1. Overview of Strimzi

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

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

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

Note
 Looking to get started with Strimzi? For step-by-step deployment instructions, see the Deploying Strimzi guide.

1.1. Kafka capabilities

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

  • Microservices and other applications to share data with extremely high throughput and low latency

  • Message ordering guarantees

  • Message rewind/replay from data storage to reconstruct an application state

  • Message compaction to remove old records when using a key-value log

  • Horizontal scalability in a cluster configuration

  • Replication of data to control fault tolerance

  • Retention of high volumes of data for immediate access

1.2. Kafka use cases

Kafka’s capabilities make it suitable for:

  • Event-driven architectures

  • Event sourcing to capture changes to the state of an application as a log of events

  • Message brokering

  • Website activity tracking

  • Operational monitoring through metrics

  • Log collection and aggregation

  • Commit logs for distributed systems

  • Stream processing so that applications can respond to data in real time

1.3. How Strimzi supports Kafka

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

Operators simplify the process of:

  • Deploying and running Kafka clusters

  • Deploying and running Kafka components

  • Configuring access to Kafka

  • Securing access to Kafka

  • Upgrading Kafka

  • Managing brokers

  • Creating and managing topics

  • Creating and managing users

1.4. Strimzi Operators

Strimzi supports Kafka using Operators to deploy and manage the components and dependencies of Kafka to Kubernetes.

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

Operators

Strimzi provides Operators for managing a Kafka cluster running within a Kubernetes cluster.

Cluster Operator

Deploys and manages Apache Kafka clusters, Kafka Connect, Kafka MirrorMaker, Kafka Bridge, Kafka Exporter, and the Entity Operator

Entity Operator

Comprises the Topic Operator and User Operator

Topic Operator

Manages Kafka topics

User Operator

Manages Kafka users

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

Operators within the Strimzi architecture

Operators within the Strimzi architecture

1.4.1. Cluster Operator

Strimzi uses the Cluster Operator to deploy and manage clusters for:

  • Kafka (including ZooKeeper, Entity Operator, Kafka Exporter, and Cruise Control)

  • Kafka Connect

  • Kafka MirrorMaker

  • Kafka Bridge

Custom resources are used to deploy the clusters.

For example, to deploy a Kafka cluster:

  • A Kafka resource with the cluster configuration is created within the Kubernetes cluster.

  • The Cluster Operator deploys a corresponding Kafka cluster, based on what is declared in the Kafka resource.

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

  • A Topic Operator to provide operator-style topic management through KafkaTopic custom resources

  • A User Operator to provide operator-style user management through KafkaUser custom resources

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

Example architecture for the Cluster Operator

The Cluster Operator creates and deploys Kafka and ZooKeeper clusters

1.4.2. Topic Operator

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

Example architecture for the Topic Operator

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

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

Specifically, if a KafkaTopic is:

  • Created, the Topic Operator creates the topic

  • Deleted, the Topic Operator deletes the topic

  • Changed, the Topic Operator updates the topic

Working in the other direction, if a topic is:

  • Created within the Kafka cluster, the Operator creates a KafkaTopic

  • Deleted from the Kafka cluster, the Operator deletes the KafkaTopic

  • Changed in the Kafka cluster, the Operator updates the KafkaTopic

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

If the topic is reconfigured or reassigned to different Kafka nodes, the KafkaTopic will always be up to date.

1.4.3. User Operator

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

For example, if a KafkaUser is:

  • Created, the User Operator creates the user it describes

  • Deleted, the User Operator deletes the user it describes

  • Changed, the User Operator updates the user it describes

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

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

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

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

1.5. Strimzi custom resources

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

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

CRDs also allow Strimzi resources to benefit from native Kubernetes features like CLI accessibility and configuration validation.

Additional resources

1.5.1. Strimzi custom resource example

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

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

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

Note
 Access to manage custom resources is limited to Strimzi administrators. For more information, see Designating Strimzi administrators in the Deploying Strimzi guide.

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

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

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

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

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

Kafka topic CRD
apiVersion: kafka.strimzi.io/v1beta1
kind: CustomResourceDefinition
metadata: (1)
  name: kafkatopics.kafka.strimzi.io
  labels:
    app: strimzi
spec: (2)
  group: kafka.strimzi.io
  versions:
    v1beta1
  scope: Namespaced
  names:
    # ...
    singular: kafkatopic
    plural: kafkatopics
    shortNames:
    - kt (3)
  additionalPrinterColumns: (4)
      # ...
  subresources:
    status: {} (5)
  validation: (6)
    openAPIV3Schema:
      properties:
        spec:
          type: object
          properties:
            partitions:
              type: integer
              minimum: 1
            replicas:
              type: integer
              minimum: 1
              maximum: 32767
      # ...

  1. The metadata for the topic CRD, its name and a label to identify the CRD.

  2. The specification for this CRD, including the group (domain) name, the plural name and the supported schema version, which are used in the URL to access the API of the topic. The other names are used to identify instance resources in the CLI. For example, kubectl get kafkatopic my-topic or kubectl get kafkatopics.

  3. The shortname can be used in CLI commands. For example, kubectl get kt can be used as an abbreviation instead of kubectl get kafkatopic.

  4. The information presented when using a get command on the custom resource.

  5. The current status of the CRD as described in the schema reference for the resource.

  6. openAPIV3Schema validation provides validation for the creation of topic custom resources. For example, a topic requires at least one partition and one replica.

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

Here is a corresponding example of a KafkaTopic custom resource.

Kafka topic custom resource
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic (1)
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster (2)
spec: (3)
  partitions: 1
  replicas: 1
  config:
    retention.ms: 7200000
    segment.bytes: 1073741824
status:
  conditions: (4)
    lastTransitionTime: "2019-08-20T11:37:00.706Z"
    status: "True"
    type: Ready
  observedGeneration: 1
  / ...

  1. The kind and apiVersion identify the CRD of which the custom resource is an instance.

  2. A label, applicable only to KafkaTopic and KafkaUser resources, that defines the name of the Kafka cluster (which is same as the name of the Kafka resource) to which a topic or user belongs.

  3. The spec shows the number of partitions and replicas for the topic as well as the configuration parameters for the topic itself. In this example, the retention period for a message to remain in the topic and the segment file size for the log are specified.

  4. Status conditions for the KafkaTopic resource. The type condition changed to Ready at the lastTransitionTime.

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

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

1.6. Listener configuration

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.

Generic listener configuration

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

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

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

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

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

You might want to configure multiple external listeners, for example, to handle access from networks that require different authentication mechanisms. Or you might need to join your 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.

Configuring listeners to secure access to Kafka brokers

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

Configuring external listeners for client access outside 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 Configuring external listeners.

Listener certificates

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

1.7. Document Conventions

Replaceables

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

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

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

2. Deployment configuration

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

  • Kafka clusters

  • Kafka Connect clusters

  • Kafka Connect clusters with Source2Image support

  • Kafka MirrorMaker

  • Kafka Bridge

  • Cruise Control

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

The Deploying Strimzi guide describes how to monitor your Strimzi deployment.

2.1. Kafka cluster configuration

The full schema of the Kafka resource is described in the Kafka schema reference. All labels that are applied to the desired Kafka resource will also be applied to the Kubernetes resources making up the Kafka cluster. This provides a convenient mechanism for resources to be labeled as required.

2.1.1. Sample Kafka YAML configuration

For help in understanding the configuration options available for your Kafka deployment, refer to sample YAML file provided here.

The sample 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)

  • Authentication

  • Storage

  • Rack awareness

  • Metrics

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    replicas: 3 (1)
    version: 0.20.0 (2)
    resources: (3)
      requests:
        memory: 64Gi
        cpu: "8"
      limits: (4)
        memory: 64Gi
        cpu: "12"
    jvmOptions: (5)
      -Xms: 8192m
      -Xmx: 8192m
    listeners: (6)
      - name: plain (7)
        port: 9092 (8)
        type: internal (9)
        tls: false (10)
        configuration:
          useServiceDnsDomain: true (11)
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication: (12)
          type: tls
      - name: external (13)
        port: 9094
        type: route
        tls: true
        configuration:
          brokerCertChainAndKey: (14)
            secretName: my-secret
            certificate: my-certificate.crt
            key: my-key.key
    authorization: (15)
      type: simple
    config: (16)
      auto.create.topics.enable: "false"
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (17)
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
    storage: (18)
      type: persistent-claim (19)
      size: 10000Gi (20)
    rack: (21)
      topologyKey: topology.kubernetes.io/zone
    metrics: (22)
      lowercaseOutputName: true
      rules: (23)
      # Special cases and very specific rules
      - pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value
        name: kafka_server_$1_$2
        type: GAUGE
        labels:
          clientId: "$3"
          topic: "$4"
          partition: "$5"
        # ...
  zookeeper: (24)
    replicas: 3
    resources:
      requests:
        memory: 8Gi
        cpu: "2"
      limits:
        memory: 8Gi
        cpu: "2"
    jvmOptions:
      -Xms: 4096m
      -Xmx: 4096m
    storage:
      type: persistent-claim
      size: 1000Gi
    metrics:
      # ...
  entityOperator: (25)
    topicOperator:
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
    userOperator:
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
  kafkaExporter: (26)
    # ...
  cruiseControl: (27)
    # ...

  1. Replicas specifies the number of broker nodes.

  2. Kafka version, which can be changed by following the upgrade procedure.

  3. Resource requests specify the resources to reserve for a given container.

  4. Resource limits specify the maximum resources that can be consumed by a container.

  5. JVM options can specify the minimum (-Xms) and maximum (-Xmx) memory allocation for JVM.

  6. Listeners configure how clients connect to the Kafka cluster via bootstrap addresses. Listeners are configured as internal or external listeners for connection inside or outside the Kubernetes cluster.

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

  8. 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.

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

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

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

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

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

  14. Optional configuration for a Kafka listener certificate managed by an external Certificate Authority. The brokerCertChainAndKey property specifies a Secret that holds a server certificate and a private key. Kafka listener certificates can also be configured for TLS listeners.

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

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

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

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

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

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

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

  22. Kafka metrics configuration for use with Prometheus.

  23. Kafka rules for exporting metrics to a Grafana dashboard through the JMX Exporter. A set of rules provided with Strimzi may be copied to your Kafka resource configuration.

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

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

  26. Kafka Exporter configuration, which is used to expose data as Prometheus metrics.

  27. Cruise Control configuration, which is used to rebalance the Kafka cluster.

2.1.2. 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.

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

Note
 Strimzi does not require Kubernetes raw block volumes.
File systems

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

Apache Kafka and ZooKeeper storage

Use separate disks for Apache Kafka and ZooKeeper.

Three types of data storage are supported:

  • Ephemeral (Recommended for development only)

  • Persistent

  • JBOD (Just a Bunch of Disks, suitable for Kafka only)

For more information, see Kafka and ZooKeeper storage.

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

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

2.1.3. 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.

Warning
 The storage type cannot be changed after a Kafka cluster is deployed.
Additional resources
Ephemeral storage

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

Important
 emptyDir volumes are not persistent and the data stored in them will be lost when the Pod is restarted. After the new pod is started, it has to recover all data from other nodes of the cluster. Ephemeral storage is not suitable for use with single node ZooKeeper clusters and for Kafka topics with replication factor 1, because it will lead to data loss.
An example of Ephemeral storage
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: ephemeral
    # ...
  zookeeper:
    # ...
    storage:
      type: ephemeral
    # ...
Log directories

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

/var/lib/kafka/data/kafka-log_idx_

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/v1beta1
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 will be used by the Kafka brokers as log directories mounted into the following path:

/var/lib/kafka/data/kafka-log_idx_

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/v1beta1
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.

    Use kubectl apply:

    kubectl apply -f your-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

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

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 change the size of a persistent storage volume after it has been provisioned.

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/v1beta1
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.

    This can be done using kubectl apply:

    kubectl apply -f KAFKA-CONFIG-FILE

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

Additional resources

For more information about reassigning topics, see Partition reassignment.

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/v1beta1
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.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

For more information about reassigning topics, see Partition reassignment.

2.1.4. Kafka broker replicas

A Kafka cluster can run with many brokers. You can configure the number of brokers used for the Kafka cluster in Kafka.spec.kafka.replicas. The best number of brokers for your cluster has to be determined based on your specific use case.

Configuring the number of broker nodes

This procedure describes how to configure the number of Kafka broker nodes in a new cluster. It only applies to new clusters with no partitions. If your cluster already has topics defined, see Scaling clusters.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • A Kafka cluster with no topics defined yet

Procedure

  1. Edit the replicas property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    replicas: 3
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

If your cluster already has topics defined, see Scaling clusters.

2.1.5. Kafka broker configuration

Strimzi allows you to customize the configuration of the Kafka brokers in your Kafka cluster. You can specify and configure most of the options listed in the "Broker Configs" section of the Apache Kafka documentation. You cannot configure options that are related to the following areas:

  • Security (Encryption, Authentication, and Authorization)

  • Listener configuration

  • Broker ID configuration

  • Configuration of log data directories

  • Inter-broker communication

  • ZooKeeper connectivity

These options are automatically configured by Strimzi.

For more information on broker configuration, see the KafkaClusterSpec schema.

Listener configuration

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

Authorizing access to Kafka

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

Configuring Kafka brokers

You can configure an existing Kafka broker, or create a new Kafka broker with a specified configuration.

Prerequisites

  • A Kubernetes cluster is available.

  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.

  2. In the spec.kafka.config property in the Kafka resource, enter one or more Kafka configuration settings. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    config:
      default.replication.factor: 3
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 1
    # ...
  zookeeper:
    # ...

  3. Apply the new configuration to create or update the resource.

    Use kubectl apply:

    kubectl apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

2.1.6. Listener configuration

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.

Generic listener configuration

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

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

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

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

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

You might want to configure multiple external listeners, for example, to handle access from networks that require different authentication mechanisms. Or you might need to join your 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.

Configuring listeners to secure access to Kafka brokers

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

Configuring external listeners for client access outside 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 Configuring external listeners.

Listener certificates

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

2.1.7. ZooKeeper replicas

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.

Three-node cluster

A three-node ZooKeeper cluster requires at least two nodes to be up and running in order to maintain the quorum. It can tolerate only one node being unavailable.

Five-node cluster

A five-node ZooKeeper cluster requires at least three nodes to be up and running in order to maintain the quorum. It can tolerate two nodes being unavailable.

Seven-node cluster

A seven-node ZooKeeper cluster requires at least four nodes to be up and running in order to maintain the quorum. It can tolerate three nodes being unavailable.

Note
 For development purposes, it is also possible to run ZooKeeper with a single node.

Having more nodes does not necessarily mean better performance, as the costs to maintain the quorum will rise with the number of nodes in the cluster. Depending on your availability requirements, you can decide for the number of nodes to use.

Number of ZooKeeper nodes

The number of ZooKeeper nodes can be configured using the replicas property in Kafka.spec.zookeeper.

An example showing replicas configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    replicas: 3
    # ...
Changing the number of ZooKeeper replicas
Prerequisites

  • A Kubernetes cluster is available.

  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.

  2. In the spec.zookeeper.replicas property in the Kafka resource, enter the number of replicated ZooKeeper servers. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    replicas: 3
    # ...

  3. Apply the new configuration to create or update the resource.

    Use kubectl apply:

    kubectl apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

2.1.8. ZooKeeper configuration

Strimzi allows you to customize the configuration of Apache ZooKeeper nodes. You can specify and configure most of the options listed in the ZooKeeper documentation.

Options which cannot be configured are those related to the following areas:

  • Security (Encryption, Authentication, and Authorization)

  • Listener configuration

  • Configuration of data directories

  • ZooKeeper cluster composition

These options are automatically configured by Strimzi.

ZooKeeper configuration

ZooKeeper nodes are configured using the config property in Kafka.spec.zookeeper. This property contains the ZooKeeper configuration options as keys. The values can be described using one of the following JSON types:

  • String

  • Number

  • Boolean

Users can specify and configure the options listed in ZooKeeper documentation with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • server.

  • dataDir

  • dataLogDir

  • clientPort

  • authProvider

  • quorum.auth

  • requireClientAuthScheme

When one of the forbidden options is present in the config property, it is ignored and a warning message is printed to the Cluster Operator log file. All other options are passed to ZooKeeper.

Important
 The Cluster Operator does not validate keys or values in the provided config object. When invalid configuration is provided, the ZooKeeper cluster might not start or might become unstable. In such cases, the configuration in the Kafka.spec.zookeeper.config object should be fixed and the Cluster Operator will roll out the new configuration to all ZooKeeper nodes.

Selected options have default values:

  • timeTick with default value 2000

  • initLimit with default value 5

  • syncLimit with default value 2

  • autopurge.purgeInterval with default value 1

These options will be automatically configured when they are not present in the Kafka.spec.zookeeper.config property.

Use the three allowed ssl configuration options for client connection using a specific cipher suite for a TLS version. A cipher suite combines algorithms for secure connection and data transfer.

Example ZooKeeper configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    config:
      autopurge.snapRetainCount: 3
      autopurge.purgeInterval: 1
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (1)
      ssl.enabled.protocols: "TLSv1.2" (2)
      ssl.protocol: "TLSv1.2" (3)
    # ...

  1. The cipher suite for TLS using a combination of ECDHE key exchange mechanism, RSA authentication algorithm, AES bulk encyption algorithm and SHA384 MAC algorithm.

  2. The SSl protocol TLSv1.2 is enabled.

  3. Specifies the TLSv1.2 protocol to generate the SSL context. Allowed values are TLSv1.1 and TLSv1.2.

Configuring ZooKeeper
Prerequisites

  • A Kubernetes cluster is available.

  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.

  2. In the spec.zookeeper.config property in the Kafka resource, enter one or more ZooKeeper configuration settings. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    config:
      autopurge.snapRetainCount: 3
      autopurge.purgeInterval: 1
    # ...

  3. Apply the new configuration to create or update the resource.

    Use kubectl apply:

    kubectl apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

2.1.9. ZooKeeper connection

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.

Connecting to ZooKeeper from a terminal

Most Kafka CLI tools can connect directly to Kafka. So you should under normal circumstances not need to connect to ZooKeeper. In case it is needed, you can follow this procedure. Open a terminal inside a ZooKeeper container to use Kafka CLI tools that require a ZooKeeper connection.

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 -it 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.

2.1.10. Entity Operator

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

The Entity Operator comprises the:

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

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

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

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. For more information on configuring the TLS sidecar, see TLS sidecar.

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 to configure the Entity Operator, see the EntityUserOperatorSpec schema reference.

Example of basic configuration enabling both operators
apiVersion: kafka.strimzi.io/v1beta1
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 90.

zookeeperSessionTimeoutSeconds

The ZooKeeper session timeout in seconds. Default 20.

topicMetadataMaxAttempts

The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation could 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 Container images.

resources

The resources property configures the amount of resources allocated to the Topic Operator. For more details about resource request and limit configuration, see CPU and memory resources.

logging

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

Example of Topic Operator configuration
apiVersion: kafka.strimzi.io/v1beta1
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.

zookeeperSessionTimeoutSeconds

The ZooKeeper session timeout in seconds. Default 6.

image

The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Container images.

resources

The resources property configures the amount of resources allocated to the User Operator. For more details about resource request and limit configuration, see CPU and memory resources.

logging

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

Example of User Operator configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60
    # ...
Operator loggers

The Topic Operator and User Operator have a configurable logger:

  • rootLogger.level

The operators use the Apache log4j2 logger implementation.

Use the logging property in the Kafka resource 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.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j2.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
    # ...
    userOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
# ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging:
        type: external
        name: customConfigMap
# ...
Additional resources
Configuring the Entity Operator
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the entityOperator property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

2.1.11. CPU and memory resources

For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.

Strimzi supports two types of resources:

  • CPU

  • Memory

Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.

Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.kafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Additional resources
Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important
 If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by Strimzi:

  • cpu

  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources
# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...
Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by Strimzi:

  • cpu

  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration
# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...
Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).

  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units
# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...
Note
 The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed.
Additional resources
Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.

  • To specify memory in gigabytes, use the G suffix. For example 1G.

  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.

  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units
# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...
Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

Configuring resource requests and limits
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    resources:
      requests:
        cpu: "8"
        memory: 64Gi
      limits:
        cpu: "12"
        memory: 128Gi
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

2.1.12. Kafka loggers

Kafka has its own configurable loggers:

  • log4j.logger.org.I0Itec.zkclient.ZkClient

  • log4j.logger.org.apache.zookeeper

  • log4j.logger.kafka

  • log4j.logger.org.apache.kafka

  • log4j.logger.kafka.request.logger

  • log4j.logger.kafka.network.Processor

  • log4j.logger.kafka.server.KafkaApis

  • log4j.logger.kafka.network.RequestChannel$

  • log4j.logger.kafka.controller

  • log4j.logger.kafka.log.LogCleaner

  • log4j.logger.state.change.logger

  • log4j.logger.kafka.authorizer.logger

ZooKeeper also has a configurable logger:

  • zookeeper.root.logger

Kafka and ZooKeeper use the Apache log4j logger implementation.

Operators use the Apache log4j2 logger implementation, so the logging configuration is described inside the ConfigMap using log4j2.properties. For more information, see Operator loggers.

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.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  # ...
  kafka:
    # ...
    logging:
      type: inline
      loggers:
        kafka.root.logger.level: "INFO"
  # ...
  zookeeper:
    # ...
    logging:
      type: inline
      loggers:
        zookeeper.root.logger: "INFO"
  # ...
  entityOperator:
    # ...
    topicOperator:
      # ...
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
    # ...
    userOperator:
      # ...
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
    # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...

Changes to both external and inline logging levels will be applied to Kafka brokers without a restart.

Additional resources

  • Garbage collector (GC) logging can also be enabled (or disabled). For more information on garbage collection, see JVM configuration

  • For more information about log levels, see Apache logging services.

2.1.13. Kafka rack awareness

The rack awareness feature in Strimzi helps to spread the Kafka broker pods and Kafka topic replicas across different racks. Enabling rack awareness helps to improve availability of Kafka brokers and the topics they are hosting.

Note
 "Rack" might represent an availability zone, data center, or an actual rack in your data center.
Configuring rack awareness in Kafka brokers

Kafka rack awareness can be configured in the rack property of Kafka.spec.kafka. The rack object has one mandatory field named topologyKey. This key needs to match one of the labels assigned to the Kubernetes cluster nodes. The label is used by Kubernetes when scheduling the Kafka broker pods to nodes. If the Kubernetes cluster is running on a cloud provider platform, that label should represent the availability zone where the node is running. Usually, the nodes are labeled with topology.kubernetes.io/zone label (or failure-domain.beta.kubernetes.io/zone on older Kubernetes versions) that can be used as the topologyKey value. For more information about Kubernetes node labels, see Well-Known Labels, Annotations and Taints. This has the effect of spreading the broker pods across zones, and also setting the brokers' broker.rack configuration parameter inside Kafka broker.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Consult your Kubernetes administrator regarding the node label that represents the zone / rack into which the node is deployed.

  2. Edit the rack property in the Kafka resource using the label as the topology key.

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    rack:
      topologyKey: topology.kubernetes.io/zone
    # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

  • For information about Configuring init container image for Kafka rack awareness, see Container images.

2.1.14. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.

Kubernetes supports two types of Healthcheck probes:

  • Liveness probes

  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.

Users can configure selected options for liveness and readiness probes.

Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.kafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds

  • timeoutSeconds

  • periodSeconds

  • successThreshold

  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Probe schema reference.

An example of liveness and readiness probe configuration
# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...
Configuring healthchecks
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    readinessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

2.1.15. Prometheus metrics

Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.

For more information about setting up and deploying Prometheus and Grafana, see Introducing Metrics to Kafka in the Deploying Strimzi guide.

Metrics configuration

Prometheus metrics are enabled by configuring the metrics property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

When the metrics property is not defined in the resource, the Prometheus metrics will be disabled. To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

Example of enabling metrics without any further configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics: {}
    # ...
  zookeeper:
    # ...

The metrics property might contain additional configuration for the Prometheus JMX exporter.

Example of enabling metrics with additional Prometheus JMX Exporter configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics:
      lowercaseOutputName: true
      rules:
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
          name: "kafka_server_$1_$2_total"
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
          name: "kafka_server_$1_$2_total"
          labels:
            topic: "$3"
    # ...
  zookeeper:
    # ...
Configuring Prometheus metrics
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the metrics property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    metrics:
      lowercaseOutputName: true
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

2.1.16. JMX Options

Strimzi supports obtaining JMX metrics from the Kafka brokers by opening a JMX port on 9999. You can obtain various metrics about each Kafka broker, for example, usage data such as the BytesPerSecond value or the request rate of the network of the broker. Strimzi supports opening a password and username protected JMX port or a non-protected JMX port.

Configuring JMX options

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

You can configure JMX options by using the jmxOptions property in the following resources:

  • Kafka.spec.kafka

You can configure username and password protection for the JMX port that is opened on the Kafka brokers.

Securing the JMX Port

You can secure the JMX port to prevent unauthorized pods from accessing the port. Currently the JMX port can only be secured using a username and password. To enable security for the JMX port, set the type parameter in the authentication field to password.:

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jmxOptions:
      authentication:
        type: "password"
    # ...
  zookeeper:
    # ...

This allows you to deploy a pod internally into a cluster and obtain JMX metrics by using the headless service and specifying which broker you want to address. To get JMX metrics from broker 0 we address the headless service appending broker 0 in front of the headless service:

"<cluster-name>-kafka-0-<cluster-name>-<headless-service-name>"

If the JMX port is secured, you can get the username and password by referencing them from the JMX secret in the deployment of your pod.

Using an open JMX port

To disable security for the JMX port, do not fill in the authentication field

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jmxOptions: {}
    # ...
  zookeeper:
    # ...

This will just open the JMX Port on the headless service and you can follow a similar approach as described above to deploy a pod into the cluster. The only difference is that any pod will be able to read from the JMX port.

2.1.17. Retrieving JMX metrics with JMXTrans

JmxTrans is a way of retrieving JMX metrics data from Java processes and pushing that data in various formats to remote sinks inside or outside of the cluster. JmxTrans can communicate with a secure JMX port. Strimzi supports using JmxTrans to read JMX data from Kafka brokers.

Jmxtrans

JmxTrans reads JMX metrics data from secure or insecure Kafka brokers and pushes the data to remote sinks in various data formats. An example use case of the Jmxtrans would be to obtain JMX metrics about the request rate of each Kafka broker’s network and push it to a Logstash database outside of the Kubernetes cluster.

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, JmxTransSpec schema reference.

Note
 JmxTrans will not come up enable you specify that JmxOptions on the Kafka broker. For more information see, Kafka Jmx Options .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/v1beta1
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/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  jmxTrans:
    kafkaQueries:
      - targetMBean: "kafka.server:type=BrokerTopicMetrics,*"
        typeNames: ["name"]
        attributes:  ["Count"]
        outputs: ["standardOut"]
  zookeeper:
    # ...
Additional resources

For more information about the Jmxtrans see Jmxtrans github

2.1.18. JVM Options

The following components of Strimzi run inside a Virtual Machine (VM):

  • Apache Kafka

  • Apache ZooKeeper

  • Apache Kafka Connect

  • Apache Kafka MirrorMaker

  • Strimzi Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.

JVM configuration

Use the jvmOptions property to configure supported options for the JVM on which the component is running.

Supported JVM options help to optimize performance for different platforms and architectures.

Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jvmOptions:
      "-Xmx": "8g"
      "-Xms": "8g"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

2.1.19. Container images

Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.

Container image configurations

Use the image property to specify which container image to use.

Warning
 Overriding container images is recommended only in special situations.
Configuring container images
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

2.1.20. TLS sidecar

A sidecar is a container that runs in a pod but serves a supporting purpose. In Strimzi, the TLS sidecar uses TLS to encrypt and decrypt all communication between the various components and ZooKeeper.

The TLS sidecar is used in:

  • Entity Operator

  • Cruise Control

TLS sidecar configuration

The TLS sidecar can be configured using the tlsSidecar property in:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

The TLS sidecar supports the following additional options:

  • image

  • resources

  • logLevel

  • readinessProbe

  • livenessProbe

The resources property can be used to specify the memory and CPU resources allocated for the TLS sidecar.

The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Container images.

The logLevel property is used to specify the logging level. Following logging levels are supported:

  • emerg

  • alert

  • crit

  • err

  • warning

  • notice

  • info

  • debug

The default value is notice.

For more information about configuring the readinessProbe and livenessProbe properties for the healthchecks, see Healthcheck configurations.

Example of TLS sidecar configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    tlsSidecar:
      image: my-org/my-image:latest
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
      logLevel: debug
      readinessProbe:
        initialDelaySeconds: 15
        timeoutSeconds: 5
      livenessProbe:
        initialDelaySeconds: 15
        timeoutSeconds: 5
    # ...
  zookeeper:
    # ...
Configuring TLS sidecar
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the tlsSidecar property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    tlsSidecar:
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
    # ...
  cruiseControl:
    # ...
    tlsSidecar:
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

2.1.21. Configuring pod scheduling

Important
 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.
Scheduling pods based on other applications
Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring pod anti-affinity in Kafka components
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/v1beta1
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 your-file
Scheduling pods to specific nodes
Node scheduling

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.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

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 your-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/v1beta1
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 your-file
Using dedicated nodes
Dedicated nodes

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.

Taints can be used to create dedicated nodes. 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.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the Kubernetes specification. For more details, see the Kubernetes taints and tolerations.

