topic replication factor of 3, with two in-sync replicas on other brokers. In this configuration, the producer can continue unaffected if a single broker is unavailable. If a second broker becomes unavailable, the producer won’t receive acknowledgments and won’t be able to produce more messages.

Topic configuration to support acks=all
# ...
min.insync.replicas=2 (1)
# ...

  1. Use 2 in-sync replicas. The default is 1.

Note
 If the system fails, there is a risk of unsent data in the buffer being lost.

11.4.3. Ordered delivery

Idempotent producers avoid duplicates as messages are delivered exactly once. IDs and sequence numbers are assigned to messages to ensure the order of delivery, even in the event of failure. If you are using acks=all for data consistency, using idempotency makes sense for ordered delivery. Idempotency is enabled for producers by default. With idempotency enabled, you can set the number of concurrent in-flight requests to a maximum of 5 for message ordering to be preserved.

Ordered delivery with idempotency
# ...
enable.idempotence=true (1)
max.in.flight.requests.per.connection=5 (2)
acks=all (3)
retries=2147483647 (4)
# ...

  1. Set to true to enable the idempotent producer.

  2. With idempotent delivery the number of in-flight requests may be greater than 1 while still providing the message ordering guarantee. The default is 5 in-flight requests.

  3. Set acks to all.

  4. Set the number of attempts to resend a failed message request.

If you choose not to use acks=all and disable idempotency because of the performance cost, set the number of in-flight (unacknowledged) requests to 1 to preserve ordering. Otherwise, a situation is possible where Message-A fails only to succeed after Message-B was already written to the broker.

Ordered delivery without idempotency
# ...
enable.idempotence=false (1)
max.in.flight.requests.per.connection=1 (2)
retries=2147483647
# ...

  1. Set to false to disable the idempotent producer.

  2. Set the number of in-flight requests to exactly 1.

11.4.4. Reliability guarantees

Idempotence is useful for exactly once writes to a single partition. Transactions, when used with idempotence, allow exactly once writes across multiple partitions.

Transactions guarantee that messages using the same transactional ID are produced once, and either all are successfully written to the respective logs or none of them are.

# ...
enable.idempotence=true
max.in.flight.requests.per.connection=5
acks=all
retries=2147483647
transactional.id=UNIQUE-ID (1)
transaction.timeout.ms=900000 (2)
# ...

  1. Specify a unique transactional ID.

  2. Set the maximum allowed time for transactions in milliseconds before a timeout error is returned. The default is 900000 or 15 minutes.

The choice of transactional.id is important in order that the transactional guarantee is maintained. Each transactional id should be used for a unique set of topic partitions. For example, this can be achieved using an external mapping of topic partition names to transactional ids, or by computing the transactional id from the topic partition names using a function that avoids collisions.

11.4.5. Optimizing throughput and latency

Usually, the requirement of a system is to satisfy a particular throughput target for a proportion of messages within a given latency. For example, targeting 500,000 messages per second with 95% of messages being acknowledged within 2 seconds.

It’s likely that the messaging semantics (message ordering and durability) of your producer are defined by the requirements for your application. For instance, it’s possible that you don’t have the option of using acks=0 or acks=1 without breaking some important property or guarantee provided by your application.

Broker restarts have a significant impact on high percentile statistics. For example, over a long period the 99th percentile latency is dominated by behavior around broker restarts. This is worth considering when designing benchmarks or comparing performance numbers from benchmarking with performance numbers seen in production.

Depending on your objective, Kafka offers a number of configuration parameters and techniques for tuning producer performance for throughput and latency.

Message batching (linger.ms and batch.size)

Message batching delays sending messages in the hope that more messages destined for the same broker will be sent, allowing them to be batched into a single produce request. Batching is a compromise between higher latency in return for higher throughput. Time-based batching is configured using linger.ms, and size-based batching is configured using batch.size.

Compression (compression.type)

Message compression adds latency in the producer (CPU time spent compressing the messages), but makes requests (and potentially disk writes) smaller, which can increase throughput. Whether compression is worthwhile, and the best compression to use, will depend on the messages being sent. Compression happens on the thread which calls KafkaProducer.send(), so if the latency of this method matters for your application you should consider using more threads.

Pipelining (max.in.flight.requests.per.connection)

Pipelining means sending more requests before the response to a previous request has been received. In general more pipelining means better throughput, up to a threshold at which other effects, such as worse batching, start to counteract the effect on throughput.