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 your-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 your-node dedicated=Kafka

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

    apiVersion: kafka.strimzi.io/v1beta1
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 your-file

2.1.22. Kafka Exporter

You can configure the Kafka resource to automatically deploy Kafka Exporter in your cluster.

Kafka Exporter extracts data for analysis as Prometheus metrics, primarily data relating to offsets, consumer groups, consumer lag and topics.

For information on setting up Kafka Exporter and why it is important to monitor consumer lag for performance, see Kafka Exporter in the Deploying Strimzi guide.

2.1.23. Performing a rolling update of a Kafka cluster

This procedure describes how to manually trigger a rolling update of an existing Kafka cluster by using a Kubernetes annotation.

Prerequisites

See the Deploying Strimzi guide for instructions on running a:

Procedure

  1. Find the name of the StatefulSet that controls the Kafka pods you want to manually update.

    For example, if your Kafka cluster is named my-cluster, the corresponding StatefulSet is named my-cluster-kafka.

  2. Annotate the StatefulSet resource in Kubernetes. For example, using kubectl annotate:

    kubectl annotate statefulset cluster-name-kafka 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 StatefulSet 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 StatefulSet.

2.1.24. Performing a rolling update of a ZooKeeper cluster

This procedure describes how to manually trigger a rolling update of an existing ZooKeeper cluster by using a Kubernetes annotation.

Prerequisites

See the Deploying Strimzi guide for instructions on running a:

Procedure

  1. Find the name of the StatefulSet that controls the ZooKeeper pods you want to manually update.

    For example, if your Kafka cluster is named my-cluster, the corresponding StatefulSet is named my-cluster-zookeeper.

  2. Annotate the StatefulSet resource in Kubernetes. For example, using kubectl annotate:

    kubectl annotate statefulset 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 StatefulSet 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 StatefulSet.

2.1.25. Scaling clusters

Scaling Kafka clusters
Adding brokers to a cluster

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

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

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

Removing brokers from a cluster

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

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

Partition reassignment

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

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

It has three different modes:

--generate

Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you just need to reassign some of the 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 of the partitions in the file have been moved to their intended brokers. If the reassignment is complete, --verify also removes any throttles that are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.

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

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 topic topic-a, partition 4 to brokers 2, 4 and 7, and topic topic-b partition 2 to brokers 1, 5 and 7:

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

Partitions not included in the JSON are not changed.

Reassigning partitions between JBOD volumes

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

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

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

For example:

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

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

Prerequisites

  • A running Cluster Operator

  • A Kafka resource

  • A set of topics to reassign the partitions of

Procedure

  1. Prepare a JSON file named topics.json that lists the topics to move. It must have the following structure:

    {
  "version": 1,
  "topics": [
    <TopicObjects>
  ]
}

    where <TopicObjects> is a comma-separated list of objects like:

    {
  "topic": <TopicName>
}

    For example if you want to reassign all the partitions of topic-a and topic-b, you would need to prepare a topics.json file like this:

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

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

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

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

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

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

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

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

Reassignment throttles

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

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

  • If the throttle is too high then clients will be impacted.

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

Scaling up a Kafka cluster

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

Prerequisites

  • An existing Kafka cluster.

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

Procedure

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

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

    For example:

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

  4. Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool from the same broker pod.

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

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

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 5000000 \
  --execute

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

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

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

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

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

    For example,

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

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

Scaling down a Kafka cluster
Additional resources

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

Prerequisites

  • An existing Kafka cluster.

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

Procedure

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

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

    For example:

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

  2. Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool from the same broker pod.

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

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

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 5000000 \
  --execute

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

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

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

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

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

    For example,

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

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

  6. Once all the partition reassignments have finished, the broker(s) being removed should not have responsibility for any of the partitions in the cluster. You can verify this by checking that the broker’s data log directory does not contain any live partition logs. If the log directory on the broker contains a directory that does not match the extended regular expression [a-zA-Z0-9.-]+\.[a-z0-9]+-delete$ then the broker still has live partitions and it should not be stopped.

    You can check this by executing the command:

    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 Pod(s) being deleted.

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

  7. Once you have confirmed that the broker has no live partitions you can edit the Kafka.spec.kafka.replicas of your Kafka resource, which will scale down the StatefulSet, deleting the highest numbered broker Pod(s).

2.1.26. Deleting Kafka nodes manually

Additional resources

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 Strimzi guide for instructions on running a:

Procedure

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

    For example, if the cluster is named cluster-name, the pods are named cluster-name-kafka-index, where index starts at zero and ends at the total number of replicas.

  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.

2.1.27. 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 Strimzi guide for instructions on running a:

Procedure

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

    For example, if the cluster is named cluster-name, the pods are named cluster-name-zookeeper-index, where index starts at zero and ends at the total number of replicas.

  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.

2.1.28. 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/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  maintenanceTimeWindows:
    - "* * 8-10 * * ?"
    - "* * 14-15 * * ?"

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

2.1.29. Renewing CA certificates manually

Cluster and clients CA certificates auto-renew at the start of their respective certificate renewal periods. If Kafka.spec.clusterCa.generateCertificateAuthority and Kafka.spec.clientsCa.generateCertificateAuthority are set to false, the CA certificates do not auto-renew.

You can 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.

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 1. 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

2.1.30. Replacing private keys

You can replace the private keys used by the cluster CA and clients CA certificates. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.

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 2. 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

2.1.31. List of resources created as part of Kafka cluster

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

Shared resources
cluster-name-cluster-ca

Secret with the Cluster CA 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 certiticates

cluster-name-clients-ca-cert

Secret with the Clients CA public key. This key can be used to verify the identity of the Kafka users.

cluster-name-cluster-operator-certs

Secret with Cluster operators keys for communication with Kafka and ZooKeeper.

Zookeeper nodes
cluster-name-zookeeper

StatefulSet which is in charge of managing the ZooKeeper node pods.

cluster-name-zookeeper-idx

Pods created by the Zookeeper StatefulSet.

cluster-name-zookeeper-nodes

Headless Service needed to have DNS resolve the ZooKeeper pods IP addresses directly.

cluster-name-zookeeper-client

Service used by Kafka brokers to connect to ZooKeeper nodes as clients.

cluster-name-zookeeper-config

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

cluster-name-zookeeper-nodes

Secret with ZooKeeper node keys.

cluster-name-zookeeper

Service account used by the Zookeeper nodes.

cluster-name-zookeeper

Pod Disruption Budget configured for the ZooKeeper nodes.

cluster-name-network-policy-zookeeper

Network policy managing access to the ZooKeeper services.

data-cluster-name-zookeeper-idx

Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod idx. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.

Kafka brokers
cluster-name-kafka

StatefulSet which is in charge of managing the Kafka broker pods.

cluster-name-kafka-idx

Pods created by the Kafka StatefulSet.

cluster-name-kafka-brokers

Service needed to have DNS resolve the Kafka broker pods IP addresses directly.

cluster-name-kafka-bootstrap

Service can be used as bootstrap servers for Kafka clients.

cluster-name-kafka-external-bootstrap

Bootstrap service for clients connecting from outside of the Kubernetes cluster. This resource will be created only when external listener is enabled.

cluster-name-kafka-pod-id

Service used to route traffic from outside of the Kubernetes cluster to individual pods. This resource will be created only when external listener is enabled.

cluster-name-kafka-external-bootstrap

Bootstrap route for clients connecting from outside of the Kubernetes cluster. This resource will be created only when external listener is enabled and set to type route.

cluster-name-kafka-pod-id

Route for traffic from outside of the Kubernetes cluster to individual pods. This resource will be created only when external listener is enabled and set to type route.

cluster-name-kafka-config

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

cluster-name-kafka-brokers

Secret with Kafka broker keys.

cluster-name-kafka

Service account used by the Kafka brokers.

cluster-name-kafka

Pod Disruption Budget configured for the Kafka brokers.

cluster-name-network-policy-kafka

Network policy managing access to the Kafka services.

strimzi-namespace-name-cluster-name-kafka-init

Cluster role binding used by the Kafka brokers.

cluster-name-jmx

Secret with JMX username and password used to secure the Kafka broker port. This resource will be 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 will be 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 only created if persistent storage is selected for JBOD volumes when provisioning persistent volumes to store data.

Entity Operator

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

cluster-name-entity-operator

Deployment with Topic and User Operators.

cluster-name-entity-operator-random-string

Pod created by the Entity Operator deployment.

cluster-name-entity-topic-operator-config

ConfigMap with ancillary configuration for Topic Operators.

cluster-name-entity-user-operator-config

ConfigMap with ancillary configuration for User Operators.

cluster-name-entity-operator-certs

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

cluster-name-entity-operator

Service account used by the Entity Operator.

strimzi-cluster-name-topic-operator

Role binding used by the Entity Operator.

strimzi-cluster-name-user-operator

Role binding used by the Entity Operator.

Kafka Exporter

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

cluster-name-kafka-exporter

Deployment with Kafka Exporter.

cluster-name-kafka-exporter-random-string

Pod created by the Kafka Exporter deployment.

cluster-name-kafka-exporter

Service used to collect consumer lag metrics.

cluster-name-kafka-exporter

Service account used by the Kafka Exporter.

Cruise Control

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

cluster-name-cruise-control

Deployment with Cruise Control.

cluster-name-cruise-control-random-string

Pod created by the Cruise Control deployment.

cluster-name-cruise-control-config

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

cluster-name-cruise-control-certs

Secret with Cruise Control keys for communication with Kafka and ZooKeeper.

cluster-name-cruise-control

Service used to communicate with Cruise Control.

cluster-name-cruise-control

Service account used by Cruise Control.

cluster-name-network-policy-cruise-control

Network policy managing access to the Cruise Control service.

JMXTrans

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

cluster-name-jmxtrans

Deployment with 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.

cluster-name-jmxtrans

Service account used by JMXTrans.

2.2. Kafka Connect/S2I cluster configuration

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

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

Additional resources

2.2.1. Configuring Kafka Connect

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

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

Kafka connector configuration

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

In the configuration, you enable KafkaConnectors for a Kafka Connect cluster by adding the strimzi.io/use-connector-resources annotation. You can also specify external configuration for Kafka Connect connectors through the externalConfiguration property.

Connectors are created, reconfigured, and deleted using the Kafka Connect HTTP REST interface, or by using KafkaConnectors. For more information on these methods, see Creating and managing connectors in the Deploying Strimzi guide.

The 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 (this 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 Strimzi guide for instructions on running a:

Procedure

  1. Edit the spec properties for the KafkaConnect or KafkaConnectS2I resource.

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

    apiVersion: kafka.strimzi.io/v1beta1
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
  externalConfiguration: (8)
    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: (9)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: (10)
    type: inline
    loggers:
      log4j.rootLogger: "INFO"
  readinessProbe: (11)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  metrics: (12)
    lowercaseOutputName: true
    lowercaseOutputLabelNames: true
    rules:
      - pattern: kafka.connect<type=connect-worker-metrics><>([a-z-]+)
        name: kafka_connect_worker_$1
        help: "Kafka Connect JMX metric worker"
        type: GAUGE
      - pattern: kafka.connect<type=connect-worker-rebalance-metrics><>([a-z-]+)
        name: kafka_connect_worker_rebalance_$1
        help: "Kafka Connect JMX metric rebalance information"
        type: GAUGE
  jvmOptions: (13)
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest (14)
  template: (15)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: (16)
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"

    1. Use KafkaConnect or KafkaConnectS2I, as required.

    2. Enables KafkaConnectors for the Kafka Connect cluster.

    3. The number of replica nodes.

    4. Authentication for the Kafka Connect cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism. By default, Kafka Connect connects to Kafka brokers using a plain text connection.

    5. Bootstrap server for connection to the Kafka Connect cluster.

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

    7. Kafka Connect configuration of workers (not connectors). Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.

    8. External configuration for Kafka connectors using environment variables, as shown here, or volumes.

    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 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.

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

    12. Prometheus metrics, which are enabled with configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using metrics: {}.

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

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

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

    16. 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/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: connect-cluster (1)
    offset.storage.topic: connect-cluster-offsets (2)
    config.storage.topic: connect-cluster-configs (3)
    status.storage.topic: connect-cluster-status  (4)
    # ...
# ...

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

  2. Kafka topic that stores connector offsets.

  3. Kafka topic that stores connector and task status configurations.

  4. Kafka topic that stores connector and task status updates.

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

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

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

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

2.2.3. Configuring Kafka Connect user authorization

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

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

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

Example configuration properties in the KafkaConnect resource
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: my-connect-cluster (1)
    offset.storage.topic: my-connect-cluster-offsets (2)
    config.storage.topic: my-connect-cluster-configs (3)
    status.storage.topic: my-connect-cluster-status (4)
    # ...
  # ...

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

  2. Kafka topic that stores connector offsets.

  3. Kafka topic that stores connector and task status configurations.

  4. Kafka topic that stores connector and task status updates.

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

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

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

  • 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/v1beta1
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.

2.2.5. List of Kafka Connect (S2I) cluster resources

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

connect-cluster-name-connect-source

ImageStream which is used as the base image for the newly-built Docker images.

connect-cluster-name-connect

BuildConfig which is responsible for building the new Kafka Connect Docker images.

connect-cluster-name-connect

ImageStream where the newly built Docker images will be pushed.

connect-cluster-name-connect

DeploymentConfig which is in charge of creating the Kafka Connect worker node pods.

connect-cluster-name-connect-api

Service which exposes the REST interface for managing the Kafka Connect cluster.

connect-cluster-name-config

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

connect-cluster-name-connect

Pod Disruption Budget configured for the Kafka Connect worker nodes.

2.3. 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.

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 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/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  replicas: 3 (1)
  consumer:
    bootstrapServers: my-source-cluster-kafka-bootstrap:9092 (2)
    groupId: "my-group" (3)
    numStreams: 2 (4)
    offsetCommitInterval: 120000 (5)
    tls: (6)
      trustedCertificates:
      - secretName: my-source-cluster-ca-cert
        certificate: ca.crt
    authentication: (7)
      type: tls
      certificateAndKey:
        secretName: my-source-secret
        certificate: public.crt
        key: private.key
    config: (8)
      max.poll.records: 100
      receive.buffer.bytes: 32768
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (9)
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      ssl.endpoint.identification.algorithm: HTTPS (10)
  producer:
    bootstrapServers: my-target-cluster-kafka-bootstrap:9092
    abortOnSendFailure: false (11)
    tls:
      trustedCertificates:
      - secretName: my-target-cluster-ca-cert
        certificate: ca.crt
    authentication:
      type: tls
      certificateAndKey:
        secretName: my-target-secret
        certificate: public.crt
        key: private.key
    config:
      compression.type: gzip
      batch.size: 8192
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (12)
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      ssl.endpoint.identification.algorithm: HTTPS (13)
  whitelist: "my-topic|other-topic" (14)
  resources: (15)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: (16)
    type: inline
    loggers:
      mirrormaker.root.logger: "INFO"
  readinessProbe: (17)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  metrics: (18)
    lowercaseOutputName: true
    rules:
      - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
        name: "kafka_server_$1_$2_total"
      - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*,
        topic=(.+)><>Count"
        name: "kafka_server_$1_$2_total"
        labels:
          topic: "$3"
  jvmOptions: (19)
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest (20)
  template: (21)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: (22)
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"
  tracing: (23)
    type: jaeger

    1. The number of replica nodes.

    2. Bootstrap servers for consumer and producer.

    3. Group ID for the consumer.

    4. The number of consumer streams.

    5. The offset auto-commit interval in milliseconds.

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

    7. Authentication for consumer or producer, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.

    8. Kafka configuration options for consumer and producer.

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

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

    11. If the abortOnSendFailure property is set to true, Kafka MirrorMaker will exit and the container will restart following a send failure for a message.

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

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

    14. A whitelist of topics mirrored from source to target Kafka cluster.

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

    16. Specified loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties or log4j2.properties key. MirrorMaker has a single logger called mirrormaker.root.logger. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

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

    18. Prometheus metrics, which are enabled with configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using metrics: {}.

    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.

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

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

How MirrorMaker 2.0 replicates data is described here:

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

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

2.4.1. MirrorMaker 2.0 data replication

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

MirrorMaker 2.0 uses:

  • Source cluster configuration to consume data from the source cluster

  • Target cluster configuration to output data to the target cluster

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

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

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

MirrorMaker 2.0 replication
Figure 1. Replication across two clusters

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 of the KafkaMirrorMaker2 resource. 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 sync topic maps the source and target offsets for replicated topic partitions from record metadata

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

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

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

Connectivity checks

A heartbeat internal topic checks connectivity between clusters.

The heartbeat topic is replicated from the source cluster.

Target clusters use the topic to check:

  • The connector managing connectivity between clusters is running

  • The source cluster is available

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

2.4.3. ACL rules synchronization

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

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

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

2.4.4. Synchronizing data between Kafka clusters using MirrorMaker 2.0

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

The configuration must specify:

  • Each Kafka cluster

  • Connection information for each cluster, including TLS authentication

  • The replication flow and direction

    • Cluster to cluster

    • Topic to topic

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

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

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

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 2.6.0
  connectCluster: "my-cluster-target"
  clusters:
  - alias: "my-cluster-source"
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092
  - alias: "my-cluster-target"
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector: {}

You can configure access control for source and target clusters using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and authentication for the source and target cluster.

Prerequisites

  • See the Deploying Strimzi guide for instructions on running a:

  • Source and target Kafka clusters must be available

Procedure

  1. Edit the spec properties for the KafkaMirrorMaker2 resource.

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

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 2.6.0 (1)
  replicas: 3 (2)
  connectCluster: "my-cluster-target" (3)
  clusters: (4)
  - alias: "my-cluster-source" (5)
    authentication: (6)
      certificateAndKey:
        certificate: source.crt
        key: source.key
        secretName: my-user-source
      type: tls
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092 (7)
    tls: (8)
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-source-cluster-ca-cert
  - alias: "my-cluster-target" (9)
    authentication: (10)
      certificateAndKey:
        certificate: target.crt
        key: target.key
        secretName: my-user-target
      type: tls
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092 (11)
    config: (12)
      config.storage.replication.factor: 1
      offset.storage.replication.factor: 1
      status.storage.replication.factor: 1
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (13)
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      ssl.endpoint.identification.algorithm: HTTPS (14)
    tls: (15)
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-target-cluster-ca-cert
  mirrors: (16)
  - sourceCluster: "my-cluster-source" (17)
    targetCluster: "my-cluster-target" (18)
    sourceConnector: (19)
      config:
        replication.factor: 1 (20)
        offset-syncs.topic.replication.factor: 1 (21)
        sync.topic.acls.enabled: "false" (22)
        replication.policy.separator: "" (23)
        replication.policy.class: "io.strimzi.kafka.connect.mirror.IdentityReplicationPolicy" (24)
    heartbeatConnector: (25)
      config:
        heartbeats.topic.replication.factor: 1 (26)
    checkpointConnector: (27)
      config:
        checkpoints.topic.replication.factor: 1 (28)
    topicsPattern: ".*" (29)
    groupsPattern: "group1|group2|group3" (30)
  resources: (31)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: (32)
    type: inline
    loggers:
      connect.root.logger.level: "INFO"
  readinessProbe: (33)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  jvmOptions: (34)
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest (35)
  template: (36)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    connectContainer: (37)
      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 (38)
  externalConfiguration: (39)
    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 version.

    2. The number of replica nodes.

    3. Cluster alias for Kafka Connect.

    4. Specification for the Kafka clusters being synchronized.

    5. Cluster alias for the source Kafka cluster.

    6. Authentication for the source cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.

    7. Bootstrap server for connection to the source Kafka cluster.

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

    9. Cluster alias for the target Kafka cluster.

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

    11. Bootstrap server for connection to the target Kafka cluster.

    12. Kafka Connect configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by 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. Replication factor for mirrored topics created at the target cluster.

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

    22. When ACL rules synchronization is enabled, ACLs are applied to synchronized topics. The default is true.

    23. Defines the separator used for the renaming of remote topics.

    24. Adds a policy that overrides the automatic renaming of remote topics. Instead of prepending the name with the name of the source cluster, the topic retains its original name. This optional setting is useful for active/passive backups and data migration.

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

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

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

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

    29. Topic replication from the source cluster defined as regular expression patterns. Here we request all topics.

    30. Consumer group replication from the source cluster defined as regular expression patterns. Here we request three consumer groups by name. You can use comma-separated lists.

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

    32. 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.

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

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

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

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

    37. Environment variables are also set for distributed tracing using Jaeger.

    38. Distributed tracing is enabled for Jaeger.

    39. External configuration for a Kubernetes Secret mounted to Kafka MirrorMaker as an environment variable.

  2. Create or update the resource:

    kubectl apply -f <your-file>

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 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/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  replicas: 3 (1)
  bootstrapServers: my-cluster-kafka-bootstrap:9092 (2)
  tls: (3)
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        certificate: ca.crt
      - secretName: my-cluster-cluster-cert
        certificate: ca2.crt
  authentication: (4)
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  http: (5)
    port: 8080
    cors: (6)
      allowedOrigins: "https://strimzi.io"
      allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  consumer: (7)
    config:
      auto.offset.reset: earliest
  producer: (8)
    config:
      delivery.timeout.ms: 300000
  resources: (9)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: (10)
    type: inline
    loggers:
      logger.bridge.level: "INFO"
      # enabling DEBUG just for send operation
      logger.send.name: "http.openapi.operation.send"
      logger.send.level: "DEBUG"
  jvmOptions: (11)
    "-Xmx": "1g"
    "-Xms": "1g"
  readinessProbe: (12)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  image: my-org/my-image:latest (13)
  template: (14)
    pod:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: application
                    operator: In
                    values:
                      - postgresql
                      - mongodb
              topologyKey: "kubernetes.io/hostname"
    bridgeContainer: (15)
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"

    1. The number of replica nodes.

    2. Bootstrap server for connection to the target Kafka cluster.

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

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

    5. HTTP access to Kafka brokers.

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

    7. Consumer configuration options.

    8. Producer configuration options.

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

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

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

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

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

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

    15. Environment variables are also set for distributed tracing using Jaeger.

  2. Create or update the resource:

    kubectl apply -f KAFKA-BRIDGE-CONFIG-FILE

2.5.2. List of Kafka Bridge cluster resources

The following resources are created by the Cluster Operator in the 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

Strimzi creates several Kubernetes resources, such as Deployments, StatefulSets, Pods, and Services, which 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.

However, 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 such 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

KafkaConnectS2I.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 StatefulSet:

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
  labels:
    app: my-cluster
spec:
  kafka:
    # ...
    template:
      statefulset:
        metadata:
          labels:
            mylabel: myvalue
    # ...

2.6.1. Customizing the image pull policy

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

2.7. External logging

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

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

Or you can specify external logging:

spec:
  # ...
  logging:
    type: external
    name: customConfigMap

With external logging, logging properties are defined in a ConfigMap. The name of the ConfigMap is referenced in the spec.logging.name 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.

2.7.1. Creating a ConfigMap for logging

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

The ConfigMap must contain the appropriate logging configuration.

  • log4j.properties for Kafka components, ZooKeeper, and the Kafka Bridge

  • log4j2.properties for the Topic Operator and User Operator

The configuration must be placed under these properties.

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

Procedure

  1. Create the ConfigMap.

    You can create the ConfigMap as a YAML file or from a properties file using kubectl at the command line.

    ConfigMap example with a root logger definition for Kafka:

    kind: ConfigMap
apiVersion: kafka.strimzi.io/v1beta1
metadata:
  name: logging-configmap
data:
  log4j.properties:
    kafka.root.logger.level="INFO"

    From the command line, using a properties file:

    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.name to the name of the ConfigMap.

    spec:
  # ...
  logging:
    type: external
    name: logging-configmap

  3. Create or update the resource.

    kubectl apply -f kafka.yaml

3. Configuring external listeners

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.

Note
 route is only supported on OpenShift
Additional resources

3.1. 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/v1beta1
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-CONFIG-FILE

    NodePort type services are created for each Kafka broker, as well as an external bootstrap service. The bootstrap service routes external traffic to the Kafka brokers. Node addresses used for connection are propagated to the status of the Kafka custom resource.

    The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.

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

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

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

    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.

3.2. 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/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: external
        port: 9094
        type: loadbalancer
        tls: true
        # ...
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    kubectl apply -f KAFKA-CONFIG-FILE

    loadbalancer type services and loadbalancers are created for each Kafka broker, as well as an external bootstrap service. The bootstrap service routes external traffic to all Kafka brokers. DNS names and IP addresses used for connection are propagated to the status of each service.

    The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.

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

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

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

    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.

3.3. 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/v1beta1
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-CONFIG-FILE

    ClusterIP type services are created for each Kafka broker, as well as an additional bootstrap service. These services are used by the Ingress controller to route traffic to the Kafka brokers. An Ingress resource is also created for each service to expose them using the Ingress controller. The Ingress hosts are propagated to the status of each service.

    The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.

    Use the address for the bootstrap host you specified in the configuration and port 443 (BOOTSTRAP-HOST:443) in your Kafka client as the bootstrap address to connect to the Kafka cluster.

  3. Extract the public certificate of the broker certificate authority.

    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.

3.4. 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/v1beta1
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-CONFIG-FILE

    ClusterIP type services are created for each Kafka broker, as well as an external bootstrap service. The services route the traffic from the OpenShift Routes to the Kafka brokers. An OpenShift Route resource is also created for each service to expose them using the HAProxy load balancer. DNS addresses used for connection are propagated to the status of each service.

    The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.

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

    kubectl get kafka KAFKA-CLUSTER-NAME -o=jsonpath='{.status.listeners[?(@.type=="external")].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.

4. Managing secure access to Kafka

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

A secure connection between Kafka brokers and clients can encompass:

  • Encryption for data exchange

  • Authentication to prove identity

  • Authorization to allow or decline actions executed by users

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

  • Security options for Kafka clusters and clients

  • How to secure Kafka brokers

  • How to use an authorization server for OAuth 2.0 token-based authentication and authorization

4.1. Security options for Kafka

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

4.1.1. Listener authentication

For clients inside the 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 Configuring external listeners.

Supported authentication options:

  1. Mutual TLS authentication (only on the listeners with TLS enabled encryption)

  2. SCRAM-SHA-512 authentication

  3. OAuth 2.0 token based authentication

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

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 set aside for interbroker communication (9091) and metrics (9404).
An example showing listener authentication configuration
# ...
listeners:
  - name: plain
    port: 9092
    type: internal
    tls: true
    authentication:
      type: scram-sha-512
  - name: tls
    port: 9093
    type: internal
    tls: true
    authentication:
      type: tls
  - name: external
    port: 9094
    type: loadbalancer
    tls: true
    authentication:
      type: tls
# ...
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

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

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

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

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

Note
 Your configuration of 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.

4.1.2. Kafka authorization

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

Supported authorization options:

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
authorization:
  type: simple
  superUsers:
    - CN=client_1
    - user_2
    - CN=client_3

4.2. Security options for Kafka clients

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

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

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

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

4.2.1. Identifying a Kafka cluster for user handling

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

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster

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

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

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

4.2.2. User authentication

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

Supported authentication mechanisms:

  • TLS client authentication

  • SCRAM-SHA-512 authentication

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

Additional resources
TLS Client Authentication

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

An example KafkaUser with TLS client authentication enabled
apiVersion: kafka.strimzi.io/v1beta1
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.

An example Secret with user credentials
apiVersion: v1
kind: Secret
metadata:
  name: my-user
  labels:
    strimzi.io/kind: KafkaUser
    strimzi.io/cluster: my-cluster
type: Opaque
data:
  ca.crt: # Public key of the client CA
  user.crt: # User certificate that contains the public key of the user
  user.key: # Private key of the user
  user.p12: # PKCS #12 archive file for storing certificates and keys
  user.password: # Password for protecting the PKCS #12 archive file
SCRAM-SHA-512 Authentication

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

An example KafkaUser with SCRAM-SHA-512 authentication enabled
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: scram-sha-512
  # ...

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

An example Secret with user credentials
apiVersion: v1
kind: Secret
metadata:
  name: my-user
  labels:
    strimzi.io/kind: KafkaUser
    strimzi.io/cluster: my-cluster
type: Opaque
data:
  password: Z2VuZXJhdGVkcGFzc3dvcmQ= (1)

  1. The generated password, base64 encoded.

Decoding the generated password:

echo "Z2VuZXJhdGVkcGFzc3dvcmQ=" | base64 --decode

4.2.3. User authorization

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

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

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

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

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 access to Kafka brokers based on a byte threshold or a time limit of CPU utilization.

An example KafkaUser with user quotas
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  quotas:
    producerByteRate: 1048576 (1)
    consumerByteRate: 2097152 (2)
    requestPercentage: 55 (3)

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

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

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

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

4.3. Securing access to Kafka brokers

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

  • A Kafka resource to:

    • Create listeners with a specified authentication type

    • Configure authorization for the whole Kafka cluster

  • A KafkaUser resource to access the Kafka brokers securely through the listeners

Configure the Kafka resource to set up:

  • Listener authentication

  • Network policies that restrict access to Kafka listeners

  • Kafka authorization

  • Super users for unconstrained access to brokers

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

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

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

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

Configure the KafkaUser resource to set up:

  • Authentication to match the enabled listener authentication

  • Authorization to match the enabled Kafka authorization

  • Quotas to control the use of resources by clients

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

Additional resources

For more information about the schema for:

4.3.1. 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/v1beta1
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-CONFIG-FILE

    The Kafka cluster is configured with a Kafka broker listener using TLS authentication.

    A service is created for each Kafka broker pod.

    A service is created to serve as the bootstrap address for connection to the Kafka cluster.

    The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.

4.3.2. Securing user access to Kafka

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

You can use 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/v1beta1
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 Strimzi Guide.

4.3.3. Restricting access to Kafka listeners using network policies

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

Prerequisites

  • 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/v1beta1
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

4.4. Using OAuth 2.0 token-based authentication

Strimzi supports the use of OAuth 2.0 authentication using the SASL OAUTHBEARER mechanism.

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

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

OAuth 2.0 authentication can also be used in conjunction with simple or OPA-based Kafka authorization.

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

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

In the context of 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

Additional resources

4.4.1. OAuth 2.0 authentication mechanism

The Kafka SASL OAUTHBEARER mechanism is used to establish authenticated sessions with a Kafka broker.

A Kafka client initiates a session with the Kafka broker using the SASL OAUTHBEARER mechanism for credentials exchange, where credentials take the form of an access token.

Kafka brokers and clients need to be configured to use OAuth 2.0.

4.4.2. OAuth 2.0 Kafka broker configuration

Kafka broker configuration for OAuth 2.0 involves:

  • Creating the OAuth 2.0 client in the authorization server

  • Configuring OAuth 2.0 authentication in the Kafka custom resource

Note
 In relation to the authorization server, Kafka brokers and Kafka clients are both regarded as OAuth 2.0 clients.
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/v1beta1
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/v1beta1
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/v1beta1
kind: Kafka
spec:
  kafka:
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: oauth
          clientId: kafka-broker
          clientSecret:
            secretName: my-cluster-oauth
            key: clientSecret
          validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
          introspectionEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/token/introspect>
          userNameClaim: preferred_username
          maxSecondsWithoutReauthentication: 3600
          tlsTrustedCertificates:
          - secretName: oauth-server-cert
            certificate: ca.crt

4.4.3. Session re-authentication for Kafka brokers

The Kafka SASL OAUTHBEARER mechanism, which is used for OAuth 2.0 authentication in Strimzi, supports a Kafka feature called the re-authentication mechanism.

When the re-authentication mechanism is enabled in the configuration of an oauth type listener, the broker’s authenticated session expires when the access token expires. The client must then 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.

You enable session re-authentication in the Kafka resource. Set the maxSecondsWithoutReauthentication property for a TLS listener with type: oauth authentication. Session re-authentication is supported for both types of token validation (fast local JWT and introspection endpoint). For an example configuration, see Configuring OAuth 2.0 support for Kafka brokers.

For more information about the re-authentication mechanism, which was added in Kafka version 2.2, see KIP-368.

Additional resources

4.4.4. OAuth 2.0 Kafka client configuration

A Kafka client is configured with either:

  • The credentials required to obtain a valid access token from an authorization server (client ID and Secret)

  • A valid long-lived access token or refresh token, obtained using tools provided by an authorization server

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

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

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

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

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

  • Client ID and Secret

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

4.4.5. OAuth 2.0 client authentication flow

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

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

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

  • Fast local token validation based on JWT signature checking and local token introspection, without contacting the authorization server

  • An OAuth 2.0 introspection endpoint provided by the authorization server

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

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

Kafka client credentials can also be configured for:

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

  • Contact with the authorization server for a new access token to be issued

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

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

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

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

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

  2. Authorization server generates a new access token.

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

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

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

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

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

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

  2. Authorization server generates a new access token.

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

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

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

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

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

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

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

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

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

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

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

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

4.4.6. 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)
    enableECDSA: "true" (11)

    1. Listener type set to oauth.

    2. URI of the token issuer used for authentication.

    3. URI of the JWKS certificate endpoint used for local JWT validation.

    4. The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authentication flow and the authorization server used.

    5. (Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.

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

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

    8. The duration the JWKS certificates are considered valid before they expire. Default is 360 seconds. If you specify a longer time, consider the risk of allowing access to revoked certificates.

    9. The period between refreshes of JWKS certificates. The interval must be at least 60 seconds shorter than the expiry interval. Default is 300 seconds.

    10. The minimum pause in seconds between consecutive attempts to refresh JWKS public keys. When an unknown signing key is encountered, the JWKS keys refresh is scheduled outside the regular periodic schedule with at least the specified pause since the last refresh attempt. The refreshing of keys follows the rule of exponential backoff, retrying on unsuccessful refreshes with ever increasing pause, until it reaches jwksRefreshSeconds. The default value is 1.

    11. (Optional) If ECDSA is used for signing JWT tokens on authorization server, then this needs to be enabled. It installs additional crypto providers using BouncyCastle crypto library. Default is false.

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

    1. URI of the token introspection endpoint.

    2. Client ID to identify the client.

    3. Client Secret and client ID is used for authentication.

    4. The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authorization server used.

    5. (Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.

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

      # ...
  authentication:
    type: oauth
    # ...
    checkIssuer: false (1)
    fallbackUserNameClaim: client_id (2)
    fallbackUserNamePrefix: client-account- (3)
    validTokenType: bearer (4)
    userInfoEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/userinfo (5)

    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. 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.

    3. 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.

    4. (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.

    5. (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.

  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 po -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>0.6.0</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)

    1. URI of the authorization server token endpoint.

    2. Client ID, which is the name used when creating the client in the authorization server.

    3. Client secret created when creating the client in the authorization server.

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

    For example:

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

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

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

What to do next
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/v1beta1
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/v1beta1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: oauth (1)
    tokenEndpointUri: https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token (2)
    clientId: kafka-bridge
    clientSecret:
      secretName: my-bridge-oauth
      key: clientSecret
    tlsTrustedCertificates: (3)
    - secretName: oauth-server-cert
      certificate: tls.crt

    1. Authentication type set to oauth.

    2. URI of the token endpoint for authentication.

    3. Trusted certificates for TLS connection to the authorization server.

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

    # ...
spec:
  # ...
  authentication:
    # ...
    disableTlsHostnameVerification: true (1)
    checkAccessTokenType: false (2)
    accessTokenIsJwt: false (3)
    scope: any (4)

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

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

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

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

  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.

4.4.7. 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:

4.5. 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 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

4.5.1. 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.

4.5.2. 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.

  • You need to understand how to manage policies and permissions for Keycloak Authorization Services, as described in the Keycloak documentation.

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/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka
  # ...
  authorization:
    type: keycloak (1)
    tokenEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token> (2)
    clientId: kafka (3)
    delegateToKafkaAcls: false (4)
    disableTlsHostnameVerification: false (5)
    superUsers: (6)
      - CN=fred
      - sam
      - CN=edward
    tlsTrustedCertificates: (7)
    - secretName: oauth-server-cert
      certificate: ca.crt
    grantsRefreshPeriodSeconds: 60 (8)
    grantsRefreshPoolSize: 5 (9)
  #...

    1. Type keycloak enables Keycloak authorization.

    2. URI of the Keycloak token endpoint. For production, always use HTTPs.

    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.

  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 po -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. Using Strimzi Operators

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

5.1. Using the Cluster Operator

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

The Cluster Operator is deployed using YAML installation files.

For information on deploying the Cluster Operator, see Deploying the Cluster Operator in the Deploying Strimzi guide.

For information on the deployment options available for Kafka, see Kafka Cluster configuration.

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

5.1.1. Cluster Operator configuration

The Cluster Operator can be configured through the following supported environment variables and through the logging configuration.

STRIMZI_NAMESPACE

A comma-separated list of namespaces that the operator should operate in. When not set, set to empty string, or 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. See the example below:

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_KAFKA_IMAGES

Required. This provides a mapping from Kafka version to the corresponding Docker image containing a Kafka broker of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.5.0=strimzi/kafka:0.20.0-kafka-2.5.0, 2.6.0=strimzi/kafka:0.20.0-kafka-2.6.0. This is used when a Kafka.spec.kafka.version property is specified but not the Kafka.spec.kafka.image, as described in Container images.

STRIMZI_DEFAULT_KAFKA_INIT_IMAGE

Optional, default strimzi/operator:0.20.0. The image name to use as default for the init container started before the broker for initial configuration work (that is, rack support), if no image is specified as the kafka-init-image in the Container images.

STRIMZI_KAFKA_CONNECT_IMAGES

Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.5.0=strimzi/kafka:0.20.0-kafka-2.5.0, 2.6.0=strimzi/kafka:0.20.0-kafka-2.6.0. This is used when a KafkaConnect.spec.version property is specified but not the KafkaConnect.spec.image, as described in image.

STRIMZI_KAFKA_CONNECT_S2I_IMAGES

Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.5.0=strimzi/kafka:0.20.0-kafka-2.5.0, 2.6.0=strimzi/kafka:0.20.0-kafka-2.6.0. This is used when a KafkaConnectS2I.spec.version property is specified but not the KafkaConnectS2I.spec.image, as described in image.

STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka mirror maker of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.5.0=strimzi/kafka:0.20.0-kafka-2.5.0, 2.6.0=strimzi/kafka:0.20.0-kafka-2.6.0. This is used when a KafkaMirrorMaker.spec.version property is specified but not the KafkaMirrorMaker.spec.image, as described in image.

STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE

Optional, default strimzi/operator:0.20.0. The image name to use as the default when deploying the topic operator, if no image is specified as the Kafka.spec.entityOperator.topicOperator.image in the Container images of the Kafka resource.

STRIMZI_DEFAULT_USER_OPERATOR_IMAGE

Optional, default strimzi/operator:0.20.0. The image name to use as the default when deploying the user operator, if no image is specified as the Kafka.spec.entityOperator.userOperator.image in the Container images of the Kafka resource.

STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE

Optional, default strimzi/kafka:0.20.0-kafka-2.6.0. 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 Container images.

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. See the example below:

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.

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 apply -f install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml

Alternatively, edit the ConfigMap directly:

kubectl edit cm strimzi-cluster-operator

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

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

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

Note
 Do not remove the monitorInterval option from the ConfigMap.
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.

5.1.2. 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 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 and KafkaConnectS2I resources use a ServiceAccount called cluster-name-cluster-connect

  • The pods for KafkaMirrorMaker use a ServiceAccount called cluster-name-mirror-maker

  • The pods for KafkaMirrorMaker2 use a ServiceAccount called cluster-name-mirrormaker2

  • The pods for KafkaBridge use a ServiceAccount called cluster-name-bridge

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

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

and

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

ClusterRoles containing only namespaced resources are bound using RoleBindings only.

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

5.2. Using the Topic Operator

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

The Deploying Strimzi guide provides instructions to deploy the Topic Operator:

5.2.1. Kafka topic resource

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

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

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/v1beta1
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.

Handling changes to topics

A fundamental problem that the Topic Operator has 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 operator. Complicating this, the Topic Operator might not always be able to observe changes at each end in real time (for example, the operator might be down).

To resolve this, the operator maintains its own private copy of the information about each topic. When a change happens either in the Kafka cluster, or in Kubernetes, it looks at both the state of the other system and at its private copy in order to determine what needs to change to keep everything in sync. The same thing happens whenever the operator starts, and periodically while it is running.

For example, suppose the Topic Operator is not running, and a KafkaTopic my-topic gets created. When the operator starts it will lack a private copy of "my-topic", so it can infer that the KafkaTopic has been created since it was last running. The operator will create the topic corresponding to my-topic, and also store a private copy of the metadata for my-topic.

The private copy allows the operator to cope with scenarios where the topic configuration gets changed both in Kafka and in Kubernetes, so long as the changes are not incompatible (for example, both changing the same topic config key, but to different values). In the case of incompatible changes, the Kafka configuration wins, and the KafkaTopic will be updated to reflect that.

The private copy is held in the same ZooKeeper ensemble used by Kafka itself. This mitigates availability concerns, because if ZooKeeper is not running then Kafka itself cannot run, so the operator will be no less available than it would even if it was stateless.

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
 Recommendations for identifiers and names in Kubernetes are outlined in Identifiers and Names in Kubernetes community article.
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: {KafkaApiVersion}
kind: KafkaTopic
metadata:
  name: topic-name-1
spec:
  topicName: topicName-1 (1)
  # ...

  1. Upper case is invalid in Kubernetes.

cannot be changed to:

apiVersion: {KafkaApiVersion}
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 valid names based on the Kafka names. Invalid characters are replaced and a hash is appended to the name.

5.2.2. 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/v1beta1
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

5.2.3. 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/v1beta1
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-CONFIG-FILE

5.3. Using the User Operator

When you create, modify or delete a user using the KafkaUser resource, the User Operator ensures those changes are reflected in the Kafka cluster.

The Deploying 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.

5.3.1. Configuring the User Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the User Operator and set a limit on the amount of resources it can consume.

Prerequisites

  • The Cluster Operator is running.

Procedure

  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/v1beta1
kind: Kafka
spec:
  # Kafka and ZooKeeper sections...
  entityOperator:
    userOperator:
      resources:
        requests:
          cpu: "1"
          memory: 500Mi
        limits:
          cpu: "1"
          memory: 500Mi

    Save the file and exit the editor. The Cluster Operator applies the changes automatically.

5.4. Monitoring operators using Prometheus metrics

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 Strimzi guide.

6. Kafka Bridge

This chapter provides an overview of the Strimzi Kafka Bridge and helps you get started using its REST API to interact with Strimzi.

6.1. Kafka Bridge overview

You can use the Strimzi Kafka Bridge as an interface to make specific types of HTTP requests to the Kafka cluster.

6.1.1. Kafka Bridge interface

The Kafka Bridge provides a RESTful interface that allows HTTP-based clients to interact with a Kafka cluster.  It offers the advantages of a web API connection to Strimzi, without the need for client applications to interpret the Kafka protocol.

The API has two main resources — consumers and topics — that are exposed and made accessible through endpoints to interact with consumers and producers in your Kafka cluster. The resources relate only to the Kafka Bridge, not the consumers and producers connected directly to Kafka.

HTTP requests

The Kafka Bridge supports HTTP requests to a Kafka cluster, with methods to:

  • Send messages to a topic.

  • Retrieve messages from topics.

  • Retrieve a list of partitions for a topic.

  • Create and delete consumers.

  • Subscribe consumers to topics, so that they start receiving messages from those topics.

  • Retrieve a list of topics that a consumer is subscribed to.

  • Unsubscribe consumers from topics.

  • Assign partitions to consumers.

  • Commit a list of consumer offsets.

  • Seek on a partition, so that a consumer starts receiving messages from the first or last offset position, or a given offset position.

The methods provide JSON responses and HTTP response code error handling. Messages can be sent in JSON or binary formats.

Clients can produce and consume messages without the requirement to use the native Kafka protocol.

Additional resources

6.1.2. Supported clients for the Kafka Bridge

You can use the Kafka Bridge to integrate both internal and external HTTP client applications with your Kafka cluster.

Internal clients

Internal clients are container-based HTTP clients running in the same Kubernetes cluster as the Kafka Bridge itself. Internal clients can access the Kafka Bridge on the host and port defined in the KafkaBridge custom resource.

External clients

External clients are HTTP clients running outside the Kubernetes cluster in which the Kafka Bridge is deployed and running. External clients can access the Kafka Bridge through an OpenShift Route, a loadbalancer service, or using an Ingress.

HTTP internal and external client integration

Internal and external HTTP producers and consumers exchange data with the Kafka brokers through the Kafka Bridge

6.1.3. Securing the Kafka Bridge

Strimzi does not currently provide any encryption, authentication, or authorization for the Kafka Bridge. This means that requests sent from external clients to the Kafka Bridge are:

  • Not encrypted, and must use HTTP rather than HTTPS

  • Sent without authentication

However, you can secure the Kafka Bridge using other methods, such as:

  • Kubernetes Network Policies that define which pods can access the Kafka Bridge.

  • Reverse proxies with authentication or authorization, for example, OAuth2 proxies.

  • API Gateways.

  • Ingress or OpenShift Routes with TLS termination.

The Kafka Bridge supports TLS encryption and TLS and SASL authentication when connecting to the Kafka Brokers. Within your Kubernetes cluster, you can configure:

  • TLS or SASL-based authentication between the Kafka Bridge and your Kafka cluster

  • A TLS-encrypted connection between the Kafka Bridge and your Kafka cluster.

For more information, see Configuring the Kafka Bridge.

You can use ACLs in Kafka brokers to restrict the topics that can be consumed and produced using the Kafka Bridge.

6.1.4. Accessing the Kafka Bridge outside of Kubernetes

After deployment, the Strimzi Kafka Bridge can only be accessed by applications running in the same Kubernetes cluster. These applications use the kafka-bridge-name-bridge-service Service to access the API.

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

  • Services of types LoadBalancer or NodePort

  • Ingress resources

  • OpenShift Routes

If you decide to create Services, use the following labels in the selector to configure the pods to which the service will route the traffic:

  # ...
  selector:
    strimzi.io/cluster: kafka-bridge-name (1)
    strimzi.io/kind: KafkaBridge
  #...

  1. Name of the Kafka Bridge custom resource in your Kubernetes cluster.

6.1.5. Requests to the Kafka Bridge

Specify data formats and HTTP headers to ensure valid requests are submitted to the Kafka Bridge.

Content Type headers

API request and response bodies are always encoded as JSON.

  • When performing consumer operations, POST requests must provide the following Content-Type header if there is a non-empty body:

    Content-Type: application/vnd.kafka.v2+json

  • When performing producer operations, POST requests must provide Content-Type headers specifying the embedded data format of the messages produced. This can be either json or binary.

    Embedded data format Content-Type header

    JSON

    Content-Type: application/vnd.kafka.json.v2+json

    Binary

    Content-Type: application/vnd.kafka.binary.v2+json

The embedded data format is set per consumer, as described in the next section.

The Content-Type must not be set if the POST request has an empty body. An empty body can be used to create a consumer with the default values.

Embedded data format

The embedded data format is the format of the Kafka messages that are transmitted, over HTTP, from a producer to a consumer using the Kafka Bridge. Two embedded data formats are supported: JSON and binary.

When creating a consumer using the /consumers/groupid endpoint, the POST request body must specify an embedded data format of either JSON or binary. This is specified in the format field, for example:

{
  "name": "my-consumer",
  "format": "binary", (1)
...
}

  1. A binary embedded data format.

The embedded data format specified when creating a consumer must match the data format of the Kafka messages it will consume.

If you choose to specify a binary embedded data format, subsequent producer requests must provide the binary data in the request body as Base64-encoded strings. For example, when sending messages using the /topics/topicname endpoint, records.value must be encoded in Base64:

{
  "records": [
    {
      "key": "my-key",
      "value": "ZWR3YXJkdGhldGhyZWVsZWdnZWRjYXQ="
    },
  ]
}

Producer requests must also provide a Content-Type header that corresponds to the embedded data format, for example, Content-Type: application/vnd.kafka.binary.v2+json.

Message format

When sending messages using the /topics endpoint, you enter the message payload in the request body, in the records parameter.

The records parameter can contain any of these optional fields:

  • Message headers

  • Message key

  • Message value

  • Destination partition

Example POST request to /topics
curl -X POST \
  http://localhost:8080/topics/my-topic \
  -H 'content-type: application/vnd.kafka.json.v2+json' \
  -d '{
    "records": [
        {
            "key": "my-key",
            "value": "sales-lead-0001"
            "partition": 2
            "headers": [
              {
                "key": "key1",
                "value": "QXBhY2hlIEthZmthIGlzIHRoZSBib21iIQ==" (1)
              }
            ]
        },
    ]
}'

  1. The header value in binary format and encoded as Base64.

Accept headers

After creating a consumer, all subsequent GET requests must provide an Accept header in the following format:

Accept: application/vnd.kafka.EMBEDDED-DATA-FORMAT.v2+json

The EMBEDDED-DATA-FORMAT is either json or binary.

For example, when retrieving records for a subscribed consumer using an embedded data format of JSON, include this Accept header:

Accept: application/vnd.kafka.json.v2+json

6.1.6. CORS

Cross-Origin Resource Sharing (CORS) allows you to specify allowed methods and originating URLs for accessing the Kafka cluster in your Kafka Bridge HTTP configuration.

Example CORS configuration for Kafka Bridge
# ...
cors:
  allowedOrigins: "https://strimzi.io"
  allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  # ...

CORS allows for simple and preflighted requests between origin sources on different domains.

Simple requests are suitable for standard requests using GET, HEAD, POST methods.

A preflighted request sends a HTTP OPTIONS request as an initial check that the actual request is safe to send. On confirmation, the actual request is sent. Preflight requests are suitable for methods that require greater safeguards, such as PUT and DELETE, and use non-standard headers.

All requests require an Origin value in their header, which is the source of the HTTP request.

Simple request

For example, this simple request header specifies the origin as https://strimzi.io.

Origin: https://strimzi.io

The header information is added to the request.

curl -v -X GET HTTP-ADDRESS/bridge-consumer/records \
-H 'Origin: https://strimzi.io'\
-H 'content-type: application/vnd.kafka.v2+json'

In the response from the Kafka Bridge, an Access-Control-Allow-Origin header is returned.

HTTP/1.1 200 OK
Access-Control-Allow-Origin: * (1)

  1. Returning an asterisk (*) shows the resource can be accessed by any domain.

Preflighted request

An initial preflight request is sent to Kafka Bridge using an OPTIONS method. The HTTP OPTIONS request sends header information to check that Kafka Bridge will allow the actual request.

Here the preflight request checks that a POST request is valid from https://strimzi.io.

OPTIONS /my-group/instances/my-user/subscription HTTP/1.1
Origin: https://strimzi.io
Access-Control-Request-Method: POST (1)
Access-Control-Request-Headers: Content-Type (2)

  1. Kafka Bridge is alerted that the actual request is a POST request.

  2. The actual request will be sent with a Content-Type header.

OPTIONS is added to the header information of the preflight request.

curl -v -X OPTIONS -H 'Origin: https://strimzi.io' \
-H 'Access-Control-Request-Method: POST' \
-H 'content-type: application/vnd.kafka.v2+json'

Kafka Bridge responds to the initial request to confirm that the request will be accepted. The response header returns allowed origins, methods and headers.

HTTP/1.1 200 OK
Access-Control-Allow-Origin: https://strimzi.io
Access-Control-Allow-Methods: GET,POST,PUT,DELETE,OPTIONS,PATCH
Access-Control-Allow-Headers: content-type

If the origin or method is rejected, an error message is returned.

The actual request does not require Access-Control-Request-Method header, as it was confirmed in the preflight request, but it does require the origin header.

curl -v -X POST HTTP-ADDRESS/topics/bridge-topic \
-H 'Origin: https://strimzi.io' \
-H 'content-type: application/vnd.kafka.v2+json'

The response shows the originating URL is allowed.

HTTP/1.1 200 OK
Access-Control-Allow-Origin: https://strimzi.io
Additional resources

Fetch CORS specification

6.1.7. Kafka Bridge API resources

For the full list of REST API endpoints and descriptions, including example requests and responses, see the Kafka Bridge API reference.

6.1.8. Kafka Bridge deployment

You deploy the Kafka Bridge into your Kubernetes cluster by using the Cluster Operator.

After the Kafka Bridge is deployed, the Cluster Operator creates Kafka Bridge objects in your Kubernetes cluster. Objects include the deployment, service, and pod, each named after the name given in the custom resource for the Kafka Bridge.

Additional resources

6.2. Kafka Bridge quickstart

Use this quickstart to try out the Strimzi Kafka Bridge in your local development environment. You will learn how to:

  • Deploy the Kafka Bridge to your Kubernetes cluster

  • Expose the Kafka Bridge service to your local machine by using port-forwarding

  • Produce messages to topics and partitions in your Kafka cluster

  • Create a Kafka Bridge consumer

  • Perform basic consumer operations, such as subscribing the consumer to topics and retrieving the messages that you produced

In this quickstart, HTTP requests are formatted as curl commands that you can copy and paste to your terminal. Access to a Kubernetes cluster is required; to run and manage a local Kubernetes cluster, use a tool such as Minikube, CodeReady Containers, or MiniShift.

Ensure you have the prerequisites and then follow the tasks in the order provided in this chapter.

About data formats

In this quickstart, you will produce and consume messages in JSON format, not binary. For more information on the data formats and HTTP headers used in the example requests, see Requests to the Kafka Bridge.

Prerequisites for the quickstart

  • Cluster administrator access to a local or remote Kubernetes cluster.

  • Strimzi is installed.

  • A running Kafka cluster, deployed by the Cluster Operator, in a Kubernetes namespace.

  • The Entity Operator is deployed and running as part of the Kafka cluster.

6.2.1. Deploying the Kafka Bridge to your Kubernetes cluster

Strimzi includes a YAML example that specifies the configuration of the Strimzi Kafka Bridge. Make some minimal changes to this file and then deploy an instance of the Kafka Bridge to your Kubernetes cluster.

Procedure

  1. Edit the examples/bridge/kafka-bridge.yaml file.

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: quickstart (1)
spec:
  replicas: 1
  bootstrapServers: <cluster-name>-kafka-bootstrap:9092 (2)
  http:
    port: 8080

    1. When the Kafka Bridge is deployed, -bridge is appended to the name of the deployment and other related resources. In this example, the Kafka Bridge deployment is named quickstart-bridge and the accompanying Kafka Bridge service is named quickstart-bridge-service.

    2. In the bootstrapServers property, enter the name of the Kafka cluster as the <cluster-name>.

  2. Deploy the Kafka Bridge to your Kubernetes cluster:

    kubectl apply -f examples/bridge/kafka-bridge.yaml

    A quickstart-bridge deployment, service, and other related resources are created in your Kubernetes cluster.

  3. Verify that the Kafka Bridge was successfully deployed:

    kubectl get deployments
    NAME                             READY   UP-TO-DATE   AVAILABLE   AGE
quickstart-bridge                  1/1     1            1          34m
my-cluster-connect                 1/1     1            1          24h
my-cluster-entity-operator         1/1     1            1          24h
#...
What to do next

After deploying the Kafka Bridge to your Kubernetes cluster, expose the Kafka Bridge service to your local machine.

Additional resources

6.2.2. Exposing the Kafka Bridge service to your local machine

Next, use port forwarding to expose the Strimzi Kafka Bridge service to your local machine on http://localhost:8080.

Note
 Port forwarding is only suitable for development and testing purposes.
Procedure

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

    kubectl get pods -o name

pod/kafka-consumer
# ...
pod/quickstart-bridge-589d78784d-9jcnr
pod/strimzi-cluster-operator-76bcf9bc76-8dnfm

  2. Connect to the quickstart-bridge pod on port 8080:

    kubectl port-forward pod/quickstart-bridge-589d78784d-9jcnr 8080:8080 &
    Note
    		 If port 8080 on your local machine is already in use, use an alternative HTTP port, such as 8008.

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

6.2.3. Producing messages to topics and partitions

Next, produce messages to topics in JSON format by using the topics endpoint. You can specify destination partitions for messages in the request body, as shown here. The partitions endpoint provides an alternative method for specifying a single destination partition for all messages as a path parameter.

Procedure

  1. In a text editor, create a YAML definition for a Kafka topic with three partitions.

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
  name: bridge-quickstart-topic
  labels:
    strimzi.io/cluster: <kafka-cluster-name> (1)
spec:
  partitions: 3 (2)
  replicas: 1
  config:
    retention.ms: 7200000
    segment.bytes: 1073741824

    1. The name of the Kafka cluster in which the Kafka Bridge is deployed.

    2. The number of partitions for the topic.

  2. Save the file to the examples/topic directory as bridge-quickstart-topic.yaml.

  3. Create the topic in your Kubernetes cluster:

    kubectl apply -f examples/topic/bridge-quickstart-topic.yaml

  4. Using the Kafka Bridge, produce three messages to the topic you created:

    curl -X POST \
  http://localhost:8080/topics/bridge-quickstart-topic \
  -H 'content-type: application/vnd.kafka.json.v2+json' \
  -d '{
    "records": [
        {
            "key": "my-key",
            "value": "sales-lead-0001"
        },
        {
            "value": "sales-lead-0002",
            "partition": 2
        },
        {
            "value": "sales-lead-0003"
        }
    ]
}'

    • sales-lead-0001 is sent to a partition based on the hash of the key.

    • sales-lead-0002 is sent directly to partition 2.

    • sales-lead-0003 is sent to a partition in the bridge-quickstart-topic topic using a round-robin method.

  5. If the request is successful, the Kafka Bridge returns an offsets array, along with a 200 code and a content-type header of application/vnd.kafka.v2+json. For each message, the offsets array describes:

    • The partition that the message was sent to

    • The current message offset of the partition

      Example response
      #...
{
  "offsets":[
    {
      "partition":0,
      "offset":0
    },
    {
      "partition":2,
      "offset":0
    },
    {
      "partition":0,
      "offset":1
    }
  ]
}
What to do next

After producing messages to topics and partitions, create a Kafka Bridge consumer.

Additional resources

6.2.4. Creating a Kafka Bridge consumer

Before you can perform any consumer operations in the Kafka cluster, you must first create a consumer by using the consumers endpoint. The consumer is referred to as a Kafka Bridge consumer.

Procedure

  1. Create a Kafka Bridge consumer in a new consumer group named bridge-quickstart-consumer-group:

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "name": "bridge-quickstart-consumer",
    "auto.offset.reset": "earliest",
    "format": "json",
    "enable.auto.commit": false,
    "fetch.min.bytes": 512,
    "consumer.request.timeout.ms": 30000
  }'

    • The consumer is named bridge-quickstart-consumer and the embedded data format is set as json.

    • Some basic configuration settings are defined.

    • The consumer will not commit offsets to the log automatically because the enable.auto.commit setting is false. You will commit the offsets manually later in this quickstart.

      If the request is successful, the Kafka Bridge returns the consumer ID (instance_id) and base URL (base_uri) in the response body, along with a 200 code.