Lowering latency

When your application calls KafkaProducer.send() the messages are:

  • Processed by any interceptors

  • Serialized

  • Assigned to a partition

  • Compressed

  • Added to a batch of messages in a per-partition queue

At which point the send() method returns. So the time send() is blocked is determined by:

  • The time spent in the interceptors, serializers and partitioner

  • The compression algorithm used

  • The time spent waiting for a buffer to use for compression

Batches will remain in the queue until one of the following occurs:

  • The batch is full (according to batch.size)

  • The delay introduced by linger.ms has passed

  • The sender is about to send message batches for other partitions to the same broker, and it is possible to add this batch too

  • The producer is being flushed or closed

Look at the configuration for batching and buffering to mitigate the impact of send() blocking on latency.

# ...
linger.ms=100 (1)
batch.size=16384 (2)
buffer.memory=33554432 (3)
# ...

  1. The linger property adds a delay in milliseconds so that larger batches of messages are accumulated and sent in a request. The default is 0'.

  2. If a maximum batch.size in bytes is used, a request is sent when the maximum is reached, or messages have been queued for longer than linger.ms (whichever comes sooner). Adding the delay allows batches to accumulate messages up to the batch size.

  3. The buffer size must be at least as big as the batch size, and be able to accommodate buffering, compression and in-flight requests.

Increasing throughput

Improve throughput of your message requests by adjusting the maximum time to wait before a message is delivered and completes a send request.

You can also direct messages to a specified partition by writing a custom partitioner to replace the default.

# ...
delivery.timeout.ms=120000 (1)
partitioner.class=my-custom-partitioner (2)

# ...

  1. The maximum time in milliseconds to wait for a complete send request. You can set the value to MAX_LONG to delegate to Kafka an indefinite number of retries. The default is 120000 or 2 minutes.

  2. Specify the class name of the custom partitioner.

11.5. Kafka consumer configuration tuning

Use a basic consumer configuration with optional properties that are tailored to specific use cases.

When tuning your consumers your primary concern will be ensuring that they cope efficiently with the amount of data ingested. As with the producer tuning, be prepared to make incremental changes until the consumers operate as expected.

11.5.1. Basic consumer configuration

Connection and deserializer properties are required for every consumer. Generally, it is good practice to add a client id for tracking.

In a consumer configuration, irrespective of any subsequent configuration:

  • The consumer fetches from a given offset and consumes the messages in order, unless the offset is changed to skip or re-read messages.

  • The broker does not know if the consumer processed the responses, even when committing offsets to Kafka, because the offsets might be sent to a different broker in the cluster.

Basic consumer configuration properties
# ...
bootstrap.servers=localhost:9092 (1)
key.deserializer=org.apache.kafka.common.serialization.StringDeserializer  (2)
value.deserializer=org.apache.kafka.common.serialization.StringDeserializer  (3)
client.id=my-client (4)
group.id=my-group-id (5)
# ...

  1. (Required) Tells the consumer to connect to a Kafka cluster using a host:port bootstrap server address for a Kafka broker. The consumer uses the address to discover and connect to all brokers in the cluster. Use a comma-separated list to specify two or three addresses in case a server is down, but it is not necessary to provide a list of all the brokers in the cluster. If you are using a loadbalancer service to expose the Kafka cluster, you only need the address for the service because the availability is handled by the loadbalancer.

  2. (Required) Deserializer to transform the bytes fetched from the Kafka broker into message keys.

  3. (Required) Deserializer to transform the bytes fetched from the Kafka broker into message values.

  4. (Optional) The logical name for the client, which is used in logs and metrics to identify the source of a request. The id can also be used to throttle consumers based on processing time quotas.

  5. (Conditional) A group id is required for a consumer to be able to join a consumer group.

11.5.2. Scaling data consumption using consumer groups

Consumer groups share a typically large data stream generated by one or multiple producers from a given topic. Consumers are grouped using a group.id property, allowing messages to be spread across the members. One of the consumers in the group is elected leader and decides how the partitions are assigned to the consumers in the group. Each partition can only be assigned to a single consumer.

If you do not already have as many consumers as partitions, you can scale data consumption by adding more consumer instances with the same group.id. Adding more consumers to a group than there are partitions will not help throughput, but it does mean that there are consumers on standby should one stop functioning. If you can meet throughput goals with fewer consumers, you save on resources.