      Example response
      #...
{
  "instance_id": "bridge-quickstart-consumer",
  "base_uri":"http://<bridge-name>-bridge-service:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer"
}

  2. Copy the base URL (base_uri) to use in the other consumer operations in this quickstart.

What to do next

Now that you have created a Kafka Bridge consumer, you can subscribe it to topics.

Additional resources

6.2.5. Subscribing a Kafka Bridge consumer to topics

After you have created a Kafka Bridge consumer, subscribe it to one or more topics by using the subscription endpoint. Once subscribed, the consumer starts receiving all messages that are produced to the topic.

Procedure

  • Subscribe the consumer to the bridge-quickstart-topic topic that you created earlier, in Producing messages to topics and partitions:

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/subscription \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "topics": [
        "bridge-quickstart-topic"
    ]
}'

    The topics array can contain a single topic (as shown here) or multiple topics. If you want to subscribe the consumer to multiple topics that match a regular expression, you can use the topic_pattern string instead of the topics array.

    If the request is successful, the Kafka Bridge returns a 204 (No Content) code only.

What to do next

After subscribing a Kafka Bridge consumer to topics, you can retrieve messages from the consumer.

Additional resources

6.2.6. Retrieving the latest messages from a Kafka Bridge consumer

Next, retrieve the latest messages from the Kafka Bridge consumer by requesting data from the records endpoint. In production, HTTP clients can call this endpoint repeatedly (in a loop).

Procedure

  1. Produce additional messages to the Kafka Bridge consumer, as described in Producing messages to topics and partitions.

  2. Submit a GET request to the records endpoint:

    curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \
  -H 'accept: application/vnd.kafka.json.v2+json'

    After creating and subscribing to a Kafka Bridge consumer, a first GET request will return an empty response because the poll operation starts a rebalancing process to assign partitions.

  3. Repeat step two to retrieve messages from the Kafka Bridge consumer.

    The Kafka Bridge returns an array of messages — describing the topic name, key, value, partition, and offset — in the response body, along with a 200 code. Messages are retrieved from the latest offset by default.

    HTTP/1.1 200 OK
content-type: application/vnd.kafka.json.v2+json
#...
[
  {
    "topic":"bridge-quickstart-topic",
    "key":"my-key",
    "value":"sales-lead-0001",
    "partition":0,
    "offset":0
  },
  {
    "topic":"bridge-quickstart-topic",
    "key":null,
    "value":"sales-lead-0003",
    "partition":0,
    "offset":1
  },
#...
    Note
    		 If an empty response is returned, produce more records to the consumer as described in Producing messages to topics and partitions, and then try retrieving messages again.
What to do next

After retrieving messages from a Kafka Bridge consumer, try committing offsets to the log.

Additional resources

6.2.7. Commiting offsets to the log

Next, use the offsets endpoint to manually commit offsets to the log for all messages received by the Kafka Bridge consumer. This is required because the Kafka Bridge consumer that you created earlier, in Creating a Kafka Bridge consumer, was configured with the enable.auto.commit setting as false.

Procedure

  • Commit offsets to the log for the bridge-quickstart-consumer:

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/offsets

    Because no request body is submitted, offsets are committed for all the records that have been received by the consumer. Alternatively, the request body can contain an array (OffsetCommitSeekList) that specifies the topics and partitions that you want to commit offsets for.

    If the request is successful, the Kafka Bridge returns a 204 code only.

What to do next

After committing offsets to the log, try out the endpoints for seeking to offsets.

Additional resources

6.2.8. Seeking to offsets for a partition

Next, use the positions endpoints to configure the Kafka Bridge consumer to retrieve messages for a partition from a specific offset, and then from the latest offset. This is referred to in Apache Kafka as a seek operation.

Procedure

  1. Seek to a specific offset for partition 0 of the quickstart-bridge-topic topic:

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "offsets": [
        {
            "topic": "bridge-quickstart-topic",
            "partition": 0,
            "offset": 2
        }
    ]
}'

    If the request is successful, the Kafka Bridge returns a 204 code only.

  2. Submit a GET request to the records endpoint:

    curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \
  -H 'accept: application/vnd.kafka.json.v2+json'

    The Kafka Bridge returns messages from the offset that you seeked to.

  3. Restore the default message retrieval behavior by seeking to the last offset for the same partition. This time, use the positions/end endpoint.

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions/end \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "partitions": [
        {
            "topic": "bridge-quickstart-topic",
            "partition": 0
        }
    ]
}'

    If the request is successful, the Kafka Bridge returns another 204 code.

Note
 You can also use the positions/beginning endpoint to seek to the first offset for one or more partitions.
What to do next

In this quickstart, you have used the Strimzi Kafka Bridge to perform several common operations on a Kafka cluster. You can now delete the Kafka Bridge consumer that you created earlier.

Additional resources

6.2.9. Deleting a Kafka Bridge consumer

Finally, delete the Kafa Bridge consumer that you used throughout this quickstart.

Procedure

  • Delete the Kafka Bridge consumer by sending a DELETE request to the instances endpoint.

    curl -X DELETE http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer

    If the request is successful, the Kafka Bridge returns a 204 code only.

Additional resources

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.

Example YAML 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. Replica capacity

  3. Capacity: Disk capacity, Network inbound capacity, Network outbound capacity, CPU capacity

  4. Replica distribution

  5. Potential network output

  6. 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.

  7. Leader bytes-in rate distribution

  8. Topic replica distribution

  9. Leader replica distribution

  10. Preferred leader election

For more information on each optimization goal, see Goals in the Cruise Control Wiki.

Note
 Intra-broker disk goals, "Write your own" goals, and Kafka assigner goals are not yet supported.

Goals configuration in 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 master, default, and user-provided optimization goals. Optimization goals for resource distribution (disk, network inbound, network outbound, and CPU) are subject to capacity limits on broker resources.

The following sections describe each goal configuration in more detail.

Hard goals and soft goals

Hard goals are goals that must be satisfied in optimization proposals. Goals that are not configured as hard goals are known as soft goals. You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations. An optimization proposal that violates one or more soft goals, but satisfies all hard goals, is valid.

Cruise Control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by Cruise Control and not sent to the user for approval.

Note
 For example, you might have a soft goal to distribute a topic’s replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met.

In Cruise Control, the following master optimization goals are preset as hard goals:

RackAwareGoal; 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/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl:
    brokerCapacity:
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    config:
      hard.goals: >
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,
        com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal
      # ...

Increasing the number of configured hard goals will reduce the likelihood of Cruise Control generating valid optimization proposals.

If skipHardGoalCheck: true is specified in the KafkaRebalance custom resource, Cruise Control does not check that the list of user-provided optimization goals (in KafkaRebalance.spec.goals) contains all the configured hard goals (hard.goals). Therefore, if some, but not all, of the user-provided optimization goals are in the hard.goals list, Cruise Control will still treat them as hard goals even if skipHardGoalCheck: true is specified.

Master optimization goals

The master optimization goals are available to all users. Goals that are not listed in the master optimization goals are not available for use in Cruise Control operations.

Unless you change the Cruise Control deployment configuration, Strimzi will inherit the following master optimization goals from Cruise Control, in descending priority order:

RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal

Six of these goals are preset as hard goals.

To reduce complexity, we recommend that you use the inherited master optimization goals, unless you need to completely exclude one or more goals from use in KafkaRebalance resources. The priority order of the master optimization goals can be modified, if desired, in the configuration for default optimization goals.

You configure master optimization goals, if necessary, in the Cruise Control deployment configuration: Kafka.spec.cruiseControl.config.goals

  • To accept the inherited master optimization goals, do not specify the goals property in Kafka.spec.cruiseControl.config.

  • If you need to modify the inherited master optimization goals, specify a list of goals, in descending priority order, in the goals configuration option.

Note
 If you change the inherited master optimization goals, you must ensure that the hard goals, if configured in the hard.goals property in Kafka.spec.cruiseControl.config, are a subset of the master optimization goals that you configured. Otherwise, errors will occur when generating optimization proposals.
Default optimization goals

Cruise Control uses the default optimization goals to generate the cached optimization proposal. For more information about the cached optimization proposal, see Optimization proposals overview.

You can override the default optimization goals by setting user-provided optimization goals in a KafkaRebalance custom resource.

Unless you specify default.goals in the Cruise Control deployment configuration, the master optimization goals are used as the default optimization goals. In this case, the cached optimization proposal is generated using the master optimization goals.

  • To use the master optimization goals as the default goals, do not specify the default.goals property in Kafka.spec.cruiseControl.config.

  • To modify the default optimization goals, edit the default.goals property in Kafka.spec.cruiseControl.config. You must use a subset of the master optimization goals.

Example Kafka configuration for default optimization goals
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl:
    brokerCapacity:
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    config:
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal
      # ...

If no default optimization goals are specified, the cached proposal is generated using the master optimization goals.

User-provided optimization goals

User-provided optimization goals narrow down the configured default goals for a particular optimization proposal. You can set them, as required, in spec.goals in a KafkaRebalance custom resource:

KafkaRebalance.spec.goals

User-provided optimization goals can generate optimization proposals for different scenarios. For example, you might want to optimize leader replica distribution across the Kafka cluster without considering disk capacity or disk utilization. So, you create a KafkaRebalance custom resource containing a single user-provided goal for leader replica distribution.

User-provided optimization goals must:

  • Include all configured hard goals, or an error occurs

  • Be a subset of the master optimization goals

To ignore the configured hard goals when generating an optimization proposal, add the skipHardGoalCheck: true property to the KafkaRebalance custom resource. See 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.

An optimization proposal is contained in the Status.Optimization Result property of a KafkaRebalance custom resource. The information provided is a summary of the full optimization proposal. Use the summary to decide whether to:

  • Approve the optimization proposal. This instructs Cruise Control to apply the proposal to the Kafka cluster and start a cluster rebalance operation.

  • Reject the optimization proposal. You can change the optimization goals and then generate another proposal.

All optimization proposals are dry runs: you cannot approve a cluster rebalance without first generating an optimization proposal. There is no limit to the number of optimization proposals that can be generated.

Cached optimization proposal

Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster. If you generate an optimization proposal using the default optimization goals, Cruise Control returns the most recent cached proposal.

To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms setting in the Cruise Control deployment configuration. Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server.

Contents of optimization proposals

The following table describes the properties contained in an optimization proposal:

Table 3. Properties contained in an optimization proposal
JSON property Description

numIntraBrokerReplicaMovements

The total number of partition replicas that will be transferred between the disks of the cluster’s brokers.

Performance impact during rebalance operation: Relatively high, but lower than numReplicaMovements.

excludedBrokersForLeadership

Not yet supported. An empty list is returned.

numReplicaMovements

The number of partition replicas that will be moved between separate brokers.

Performance impact during rebalance operation: Relatively high.

onDemandBalancednessScoreBefore, onDemandBalancednessScoreAfter

A measurement of the overall balancedness of a Kafka Cluster, before and after the optimization proposal was generated.

The score is calculated by subtracting the sum of the BalancednessScore of each violated soft goal from 100. Cruise Control assigns a BalancednessScore to every optimization goal based on several factors, including priority—​the goal’s position in the list of default.goals or user-provided goals.

The Before score is based on the current configuration of the Kafka cluster. The After score is based on the generated optimization proposal.

intraBrokerDataToMoveMB

The sum of the size of each partition replica that will be moved between disks on the same broker (see also numIntraBrokerReplicaMovements).

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see dataToMoveMB).

recentWindows

The number of metrics windows upon which the optimization proposal is based.

dataToMoveMB

The sum of the size of each partition replica that will be moved to a separate broker (see also numReplicaMovements).

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete.

monitoredPartitionsPercentage

The percentage of partitions in the Kafka cluster covered by the optimization proposal. Affected by the number of excludedTopics.

excludedTopics

If you specified a regular expression in the spec.excludedTopicsRegex property in the KafkaRebalance resource, all topic names matching that expression are listed here. These topics are excluded from the calculation of partition replica/leader movements in the optimization proposal.

numLeaderMovements

The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration.

Performance impact during rebalance operation: Relatively low.

excludedBrokersForReplicaMove

Not yet supported. An empty list is returned.
Additional resources

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.

Partition reassignment commands

Optimization proposals are comprised of separate partition reassignment commands. When you approve a proposal, the Cruise Control server applies these commands to the Kafka cluster.

A partition reassignment command consists of either of the following types of operations:

  • Partition movement: Involves transferring the partition replica and its data to a new location. Partition movements can take one of two forms:

    • Inter-broker movement: The partition replica is moved to a log directory on a different broker.

    • Intra-broker movement: The partition replica is moved to a different log directory on the same broker.

  • Leadership movement: This involves switching the leader of the partition’s replicas.

Cruise Control issues partition reassignment commands to the Kafka cluster in batches. The performance of the cluster during the rebalance is affected by the number of each type of movement contained in each batch.

Replica movement strategies

Cluster rebalance performance is also influenced by the replica movement strategy that is applied to the batches of partition reassignment commands. By default, Cruise Control uses the BaseReplicaMovementStrategy, which simply applies the commands in the order they were generated. However, if there are some very large partition reassignments early in the proposal, this strategy can slow down the application of the other reassignments.

Cruise Control provides three alternative replica movement strategies that can be applied to optimization proposals:

  • PrioritizeSmallReplicaMovementStrategy: Order reassignments in order of ascending size.

  • PrioritizeLargeReplicaMovementStrategy: Order reassignments in order of descending size.

  • PostponeUrpReplicaMovementStrategy: Prioritize reassignments for replicas of partitions which have no out-of-sync replicas.

These strategies can be configured as a sequence. The first strategy attempts to compare two partition reassignments using its internal logic. If the reassignments are equivalent, then it passes them to the next strategy in the sequence to decide the order, and so on.

Rebalance tuning options

Cruise Control provides several configuration options for tuning the rebalance parameters discussed above. You can set these tuning options at either the Cruise Control server or optimization proposal levels:

  • The Cruise Control server setting can be set in the Kafka custom resource under Kafka.spec.cruiseControl.config.

  • The individual rebalance performance configurations can be set under KafkaRebalance.spec.

The relevant configurations are summarized below:

Server and KafkaRebalance Configuration Description Default Value

num.concurrent.partition.movements.per.broker

The maximum number of inter-broker partition movements in each partition reassignment batch

5

concurrentPartitionMovementsPerBroker

num.concurrent.intra.broker.partition.movements

The maximum number of intra-broker partition movements in each partition reassignment batch

2

concurrentIntraBrokerPartitionMovements

num.concurrent.leader.movements

The maximum number of partition leadership changes in each partition reassignment batch

1000

concurrentLeaderMovements

default.replication.throttle

The bandwidth (in bytes per second) to be assigned to the reassigning of partitions

No Limit

replicationThrottle

default.replica.movement.strategies

The list of strategies (in priority order) used to determine the order in which partition reassignment commands are executed for generated proposals.

For the server setting, use a comma separated string with the fully qualified names of the strategy class (add com.linkedin.kafka.cruisecontrol.executor.strategy. to the start of each class name). For the KafkaRebalance resource setting use a YAML array of strategy class names.

BaseReplicaMovementStrategy

replicaMovementStrategies

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

Note
 Strings that look like JSON or YAML will need to be explicitly quoted.

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/v1beta1
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
    # ...

Capacity configuration

Cruise Control uses capacity limits to determine if optimization goals for resource distribution are being broken. There are four goals of this type:

  • DiskUsageDistributionGoal - Disk utilization distribution

  • CpuUsageDistributionGoal - CPU utilization distribution

  • NetworkInboundUsageDistributionGoal - Network inbound utilization distribution

  • NetworkOutboundUsageDistributionGoal - Network outbound utilization distribution

You specify capacity limits for Kafka broker resources in the brokerCapacity property in Kafka.spec.cruiseControl . They are enabled by default and you can change their default values. Capacity limits can be set for the following broker resources, using the standard Kubernetes byte units (K, M, G and T) or their bibyte (power of two) equivalents (Ki, Mi, Gi and Ti):

  • disk - Disk storage per broker (Default: 100000Mi)

  • cpuUtilization - CPU utilization as a percentage (Default: 100)

  • inboundNetwork - Inbound network throughput in byte units per second (Default: 10000KiB/s)

  • outboundNetwork - Outbound network throughput in byte units per second (Default: 10000KiB/s)

Because Strimzi Kafka brokers are homogeneous, Cruise Control applies the same capacity limits to every broker it is monitoring.

An example Cruise Control brokerCapacity configuration using bibyte units
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    brokerCapacity:
      disk: 100Gi
      cpuUtilization: 100
      inboundNetwork: 10000KiB/s
      outboundNetwork: 10000KiB/s
    # ...
Additional resources

For more information, refer to the BrokerCapacity schema reference.

Logging configuration

Cruise Control has its own configurable logger:

  • cruisecontrol.root.logger

Cruise Control uses the Apache log4j 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.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
# ...
spec:
  cruiseControl:
    # ...
    logging:
      type: inline
      loggers:
        cruisecontrol.root.logger: "INFO"
    # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
# ...
spec:
  cruiseControl:
    # ...
    logging:
      type: external
      name: customConfigMap
    # ...

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/v1beta1
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: 200m
        memory: 64Mi
      limits:
        cpu: 500m
        memory: 128Mi
    logging: (4)
        type: inline
        loggers:
          cruisecontrol.root.logger: "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 master 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 CPU and memory 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 cruisecontrol.root.logger. 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. Verify that Cruise Control was successfully deployed:

    kubectl get deployments -l app.kubernetes.io/name=strimzi

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.

Table 4. Auto-created topics
Auto-created topic Created by Function

strimzi.cruisecontrol.metrics

Strimzi Metrics Reporter

Stores the raw metrics from the Metrics Reporter in each Kafka broker.

strimzi.cruisecontrol.partitionmetricsamples

Cruise Control

Stores the derived metrics for each partition. These are created by the Metric Sample Aggregator.

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.

What to do next

After configuring and deploying Cruise Control, you can generate optimization proposals.

Additional resources

CruiseControlTemplate schema reference.

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.

Prerequisites
Procedure

  1. Create a KafkaRebalance resource:

    1. To use the default optimization goals defined in the Kafka resource, leave the spec property empty:

      apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec: {}

    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/v1alpha1
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/v1alpha1
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.

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. The KafkaRebalance custom resource cannot be reused.

    • 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 (including Kafka Connect with Source2Image support)

  • 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

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 more information, 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>0.1.13</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>0.1.13</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 (including Kafka Connect with Source2Image support), 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, KafkaConnectS2I, 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, KafkaConnectS2I, 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/v1beta1
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/v1beta1
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/v1alpha1
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/v1beta1
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 encrypted communication between the Kafka and Strimzi components using the TLS protocol. Communication between Kafka brokers (interbroker communication), between ZooKeeper nodes (internodal communication), and between these and the Strimzi operators is always encrypted. 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. Certificates provided by users are not renewed.

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.

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.

You can use this procedure to install your own cluster or client CA certificates.

The procedure describes renewal of CA certificates in PEM format. You can also use certificates in PKCS #12 format.

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 as a CA in the X509v3 Basic Constraints.

Procedure

  1. Put your CA certificate in the corresponding Secret.

    1. Delete the existing secret:

      kubectl delete secret CA-CERTIFICATE-SECRET

      CA-CERTIFICATE-SECRET is the name of the Secret, which is CLUSTER-NAME-cluster-ca-cert for the cluster CA certificate and CLUSTER-NAME-clients-ca-cert for the clients CA certificate.

      Ignore any "Not Exists" errors.

    2. Create and label the new secret

      kubectl create secret generic CA-CERTIFICATE-SECRET --from-file=ca.crt=CA-CERTIFICATE-FILENAME

  2. Put your CA key in the corresponding Secret.

    1. Delete the existing secret:

      kubectl delete secret CA-KEY-SECRET

      CA-KEY-SECRET is the name of CA key, which is CLUSTER-NAME-cluster-ca for the cluster CA key and 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-SECRET-FILENAME

  3. Label the secrets with the labels strimzi.io/kind=Kafka and strimzi.io/cluster=CLUSTER-NAME:

    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

  4. 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/v1beta1
spec:
  # ...
  clusterCa:
    generateCertificateAuthority: false
Additional resources

9.2. Secrets

Strimzi uses Secrets to store private keys and certificates for Kafka cluster components and clients. Secrets are used for establishing TLS encrypted connections between Kafka brokers, and between brokers and clients. They are also used for mutual TLS authentication.

  • A Cluster Secret contains a cluster CA certificate to sign Kafka broker certificates, and is used by a connecting client to establish a TLS encrypted connection with the Kafka cluster to validate broker identity.

  • A Client Secret contains a client CA certificate for a user to sign its own client certificate to allow mutual authentication against the Kafka cluster. The broker validates the client identity through the client CA certificate itself.

  • A User Secret contains a private key and certificate, which are generated and signed by the client CA certificate when a new user is created. The key and certificate are used for authentication and authorization when accessing the cluster.

Secrets provide private keys and certificates in PEM and PKCS #12 formats. 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 Java applications. PKCS #12 storage provides a truststore (or keystore) that can be used directly.

All keys are 2048 bits in size.

9.2.1. PKCS #12 storage

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.

9.2.2. Cluster CA Secrets

The following tables describe the Cluster Secrets that are managed by the Cluster Operator in a Kafka cluster.

Only the <cluster>-cluster-ca-cert Secret needs to be used by clients. All other Secrets described only need to be accessed by the Strimzi components. You can enforce this using Kubernetes role-based access controls, if necessary.

Table 5. Fields in the <cluster>-cluster-ca Secret
Field Description

ca.key

The current private key for the cluster CA.
Table 6. Fields in the <cluster>-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.
Note
 The CA certificates in <cluster>-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>-kafka-brokers Secret
Field Description

<cluster>-kafka-<num>.p12

PKCS #12 archive file for storing certificates and keys.

<cluster>-kafka-<num>.password

Password for protecting the PKCS #12 archive file.

<cluster>-kafka-<num>.crt

Certificate for Kafka broker pod <num>. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

<cluster>-kafka-<num>.key

Private key for Kafka broker pod <num>.
Table 8. Fields in the <cluster>-zookeeper-nodes Secret
Field Description

<cluster>-zookeeper-<num>.p12

PKCS #12 archive file for storing certificates and keys.

<cluster>-zookeeper-<num>.password

Password for protecting the PKCS #12 archive file.

<cluster>-zookeeper-<num>.crt

Certificate for ZooKeeper node <num>. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

<cluster>-zookeeper-<num>.key

Private key for ZooKeeper pod <num>.
Table 9. Fields in the <cluster>-entity-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 Entity Operator and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

entity-operator.key

Private key for TLS communication between the Entity Operator and Kafka or ZooKeeper.

9.2.3. Client CA Secrets

Table 10. Clients CA Secrets managed by the Cluster Operator in <cluster>
Secret name Field within Secret Description

<cluster>-clients-ca

ca.key

The current private key for the clients CA.

<cluster>-clients-ca-cert

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.

The certificates in <cluster>-clients-ca-cert are those which the Kafka brokers trust.

Note
 <cluster>-clients-ca is used to sign certificates of client applications. It needs to 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.

9.2.4. User Secrets

Table 11. Secrets managed by the User Operator
Secret name Field within Secret Description

<user>

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

9.3. 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 auto-generated CA certificates, 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:

  • 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/v1beta1
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 whether the relevant setting is enabled in either Kafka.spec.clusterCa.generateCertificateAuthority or Kafka.spec.clientsCa.generateCertificateAuthority.

9.3.1. Renewal process with generated CAs

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 applications

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.

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.

Client certificate renewal

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.

9.3.3. Renewing CA certificates manually

Cluster and clients CA certificates auto-renew at the start of their respective certificate renewal periods. If Kafka.spec.clusterCa.generateCertificateAuthority and Kafka.spec.clientsCa.generateCertificateAuthority are set to false, the CA certificates do not auto-renew.

You can 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.

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 12. 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. Renewing your own CA certificates

This procedure describes how to renew CA certificates and keys that you previously installed. You need to follow this procedure during the renewal period in order to replace CA certificates which will soon expire.

You can use this procedure to install your own cluster or client CA certificates.

The procedure describes renewal of CA certificates in PEM format. You can also use certificates in PKCS #12 format.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster in which you previously installed your own CA certificates and private keys.

  • New cluster and clients X.509 certificates and keys in PEM format.

These could be generated using an openssl command, such as:

openssl req -x509 -new -days NUMBER-OF-DAYS-VALID --nodes -out ca.crt -keyout ca.key
Procedure

  1. Check the details of the current CA certificates in the Secret:

    kubectl describe secret CA-CERTIFICATE-SECRET

    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.

  2. Create a directory to contain the existing CA certificates in the secret.

    mkdir new-ca-cert-secret
cd new-ca-cert-secret

  3. Fetch the secret for each CA certificate you wish to renew:

    kubectl get secret CA-CERTIFICATE-SECRET -o 'jsonpath={.data.CA-CERTIFICATE}' | base64 -d > CA-CERTIFICATE

    Replace CA-CERTIFICATE with the name of each CA certificate.

  4. Rename the old ca.crt file as ca-DATE.crt, where DATE is the certificate expiry date in the format YEAR-MONTH-DAYTHOUR-MINUTE-SECONDZ.

    For example ca-2018-09-27T17-32-00Z.crt.

    mv ca.crt ca-$(date -u -d$(openssl x509 -enddate -noout -in ca.crt | sed 's/.*=//') +'%Y-%m-%dT%H-%M-%SZ').crt

  5. Copy your new CA certificate into the directory, naming it ca.crt:

    cp PATH-TO-NEW-CERTIFICATE ca.crt

  6. Put your CA certificate in the corresponding Secret.

    1. Delete the existing secret:

      kubectl delete secret CA-CERTIFICATE-SECRET

      CA-CERTIFICATE-SECRET is the name of the Secret, as returned in the first step.

      Ignore any "Not Exists" errors.

    2. Recreate the secret:

      kubectl create secret generic CA-CERTIFICATE-SECRET --from-file=.

  7. Delete the directory you created:

    cd ..
rm -r new-ca-cert-secret

  8. Put your CA key in the corresponding Secret.

    1. Delete the existing secret:

      kubectl delete secret CA-KEY-SECRET

      CA-KEY-SECRET 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.

    2. Recreate the secret with the new CA key:

      kubectl create secret generic CA-KEY-SECRET --from-file=ca.key=CA-KEY-SECRET-FILENAME

  9. Label the secrets with the labels strimzi.io/kind=Kafka and strimzi.io/cluster=KAFKA-CLUSTER-NAME:

    kubectl label secret CA-CERTIFICATE-SECRET strimzi.io/kind=Kafka strimzi.io/cluster=KAFKA-CLUSTER-NAME
kubectl label secret CA-KEY-SECRET strimzi.io/kind=Kafka strimzi.io/cluster=KAFKA-CLUSTER-NAME

9.4. Replacing private keys

You can replace the private keys used by the cluster CA and clients CA certificates. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.

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 13. 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

9.5. TLS connections

9.5.1. ZooKeeper communication

Communication between the ZooKeeper nodes on all ports as well as between clients and ZooKeeper is encrypted.

9.5.2. Kafka interbroker communication

Communication between Kafka brokers is done through an internal listener on port 9091, which is encrypted by default and not accessible to Kafka clients.

Communication between Kafka brokers and ZooKeeper nodes is also encrypted.

9.5.3. 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.

9.5.4. Cruise Control

Cruise Control uses encryption for communication with both Kafka and ZooKeeper. A TLS sidecar is used when communicating with ZooKeeper.

9.5.5. Kafka Client connections

Encrypted or unencrypted communication between Kafka brokers and clients is configured using the tls property for spec.kafka.listeners.

9.6. 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.

9.7. 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 generated <cluster-name>-cluster-ca-cert Secret.

    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

  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 generated <cluster-name>-cluster-ca-cert Secret.

    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.

9.8. Kafka listener certificates

You can provide your own server certificates and private keys for the following types of listeners:

  • Internal TLS listeners for communication within the Kubernetes cluster

  • External listeners (route, loadbalancer, ingress, and nodeport types), which have TLS encryption enabled, for communication between Kafka clients and Kafka brokers

These user-provided certificates are called Kafka listener certificates.

Providing Kafka listener certificates for external listeners allows you to leverage existing security infrastructure, such as your organization’s private CA or a public CA. Kafka clients will connect to Kafka brokers using Kafka listener certificates rather than certificates signed by the cluster CA or clients CA.

You must manually renew Kafka listener certificates when needed.

9.8.1. 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

9.8.2. 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 14. 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 15. 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 Connect S2I

kafkaconnects2i

kcs2i

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. Usng -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[?(@.type=="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 tls listeners.

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

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

By changing the type condition to @.type=="external" or @.type=="plain" you can also get the address of the other Kafka listeners.

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

192.168.1.247:9094

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 16. 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.

KafkaConnectS2I

KafkaConnectS2IStatus schema reference

The Kafka Connect cluster with Source-to-Image support, 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:

  • 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/v1beta1
kind: Kafka
metadata:
spec:
  # ...
status:
  conditions: (1)
  - lastTransitionTime: 2019-07-23T23:46:57+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
    # ...

  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.

    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. 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

10.2.1. 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.

10.3. 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

10.3.1. 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.

10.3.2. 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.

10.3.3. 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.

    This time you deploy your cluster without the Topic Operator, delete the Topic Operator data in ZooKeeper, and then redeploy it so that the Topic Operator 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 apply -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. Run an exec command to one of the Kafka broker pods to open the ZooKeeper shell script.

      For example, where my-cluster-kafka-0 is the name of the broker pod:

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

    3. Delete the whole /strimzi path to remove the Topic Operator storage:

      deleteall /strimzi

    4. Enable the Topic Operator by redeploying the Kafka cluster with the topicOperator property to recreate the KafkaTopic resources.

      For example:

      apiVersion: kafka.strimzi.io/v1beta1
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

10.4. Tuning client configuration

Use configuration properties to optimize the performance of Kafka 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. For example, for producers you can tune latency and throughput of messages so that clients can respond to data in real time. Or you can change the configuration to provide stronger message durability guarantees.

You might start by analyzing client metrics to gauge where to make your initial configurations, then make incremental changes and further comparisons until you have the configuration you need.

10.4.1. 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.

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.

# ...
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.

Data durability

You can apply greater data durability, to minimize the likelihood that messages are lost, using message delivery acknowledgments.

# ...
acks=all (1)
# ...

  1. Specifying acks=all forces a partition leader to replicate messages to a certain number of followers before acknowledging that the message request was successfully received. Because of the additional checks, acks=all increases the latency between the producer sending a message and receiving acknowledgment.

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.
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, enabling idempotency makes sense for ordered delivery.

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 are not using acks=all and 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.

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.

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.

10.4.2. 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.

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.

# ...
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.

Consumer groups are used to share a typically large data stream generated by multiple producers from a given topic. Consumers are grouped using a group.id, allowing messages to be spread across the members.

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 with the same group.id property are in the same group. 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.

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.

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.

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.

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.

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=10000 (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.

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 is assigned a new member id, but as a static member it continues with the same instance id, and the same assignment of topic partitions is made.

If the consumer application does not make a call to poll at least every max.poll.interval.ms milliseconds, the consumer is considered to be failed, causing a rebalance. If the application cannot process all the records returned from poll in time, you can avoid a rebalance by using the max.poll.interval.ms property to specify the interval in milliseconds between polls for new messages from a consumer. Or you can use the max.poll.records property to set a maximum limit on the number of records returned from the consumer buffer, allowing your application to process fewer records within the max.poll.interval.ms limit.

# ...
group.instance.id=UNIQUE-ID (1)
max.poll.interval.ms=300000 (2)
max.poll.records=500 (3)
# ...

  1. The unique instance id ensures that a new consumer instance receives the same assignment of topic partitions.

  2. Set the interval to check the consumer is continuing to process messages.

  3. Sets the number of processed records returned from the consumer.

10.5. Uninstalling Strimzi

This procedure describes how to uninstall Strimzi and remove resources related to the deployment.

Prerequisites

In order to perform this procedure, identify resources created specifically for a deployment and referenced from the Strimzi resource.

Such resources include:

  • Secrets (Custom CAs and certificates, Kafka Connect secrets, and other Kafka secrets)

  • Logging ConfigMaps (of type external)

These are resources referenced by Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge configuration.

Procedure

  1. Delete the Cluster Operator Deployment, related CustomResourceDefinitions, and RBAC resources:

    kubectl delete -f install/cluster-operator
    Warning
    		 Deleting CustomResourceDefinitions results in the garbage collection of the corresponding custom resources (Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge) and the resources dependent on them (Deployments, StatefulSets, and other dependent resources).

  2. Delete the resources you identified in the prerequisites.

Appendix A: Frequently asked questions

A.1.1. 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:

A.1.2. 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.

A.1.3. 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 Strimzi guide.

A.1.4. 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.

A.1.5. 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", "");

Appendix B: Custom Resource API Reference

B.1. Common configuration properties

Common configuration properties apply to more than one resource.

B.1.1. replicas

Use the replicas property to configure replicas.

The type of replication depends on the resource.

  • KafkaTopic uses a replication factor to configure the number of replicas of each partition within a Kafka cluster.

  • Kafka components use replicas to configure the number of pods in a deployment to provide better availability and scalability.

Note
 When running a Kafka component on Kubernetes it may not be necessary to run multiple replicas for high availability. When the node where the component is deployed crashes, Kubernetes will automatically reschedule the Kafka component pod to a different node. However, running Kafka components with multiple replicas can provide faster failover times as the other nodes will be up and running.

B.1.2. bootstrapServers

Use the bootstrapServers property to configure a list of bootstrap servers.

The bootstrap server lists can refer to Kafka clusters that are not deployed in the same Kubernetes cluster. They can also refer to a Kafka cluster not deployed by Strimzi.

If on the same Kubernetes cluster, each list must ideally contain the Kafka cluster bootstrap service which is named CLUSTER-NAME-kafka-bootstrap and a port number. If deployed by Strimzi but on different Kubernetes clusters, the list content depends on the approach used for exposing the clusters (routes, nodeports or loadbalancers).

When using Kafka with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the given cluster.

B.1.3. ssl

Use the three allowed ssl configuration options for client connection using a specific cipher suite for a TLS version. A cipher suite combines algorithms for secure connection and data transfer.

You can also configure the ssl.endpoint.identification.algorithm property to enable or disable hostname verification.

Example SSL configuration
# ...
spec:
  config:
    ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (1)
    ssl.enabled.protocols: "TLSv1.2" (2)
    ssl.protocol: "TLSv1.2" (3)
    ssl.endpoint.identification.algorithm: HTTPS (4)
# ...

  1. The cipher suite for TLS using a combination of ECDHE key exchange mechanism, RSA authentication algorithm, AES bulk encyption algorithm and SHA384 MAC algorithm.

  2. The SSl protocol TLSv1.2 is enabled.

  3. Specifies the TLSv1.2 protocol to generate the SSL context. Allowed values are TLSv1.1 and TLSv1.2.

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

B.1.4. trustedCertificates

Having set tls to configure TLS encryption, use the trustedCertificates property to provide a list of secrets with key names under which the certificates are stored in X.509 format.

You can use the secrets created by the Cluster Operator for the Kafka cluster, or you can create your own TLS certificate file, then create a Secret from the file:

kubectl create secret generic MY-SECRET \
--from-file=MY-TLS-CERTIFICATE-FILE.crt
Example TLS encryption configuration
tls:
  trustedCertificates:
    - secretName: my-cluster-cluster-cert
      certificate: ca.crt
    - secretName: my-cluster-cluster-cert
      certificate: ca2.crt

If certificates are stored in the same secret, it can be listed multiple times.

If you want to enable TLS, but use the default set of public certification authorities shipped with Java, you can specify trustedCertificates as an empty array:

Example of enabling TLS with the default Java certificates
tls:
  trustedCertificates: []

For information on configuring TLS client authentication, see KafkaClientAuthenticationTls schema reference.

B.1.5. resources

You request CPU and memory resources for components. Limits specify the maximum resources that can be consumed by a given container.

Resource requests and limits for the Topic Operator and User Operator are set in the Kafka resource.

Use the reources.requests and resources.limits properties to configure resource requests and limits.

For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.

Strimzi supports requests and limits for the following types of resources:

  • cpu

  • memory

Strimzi uses the Kubernetes syntax for specifying these resources.

For more information about managing computing resources on Kubernetes, see Managing Compute Resources for Containers.

Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important
 If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled.

A request may be configured for one or more supported resources.

Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

A resource may be configured for one or more supported limits.

Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).

  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Note
 The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed.

For more information on CPU specification, see the Meaning of CPU.

Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.

  • To specify memory in gigabytes, use the G suffix. For example 1G.

  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.

  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

For more details about memory specification and additional supported units, see Meaning of memory.

B.1.6. image

Use the image property to configure the container image used by the component.

Overriding container images is recommended only in special situations where you need to use a different container registry or a customized image.

For example, if your network does not allow access to the container repository used by Strimzi, you can copy the Strimzi images or build them from the source. However, if the configured image is not compatible with Strimzi images, it might not work properly.

A copy of the container image might also be customized and used for debugging.

You can specify which container image to use for a component using the image property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.jmxTrans

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaMirrorMaker2.spec

  • KafkaBridge.spec

Configuring the image property for Kafka, Kafka Connect, and Kafka MirrorMaker

Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka MirrorMaker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:

  • STRIMZI_KAFKA_IMAGES

  • STRIMZI_KAFKA_CONNECT_IMAGES

  • STRIMZI_KAFKA_CONNECT_S2I_IMAGES

  • STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

These environment variables contain mappings between the Kafka versions and their corresponding images. The mappings are used together with the image and version properties:

  • If neither image nor version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable.

  • If image is given but version is not, then the given image is used and the version is assumed to be the Cluster Operator’s default Kafka version.

  • If version is given but image is not, then the image that corresponds to the given version in the environment variable is used.

  • If both version and image are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.

The image and version for the different components can be configured in the following properties:

  • For Kafka in spec.kafka.image and spec.kafka.version.

  • For Kafka Connect, Kafka Connect S2I, and Kafka MirrorMaker in spec.image and spec.version.

Warning
 It is recommended to provide only the version and leave the image property unspecified. This reduces the chance of making a mistake when configuring the custom resource. If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.

Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.20.0 container image.

  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.20.0 container image.

  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.20.0-kafka-2.6.0 container image.

  • For Kafka Exporter:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.20.0-kafka-2.6.0 container image.

  • For Kafka Bridge:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka-bridge:0.19.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_INIT_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.20.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_JMXTRANS_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.20.0 container image.

Example of container image configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

B.1.7. livenessProbe and readinessProbe healthchecks

Use the livenessProbe and readinessProbe properties to configure healthcheck probes supported in Strimzi.

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.

For more details about the probes, see Configure Liveness and Readiness Probes.

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds

  • timeoutSeconds

  • periodSeconds

  • successThreshold

  • failureThreshold

Example of liveness and readiness probe configuration
# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...

For more information about the livenessProbe and readinessProbe options, see Probe schema reference.

B.1.8. metrics

Use the metrics property to enable and configure Prometheus metrics.

The metrics property can also contain additional configuration for the Prometheus JMX exporter. Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics.

To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

When metrics are enabled, they are exposed on port 9404.

When the metrics property is not defined in the resource, the Prometheus metrics are disabled.

For more information about setting up and deploying Prometheus and Grafana, see Introducing Metrics to Kafka in the Deploying Strimzi guide.

B.1.9. jvmOptions

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaMirrorMaker2.spec

  • KafkaBridge.spec

Only the following JVM options are supported:

-Xms

Configures the minimum initial allocation heap size when the JVM starts.

-Xmx

Configures the maximum heap size.

Note
 The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container.

  • If there is a memory limit, the JVM’s minimum and maximum memory is set to a value corresponding to the limit.

  • If there is no memory limit, the JVM’s minimum memory is set to 128M. The JVM’s maximum memory is not defined to allow the memory to grow as needed, which is ideal for single node environments in test and development.

Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.

  • If -Xmx is set without also setting an appropriate Kubernetes memory limit, it is possible that the container will be killed should the Kubernetes node experience memory pressure (from other Pods running on it).

  • If -Xmx is set without also setting an appropriate Kubernetes memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • Set the memory request and the memory limit to the same value

  • Use a memory request that is at least 4.5 × the -Xmx

  • Consider setting -Xms to the same value as -Xmx

Important
 Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as an operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and ZooKeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation is multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server
# ...
jvmOptions:
  "-server": true
# ...
Note
 When neither of the two options (-server and -XX) are specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS is used.

-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object
jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note
 When neither of the two options (-server and -XX) are specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS is used.

B.1.10. Garbage collector logging

The jvmOptions property also allows you to enable and disable garbage collector (GC) logging. GC logging is disabled by default. To enable it, set the gcLoggingEnabled property as follows:

Example of enabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: true
# ...

B.2. Kafka schema reference

Property Description

spec

The specification of the Kafka and ZooKeeper clusters, and Topic Operator.

KafkaSpec

status

The status of the Kafka and ZooKeeper clusters, and Topic Operator.

KafkaStatus

B.3. KafkaSpec schema reference

Used in: Kafka

Property Description

kafka

Configuration of the Kafka cluster.

KafkaClusterSpec

zookeeper

Configuration of the ZooKeeper cluster.

ZookeeperClusterSpec

topicOperator

The property topicOperator has been deprecated. This feature should now be configured at path spec.entityOperator.topicOperator. Configuration of the Topic Operator.

TopicOperatorSpec

entityOperator

Configuration of the Entity Operator.

EntityOperatorSpec

clusterCa

Configuration of the cluster certificate authority.

CertificateAuthority

clientsCa

Configuration of the clients certificate authority.

CertificateAuthority

cruiseControl

Configuration for Cruise Control deployment. Deploys a Cruise Control instance when specified.

CruiseControlSpec

jmxTrans

Configuration for JmxTrans. When the property is present a JmxTrans deployment is created for gathering JMX metrics from each Kafka broker. For more information see JmxTrans GitHub.

JmxTransSpec

kafkaExporter

Configuration of the Kafka Exporter. Kafka Exporter can provide additional metrics, for example lag of consumer group at topic/partition.

KafkaExporterSpec

maintenanceTimeWindows

A list of time windows for maintenance tasks (that is, certificates renewal). Each time window is defined by a cron expression.

string array

B.4. KafkaClusterSpec schema reference

Used in: KafkaSpec

Configures a Kafka cluster.

B.4.1. listeners

Use the listeners property to configure listeners to provide access to Kafka brokers.

Example configuration of a plain (unencrypted) listener without authentication
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: false
    # ...
  zookeeper:
    # ...

B.4.2. config

Use the config properties to configure Kafka brokers as keys with values in one of the following JSON types:

  • String

  • Number

  • Boolean

You can specify and configure all of the options in the "Broker Configs" section of the Apache Kafka documentation apart from those managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • listeners

  • advertised.

  • broker.

  • listener.

  • host.name

  • port

  • inter.broker.listener.name

  • sasl.

  • ssl.

  • security.

  • password.

  • principal.builder.class

  • log.dir

  • zookeeper.connect

  • zookeeper.set.acl

  • authorizer.

  • super.user

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Cluster Operator log file. All other supported options are passed to Kafka.

There are exceptions to the forbidden options. For client connection using a specific cipher suite for a TLS version, you can configure allowed ssl properties. You can also configure the zookeeper.connection.timeout.ms property to set the maximum time allowed for establishing a ZooKeeper connection.

Example Kafka broker configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    config:
      num.partitions: 1
      num.recovery.threads.per.data.dir: 1
      default.replication.factor: 3
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 1
      log.retention.hours: 168
      log.segment.bytes: 1073741824
      log.retention.check.interval.ms: 300000
      num.network.threads: 3
      num.io.threads: 8
      socket.send.buffer.bytes: 102400
      socket.receive.buffer.bytes: 102400
      socket.request.max.bytes: 104857600
      group.initial.rebalance.delay.ms: 0
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384"
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      zookeeper.connection.timeout.ms: 6000
    # ...
Property Description

replicas

The number of pods in the cluster.

integer

image

The docker image for the pods. The default value depends on the configured Kafka.spec.kafka.version.

string

storage

Storage configuration (disk). Cannot be updated. The type depends on the value of the storage.type property within the given object, which must be one of [ephemeral, persistent-claim, jbod].

EphemeralStorage, PersistentClaimStorage, JbodStorage

listeners

Configures listeners of Kafka brokers.

GenericKafkaListener array or KafkaListeners

authorization

Authorization configuration for Kafka brokers. The type depends on the value of the authorization.type property within the given object, which must be one of [simple, opa, keycloak].

KafkaAuthorizationSimple, KafkaAuthorizationOpa, KafkaAuthorizationKeycloak

config

Kafka broker config properties with the following prefixes cannot be set: listeners, advertised., broker., listener., host.name, port, inter.broker.listener.name, sasl., ssl., security., password., principal.builder.class, log.dir, zookeeper.connect, zookeeper.set.acl, zookeeper.ssl, zookeeper.clientCnxnSocket, authorizer., super.user, cruise.control.metrics.topic, cruise.control.metrics.reporter.bootstrap.servers (with the exception of: zookeeper.connection.timeout.ms, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols,cruise.control.metrics.topic.num.partitions, cruise.control.metrics.topic.replication.factor, cruise.control.metrics.topic.retention.ms,cruise.control.metrics.topic.auto.create.retries, cruise.control.metrics.topic.auto.create.timeout.ms).

map

rack

Configuration of the broker.rack broker config.

Rack

brokerRackInitImage

The image of the init container used for initializing the broker.rack.

string

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.kafka.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.kafka.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

jmxOptions

JMX Options for Kafka brokers.

KafkaJmxOptions

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

logging

Logging configuration for Kafka. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

tlsSidecar

The property tlsSidecar has been deprecated. TLS sidecar configuration.

TlsSidecar

template

Template for Kafka cluster resources. The template allows users to specify how are the StatefulSet, Pods and Services generated.

KafkaClusterTemplate

version

The kafka broker version. Defaults to 2.6.0. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

B.5. EphemeralStorage schema reference

Used in: JbodStorage, KafkaClusterSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type EphemeralStorage from PersistentClaimStorage. It must have the value ephemeral for the type EphemeralStorage.

Property Description

id

Storage identification number. It is mandatory only for storage volumes defined in a storage of type 'jbod'.

integer

sizeLimit

When type=ephemeral, defines the total amount of local storage required for this EmptyDir volume (for example 1Gi).

string

type

Must be ephemeral.

string

B.6. PersistentClaimStorage schema reference

Used in: JbodStorage, KafkaClusterSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type PersistentClaimStorage from EphemeralStorage. It must have the value persistent-claim for the type PersistentClaimStorage.

Property Description

type

Must be persistent-claim.

string

size

When type=persistent-claim, defines the size of the persistent volume claim (i.e 1Gi). Mandatory when type=persistent-claim.

string

selector

Specifies a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume.

map

deleteClaim

Specifies if the persistent volume claim has to be deleted when the cluster is un-deployed.

boolean

class

The storage class to use for dynamic volume allocation.

string

id

Storage identification number. It is mandatory only for storage volumes defined in a storage of type 'jbod'.

integer

overrides

Overrides for individual brokers. The overrides field allows to specify a different configuration for different brokers.

PersistentClaimStorageOverride array

B.7. PersistentClaimStorageOverride schema reference

Used in: PersistentClaimStorage

Property Description

class

The storage class to use for dynamic volume allocation for this broker.

string

broker

Id of the kafka broker (broker identifier).

integer

B.8. JbodStorage schema reference

Used in: KafkaClusterSpec

The type property is a discriminator that distinguishes the use of the type JbodStorage from EphemeralStorage, PersistentClaimStorage. It must have the value jbod for the type JbodStorage.

Property Description

type

Must be jbod.

string

volumes

List of volumes as Storage objects representing the JBOD disks array.

EphemeralStorage, PersistentClaimStorage array

B.9. GenericKafkaListener schema reference

Used in: KafkaClusterSpec

Configures listeners to connect to Kafka brokers within and outside Kubernetes.

You configure the listeners in the Kafka resource.

Example Kafka resource showing listener configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    #...
    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: false
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
      - name: external1
        port: 9094
        type: route
        tls: true
      - name: external2
        port: 9095
        type: ingress
        tls: false
        authentication:
          type: tls
        configuration:
          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
    #...

B.9.1. listeners

You configure Kafka broker listeners using the listeners property in the Kafka resource. Listeners are defined as an array.

Example listener configuration
listeners:
  - name: plain
    port: 9092
    type: internal
    tls: false

The name and port must be unique within the Kafka cluster. The name can be up to 25 characters long, comprising lower-case letters and numbers. Allowed port numbers are 9092 and higher with the exception of ports 9404 and 9999, which are already used for Prometheus and JMX.

By specifying a unique name and port for each listener, you can configure multiple listeners.

B.9.2. type

The type is set as internal, or for external listeners, as route, loadbalancer, nodeport or ingress.

internal

You can configure internal listeners with or without encryption using the tls property.

Example internal listener configuration
#...
spec:
  kafka:
    #...
    listeners:
      #...
      - name: plain
        port: 9092
        type: internal
        tls: false
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
    #...
route

Configures an external listener to expose Kafka using OpenShift Routes and the HAProxy router.

A dedicated Route is created for every Kafka broker pod. An additional Route is created to serve as a Kafka bootstrap address. Kafka clients can use these Routes to connect to Kafka on port 443. The client connects on port 443, the default router port, but traffic is then routed to the port you configure, which is 9094 in this example.

Example route listener configuration
#...
spec:
  kafka:
    #...
    listeners:
      #...
      - name: external1
        port: 9094
        type: route
        tls: true
    #...
ingress

Configures an external listener to expose Kafka using Kubernetes Ingress and the NGINX Ingress Controller for Kubernetes.

A dedicated Ingress resource is created for every Kafka broker pod. An additional Ingress resource is created to serve as a Kafka bootstrap address. Kafka clients can use these Ingress resources to connect to Kafka on port 443. The client connects on port 443, the default controller port, but traffic is then routed to the port you configure, which is 9095 in the following example.

You must specify the hostnames used by the bootstrap and per-broker services using GenericKafkaListenerConfigurationBootstrap and GenericKafkaListenerConfigurationBroker properties.

Example ingress listener configuration
#...
spec:
  kafka:
    #...
    listeners:
      #...
      - name: external2
        port: 9095
        type: ingress
        tls: false
        authentication:
          type: tls
        configuration:
          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
  #...
Note
 External listeners using Ingress are currently only tested with the NGINX Ingress Controller for Kubernetes.
loadbalancer

Configures an external listener to expose Kafka Loadbalancer type Services.

A new loadbalancer service is created for every Kafka broker pod. An additional loadbalancer is created to serve as a Kafka bootstrap address. Loadbalancers listen to the specified port number, which is port 9094 in the following example.

You can use the loadBalancerSourceRanges property to configure source ranges to restrict access to the specified IP addresses.

Example loadbalancer listener configuration
#...
spec:
  kafka:
    #...
    listeners:
      - name: external3
        port: 9094
        type: loadbalancer
        tls: true
        configuration:
          loadBalancerSourceRanges:
            - 10.0.0.0/8
            - 88.208.76.87/32
    #...
nodeport

Configures an external listener to expose Kafka using NodePort type Services.

Kafka clients connect directly to the nodes of Kubernetes. An additional NodePort type of service is created to serve as a Kafka bootstrap address.

When configuring the advertised addresses for the Kafka broker pods, Strimzi uses the address of the node on which the given pod is running. You can use preferredNodePortAddressType property to configure the first address type checked as the node address.

Example nodeport listener configuration
#...
spec:
  kafka:
    #...
    listeners:
      #...
      - name: external4
        port: 9095
        type: nodeport
        tls: false
        configuration:
          preferredNodePortAddressType: InternalDNS
    #...
Note
 TLS hostname verification is not currently supported when exposing Kafka clusters using node ports.

B.9.3. port

The port number is the port used in the Kafka cluster, which might not be the same port used for access by a client.

  • loadbalancer listeners use the specified port number, as do internal listeners

  • ingress and route listeners use port 443 for access

  • nodeport listeners use the port number assigned by Kubernetes

For client connection, use the address and port for the bootstrap service of the listener. You can retrieve this from the status of the Kafka resource.

Example command to retrieve the address and port for client connection
kubectl get kafka KAFKA-CLUSTER-NAME -o=jsonpath='{.status.listeners[?(@.type=="external")].bootstrapServers}{"\n"}'
Note
 Listeners cannot be configured to use the ports set aside for interbroker communication (9091) and metrics (9404).

B.9.4. tls

The TLS property is required.

By default, TLS encryption is not enabled. To enable it, set the tls property to true.

TLS encryption is always used with route listeners.

B.9.5. authentication

Authentication for the listener can be specified as:

  • Mutual TLS (tls)

  • SCRAM-SHA-512 (scram-sha-512)

  • Token-based OAuth 2.0 (oauth).

B.9.6. networkPolicyPeers

Use networkPolicyPeers to configure network policies that restrict access to a listener at the network level. The following example shows a networkPolicyPeers configuration for a plain and a tls listener.

listeners:
  #...
  - name: plain
    port: 9092
    type: internal
    tls: true
    authentication:
      type: scram-sha-512
    networkPolicyPeers:
      - podSelector:
          matchLabels:
            app: kafka-sasl-consumer
      - podSelector:
          matchLabels:
            app: kafka-sasl-producer
  - name: tls
    port: 9093
    type: internal
    tls: true
    authentication:
      type: tls
    networkPolicyPeers:
      - namespaceSelector:
          matchLabels:
            project: myproject
      - namespaceSelector:
          matchLabels:
            project: myproject2
# ...

In the example:

  • Only application pods matching the labels app: kafka-sasl-consumer and app: kafka-sasl-producer can connect to the plain listener. The application pods must be running in the same namespace as the Kafka broker.

  • Only application pods running in namespaces matching the labels project: myproject and project: myproject2 can connect to the tls listener.

The syntax of the networkPolicyPeers field is the same as the from field in NetworkPolicy resources.

Backwards compatibility with KafkaListeners

GenericKafkaListener replaces the KafkaListeners schema, which is now deprecated.

To convert the listeners configured using the KafkaListeners schema into the format of the GenericKafkaListener schema, with backwards compatibility, use the following names, ports and types:

listeners:
  #...
  - name: plain
    port: 9092
    type: internal
    tls: false
  - name: tls
    port: 9093
    type: internal
    tls: true
  - name: external
    port: 9094
    type: EXTERNAL-LISTENER-TYPE (1)
    tls: true
# ...

  1. Options: ingress, loadbalancer, nodeport, route

Property Description

name

Name of the listener. The name will be used to identify the listener and the related Kubernetes objects. The name has to be unique within given a Kafka cluster. The name can consist of lowercase characters and numbers and be up to 11 characters long.

string

port

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.

integer

type

Type of the listener. Currently the supported types are internal, route, loadbalancer, nodeport and ingress.

* internal type exposes Kafka internally only within the Kubernetes cluster. * route type uses OpenShift Routes to expose Kafka. * loadbalancer type uses LoadBalancer type services to expose Kafka. * nodeport type uses NodePort type services to expose Kafka. * ingress type uses Kubernetes Nginx Ingress to expose Kafka. .

string (one of [ingress, internal, route, loadbalancer, nodeport])

tls

Enables TLS encryption on the listener. This is a required property.

boolean

authentication

Authentication configuration for this listener. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

configuration

Additional listener configuration.

GenericKafkaListenerConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.10. KafkaListenerAuthenticationTls schema reference

Used in: GenericKafkaListener, KafkaListenerExternalIngress, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalRoute, KafkaListenerPlain, KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationTls from KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth. It must have the value tls for the type KafkaListenerAuthenticationTls.

Property Description

type

Must be tls.

string

B.11. KafkaListenerAuthenticationScramSha512 schema reference

Used in: GenericKafkaListener, KafkaListenerExternalIngress, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalRoute, KafkaListenerPlain, KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationScramSha512 from KafkaListenerAuthenticationTls, KafkaListenerAuthenticationOAuth. It must have the value scram-sha-512 for the type KafkaListenerAuthenticationScramSha512.

Property Description

type

Must be scram-sha-512.

string

B.12. KafkaListenerAuthenticationOAuth schema reference

Used in: GenericKafkaListener, KafkaListenerExternalIngress, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalRoute, KafkaListenerPlain, KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationOAuth from KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512. It must have the value oauth for the type KafkaListenerAuthenticationOAuth.

Property Description

accessTokenIsJwt

Configure whether the access token is treated as JWT. This must be set to false if the authorization server returns opaque tokens. Defaults to true.

boolean

checkAccessTokenType

Configure whether the access token type check is performed or not. This should be set to false if the authorization server does not include 'typ' claim in JWT token. Defaults to true.

boolean

checkIssuer

Enable or disable issuer checking. By default issuer is checked using the value configured by validIssuerUri. Default value is true.

boolean

clientId

OAuth Client ID which the Kafka broker can use to authenticate against the authorization server and use the introspect endpoint URI.

string

clientSecret

Link to Kubernetes Secret containing the OAuth client secret which the Kafka broker can use to authenticate against the authorization server and use the introspect endpoint URI.

GenericSecretSource

disableTlsHostnameVerification

Enable or disable TLS hostname verification. Default value is false.

boolean

enableECDSA

Enable or disable ECDSA support by installing BouncyCastle crypto provider. Default value is false.

boolean

fallbackUserNameClaim

The fallback username claim to be used for the user id if the claim specified by userNameClaim is not present. This is useful when client_credentials authentication only results in the client id being provided in another claim. It only takes effect if userNameClaim is set.

string

fallbackUserNamePrefix

The prefix to use with the value of fallbackUserNameClaim to construct the user id. This only takes effect if fallbackUserNameClaim is true, and the value is present for the claim. Mapping usernames and client ids into the same user id space is useful in preventing name collisions.

string

introspectionEndpointUri

URI of the token introspection endpoint which can be used to validate opaque non-JWT tokens.

string

jwksEndpointUri

URI of the JWKS certificate endpoint, which can be used for local JWT validation.

string

jwksExpirySeconds

Configures how often are the JWKS certificates considered valid. The expiry interval has to be at least 60 seconds longer then the refresh interval specified in jwksRefreshSeconds. Defaults to 360 seconds.

integer

jwksMinRefreshPauseSeconds

The minimum pause between two consecutive refreshes. When an unknown signing key is encountered the refresh is scheduled immediately, but will always wait for this minimum pause. Defaults to 1 second.

integer

jwksRefreshSeconds

Configures how often are the JWKS certificates refreshed. The refresh interval has to be at least 60 seconds shorter then the expiry interval specified in jwksExpirySeconds. Defaults to 300 seconds.

integer

maxSecondsWithoutReauthentication

Maximum number of seconds the authenticated session remains valid without re-authentication. This enables Apache Kafka re-authentication feature, and causes sessions to expire when the access token expires. If the access token expires before max time or if max time is reached, the client has to re-authenticate, otherwise the server will drop the connection. Not set by default - the authenticated session does not expire when the access token expires.

integer

tlsTrustedCertificates

Trusted certificates for TLS connection to the OAuth server.