Consumers within the same consumer group send offset commits and heartbeats to the same broker. So the greater the number of consumers in the group, the higher the request load on the broker.

# ...
group.id=my-group-id (1)
# ...

  1. Add a consumer to a consumer group using a group id.

11.5.3. Message ordering guarantees

Kafka brokers receive fetch requests from consumers that ask the broker to send messages from a list of topics, partitions and offset positions.

A consumer observes messages in a single partition in the same order that they were committed to the broker, which means that Kafka only provides ordering guarantees for messages in a single partition. Conversely, if a consumer is consuming messages from multiple partitions, the order of messages in different partitions as observed by the consumer does not necessarily reflect the order in which they were sent.

If you want a strict ordering of messages from one topic, use one partition per consumer.

11.5.4. Optimizing throughput and latency

Control the number of messages returned when your client application calls KafkaConsumer.poll().

Use the fetch.max.wait.ms and fetch.min.bytes properties to increase the minimum amount of data fetched by the consumer from the Kafka broker. Time-based batching is configured using fetch.max.wait.ms, and size-based batching is configured using fetch.min.bytes.

If CPU utilization in the consumer or broker is high, it might be because there are too many requests from the consumer. You can adjust fetch.max.wait.ms and fetch.min.bytes properties higher so that there are fewer requests and messages are delivered in bigger batches. By adjusting higher, throughput is improved with some cost to latency. You can also adjust higher if the amount of data being produced is low.

For example, if you set fetch.max.wait.ms to 500ms and fetch.min.bytes to 16384 bytes, when Kafka receives a fetch request from the consumer it will respond when the first of either threshold is reached.

Conversely, you can adjust the fetch.max.wait.ms and fetch.min.bytes properties lower to improve end-to-end latency.

# ...
fetch.max.wait.ms=500 (1)
fetch.min.bytes=16384 (2)
# ...

  1. The maximum time in milliseconds the broker will wait before completing fetch requests. The default is 500 milliseconds.

  2. If a minimum batch size in bytes is used, a request is sent when the minimum is reached, or messages have been queued for longer than fetch.max.wait.ms (whichever comes sooner). Adding the delay allows batches to accumulate messages up to the batch size.

Lowering latency by increasing the fetch request size

Use the fetch.max.bytes and max.partition.fetch.bytes properties to increase the maximum amount of data fetched by the consumer from the Kafka broker.

The fetch.max.bytes property sets a maximum limit in bytes on the amount of data fetched from the broker at one time.

The max.partition.fetch.bytes sets a maximum limit in bytes on how much data is returned for each partition, which must always be larger than the number of bytes set in the broker or topic configuration for max.message.bytes.

The maximum amount of memory a client can consume is calculated approximately as:

NUMBER-OF-BROKERS * fetch.max.bytes and NUMBER-OF-PARTITIONS * max.partition.fetch.bytes

If memory usage can accommodate it, you can increase the values of these two properties. By allowing more data in each request, latency is improved as there are fewer fetch requests.

# ...
fetch.max.bytes=52428800 (1)
max.partition.fetch.bytes=1048576 (2)
# ...

  1. The maximum amount of data in bytes returned for a fetch request.

  2. The maximum amount of data in bytes returned for each partition.

11.5.5. Avoiding data loss or duplication when committing offsets

The Kafka auto-commit mechanism allows a consumer to commit the offsets of messages automatically. If enabled, the consumer will commit offsets received from polling the broker at 5000ms intervals.

The auto-commit mechanism is convenient, but it introduces a risk of data loss and duplication. If a consumer has fetched and transformed a number of messages, but the system crashes with processed messages in the consumer buffer when performing an auto-commit, that data is lost. If the system crashes after processing the messages, but before performing the auto-commit, the data is duplicated on another consumer instance after rebalancing.

Auto-committing can avoid data loss only when all messages are processed before the next poll to the broker, or the consumer closes.

To minimize the likelihood of data loss or duplication, you can set enable.auto.commit to false and develop your client application to have more control over committing offsets. Or you can use auto.commit.interval.ms to decrease the intervals between commits.

# ...
enable.auto.commit=false (1)
# ...

  1. Auto commit is set to false to provide more control over committing offsets.

By setting to enable.auto.commit to false, you can commit offsets after all processing has been performed and the message has been consumed. For example, you can set up your application to call the Kafka commitSync and commitAsync commit APIs.