CertSecretSource array

type

Must be oauth.

string

userInfoEndpointUri

URI of the User Info Endpoint to use as a fallback to obtaining the user id when the Introspection Endpoint does not return information that can be used for the user id.

string

userNameClaim

Name of the claim from the JWT authentication token, Introspection Endpoint response or User Info Endpoint response which will be used to extract the user id. Defaults to sub.

string

validIssuerUri

URI of the token issuer used for authentication.

string

validTokenType

Valid value for the token_type attribute returned by the Introspection Endpoint. No default value, and not checked by default.

string

B.13. GenericSecretSource schema reference

Used in: KafkaClientAuthenticationOAuth, KafkaListenerAuthenticationOAuth

Property Description

key

The key under which the secret value is stored in the Kubernetes Secret.

string

secretName

The name of the Kubernetes Secret containing the secret value.

string

B.14. CertSecretSource schema reference

Used in: KafkaAuthorizationKeycloak, KafkaBridgeTls, KafkaClientAuthenticationOAuth, KafkaConnectTls, KafkaListenerAuthenticationOAuth, KafkaMirrorMaker2Tls, KafkaMirrorMakerTls

Property Description

certificate

The name of the file certificate in the Secret.

string

secretName

The name of the Secret containing the certificate.

string

B.15. GenericKafkaListenerConfiguration schema reference

Used in: GenericKafkaListener

Configuration for Kafka listeners.

B.15.1. brokerCertChainAndKey

The brokerCertChainAndKey property is only used with listeners that have TLS encryption enabled. You can use the property to providing your own Kafka listener certificates.

Example configuration for a loadbalancer external listener with TLS encryption enabled
listeners:
  #...
  - 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
# ...

B.15.2. externalTrafficPolicy

The externalTrafficPolicy property is used with loadbalancer and nodeport listeners. When exposing Kafka outside of Kubernetes you can choose Local or Cluster. Local avoids hops to other nodes and preserves the client IP, whereas Cluster does neither. The default is Cluster.

B.15.3. loadBalancerSourceRanges

The loadBalancerSourceRanges property is only used with loadbalancer listeners. When exposing Kafka outside of Kubernetes use source ranges, in addition to labels and annotations, to customize how a service is created.

Example source ranges configured for a loadbalancer listener
listeners:
  #...
  - name: external
    port: 9094
    type: loadbalancer
    tls: false
    configuration:
      externalTrafficPolicy: Local
      loadBalancerSourceRanges:
        - 10.0.0.0/8
        - 88.208.76.87/32
      # ...
# ...

B.15.4. class

The class property is only used with ingress listeners.

By default, the Ingress class is set to nginx. You can change the Ingress class using the class property.

Example of an external listener of type ingress using Ingress class nginx-internal
listeners:
  #...
  - name: external
    port: 9094
    type: ingress
    tls: false
    configuration:
      class: nginx-internal
    # ...
# ...

B.15.5. preferredNodePortAddressType

The preferredNodePortAddressType property is only used with nodeport listeners.

Use the preferredNodePortAddressType property in your listener configuration to specify the first address type checked as the node address. This property is useful, for example, if your deployment does not have DNS support, or you only want to expose a broker internally through an internal DNS or IP address. If an address of this type is found, it is used. If the preferred address type is not found, Strimzi proceeds through the types in the standard order of priority:

  1. ExternalDNS

  2. ExternalIP

  3. Hostname

  4. InternalDNS

  5. InternalIP

Example of an external listener configured with a preferred node port address type
listeners:
  #...
  - name: external
    port: 9094
    type: nodeport
    tls: false
    configuration:
      preferredNodePortAddressType: InternalDNS
      # ...
# ...

B.15.6. useServiceDnsDomain

The useServiceDnsDomain property is only used with internal listeners. It defines whether the fully-qualified DNS names that include the cluster service suffix (usually .cluster.local) are used. With useServiceDnsDomain set as false, the advertised addresses are generated without the service suffix; for example, my-cluster-kafka-0.my-cluster-kafka-brokers.myproject.svc. With useServiceDnsDomain set as true, the advertised addresses are generated with the service suffix; for example, my-cluster-kafka-0.my-cluster-kafka-brokers.myproject.svc.cluster.local. Default is false.

Example of an internal listener configured to use the Service DNS domain
listeners:
  #...
  - name: plain
    port: 9092
    type: internal
    tls: false
    configuration:
      useServiceDnsDomain: true
      # ...
# ...

If your Kubernetes cluster uses a different service suffix than .cluster.local, you can configure the suffix using the KUBERNETES_SERVICE_DNS_DOMAIN environment variable in the Cluster Operator configuration. See Cluster Operator configuration for more details.

Property Description

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair which will be used for this listener. The certificate can optionally contain the whole chain. This field can be used only with listeners with enabled TLS encryption.

CertAndKeySecretSource

externalTrafficPolicy

Specifies whether the service routes external traffic to node-local or cluster-wide endpoints. Cluster may cause a second hop to another node and obscures the client source IP. Local avoids a second hop for LoadBalancer and Nodeport type services and preserves the client source IP (when supported by the infrastructure). If unspecified, Kubernetes will use Cluster as the default.This field can be used only with loadbalancer or nodeport type listener.

string (one of [Local, Cluster])

loadBalancerSourceRanges

A list of CIDR ranges (for example 10.0.0.0/8 or 130.211.204.1/32) from which clients can connect to load balancer type listeners. If supported by the platform, traffic through the loadbalancer is restricted to the specified CIDR ranges. This field is applicable only for loadbalancer type services and is ignored if the cloud provider does not support the feature. For more information, see https://v1-17.docs.kubernetes.io/docs/tasks/access-application-cluster/configure-cloud-provider-firewall/. This field can be used only with loadbalancer type listener.

string array

bootstrap

Bootstrap configuration.

GenericKafkaListenerConfigurationBootstrap

brokers

Per-broker configurations.

GenericKafkaListenerConfigurationBroker array

class

Configures the Ingress class that defines which Ingress controller will be used. If not set, the Ingress class is set to nginx. This field can be used only with ingress type listener.

string

preferredNodePortAddressType

Defines which address type should be used as the node address. Available types are: ExternalDNS, ExternalIP, InternalDNS, InternalIP and Hostname. By default, the addresses will be used in the following order (the first one found will be used): * ExternalDNS * ExternalIP * InternalDNS * InternalIP * Hostname

This field can be used to select the address type which will be used as the preferred type and checked first. In case no address will be found for this address type, the other types will be used in the default order.This field can be used only with nodeport type listener..

string (one of [ExternalDNS, ExternalIP, Hostname, InternalIP, InternalDNS])

useServiceDnsDomain

Configures whether the Kubernetes service DNS domain should be used or not. If set to true, the generated addresses with contain the service DNS domain suffix (by default .cluster.local, can be configured using environment variable KUBERNETES_SERVICE_DNS_DOMAIN). Defaults to false.This field can be used only with internal type listener.

boolean

B.16. CertAndKeySecretSource schema reference

Used in: GenericKafkaListenerConfiguration, IngressListenerConfiguration, KafkaClientAuthenticationTls, KafkaListenerExternalConfiguration, NodePortListenerConfiguration, TlsListenerConfiguration

Property Description

certificate

The name of the file certificate in the Secret.

string

key

The name of the private key in the Secret.

string

secretName

The name of the Secret containing the certificate.

string

B.17. GenericKafkaListenerConfigurationBootstrap schema reference

Used in: GenericKafkaListenerConfiguration

Configures bootstrap service overrides for external listeners.

Broker service equivalents of nodePort, host, loadBalancerIP and annotations properties are configured in the GenericKafkaListenerConfigurationBroker schema.

B.17.1. alternativeNames

You can specify alternative names for the bootstrap service. The names are added to the broker certificates and can be used for TLS hostname verification. The alternativeNames property is applicable to all types of external listeners.

Example of an external route listener configured with an additional bootstrap address
listeners:
  #...
  - name: external
    port: 9094
    type: route
    tls: true
    authentication:
      type: tls
    configuration:
      bootstrap:
        alternativeNames:
          - example.hostname1
          - example.hostname2
# ...

B.17.2. host

The host property is used with route and ingress listeners to specify the hostnames used by the bootstrap and per-broker services.

A host property value is mandatory for ingress listener configuration, as the Ingress controller does not assign any hostnames automatically. Make sure that the hostnames resolve to the Ingress endpoints. Strimzi will not perform any validation that the requested hosts are available and properly routed to the Ingress endpoints.

Example of host configuration for an ingress listener
listeners:
  #...
  - name: external
    port: 9094
    type: ingress
    tls: true
    authentication:
      type: tls
    configuration:
      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
# ...

By default, route listener hosts are automatically assigned by OpenShift. However, you can override the assigned route hosts by specifying hosts.

Strimzi does not perform any validation that the requested hosts are available. You must ensure that they are free and can be used.

Example of host configuration for a route listener
# ...
listeners:
  #...
  - name: external
    port: 9094
    type: route
    tls: true
    authentication:
      type: tls
    configuration:
      bootstrap:
        host: bootstrap.myrouter.com
      brokers:
      - broker: 0
        host: broker-0.myrouter.com
      - broker: 1
        host: broker-1.myrouter.com
      - broker: 2
        host: broker-2.myrouter.com
# ...

B.17.3. nodePort

By default, the port numbers used for the bootstrap and broker services are automatically assigned by Kubernetes. You can override the assigned node ports for nodeport listeners by specifying the requested port numbers.

Strimzi does not perform any validation on the requested ports. You must ensure that they are free and available for use.

Example of an external listener configured with overrides for node ports
# ...
listeners:
  #...
  - name: external
    port: 9094
    type: nodeport
    tls: true
    authentication:
      type: tls
    configuration:
      bootstrap:
        nodePort: 32100
      brokers:
      - broker: 0
        nodePort: 32000
      - broker: 1
        nodePort: 32001
      - broker: 2
        nodePort: 32002
# ...

B.17.4. loadBalancerIP

Use the loadBalancerIP property to request a specific IP address when creating a loadbalancer. Use this property when you need to use a loadbalancer with a specific IP address. The loadBalancerIP field is ignored if the cloud provider does not support the feature.

Example of an external listener of type loadbalancer with specific loadbalancer IP address requests
# ...
listeners:
  #...
  - name: external
    port: 9094
    type: loadbalancer
    tls: true
    authentication:
      type: tls
    configuration:
      bootstrap:
        loadBalancerIP: 172.29.3.10
      brokers:
      - broker: 0
        loadBalancerIP: 172.29.3.1
      - broker: 1
        loadBalancerIP: 172.29.3.2
      - broker: 2
        loadBalancerIP: 172.29.3.3
# ...

B.17.5. annotations

Use the annotations property to add annotations to loadbalancer, nodeport or ingress listeners. You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the loadbalancer services.

Example of an external listener of type loadbalancer using annotations
# ...
listeners:
  #...
  - name: external
    port: 9094
    type: loadbalancer
    tls: true
    authentication:
      type: tls
    configuration:
      bootstrap:
        annotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-bootstrap.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      brokers:
      - broker: 0
        annotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-0.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 1
        annotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-1.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 2
        annotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-2.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
# ...
Property Description

alternativeNames

Additional alternative names for the bootstrap service. The alternative names will be added to the list of subject alternative names of the TLS certificates.

string array

host

The bootstrap host. This field will be used in the Ingress resource or in the Route resource to specify the desired hostname. This field can be used only with route (optional) or ingress (required) type listeners.

string

nodePort

Node port for the bootstrap service. This field can be used only with nodeport type listener.

integer

loadBalancerIP

The loadbalancer is requested with the IP address specified in this field. This feature depends on whether the underlying cloud provider supports specifying the loadBalancerIP when a load balancer is created. This field is ignored if the cloud provider does not support the feature.This field can be used only with loadbalancer type listener.

string

annotations

Annotations that will be added to the Ingress or Service resource. You can use this field to configure DNS providers such as External DNS. This field can be used only with loadbalancer, nodeport, or ingress type listeners.

map

B.18. GenericKafkaListenerConfigurationBroker schema reference

Used in: GenericKafkaListenerConfiguration

Configures broker service overrides for external listeners.

You can see example configuration for the nodePort, host, loadBalancerIP and annotations properties in the GenericKafkaListenerConfigurationBootstrap schema, which configures bootstrap service overrides for external listeners.

Advertised addresses for brokers

By default, Strimzi tries to automatically determine the hostnames and ports that your Kafka cluster advertises to its clients. This is not sufficient in all situations, because the infrastructure on which Strimzi is running might not provide the right hostname or port through which Kafka can be accessed.

You can specify a broker ID and customize the advertised hostname and port in the configuration property of the external listener. Strimzi will then automatically configure the advertised address in the Kafka brokers and add it to the broker certificates so it can be used for TLS hostname verification. Overriding the advertised host and ports is available for all types of external listeners.

Example of an external route listener configured with overrides for advertised addresses
listeners:
  #...
  - name: external
    port: 9094
    type: route
    tls: true
    authentication:
      type: tls
    configuration:
      brokers:
      - broker: 0
        advertisedHost: example.hostname.0
        advertisedPort: 12340
      - broker: 1
        advertisedHost: example.hostname.1
        advertisedPort: 12341
      - broker: 2
        advertisedHost: example.hostname.2
        advertisedPort: 12342
# ...
Property Description

broker

ID of the kafka broker (broker identifier). Broker IDs start from 0 and correspond to the number of broker replicas.

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

host

The broker host. This field will be used in the Ingress resource or in the Route resource to specify the desired hostname. This field can be used only with route (optional) or ingress (required) type listeners.

string

nodePort

Node port for the per-broker service. This field can be used only with nodeport type listener.

integer

loadBalancerIP

The loadbalancer is requested with the IP address specified in this field. This feature depends on whether the underlying cloud provider supports specifying the loadBalancerIP when a load balancer is created. This field is ignored if the cloud provider does not support the feature.This field can be used only with loadbalancer type listener.

string

annotations

Annotations that will be added to the Ingress or Service resource. You can use this field to configure DNS providers such as External DNS. This field can be used only with loadbalancer, nodeport, or ingress type listeners.

map

B.19. KafkaListeners schema reference

The type KafkaListeners has been deprecated. Please use GenericKafkaListener instead.

Used in: KafkaClusterSpec

Refer to previous documentation for example configuration.

Property Description

plain

Configures plain listener on port 9092.

KafkaListenerPlain

tls

Configures TLS listener on port 9093.

KafkaListenerTls

external

Configures external listener on port 9094. The type depends on the value of the external.type property within the given object, which must be one of [route, loadbalancer, nodeport, ingress].

KafkaListenerExternalRoute, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalIngress

B.20. KafkaListenerPlain schema reference

Used in: KafkaListeners

Property Description

authentication

Authentication configuration for this listener. Since this listener does not use TLS transport you cannot configure an authentication with type: tls. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.21. KafkaListenerTls schema reference

Used in: KafkaListeners

Property Description

authentication

Authentication configuration for this listener. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

configuration

Configuration of TLS listener.

TlsListenerConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.22. TlsListenerConfiguration schema reference

Used in: KafkaListenerTls

Property Description

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair. The certificate can optionally contain the whole chain.

CertAndKeySecretSource

B.23. KafkaListenerExternalRoute schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalRoute from KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalIngress. It must have the value route for the type KafkaListenerExternalRoute.

Property Description

type

Must be route.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

RouteListenerOverride

configuration

External listener configuration.

KafkaListenerExternalConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.24. RouteListenerOverride schema reference

Used in: KafkaListenerExternalRoute

Property Description

bootstrap

External bootstrap service configuration.

RouteListenerBootstrapOverride

brokers

External broker services configuration.

RouteListenerBrokerOverride array

B.25. RouteListenerBootstrapOverride schema reference

Used in: RouteListenerOverride

Property Description

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

host

Host for the bootstrap route. This field will be used in the spec.host field of the OpenShift Route.

string

B.26. RouteListenerBrokerOverride schema reference

Used in: RouteListenerOverride

Property Description

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

host

Host for the broker route. This field will be used in the spec.host field of the OpenShift Route.

string

B.27. KafkaListenerExternalConfiguration schema reference

Used in: KafkaListenerExternalLoadBalancer, KafkaListenerExternalRoute

Property Description

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair. The certificate can optionally contain the whole chain.

CertAndKeySecretSource

B.28. KafkaListenerExternalLoadBalancer schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalLoadBalancer from KafkaListenerExternalRoute, KafkaListenerExternalNodePort, KafkaListenerExternalIngress. It must have the value loadbalancer for the type KafkaListenerExternalLoadBalancer.

Property Description

type

Must be loadbalancer.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

LoadBalancerListenerOverride

configuration

External listener configuration.

KafkaListenerExternalConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

tls

Enables TLS encryption on the listener. By default set to true for enabled TLS encryption.

boolean

B.29. LoadBalancerListenerOverride schema reference

Used in: KafkaListenerExternalLoadBalancer

Property Description

bootstrap

External bootstrap service configuration.

LoadBalancerListenerBootstrapOverride

brokers

External broker services configuration.

LoadBalancerListenerBrokerOverride array

B.30. LoadBalancerListenerBootstrapOverride schema reference

Used in: LoadBalancerListenerOverride

Property Description

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

dnsAnnotations

Annotations that will be added to the Service resource. You can use this field to configure DNS providers such as External DNS.

map

loadBalancerIP

The loadbalancer is requested with the IP address specified in this field. This feature depends on whether the underlying cloud provider supports specifying the loadBalancerIP when a load balancer is created. This field is ignored if the cloud provider does not support the feature.

string

B.31. LoadBalancerListenerBrokerOverride schema reference

Used in: LoadBalancerListenerOverride

Property Description

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

dnsAnnotations

Annotations that will be added to the Service resources for individual brokers. You can use this field to configure DNS providers such as External DNS.

map

loadBalancerIP

The loadbalancer is requested with the IP address specified in this field. This feature depends on whether the underlying cloud provider supports specifying the loadBalancerIP when a load balancer is created. This field is ignored if the cloud provider does not support the feature.

string

B.32. KafkaListenerExternalNodePort schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalNodePort from KafkaListenerExternalRoute, KafkaListenerExternalLoadBalancer, KafkaListenerExternalIngress. It must have the value nodeport for the type KafkaListenerExternalNodePort.

Property Description

type

Must be nodeport.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

NodePortListenerOverride

configuration

External listener configuration.

NodePortListenerConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

tls

Enables TLS encryption on the listener. By default set to true for enabled TLS encryption.

boolean

B.33. NodePortListenerOverride schema reference

Used in: KafkaListenerExternalNodePort

Property Description

bootstrap

External bootstrap service configuration.

NodePortListenerBootstrapOverride

brokers

External broker services configuration.

NodePortListenerBrokerOverride array

B.34. NodePortListenerBootstrapOverride schema reference

Used in: NodePortListenerOverride

Property Description

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

dnsAnnotations

Annotations that will be added to the Service resource. You can use this field to configure DNS providers such as External DNS.

map

nodePort

Node port for the bootstrap service.

integer

B.35. NodePortListenerBrokerOverride schema reference

Used in: NodePortListenerOverride

Property Description

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

nodePort

Node port for the broker service.

integer

dnsAnnotations

Annotations that will be added to the Service resources for individual brokers. You can use this field to configure DNS providers such as External DNS.

map

B.36. NodePortListenerConfiguration schema reference

Used in: KafkaListenerExternalNodePort

Property Description

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair. The certificate can optionally contain the whole chain.

CertAndKeySecretSource

preferredAddressType

Defines which address type should be used as the node address. Available types are: ExternalDNS, ExternalIP, InternalDNS, InternalIP and Hostname. By default, the addresses will be used in the following order (the first one found will be used): * ExternalDNS * ExternalIP * InternalDNS * InternalIP * Hostname

This field can be used to select the address type which will be used as the preferred type and checked first. In case no address will be found for this address type, the other types will be used in the default order..

string (one of [ExternalDNS, ExternalIP, Hostname, InternalIP, InternalDNS])

B.37. KafkaListenerExternalIngress schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalIngress from KafkaListenerExternalRoute, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort. It must have the value ingress for the type KafkaListenerExternalIngress.

Property Description

type

Must be ingress.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

class

Configures the Ingress class that defines which Ingress controller will be used. If not set, the Ingress class is set to nginx.

string

configuration

External listener configuration.

IngressListenerConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.38. IngressListenerConfiguration schema reference

Used in: KafkaListenerExternalIngress

Property Description

bootstrap

External bootstrap ingress configuration.

IngressListenerBootstrapConfiguration

brokers

External broker ingress configuration.

IngressListenerBrokerConfiguration array

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair. The certificate can optionally contain the whole chain.

CertAndKeySecretSource

B.39. IngressListenerBootstrapConfiguration schema reference

Used in: IngressListenerConfiguration

Property Description

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

dnsAnnotations

Annotations that will be added to the Ingress resource. You can use this field to configure DNS providers such as External DNS.

map

host

Host for the bootstrap route. This field will be used in the Ingress resource.

string

B.40. IngressListenerBrokerConfiguration schema reference

Used in: IngressListenerConfiguration

Property Description

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

host

Host for the broker ingress. This field will be used in the Ingress resource.

string

dnsAnnotations

Annotations that will be added to the Ingress resources for individual brokers. You can use this field to configure DNS providers such as External DNS.

map

B.41. KafkaAuthorizationSimple schema reference

Used in: KafkaClusterSpec

Simple authorization in Strimzi uses the AclAuthorizer plugin, the default Access Control Lists (ACLs) authorization plugin provided with Apache Kafka. ACLs allow you to define which users have access to which resources at a granular level.

Configure the Kafka custom resource to use simple authorization. Set the type property in the authorization section to the value simple, and configure a list of super users.

Access rules are configured for the KafkaUser, as described in the ACLRule schema reference.

B.41.1. superUsers

A list of user principals treated as super users, so that they are always allowed without querying ACL rules. For more information see Kafka authorization.

An example of simple authorization configuration
authorization:
  type: simple
  superUsers:
    - CN=client_1
    - user_2
    - CN=client_3
Note
 The super.user configuration option in the config property in Kafka.spec.kafka is ignored. Designate super users in the authorization property instead. For more information, see Kafka broker configuration.

The type property is a discriminator that distinguishes the use of the type KafkaAuthorizationSimple from KafkaAuthorizationOpa, KafkaAuthorizationKeycloak. It must have the value simple for the type KafkaAuthorizationSimple.

Property Description

type

Must be simple.

string

superUsers

List of super users. Should contain list of user principals which should get unlimited access rights.

string array

B.42. KafkaAuthorizationOpa schema reference

Used in: KafkaClusterSpec

To use Open Policy Agent authorization, set the type property in the authorization section to the value opa, and configure OPA properties as required.

B.42.1. url

The URL used to connect to the Open Policy Agent server. The URL has to include the policy which will be queried by the authorizer. Required.

B.42.2. allowOnError

Defines whether a Kafka client should be allowed or denied by default when the authorizer fails to query the Open Policy Agent, for example, when it is temporarily unavailable. Defaults to false - all actions will be denied.

B.42.3. initialCacheCapacity

Initial capacity of the local cache used by the authorizer to avoid querying the Open Policy Agent for every request. Defaults to 5000.

B.42.4. maximumCacheSize

Maximum capacity of the local cache used by the authorizer to avoid querying the Open Policy Agent for every request. Defaults to 50000.

B.42.5. expireAfterMs

The expiration of the records kept in the local cache to avoid querying the Open Policy Agent for every request. Defines how often the cached authorization decisions are reloaded from the Open Policy Agent server. In milliseconds. Defaults to 3600000 milliseconds (1 hour).

B.42.6. superUsers

A list of user principals treated as super users, so that they are always allowed without querying the open Policy Agent policy. For more information see Kafka authorization.

An example of Open Policy Agent authorizer configuration
authorization:
  type: opa
  url: http://opa:8181/v1/data/kafka/allow
  allowOnError: false
  initialCacheCapacity: 1000
  maximumCacheSize: 10000
  expireAfterMs: 60000
  superUsers:
    - CN=fred
    - sam
    - CN=edward

The type property is a discriminator that distinguishes the use of the type KafkaAuthorizationOpa from KafkaAuthorizationSimple, KafkaAuthorizationKeycloak. It must have the value opa for the type KafkaAuthorizationOpa.

Property Description

type

Must be opa.

string

url

The URL used to connect to the Open Policy Agent server. The URL has to include the policy which will be queried by the authorizer. This option is required.

string

allowOnError

Defines whether a Kafka client should be allowed or denied by default when the authorizer fails to query the Open Policy Agent, for example, when it is temporarily unavailable). Defaults to false - all actions will be denied.

boolean

initialCacheCapacity

Initial capacity of the local cache used by the authorizer to avoid querying the Open Policy Agent for every request Defaults to 5000.

integer

maximumCacheSize

Maximum capacity of the local cache used by the authorizer to avoid querying the Open Policy Agent for every request. Defaults to 50000.

integer

expireAfterMs

The expiration of the records kept in the local cache to avoid querying the Open Policy Agent for every request. Defines how often the cached authorization decisions are reloaded from the Open Policy Agent server. In milliseconds. Defaults to 3600000.

integer

superUsers

List of super users, which is specifically a list of user principals that have unlimited access rights.

string array

B.43. KafkaAuthorizationKeycloak schema reference

Used in: KafkaClusterSpec

The type property is a discriminator that distinguishes the use of the type KafkaAuthorizationKeycloak from KafkaAuthorizationSimple, KafkaAuthorizationOpa. It must have the value keycloak for the type KafkaAuthorizationKeycloak.

Property Description

type

Must be keycloak.

string

clientId

OAuth Client ID which the Kafka client can use to authenticate against the OAuth server and use the token endpoint URI.

string

tokenEndpointUri

Authorization server token endpoint URI.

string

tlsTrustedCertificates

Trusted certificates for TLS connection to the OAuth server.

CertSecretSource array

disableTlsHostnameVerification

Enable or disable TLS hostname verification. Default value is false.

boolean

delegateToKafkaAcls

Whether authorization decision should be delegated to the 'Simple' authorizer if DENIED by Keycloak Authorization Services policies. Default value is false.

boolean

grantsRefreshPeriodSeconds

The time between two consecutive grants refresh runs in seconds. The default value is 60.

integer

grantsRefreshPoolSize

The number of threads to use to refresh grants for active sessions. The more threads, the more parallelism, so the sooner the job completes. However, using more threads places a heavier load on the authorization server. The default value is 5.

integer

superUsers

List of super users. Should contain list of user principals which should get unlimited access rights.

string array

B.44. Rack schema reference

Used in: KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec

Property Description

topologyKey

A key that matches labels assigned to the Kubernetes cluster nodes. The value of the label is used to set the broker’s broker.rack config.

string

B.45. Probe schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaExporterSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TlsSidecar, TopicOperatorSpec, ZookeeperClusterSpec

Property Description

failureThreshold

Minimum consecutive failures for the probe to be considered failed after having succeeded. Defaults to 3. Minimum value is 1.

integer

initialDelaySeconds

The initial delay before first the health is first checked.

integer

periodSeconds

How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1.

integer

successThreshold

Minimum consecutive successes for the probe to be considered successful after having failed. Defaults to 1. Must be 1 for liveness. Minimum value is 1.

integer

timeoutSeconds

The timeout for each attempted health check.

integer

B.46. JvmOptions schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TopicOperatorSpec, ZookeeperClusterSpec

Property Description

-XX

A map of -XX options to the JVM.

map

-Xms

-Xms option to to the JVM.

string

-Xmx

-Xmx option to to the JVM.

string

gcLoggingEnabled

Specifies whether the Garbage Collection logging is enabled. The default is false.

boolean

javaSystemProperties

A map of additional system properties which will be passed using the -D option to the JVM.

SystemProperty array

B.47. SystemProperty schema reference

Used in: JvmOptions

Property Description

name

The system property name.

string

value

The system property value.

string

B.48. KafkaJmxOptions schema reference

Used in: KafkaClusterSpec

Property Description

authentication

Authentication configuration for connecting to the Kafka JMX port. The type depends on the value of the authentication.type property within the given object, which must be one of [password].

KafkaJmxAuthenticationPassword

B.49. KafkaJmxAuthenticationPassword schema reference

Used in: KafkaJmxOptions

The type property is a discriminator that distinguishes the use of the type KafkaJmxAuthenticationPassword from other subtypes which may be added in the future. It must have the value password for the type KafkaJmxAuthenticationPassword.

Property Description

type

Must be password.

string

B.50. InlineLogging schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TopicOperatorSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type InlineLogging from ExternalLogging. It must have the value inline for the type InlineLogging.

Property Description

type

Must be inline.

string

loggers

A Map from logger name to logger level.

map

B.51. ExternalLogging schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TopicOperatorSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type ExternalLogging from InlineLogging. It must have the value external for the type ExternalLogging.

Property Description

type

Must be external.

string

name

The name of the ConfigMap from which to get the logging configuration.

string

B.52. TlsSidecar schema reference

Used in: CruiseControlSpec, EntityOperatorSpec, KafkaClusterSpec, TopicOperatorSpec, ZookeeperClusterSpec

Property Description

image

The docker image for the container.

string

livenessProbe

Pod liveness checking.

Probe

logLevel

The log level for the TLS sidecar. Default value is notice.

string (one of [emerg, debug, crit, err, alert, warning, notice, info])

readinessProbe

Pod readiness checking.