The commitSync API commits the offsets in a message batch returned from polling. You call the API when you are finished processing all the messages in the batch. If you use the commitSync API, the application will not poll for new messages until the last offset in the batch is committed. If this negatively affects throughput, you can commit less frequently, or you can use the commitAsync API. The commitAsync API does not wait for the broker to respond to a commit request, but risks creating more duplicates when rebalancing. A common approach is to combine both commit APIs in an application, with the commitSync API used just before shutting the consumer down or rebalancing to make sure the final commit is successful.

Controlling transactional messages

Consider using transactional ids and enabling idempotence (enable.idempotence=true) on the producer side to guarantee exactly-once delivery. On the consumer side, you can then use the isolation.level property to control how transactional messages are read by the consumer.

The isolation.level property has two valid values:

  • read_committed

  • read_uncommitted (default)

Use read_committed to ensure that only transactional messages that have been committed are read by the consumer. However, this will cause an increase in end-to-end latency, because the consumer will not be able to return a message until the brokers have written the transaction markers that record the result of the transaction (committed or aborted).

# ...
enable.auto.commit=false
isolation.level=read_committed (1)
# ...

  1. Set to read_committed so that only committed messages are read by the consumer.

11.5.6. Recovering from failure to avoid data loss

Use the session.timeout.ms and heartbeat.interval.ms properties to configure the time taken to check and recover from consumer failure within a consumer group.

The session.timeout.ms property specifies the maximum amount of time in milliseconds a consumer within a consumer group can be out of contact with a broker before being considered inactive and a rebalancing is triggered between the active consumers in the group. When the group rebalances, the partitions are reassigned to the members of the group.

The heartbeat.interval.ms property specifies the interval in milliseconds between heartbeat checks to the consumer group coordinator to indicate that the consumer is active and connected. The heartbeat interval must be lower, usually by a third, than the session timeout interval.

If you set the session.timeout.ms property lower, failing consumers are detected earlier, and rebalancing can take place quicker. However, take care not to set the timeout so low that the broker fails to receive a heartbeat in time and triggers an unnecessary rebalance.

Decreasing the heartbeat interval reduces the chance of accidental rebalancing, but more frequent heartbeats increases the overhead on broker resources.

11.5.7. Managing offset policy

Use the auto.offset.reset property to control how a consumer behaves when no offsets have been committed, or a committed offset is no longer valid or deleted.

Suppose you deploy a consumer application for the first time, and it reads messages from an existing topic. Because this is the first time the group.id is used, the __consumer_offsets topic does not contain any offset information for this application. The new application can start processing all existing messages from the start of the log or only new messages. The default reset value is latest, which starts at the end of the partition, and consequently means some messages are missed. To avoid data loss, but increase the amount of processing, set auto.offset.reset to earliest to start at the beginning of the partition.

Also consider using the earliest option to avoid messages being lost when the offsets retention period (offsets.retention.minutes) configured for a broker has ended. If a consumer group or standalone consumer is inactive and commits no offsets during the retention period, previously committed offsets are deleted from __consumer_offsets.

# ...
heartbeat.interval.ms=3000 (1)
session.timeout.ms=45000 (2)
auto.offset.reset=earliest (3)
# ...

  1. Adjust the heartbeat interval lower according to anticipated rebalances.

  2. If no heartbeats are received by the Kafka broker before the timeout duration expires, the consumer is removed from the consumer group and a rebalance is initiated. If the broker configuration has a group.min.session.timeout.ms and group.max.session.timeout.ms, the session timeout value must be within that range.

  3. Set to earliest to return to the start of a partition and avoid data loss if offsets were not committed.

If the amount of data returned in a single fetch request is large, a timeout might occur before the consumer has processed it. In this case, you can lower max.partition.fetch.bytes or increase session.timeout.ms.

11.5.8. Minimizing the impact of rebalances

The rebalancing of a partition between active consumers in a group is the time it takes for:

  • Consumers to commit their offsets

  • The new consumer group to be formed

  • The group leader to assign partitions to group members

  • The consumers in the group to receive their assignments and start fetching

Clearly, the process increases the downtime of a service, particularly when it happens repeatedly during a rolling restart of a consumer group cluster.

In this situation, you can use the concept of static membership to reduce the number of rebalances. Rebalancing assigns topic partitions evenly among consumer group members. Static membership uses persistence so that a consumer instance is recognized during a restart after a session timeout.

The consumer group coordinator can identify a new consumer instance using a unique id that is specified using the group.instance.id property. During a restart, the consumer