Probe

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

B.53. KafkaClusterTemplate schema reference

Used in: KafkaClusterSpec

Property Description

statefulset

Template for Kafka StatefulSet.

StatefulSetTemplate

pod

Template for Kafka Pods.

PodTemplate

bootstrapService

Template for Kafka bootstrap Service.

ResourceTemplate

brokersService

Template for Kafka broker Service.

ResourceTemplate

externalBootstrapService

Template for Kafka external bootstrap Service.

ExternalServiceTemplate

perPodService

Template for Kafka per-pod Services used for access from outside of Kubernetes.

ExternalServiceTemplate

externalBootstrapRoute

Template for Kafka external bootstrap Route.

ResourceTemplate

perPodRoute

Template for Kafka per-pod Routes used for access from outside of OpenShift.

ResourceTemplate

externalBootstrapIngress

Template for Kafka external bootstrap Ingress.

ResourceTemplate

perPodIngress

Template for Kafka per-pod Ingress used for access from outside of Kubernetes.

ResourceTemplate

persistentVolumeClaim

Template for all Kafka PersistentVolumeClaims.

ResourceTemplate

podDisruptionBudget

Template for Kafka PodDisruptionBudget.

PodDisruptionBudgetTemplate

kafkaContainer

Template for the Kafka broker container.

ContainerTemplate

tlsSidecarContainer

The property tlsSidecarContainer has been deprecated. Template for the Kafka broker TLS sidecar container.

ContainerTemplate

initContainer

Template for the Kafka init container.

ContainerTemplate

B.54. StatefulSetTemplate schema reference

Used in: KafkaClusterTemplate, ZookeeperClusterTemplate

Property Description

metadata

Metadata applied to the resource.

MetadataTemplate

podManagementPolicy

PodManagementPolicy which will be used for this StatefulSet. Valid values are Parallel and OrderedReady. Defaults to Parallel.

string (one of [OrderedReady, Parallel])

B.55. MetadataTemplate schema reference

Used in: ExternalServiceTemplate, PodDisruptionBudgetTemplate, PodTemplate, ResourceTemplate, StatefulSetTemplate

Labels and Annotations are used to identify and organize resources, and are configured in the metadata property.

For example:

# ...
template:
    statefulset:
        metadata:
            labels:
                label1: value1
                label2: value2
            annotations:
                annotation1: value1
                annotation2: value2
# ...

The labels and annotations fields can contain any labels or annotations that do not contain the reserved string strimzi.io. Labels and annotations containing strimzi.io are used internally by Strimzi and cannot be configured.

Property Description

labels

Labels added to the resource template. Can be applied to different resources such as StatefulSets, Deployments, Pods, and Services.

map

annotations

Annotations added to the resource template. Can be applied to different resources such as StatefulSets, Deployments, Pods, and Services.

map

B.56. PodTemplate schema reference

Used in: CruiseControlTemplate, EntityOperatorTemplate, JmxTransTemplate, KafkaBridgeTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaExporterTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

An example showing the PodTemplate
# ...
template:
  pod:
    metadata:
      labels:
        label1: value1
      annotations:
        anno1: value1
    imagePullSecrets:
      - name: my-docker-credentials
    securityContext:
      runAsUser: 1000001
      fsGroup: 0
    terminationGracePeriodSeconds: 120
# ...
Property Description

metadata

Metadata applied to the resource.

MetadataTemplate

imagePullSecrets

List of references to secrets in the same namespace to use for pulling any of the images used by this Pod. When the STRIMZI_IMAGE_PULL_SECRETS environment variable in Cluster Operator and the imagePullSecrets option are specified, only the imagePullSecrets variable is used and the STRIMZI_IMAGE_PULL_SECRETS variable is ignored. See external documentation of core/v1 localobjectreference.

LocalObjectReference array

securityContext

Configures pod-level security attributes and common container settings. See external documentation of core/v1 podsecuritycontext.

PodSecurityContext

terminationGracePeriodSeconds

The grace period is the duration in seconds after the processes running in the pod are sent a termination signal, and the time when the processes are forcibly halted with a kill signal. Set this value to longer than the expected cleanup time for your process. Value must be a non-negative integer. A zero value indicates delete immediately. You might need to increase the grace period for very large Kafka clusters, so that the Kafka brokers have enough time to transfer their work to another broker before they are terminated. Defaults to 30 seconds.

integer

affinity

The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

priorityClassName

The name of the priority class used to assign priority to the pods. For more information about priority classes, see Pod Priority and Preemption.

string

schedulerName

The name of the scheduler used to dispatch this Pod. If not specified, the default scheduler will be used.

string

hostAliases

The pod’s HostAliases. HostAliases is an optional list of hosts and IPs that will be injected into the pod’s hosts file if specified. See external documentation of core/v1 HostAlias.

HostAlias array

B.57. ResourceTemplate schema reference

Used in: CruiseControlTemplate, EntityOperatorTemplate, JmxTransTemplate, KafkaBridgeTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaExporterTemplate, KafkaMirrorMakerTemplate, KafkaUserTemplate, ZookeeperClusterTemplate

Property Description

metadata

Metadata applied to the resource.

MetadataTemplate

B.58. ExternalServiceTemplate schema reference

Used in: KafkaClusterTemplate

When exposing Kafka outside of Kubernetes using loadbalancers or node ports, you can use properties, in addition to labels and annotations, to customize how a Service is created.

An example showing customized external services
# ...
template:
  externalBootstrapService:
    externalTrafficPolicy: Local
    loadBalancerSourceRanges:
      - 10.0.0.0/8
      - 88.208.76.87/32
  perPodService:
    externalTrafficPolicy: Local
    loadBalancerSourceRanges:
      - 10.0.0.0/8
      - 88.208.76.87/32
# ...
Property Description

metadata

Metadata applied to the resource.

MetadataTemplate

externalTrafficPolicy

The property externalTrafficPolicy has been deprecated. Specifies whether the service routes external traffic to node-local or cluster-wide endpoints. Cluster may cause a second hop to another node and obscures the client source IP. Local avoids a second hop for LoadBalancer and Nodeport type services and preserves the client source IP (when supported by the infrastructure). If unspecified, Kubernetes will use Cluster as the default.

string (one of [Local, Cluster])

loadBalancerSourceRanges

The property loadBalancerSourceRanges has been deprecated. A list of CIDR ranges (for example 10.0.0.0/8 or 130.211.204.1/32) from which clients can connect to load balancer type listeners. If supported by the platform, traffic through the loadbalancer is restricted to the specified CIDR ranges. This field is applicable only for loadbalancer type services and is ignored if the cloud provider does not support the feature. For more information, see https://v1-17.docs.kubernetes.io/docs/tasks/access-application-cluster/configure-cloud-provider-firewall/.

string array

B.59. PodDisruptionBudgetTemplate schema reference

Used in: CruiseControlTemplate, KafkaBridgeTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

Strimzi creates a PodDisruptionBudget for every new StatefulSet or Deployment. By default, pod disruption budgets only allow a single pod to be unavailable at a given time. You can increase the amount of unavailable pods allowed by changing the default value of the maxUnavailable property in the PodDisruptionBudget.spec resource.

An example of PodDisruptionBudget template
# ...
template:
    podDisruptionBudget:
        metadata:
            labels:
                key1: label1
                key2: label2
            annotations:
                key1: label1
                key2: label2
        maxUnavailable: 1
# ...
Property Description

metadata

Metadata to apply to the PodDistruptionBugetTemplate resource.

MetadataTemplate

maxUnavailable

Maximum number of unavailable pods to allow automatic Pod eviction. A Pod eviction is allowed when the maxUnavailable number of pods or fewer are unavailable after the eviction. Setting this value to 0 prevents all voluntary evictions, so the pods must be evicted manually. Defaults to 1.

integer

B.60. ContainerTemplate schema reference

Used in: CruiseControlTemplate, EntityOperatorTemplate, JmxTransTemplate, KafkaBridgeTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaExporterTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

You can set custom security context and environment variables for a container.

The environment variables are defined under the env property as a list of objects with name and value fields. The following example shows two custom environment variables and a custom security context set for the Kafka broker containers:

# ...
template:
  kafkaContainer:
    env:
    - name: EXAMPLE_ENV_1
      value: example.env.one
    - name: EXAMPLE_ENV_2
      value: example.env.two
    securityContext:
      runAsUser: 2000
# ...

Environment variables prefixed with KAFKA_ are internal to Strimzi and should be avoided. If you set a custom environment variable that is already in use by Strimzi, it is ignored and a warning is recorded in the log.

Property Description

env

Environment variables which should be applied to the container.

ContainerEnvVar array

securityContext

Security context for the container. See external documentation of core/v1 securitycontext.

SecurityContext

B.61. ContainerEnvVar schema reference

Used in: ContainerTemplate

Property Description

name

The environment variable key.

string

value

The environment variable value.

string

B.62. ZookeeperClusterSpec schema reference

Used in: KafkaSpec

Property Description

replicas

The number of pods in the cluster.

integer

image

The docker image for the pods.

string

storage

Storage configuration (disk). Cannot be updated. The type depends on the value of the storage.type property within the given object, which must be one of [ephemeral, persistent-claim].

EphemeralStorage, PersistentClaimStorage

config

The ZooKeeper broker config. Properties with the following prefixes cannot be set: server., dataDir, dataLogDir, clientPort, authProvider, quorum.auth, requireClientAuthScheme, snapshot.trust.empty, standaloneEnabled, reconfigEnabled, 4lw.commands.whitelist, secureClientPort, ssl., serverCnxnFactory, sslQuorum (with the exception of: ssl.protocol, ssl.quorum.protocol, ssl.enabledProtocols, ssl.quorum.enabledProtocols, ssl.ciphersuites, ssl.quorum.ciphersuites, ssl.hostnameVerification, ssl.quorum.hostnameVerification).

map

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.zookeeper.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.zookeeper.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

logging

Logging configuration for ZooKeeper. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

template

Template for ZooKeeper cluster resources. The template allows users to specify how are the StatefulSet, Pods and Services generated.

ZookeeperClusterTemplate

tlsSidecar

The property tlsSidecar has been deprecated. TLS sidecar configuration. The TLS sidecar is not used anymore and this option will be ignored.

TlsSidecar

B.63. ZookeeperClusterTemplate schema reference

Used in: ZookeeperClusterSpec

Property Description

statefulset

Template for ZooKeeper StatefulSet.

StatefulSetTemplate

pod

Template for ZooKeeper Pods.

PodTemplate

clientService

Template for ZooKeeper client Service.

ResourceTemplate

nodesService

Template for ZooKeeper nodes Service.

ResourceTemplate

persistentVolumeClaim

Template for all ZooKeeper PersistentVolumeClaims.

ResourceTemplate

podDisruptionBudget

Template for ZooKeeper PodDisruptionBudget.

PodDisruptionBudgetTemplate

zookeeperContainer

Template for the ZooKeeper container.

ContainerTemplate

tlsSidecarContainer

The property tlsSidecarContainer has been deprecated. Template for the Zookeeper server TLS sidecar container. The TLS sidecar is not used anymore and this option will be ignored.

ContainerTemplate

B.64. TopicOperatorSpec schema reference

The type TopicOperatorSpec has been deprecated. Please use EntityTopicOperatorSpec instead.

Used in: KafkaSpec

Property Description

watchedNamespace

The namespace the Topic Operator should watch.

string

image

The image to use for the Topic Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the ZooKeeper session.

integer

affinity

Pod affinity rules. See external documentation of core/v1 affinity.

Affinity

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

topicMetadataMaxAttempts

The number of attempts at getting topic metadata.

integer

tlsSidecar

TLS sidecar configuration.

TlsSidecar

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

jvmOptions

JVM Options for pods.

JvmOptions

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

B.65. EntityOperatorSpec schema reference

Used in: KafkaSpec

Property Description

topicOperator

Configuration of the Topic Operator.

EntityTopicOperatorSpec

userOperator

Configuration of the User Operator.

EntityUserOperatorSpec

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

tlsSidecar

TLS sidecar configuration.

TlsSidecar

template

Template for Entity Operator resources. The template allows users to specify how is the Deployment and Pods generated.

EntityOperatorTemplate

B.66. EntityTopicOperatorSpec schema reference

Used in: EntityOperatorSpec

Property Description

watchedNamespace

The namespace the Topic Operator should watch.

string

image

The image to use for the Topic Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the ZooKeeper session.

integer

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

topicMetadataMaxAttempts

The number of attempts at getting topic metadata.

integer

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

jvmOptions

JVM Options for pods.

JvmOptions

B.67. EntityUserOperatorSpec schema reference

Used in: EntityOperatorSpec

Property Description

watchedNamespace

The namespace the User Operator should watch.

string

image

The image to use for the User Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the ZooKeeper session.

integer

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

jvmOptions

JVM Options for pods.

JvmOptions

B.68. EntityOperatorTemplate schema reference

Used in: EntityOperatorSpec

Property Description

deployment

Template for Entity Operator Deployment.

ResourceTemplate

pod

Template for Entity Operator Pods.

PodTemplate

tlsSidecarContainer

Template for the Entity Operator TLS sidecar container.

ContainerTemplate

topicOperatorContainer

Template for the Entity Topic Operator container.

ContainerTemplate

userOperatorContainer

Template for the Entity User Operator container.

ContainerTemplate

B.69. CertificateAuthority schema reference

Used in: KafkaSpec

Configuration of how TLS certificates are used within the cluster. This applies to certificates used for both internal communication within the cluster and to certificates used for client access via Kafka.spec.kafka.listeners.tls.

Property Description

generateCertificateAuthority

If true then Certificate Authority certificates will be generated automatically. Otherwise the user will need to provide a Secret with the CA certificate. Default is true.

boolean

validityDays

The number of days generated certificates should be valid for. The default is 365.

integer

renewalDays

The number of days in the certificate renewal period. This is the number of days before the a certificate expires during which renewal actions may be performed. When generateCertificateAuthority is true, this will cause the generation of a new certificate. When generateCertificateAuthority is true, this will cause extra logging at WARN level about the pending certificate expiry. Default is 30.

integer

certificateExpirationPolicy

How should CA certificate expiration be handled when generateCertificateAuthority=true. The default is for a new CA certificate to be generated reusing the existing private key.

string (one of [replace-key, renew-certificate])

B.70. CruiseControlSpec schema reference

Used in: KafkaSpec

Property Description

image

The docker image for the pods.

string

tlsSidecar

TLS sidecar configuration.

TlsSidecar

resources

CPU and memory resources to reserve for the Cruise Control container. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

livenessProbe

Pod liveness checking for the Cruise Control container.

Probe

readinessProbe

Pod readiness checking for the Cruise Control container.

Probe

jvmOptions

JVM Options for the Cruise Control container.

JvmOptions

logging

Logging configuration (log4j1) for Cruise Control. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

template

Template to specify how Cruise Control resources, Deployments and Pods, are generated.

CruiseControlTemplate

brokerCapacity

The Cruise Control brokerCapacity configuration.

BrokerCapacity

config

The Cruise Control configuration. For a full list of configuration options refer to https://github.com/linkedin/cruise-control/wiki/Configurations. Note that properties with the following prefixes cannot be set: bootstrap.servers, client.id, zookeeper., network., security., failed.brokers.zk.path,webserver.http., webserver.api.urlprefix, webserver.session.path, webserver.accesslog., two.step., request.reason.required,metric.reporter.sampler.bootstrap.servers, metric.reporter.topic, partition.metric.sample.store.topic, broker.metric.sample.store.topic,capacity.config.file, self.healing., anomaly.detection., ssl.

map

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

B.71. CruiseControlTemplate schema reference

Used in: CruiseControlSpec

Property Description

deployment

Template for Cruise Control Deployment.

ResourceTemplate

pod

Template for Cruise Control Pods.

PodTemplate

apiService

Template for Cruise Control API Service.

ResourceTemplate

podDisruptionBudget

Template for Cruise Control PodDisruptionBudget.

PodDisruptionBudgetTemplate

cruiseControlContainer

Template for the Cruise Control container.

ContainerTemplate

tlsSidecarContainer

Template for the Cruise Control TLS sidecar container.

ContainerTemplate

B.72. BrokerCapacity schema reference

Used in: CruiseControlSpec

Property Description

disk

Broker capacity for disk in bytes, for example, 100Gi.

string

cpuUtilization

Broker capacity for CPU resource utilization as a percentage (0 - 100).

integer

inboundNetwork

Broker capacity for inbound network throughput in bytes per second, for example, 10000KB/s.

string

outboundNetwork

Broker capacity for outbound network throughput in bytes per second, for example 10000KB/s.

string

B.73. JmxTransSpec schema reference

Used in: KafkaSpec

Property Description

image

The image to use for the JmxTrans.

string

outputDefinitions

Defines the output hosts that will be referenced later on. For more information on these properties see, JmxTransOutputDefinitionTemplate schema reference.

JmxTransOutputDefinitionTemplate array

logLevel

Sets the logging level of the JmxTrans deployment.For more information see, JmxTrans Logging Level.

string

kafkaQueries

Queries to send to the Kafka brokers to define what data should be read from each broker. For more information on these properties see, JmxTransQueryTemplate schema reference.

JmxTransQueryTemplate array

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

template

Template for JmxTrans resources.

JmxTransTemplate

B.74. JmxTransOutputDefinitionTemplate schema reference

Used in: JmxTransSpec

Property Description

outputType

Template for setting the format of the data that will be pushed.For more information see JmxTrans OutputWriters.

string

host

The DNS/hostname of the remote host that the data is pushed to.

string

port

The port of the remote host that the data is pushed to.

integer

flushDelayInSeconds

How many seconds the JmxTrans waits before pushing a new set of data out.

integer

typeNames

Template for filtering data to be included in response to a wildcard query. For more information see JmxTrans queries.

string array

name

Template for setting the name of the output definition. This is used to identify where to send the results of queries should be sent.

string

B.75. JmxTransQueryTemplate schema reference

Used in: JmxTransSpec

Property Description

targetMBean

If using wildcards instead of a specific MBean then the data is gathered from multiple MBeans. Otherwise if specifying an MBean then data is gathered from that specified MBean.

string

attributes

Determine which attributes of the targeted MBean should be included.

string array

outputs

List of the names of output definitions specified in the spec.kafka.jmxTrans.outputDefinitions that have defined where JMX metrics are pushed to, and in which data format.

string array

B.76. JmxTransTemplate schema reference

Used in: JmxTransSpec

Property Description

deployment

Template for JmxTrans Deployment.

ResourceTemplate

pod

Template for JmxTrans Pods.

PodTemplate

container

Template for JmxTrans container.

ContainerTemplate

B.77. KafkaExporterSpec schema reference

Used in: KafkaSpec

Property Description

image

The docker image for the pods.

string

groupRegex

Regular expression to specify which consumer groups to collect. Default value is .*.

string

topicRegex

Regular expression to specify which topics to collect. Default value is .*.

string

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

logging

Only log messages with the given severity or above. Valid levels: [debug, info, warn, error, fatal]. Default log level is info.

string

enableSaramaLogging

Enable Sarama logging, a Go client library used by the Kafka Exporter.

boolean

template

Customization of deployment templates and pods.

KafkaExporterTemplate

livenessProbe

Pod liveness check.

Probe

readinessProbe

Pod readiness check.

Probe

B.78. KafkaExporterTemplate schema reference

Used in: KafkaExporterSpec

Property Description

deployment

Template for Kafka Exporter Deployment.

ResourceTemplate

pod

Template for Kafka Exporter Pods.

PodTemplate

service

Template for Kafka Exporter Service.

ResourceTemplate

container

Template for the Kafka Exporter container.

ContainerTemplate

B.79. KafkaStatus schema reference

Used in: Kafka

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

listeners

Addresses of the internal and external listeners.

ListenerStatus array

B.80. Condition schema reference

Used in: KafkaBridgeStatus, KafkaConnectorStatus, KafkaConnectS2IStatus, KafkaConnectStatus, KafkaMirrorMaker2Status, KafkaMirrorMakerStatus, KafkaRebalanceStatus, KafkaStatus, KafkaTopicStatus, KafkaUserStatus

Property Description

type

The unique identifier of a condition, used to distinguish between other conditions in the resource.

string

status

The status of the condition, either True, False or Unknown.

string

lastTransitionTime

Last time the condition of a type changed from one status to another. The required format is 'yyyy-MM-ddTHH:mm:ssZ', in the UTC time zone.

string

reason

The reason for the condition’s last transition (a single word in CamelCase).

string

message

Human-readable message indicating details about the condition’s last transition.

string

B.81. ListenerStatus schema reference

Used in: KafkaStatus

Property Description

type

The type of the listener. Can be one of the following three types: plain, tls, and external.

string

addresses

A list of the addresses for this listener.

ListenerAddress array

bootstrapServers

A comma-separated list of host:port pairs for connecting to the Kafka cluster using this listener.

string

certificates

A list of TLS certificates which can be used to verify the identity of the server when connecting to the given listener. Set only for tls and external listeners.

string array

B.82. ListenerAddress schema reference

Used in: ListenerStatus

Property Description

host

The DNS name or IP address of the Kafka bootstrap service.

string

port

The port of the Kafka bootstrap service.

integer

B.83. KafkaConnect schema reference

Property Description

spec

The specification of the Kafka Connect cluster.

KafkaConnectSpec

status

The status of the Kafka Connect cluster.

KafkaConnectStatus

B.84. KafkaConnectSpec schema reference

Used in: KafkaConnect

Configures a Kafka Connect cluster.

B.84.1. config

Use the config properties to configure Kafka options as keys.

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

Configuration options that cannot be configured relate to:

  • Kafka cluster bootstrap address

  • Security (Encryption, Authentication, and Authorization)

  • Listener / REST interface configuration

  • Plugin path configuration

The values can be one of the following JSON types:

  • String

  • Number

  • Boolean

You can specify and configure the options listed in the Apache Kafka documentation with the exception of those options that are managed directly by Strimzi. Specifically, configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.

  • sasl.

  • security.

  • listeners

  • plugin.path

  • rest.

  • bootstrap.servers

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Cluster Operator log file. All other options are passed to Kafka Connect.

Important
 The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object, then the Cluster Operator can roll out the new configuration to all Kafka Connect nodes.

Certain options have default values:

  • group.id with default value connect-cluster

  • offset.storage.topic with default value connect-cluster-offsets

  • config.storage.topic with default value connect-cluster-configs

  • status.storage.topic with default value connect-cluster-status

  • key.converter with default value org.apache.kafka.connect.json.JsonConverter

  • value.converter with default value org.apache.kafka.connect.json.JsonConverter

These options are automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

There are exceptions to the forbidden options. You can use three allowed ssl configuration options for client connection using a specific cipher suite for a TLS version. A cipher suite combines algorithms for secure connection and data transfer. You can also configure the ssl.endpoint.identification.algorithm property to enable or disable hostname verification.

Example Kafka Connect configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    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
    ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384"
    ssl.enabled.protocols: "TLSv1.2"
    ssl.protocol: "TLSv1.2"
    ssl.endpoint.identification.algorithm: HTTPS
  # ...

For client connection using a specific cipher suite for a TLS version, you can configure allowed ssl properties. You can also configure the ssl.endpoint.identification.algorithm property to enable or disable hostname verification.

B.84.2. logging

Kafka Connect (and Kafka Connect with Source2Image support) has its own configurable loggers:

  • connect.root.logger.level

  • log4j.logger.org.reflections

Further loggers are added depending on the Kafka Connect plugins running.

Use a curl request to get a complete list of Kafka Connect loggers running from any Kafka broker pod:

curl -s http://<connect-cluster-name>-connect-api:8083/admin/loggers/

Kafka Connect uses the Apache log4j 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.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties. For more information about log levels, see Apache logging services.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
spec:
  # ...
  logging:
    type: inline
    loggers:
      connect.root.logger.level: "INFO"
  # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...

Any available loggers that are not configured have their level set to OFF.

If Kafka Connect was deployed using the Cluster Operator, changes to Kafka Connect logging levels are applied dynamically.

If you use external logging, a rolling update is triggered when logging appenders are changed.

Garbage collector (GC)

Garbage collector logging can also be enabled (or disabled) using the jvmOptions property.

Property Description

replicas

The number of pods in the Kafka Connect group.

integer

version

The Kafka Connect version. Defaults to 2.6.0. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

image

The docker image for the pods.

string

bootstrapServers

Bootstrap servers to connect to. This should be given as a comma separated list of <hostname>:‍<port> pairs.

string

tls

TLS configuration.

KafkaConnectTls

authentication

Authentication configuration for Kafka Connect. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

config

The Kafka Connect configuration. Properties with the following prefixes cannot be set: ssl., sasl., security., listeners, plugin.path, rest., bootstrap.servers, consumer.interceptor.classes, producer.interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

resources

The maximum limits for CPU and memory resources and the requested initial resources. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

logging

Logging configuration for Kafka Connect. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

tracing

The configuration of tracing in Kafka Connect. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

template

Template for Kafka Connect and Kafka Connect S2I resources. The template allows users to specify how the Deployment, Pods and Service are generated.

KafkaConnectTemplate

externalConfiguration

Pass data from Secrets or ConfigMaps to the Kafka Connect pods and use them to configure connectors.

ExternalConfiguration

clientRackInitImage

The image of the init container used for initializing the client.rack.

string

rack

Configuration of the node label which will be used as the client.rack consumer configuration.

Rack

B.85. KafkaConnectTls schema reference

Used in: KafkaConnectS2ISpec, KafkaConnectSpec

Configures TLS trusted certificates for connecting Kafka Connect to the cluster.

B.85.1. trustedCertificates

Provide a list of secrets using the trustedCertificates property.

Property Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

B.86. KafkaClientAuthenticationTls schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2ClusterSpec, KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

To use TLS client authentication, set the type property to the value tls. TLS client authentication uses a TLS certificate to authenticate.

B.86.1. certificateAndKey

The certificate is specified in the certificateAndKey property and is always loaded from a Kubernetes secret. In the secret, the certificate must be stored in X509 format under two different keys: public and private.

You can use the secrets created by the User Operator, or you can create your own TLS certificate file, with the keys used for authentication, then create a Secret from the file:

kubectl create secret generic MY-SECRET \
--from-file=MY-PUBLIC-TLS-CERTIFICATE-FILE.crt \
--from-file=MY-PRIVATE.key
Note
 TLS client authentication can only be used with TLS connections.
Example TLS client authentication configuration
authentication:
  type: tls
  certificateAndKey:
    secretName: my-secret
    certificate: my-public-tls-certificate-file.crt
    key: private.key

The type property is a discriminator that distinguishes the use of the type KafkaClientAuthenticationTls from KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth. It must have the value tls for the type KafkaClientAuthenticationTls.

Property Description

certificateAndKey

Reference to the Secret which holds the certificate and private key pair.

CertAndKeySecretSource

type

Must be tls.

string

B.87. KafkaClientAuthenticationScramSha512 schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2ClusterSpec, KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

To configure SASL-based SCRAM-SHA-512 authentication, set the type property to scram-sha-512. The SCRAM-SHA-512 authentication mechanism requires a username and password.

B.87.1. username

Specify the username in the username property.

B.87.2. passwordSecret

In the passwordSecret property, specify a link to a Secret containing the password.

You can use the secrets created by the User Operator.

If required, you can create a text file that contains the password, in cleartext, to use for authentication:

echo -n PASSWORD > MY-PASSWORD.txt

You can then create a Secret from the text file, setting your own field name (key) for the password:

kubectl create secret generic MY-CONNECT-SECRET-NAME --from-file=MY-PASSWORD-FIELD-NAME=./MY-PASSWORD.txt
Example Secret for SCRAM-SHA-512 client authentication for Kafka Connect
apiVersion: v1
kind: Secret
metadata:
  name: my-connect-secret-name
type: Opaque
data:
  my-connect-password-field: LFTIyFRFlMmU2N2Tm

The secretName property contains the name of the Secret, and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password property.
Example SASL-based SCRAM-SHA-512 client authentication configuration for Kafka Connect
authentication:
  type: scram-sha-512
  username: my-connect-username
  passwordSecret:
    secretName: my-connect-secret-name
    password: my-connect-password-field

The type property is a discriminator that distinguishes the use of the type KafkaClientAuthenticationScramSha512 from KafkaClientAuthenticationTls, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth. It must have the value scram-sha-512 for the type KafkaClientAuthenticationScramSha512.

Property Description

passwordSecret

Reference to the Secret which holds the password.

PasswordSecretSource

type

Must be scram-sha-512.

string

username

Username used for the authentication.

string

B.88. PasswordSecretSource schema reference

Used in: KafkaClientAuthenticationPlain, KafkaClientAuthenticationScramSha512

Property Description

password

The name of the key in the Secret under which the password is stored.

string

secretName

The name of the Secret containing the password.

string

B.89. KafkaClientAuthenticationPlain schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2ClusterSpec, KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

To configure SASL-based PLAIN authentication, set the type property to plain. SASL PLAIN authentication mechanism requires a username and password.

Warning
 The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled.

B.89.1. username

Specify the username in the username property.

B.89.2. passwordSecret

In the passwordSecret property, specify a link to a Secret containing the password.

You can use the secrets created by the User Operator.

If required, create a text file that contains the password, in cleartext, to use for authentication:

echo -n PASSWORD > MY-PASSWORD.txt

You can then create a Secret from the text file, setting your own field name (key) for the password:

kubectl create secret generic MY-CONNECT-SECRET-NAME --from-file=MY-PASSWORD-FIELD-NAME=./MY-PASSWORD.txt
Example Secret for PLAIN client authentication for Kafka Connect
apiVersion: v1
kind: Secret
metadata:
  name: my-connect-secret-name
type: Opaque
data:
  my-password-field-name: LFTIyFRFlMmU2N2Tm

The secretName property contains the name of the Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password property.
An example SASL based PLAIN client authentication configuration
authentication:
  type: plain
  username: my-connect-username
  passwordSecret:
    secretName: my-connect-secret-name
    password: my-password-field-name

The type property is a discriminator that distinguishes the use of the type KafkaClientAuthenticationPlain from KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationOAuth. It must have the value plain for the type KafkaClientAuthenticationPlain.

Property Description

passwordSecret

Reference to the Secret which holds the password.

PasswordSecretSource

type

Must be plain.

string

username

Username used for the authentication.

string

B.90. KafkaClientAuthenticationOAuth schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2ClusterSpec, KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

To use OAuth client authentication, set the type property to the value oauth.

OAuth authentication can be configured using one of the following options:

  • Client ID and secret

  • Client ID and refresh token

  • Access token

  • TLS

Client ID and secret

You can configure the address of your authorization server in the tokenEndpointUri property together with the client ID and client secret used in authentication. The OAuth client will connect to the OAuth server, authenticate using the client ID and secret and get an access token which it will use to authenticate with the Kafka broker. In the clientSecret property, specify a link to a Secret containing the client secret.

An example of OAuth client authentication using client ID and client secret
authentication:
  type: oauth
  tokenEndpointUri: https://sso.myproject.svc:8443/auth/realms/internal/protocol/openid-connect/token
  clientId: my-client-id
  clientSecret:
    secretName: my-client-oauth-secret
    key: client-secret
Client ID and refresh token

You can configure the address of your OAuth server in the tokenEndpointUri property together with the OAuth client ID and refresh token. The OAuth client will connect to the OAuth server, authenticate using the client ID and refresh token and get an access token which it will use to authenticate with the Kafka broker. In the refreshToken property, specify a link to a Secret containing the refresh token.

+ .An example of OAuth client authentication using client ID and refresh token

authentication:
  type: oauth
  tokenEndpointUri: https://sso.myproject.svc:8443/auth/realms/internal/protocol/openid-connect/token
  clientId: my-client-id
  refreshToken:
    secretName: my-refresh-token-secret
    key: refresh-token
Access token

You can configure the access token used for authentication with the Kafka broker directly. In this case, you do not specify the tokenEndpointUri. In the accessToken property, specify a link to a Secret containing the access token.

An example of OAuth client authentication using only an access token
authentication:
  type: oauth
  accessToken:
    secretName: my-access-token-secret
    key: access-token
TLS

Accessing the OAuth server using the HTTPS protocol does not require any additional configuration as long as the TLS certificates used by it are signed by a trusted certification authority and its hostname is listed in the certificate.

If your OAuth server is using certificates which are self-signed or are signed by a certification authority which is not trusted, you can configure a list of trusted certificates in the custom resoruce. The tlsTrustedCertificates property contains a list of secrets with key names under which the certificates are stored. The certificates must be stored in X509 format.

An example of TLS certificates provided
authentication:
  type: oauth
  tokenEndpointUri: https://sso.myproject.svc:8443/auth/realms/internal/protocol/openid-connect/token
  clientId: my-client-id
  refreshToken:
    secretName: my-refresh-token-secret
    key: refresh-token
  tlsTrustedCertificates:
    - secretName: oauth-server-ca
      certificate: tls.crt

The OAuth client will by default verify that the hostname of your OAuth server matches either the certificate subject or one of the alternative DNS names. If it is not required, you can disable the hostname verification.

An example of disabled TLS hostname verification
authentication:
  type: oauth
  tokenEndpointUri: https://sso.myproject.svc:8443/auth/realms/internal/protocol/openid-connect/token
  clientId: my-client-id
  refreshToken:
    secretName: my-refresh-token-secret
    key: refresh-token
  disableTlsHostnameVerification: true

The type property is a discriminator that distinguishes the use of the type KafkaClientAuthenticationOAuth from KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain. It must have the value oauth for the type KafkaClientAuthenticationOAuth.

Property Description

accessToken

Link to Kubernetes Secret containing the access token which was obtained from the authorization server.

GenericSecretSource

accessTokenIsJwt

Configure whether access token should be treated as JWT. This should be set to false if the authorization server returns opaque tokens. Defaults to true.

boolean

clientId

OAuth Client ID which the Kafka client can use to authenticate against the OAuth server and use the token endpoint URI.

string

clientSecret

Link to Kubernetes Secret containing the OAuth client secret which the Kafka client can use to authenticate against the OAuth server and use the token endpoint URI.

GenericSecretSource

disableTlsHostnameVerification

Enable or disable TLS hostname verification. Default value is false.

boolean

maxTokenExpirySeconds

Set or limit time-to-live of the access tokens to the specified number of seconds. This should be set if the authorization server returns opaque tokens.

integer

refreshToken

Link to Kubernetes Secret containing the refresh token which can be used to obtain access token from the authorization server.

GenericSecretSource

scope

OAuth scope to use when authenticating against the authorization server. Some authorization servers require this to be set. The possible values depend on how authorization server is configured. By default scope is not specified when doing the token endpoint request.

string

tlsTrustedCertificates

Trusted certificates for TLS connection to the OAuth server.

CertSecretSource array

tokenEndpointUri

Authorization server token endpoint URI.

string

type

Must be oauth.

string

B.91. JaegerTracing schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec

The type property is a discriminator that distinguishes the use of the type JaegerTracing from other subtypes which may be added in the future. It must have the value jaeger for the type JaegerTracing.

Property Description

type

Must be jaeger.

string

B.92. KafkaConnectTemplate schema reference

Used in: KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec

Property Description

deployment

Template for Kafka Connect Deployment.

ResourceTemplate

pod

Template for Kafka Connect Pods.

PodTemplate

apiService

Template for Kafka Connect API Service.

ResourceTemplate

connectContainer

Template for the Kafka Connect container.

ContainerTemplate

initContainer

Template for the Kafka init container.

ContainerTemplate

podDisruptionBudget

Template for Kafka Connect PodDisruptionBudget.

PodDisruptionBudgetTemplate

B.93. ExternalConfiguration schema reference

Used in: KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec

Configures external storage properties that define configuration options for Kafka Connect connectors.

You can mount ConfigMaps or Secrets into a Kafka Connect pod as environment variables or volumes. Volumes and environment variables are configured in the externalConfiguration property in KafkaConnect.spec and KafkaConnectS2I.spec.

When applied, the environment variables and volumes are available for use when developing your connectors.

B.93.1. env

The env property is used to specify one or more environment variables. These variables can contain a value from either a ConfigMap or a Secret.

Example Secret containing values for environment variables
apiVersion: v1
kind: Secret
metadata:
  name: aws-creds
type: Opaque
data:
  awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg=
  awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=
Note
 The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_.

To mount a value from a Secret to an environment variable, use the valueFrom property and the secretKeyRef.

Example environment variables set to values from a Secret
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    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

A common use case for mounting Secrets to environment variables is when your connector needs to communicate with Amazon AWS and needs to read the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables with credentials.

To mount a value from a ConfigMap to an environment variable, use configMapKeyRef in the valueFrom property as shown in the following example.

Example environment variables set to values from a ConfigMap
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          configMapKeyRef:
            name: my-config-map
            key: my-key

B.93.2. volumes

You can also mount ConfigMaps or Secrets to a Kafka Connect pod as volumes.

Using volumes instead of environment variables is useful in the following scenarios:

  • Mounting truststores or keystores with TLS certificates

  • Mounting a properties file that is used to configure Kafka Connect connectors

Example Secret with properties
apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
stringData:
  connector.properties: |- (1)
    dbUsername: my-user (2)
    dbPassword: my-password

  1. The connector configuration in properties file format.

  2. Database username and password properties used in the configuration.

In this example, a Secret named mysecret is mounted to a volume named connector-config. In the config property, a configuration provider (FileConfigProvider) is specified, which will load configuration values from external sources. The Kafka FileConfigProvider is given the alias file, and will read and extract database username and password property values from the file to use in the connector configuration.

Example external volumes set to values from a Secret
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    config.providers: file (1)
    config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider (2)
  #...
  externalConfiguration:
    volumes:
      - name: connector-config (3)
        secret:
          secretName: mysecret (4)

  1. The alias for the configuration provider, which is used to define other configuration parameters. Use a comma-separated list if you want to add more than one provider.

  2. The FileConfigProvider is the configuration provider that provides values from properties files. The parameter uses the alias from config.providers, taking the form config.providers.${alias}.class.

  3. The name of the volume containing the Secret. Each volume must specify a name in the name property and a reference to ConfigMap or Secret.

  4. The name of the Secret.

The volumes are mounted inside the Kafka Connect containers in the path /opt/kafka/external-configuration/<volume-name>. For example, the files from a volume named connector-config would appear in the directory /opt/kafka/external-configuration/connector-config.

The FileConfigProvider is used to read the values from the mounted properties files in connector configurations.

Property Description

env

Allows to pass data from Secret or ConfigMap to the Kafka Connect pods as environment variables.

ExternalConfigurationEnv array

volumes

Allows to pass data from Secret or ConfigMap to the Kafka Connect pods as volumes.

ExternalConfigurationVolumeSource array

B.94. ExternalConfigurationEnv schema reference

Used in: ExternalConfiguration

Property Description

name

Name of the environment variable which will be passed to the Kafka Connect pods. The name of the environment variable cannot start with KAFKA_ or STRIMZI_.

string

valueFrom

Value of the environment variable which will be passed to the Kafka Connect pods. It can be passed either as a reference to Secret or ConfigMap field. The field has to specify exactly one Secret or ConfigMap.

ExternalConfigurationEnvVarSource

B.95. ExternalConfigurationEnvVarSource schema reference

Used in: ExternalConfigurationEnv

Property Description

configMapKeyRef

Refernce to a key in a ConfigMap. See external documentation of core/v1 configmapkeyselector.

ConfigMapKeySelector

secretKeyRef

Reference to a key in a Secret. See external documentation of core/v1 secretkeyselector.

SecretKeySelector

B.96. ExternalConfigurationVolumeSource schema reference

Used in: ExternalConfiguration

Property Description

configMap

Reference to a key in a ConfigMap. Exactly one Secret or ConfigMap has to be specified. See external documentation of core/v1 configmapvolumesource.

ConfigMapVolumeSource

name

Name of the volume which will be added to the Kafka Connect pods.

string

secret

Reference to a key in a Secret. Exactly one Secret or ConfigMap has to be specified. See external documentation of core/v1 secretvolumesource.

SecretVolumeSource

B.97. KafkaConnectStatus schema reference

Used in: KafkaConnect

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

url

The URL of the REST API endpoint for managing and monitoring Kafka Connect connectors.

string

connectorPlugins

The list of connector plugins available in this Kafka Connect deployment.

ConnectorPlugin array

labelSelector

Label selector for pods providing this resource.

string

replicas

The current number of pods being used to provide this resource.

integer

B.98. ConnectorPlugin schema reference

Used in: KafkaConnectS2IStatus, KafkaConnectStatus, KafkaMirrorMaker2Status

Property Description

type

The type of the connector plugin. The available types are sink and source.

string

version

The version of the connector plugin.

string

class

The class of the connector plugin.

string

B.99. KafkaConnectS2I schema reference

Property Description

spec

The specification of the Kafka Connect Source-to-Image (S2I) cluster.

KafkaConnectS2ISpec

status

The status of the Kafka Connect Source-to-Image (S2I) cluster.

KafkaConnectS2IStatus

B.100. KafkaConnectS2ISpec schema reference

Used in: KafkaConnectS2I

Configures a Kafka Connect cluster with Source-to-Image (S2I) support.

When extending Kafka Connect with connector plugins on OpenShift (only), you can use OpenShift builds and S2I to create a container image that is used by the Kafka Connect deployment.

The configuration options are similar to Kafka Connect configuration using the KafkaConnectSpec schema.

Property Description

replicas

The number of pods in the Kafka Connect group.

integer

image

The docker image for the pods.

string

buildResources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

logging

Logging configuration for Kafka Connect. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

template

Template for Kafka Connect and Kafka Connect S2I resources. The template allows users to specify how the Deployment, Pods and Service are generated.

KafkaConnectTemplate

authentication

Authentication configuration for Kafka Connect. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

bootstrapServers

Bootstrap servers to connect to. This should be given as a comma separated list of <hostname>:‍<port> pairs.

string

clientRackInitImage

The image of the init container used for initializing the client.rack.

string

config

The Kafka Connect configuration. Properties with the following prefixes cannot be set: ssl., sasl., security., listeners, plugin.path, rest., bootstrap.servers, consumer.interceptor.classes, producer.interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

externalConfiguration

Pass data from Secrets or ConfigMaps to the Kafka Connect pods and use them to configure connectors.

ExternalConfiguration

insecureSourceRepository

When true this configures the source repository with the 'Local' reference policy and an import policy that accepts insecure source tags.

boolean

rack

Configuration of the node label which will be used as the client.rack consumer configuration.

Rack

resources

The maximum limits for CPU and memory resources and the requested initial resources. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

tls

TLS configuration.

KafkaConnectTls

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

tracing

The configuration of tracing in Kafka Connect. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

version

The Kafka Connect version. Defaults to 2.6.0. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

B.101. KafkaConnectS2IStatus schema reference

Used in: KafkaConnectS2I

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

url

The URL of the REST API endpoint for managing and monitoring Kafka Connect connectors.

string

connectorPlugins

The list of connector plugins available in this Kafka Connect deployment.

ConnectorPlugin array

buildConfigName

The name of the build configuration.

string

labelSelector

Label selector for pods providing this resource.

string

replicas

The current number of pods being used to provide this resource.

integer

B.102. KafkaTopic schema reference

Property Description

spec

The specification of the topic.

KafkaTopicSpec

status

The status of the topic.

KafkaTopicStatus

B.103. KafkaTopicSpec schema reference

Used in: KafkaTopic

Property Description

partitions

The number of partitions the topic should have. This cannot be decreased after topic creation. It can be increased after topic creation, but it is important to understand the consequences that has, especially for topics with semantic partitioning.

integer

replicas

The number of replicas the topic should have.

integer

config

The topic configuration.

map

topicName

The name of the topic. When absent this will default to the metadata.name of the topic. It is recommended to not set this unless the topic name is not a valid Kubernetes resource name.

string

B.104. KafkaTopicStatus schema reference

Used in: KafkaTopic

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

B.105. KafkaUser schema reference

Property Description

spec

The specification of the user.

KafkaUserSpec

status

The status of the Kafka User.

KafkaUserStatus

B.106. KafkaUserSpec schema reference

Used in: KafkaUser

Property Description

authentication

Authentication mechanism enabled for this Kafka user. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512].

KafkaUserTlsClientAuthentication, KafkaUserScramSha512ClientAuthentication

authorization

Authorization rules for this Kafka user. The type depends on the value of the authorization.type property within the given object, which must be one of [simple].

KafkaUserAuthorizationSimple

quotas

Quotas on requests to control the broker resources used by clients. Network bandwidth and request rate quotas can be enforced.Kafka documentation for Kafka User quotas can be found at http://kafka.apache.org/documentation/#design_quotas.

KafkaUserQuotas

template

Template to specify how Kafka User Secrets are generated.

KafkaUserTemplate

B.107. KafkaUserTlsClientAuthentication schema reference

Used in: KafkaUserSpec

The type property is a discriminator that distinguishes the use of the type KafkaUserTlsClientAuthentication from KafkaUserScramSha512ClientAuthentication. It must have the value tls for the type KafkaUserTlsClientAuthentication.

Property Description

type

Must be tls.

string

B.108. KafkaUserScramSha512ClientAuthentication schema reference

Used in: KafkaUserSpec

The type property is a discriminator that distinguishes the use of the type KafkaUserScramSha512ClientAuthentication from KafkaUserTlsClientAuthentication. It must have the value scram-sha-512 for the type KafkaUserScramSha512ClientAuthentication.

Property Description

type

Must be scram-sha-512.

string

B.109. KafkaUserAuthorizationSimple schema reference

Used in: KafkaUserSpec

The type property is a discriminator that distinguishes the use of the type KafkaUserAuthorizationSimple from other subtypes which may be added in the future. It must have the value simple for the type KafkaUserAuthorizationSimple.

Property Description

type

Must be simple.

string

acls

List of ACL rules which should be applied to this user.

AclRule array

B.110. AclRule schema reference

Used in: KafkaUserAuthorizationSimple

Configures access control rule for a KafkaUser when brokers are using the AclAuthorizer.

Example KafkaUser configuration with authorization
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  authorization:
    type: simple
    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: prefix
        operation: Read

B.110.1. resource

Use the resource property to specify the resource that the rule applies to.

Simple authorization supports four resource types, which are specified in the type property:

  • Topics (topic)

  • Consumer Groups (group)

  • Clusters (cluster)

  • Transactional IDs (transactionalId)

For Topic, Group, and Transactional ID resources you can specify the name of the resource the rule applies to in the name property.

Cluster type resources have no name.

A name is specified as a literal or a prefix using the patternType property.

  • Literal names are taken exactly as they are specified in the name field.

  • Prefix names use the value from the name as a prefix, and will apply the rule to all resources with names starting with the value.

B.110.2. type

The type of rule, which is to allow or deny (not currently supported) an operation.

The type field is optional. If type is unspecified, the ACL rule is treated as an allow rule.

B.110.3. operation

Specify an operation for the rule to allow or deny.

The following operations are supported:

  • Read

  • Write

  • Delete

  • Alter

  • Describe

  • All

  • IdempotentWrite

  • ClusterAction

  • Create

  • AlterConfigs

  • DescribeConfigs

Only certain operations work with each resource.

For more details about AclAuthorizer, ACLs and supported combinations of resources and operations, see Authorization and ACLs.

B.110.4. host

Use the host property to specify a remote host from which the rule is allowed or denied.

Use an asterisk (*) to allow or deny the operation from all hosts. The host field is optional. If host is unspecified, the * value is used by default.

Property Description

host

The host from which the action described in the ACL rule is allowed or denied.

string

operation

Operation which will be allowed or denied. Supported operations are: Read, Write, Create, Delete, Alter, Describe, ClusterAction, AlterConfigs, DescribeConfigs, IdempotentWrite and All.

string (one of [Read, Write, Delete, Alter, Describe, All, IdempotentWrite, ClusterAction, Create, AlterConfigs, DescribeConfigs])

resource

Indicates the resource for which given ACL rule applies. The type depends on the value of the resource.type property within the given object, which must be one of [topic, group, cluster, transactionalId].

AclRuleTopicResource, AclRuleGroupResource, AclRuleClusterResource, AclRuleTransactionalIdResource

type

The type of the rule. Currently the only supported type is allow. ACL rules with type allow are used to allow user to execute the specified operations. Default value is allow.

string (one of [allow, deny])

B.111. AclRuleTopicResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleTopicResource from AclRuleGroupResource, AclRuleClusterResource, AclRuleTransactionalIdResource. It must have the value topic for the type AclRuleTopicResource.

Property Description

type

Must be topic.

string

name

Name of resource for which given ACL rule applies. Can be combined with patternType field to use prefix pattern.

string

patternType

Describes the pattern used in the resource field. The supported types are literal and prefix. With literal pattern type, the resource field will be used as a definition of a full topic name. With prefix pattern type, the resource name will be used only as a prefix. Default value is literal.

string (one of [prefix, literal])

B.112. AclRuleGroupResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleGroupResource from AclRuleTopicResource, AclRuleClusterResource, AclRuleTransactionalIdResource. It must have the value group for the type AclRuleGroupResource.

Property Description

type

Must be group.

string

name

Name of resource for which given ACL rule applies. Can be combined with patternType field to use prefix pattern.

string

patternType

Describes the pattern used in the resource field. The supported types are literal and prefix. With literal pattern type, the resource field will be used as a definition of a full topic name. With prefix pattern type, the resource name will be used only as a prefix. Default value is literal.

string (one of [prefix, literal])

B.113. AclRuleClusterResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleClusterResource from AclRuleTopicResource, AclRuleGroupResource, AclRuleTransactionalIdResource. It must have the value cluster for the type AclRuleClusterResource.

Property Description

type

Must be cluster.

string

B.114. AclRuleTransactionalIdResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleTransactionalIdResource from AclRuleTopicResource, AclRuleGroupResource, AclRuleClusterResource. It must have the value transactionalId for the type AclRuleTransactionalIdResource.

Property Description

type

Must be transactionalId.

string

name

Name of resource for which given ACL rule applies. Can be combined with patternType field to use prefix pattern.

string

patternType

Describes the pattern used in the resource field. The supported types are literal and prefix. With literal pattern type, the resource field will be used as a definition of a full name. With prefix pattern type, the resource name will be used only as a prefix. Default value is literal.

string (one of [prefix, literal])

B.115. KafkaUserQuotas schema reference

Used in: KafkaUserSpec

Kafka allows a user to set quotas to control the use of resources by clients.

B.115.1. quotas

Quotas split into two categories:

  • Network usage quotas, which are defined as the byte rate threshold for each group of clients sharing a quota

  • CPU utilization quotas, which are defined as the percentage of time a client can utilize on request handler I/O threads and network threads of each broker within a quota window

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

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

An example Kafka user quotas
spec:
  quotas:
    producerByteRate: 1048576
    consumerByteRate: 2097152
    requestPercentage: 55

For more info about Kafka user quotas, refer to the Apache Kafka documentation.

Property Description

consumerByteRate

A quota on the maximum bytes per-second that each client group can fetch from a broker before the clients in the group are throttled. Defined on a per-broker basis.

integer

producerByteRate

A quota on the maximum bytes per-second that each client group can publish to a broker before the clients in the group are throttled. Defined on a per-broker basis.

integer

requestPercentage

A quota on the maximum CPU utilization of each client group as a percentage of network and I/O threads.

integer

B.116. KafkaUserTemplate schema reference

Used in: KafkaUserSpec

Specify additional labels and annotations for the secret created by the User Operator.

An example showing the KafkaUserTemplate
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: tls
  template:
    secret:
      metadata:
        labels:
          label1: value1
        annotations:
          anno1: value1
  # ...
Property Description

secret

Template for KafkaUser resources. The template allows users to specify how the Secret with password or TLS certificates is generated.

ResourceTemplate

B.117. KafkaUserStatus schema reference

Used in: KafkaUser

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

username

Username.

string

secret

The name of Secret where the credentials are stored.

string

B.118. KafkaMirrorMaker schema reference

Property Description

spec

The specification of Kafka MirrorMaker.

KafkaMirrorMakerSpec

status

The status of Kafka MirrorMaker.

KafkaMirrorMakerStatus

B.119. KafkaMirrorMakerSpec schema reference

Used in: KafkaMirrorMaker

Configures Kafka MirrorMaker.

B.119.1. whitelist

Use the whitelist property to configure a list of topics that Kafka MirrorMaker mirrors from the source to the target Kafka cluster.

The property allows any regular expression from the simplest case with a single topic name to complex patterns. For example, you can mirror topics A and B using "A|B" or all topics using "*". You can also pass multiple regular expressions separated by commas to the Kafka MirrorMaker.

B.119.2. KafkaMirrorMakerConsumerSpec and KafkaMirrorMakerProducerSpec

Use the KafkaMirrorMakerConsumerSpec and KafkaMirrorMakerProducerSpec to configure source (consumer) and target (producer) clusters.

Kafka MirrorMaker always works together with two Kafka clusters (source and target). To establish a connection, the bootstrap servers for the source and the target Kafka clusters are specified as comma-separated lists of HOSTNAME:PORT pairs. Each comma-separated list contains one or more Kafka brokers or a Service pointing to Kafka brokers specified as a HOSTNAME:PORT pair.

B.119.3. logging

Kafka MirrorMaker has its own configurable logger:

  • mirrormaker.root.logger

MirrorMaker uses the Apache log4j 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.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties. For more information about log levels, see Apache logging services.

Here we see examples of inline and external logging:

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
  # ...
  logging:
    type: inline
    loggers:
      mirrormaker.root.logger: "INFO"
  # ...
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...
Garbage collector (GC)

Garbage collector logging can also be enabled (or disabled) using the jvmOptions property.

Property Description

replicas

The number of pods in the Deployment.

integer

image

The docker image for the pods.

string

whitelist

List of topics which are included for mirroring. This option allows any regular expression using Java-style regular expressions. Mirroring two topics named A and B is achieved by using the whitelist 'A|B'. Or, as a special case, you can mirror all topics using the whitelist '*'. You can also specify multiple regular expressions separated by commas.

string

consumer

Configuration of source cluster.

KafkaMirrorMakerConsumerSpec

producer

Configuration of target cluster.

KafkaMirrorMakerProducerSpec

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

jvmOptions

JVM Options for pods.

JvmOptions

logging

Logging configuration for MirrorMaker. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See JMX Exporter documentation for details of the structure of this configuration.

map

tracing

The configuration of tracing in Kafka MirrorMaker. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

template

Template to specify how Kafka MirrorMaker resources, Deployments and Pods, are generated.

KafkaMirrorMakerTemplate

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

version

The Kafka MirrorMaker version. Defaults to 2.6.0. Consult the documentation to understand the process required to upgrade or downgrade the version.

string

B.120. KafkaMirrorMakerConsumerSpec schema reference

Used in: KafkaMirrorMakerSpec

Configures a MirrorMaker consumer.

B.120.1. numStreams

Use the consumer.numStreams property to configure the number of streams for the consumer.

You can increase the throughput in mirroring topics by increasing the number of consumer threads. Consumer threads belong to the consumer group specified for Kafka MirrorMaker. Topic partitions are assigned across the consumer threads, which consume messages in parallel.

B.120.2. offsetCommitInterval

Use the consumer.offsetCommitInterval property to configure an offset auto-commit interval for the consumer.

You can specify the regular time interval at which an offset is committed after Kafka MirrorMaker has consumed data from the source Kafka cluster. The time interval is set in milliseconds, with a default value of 60,000.

B.120.3. config

Use the consumer.config properties to configure Kafka options for the consumer.

The config property contains the Kafka MirrorMaker consumer configuration options as keys, with values set in one of the following JSON types:

  • String

  • Number

  • Boolean

For client connection using a specific cipher suite for a TLS version, you can configure allowed ssl properties. You can also configure the ssl.endpoint.identification.algorithm property to enable or disable hostname verification.

Exceptions

You can specify and configure the options listed in the Apache Kafka configuration documentation for consumers.

However, there are exceptions for options automatically configured and managed directly by Strimzi related to:

  • Kafka cluster bootstrap address

  • Security (encryption, authentication, and authorization)

  • Consumer group identifier

  • Interceptors

Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Cluster Operator log file. All other options are passed to Kafka MirrorMaker.

Important
 The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka MirrorMaker might not start or might become unstable. In such cases, the configuration in the KafkaMirrorMaker.spec.consumer.config object should be fixed and the Cluster Operator will roll out the new configuration for Kafka MirrorMaker.

B.120.4. groupId

Use the consumer.groupId property to configure a consumer group identifier for the consumer.

Kafka MirrorMaker uses a Kafka consumer to consume messages, behaving like any other Kafka consumer client. Messages consumed from the source Kafka cluster are mirrored to a target Kafka cluster. A group identifier is required, as the consumer needs to be part of a consumer group for the assignment of partitions.

Property Description

numStreams

Specifies the number of consumer stream threads to create.

integer

offsetCommitInterval

Specifies the offset auto-commit interval in ms. Default value is 60000.

integer

groupId

A unique string that identifies the consumer group this consumer belongs to.

string

bootstrapServers

A list of host:port pairs for establishing the initial connection to the Kafka cluster.

string

authentication

Authentication configuration for connecting to the cluster. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

config

The MirrorMaker consumer config. Properties with the following prefixes cannot be set: ssl., bootstrap.servers, group.id, sasl., security., interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

tls

TLS configuration for connecting MirrorMaker to the cluster.

KafkaMirrorMakerTls

B.121. KafkaMirrorMakerTls schema reference

Used in: KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

Configures TLS trusted certificates for connecting MirrorMaker to the cluster.

B.121.1. trustedCertificates

Provide a list of secrets using the trustedCertificates property.

Property Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

B.122. KafkaMirrorMakerProducerSpec schema reference

Used in: KafkaMirrorMakerSpec

Configures a MirrorMaker producer.

B.122.1. abortOnSendFailure

Use the producer.abortOnSendFailure property to configure how to handle message send failure from the producer.

By default, if an error occurs when sending a message from Kafka MirrorMaker to a Kafka cluster:

  • The Kafka MirrorMaker container is terminated in Kubernetes.

  • The container is then recreated.

If the abortOnSendFailure option is set to false, message sending errors are ignored.

B.122.2. config

Use the producer.config properties to configure Kafka options for the producer.

The config property contains the Kafka MirrorMaker producer configuration options as keys, with values set in one of the following JSON types:

  • String

  • Number

  • Boolean

For client connection using a specific cipher suite for a TLS version, you can configure allowed ssl properties. You can also configure the ssl.endpoint.identification.algorithm property to enable or disable hostname verification.

Exceptions

You can specify and configure the options listed in the Apache Kafka configuration documentation for producers.

However, there are exceptions for options automatically configured and managed directly by Strimzi related to:

  • Kafka cluster bootstrap address

  • Security (encryption, authentication, and authorization)

  • Interceptors

Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Cluster Operator log file. All other options are passed to Kafka MirrorMaker.

Important
 The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka MirrorMaker might not start or might become unstable. In such cases, the configuration in the KafkaMirrorMaker.spec.producer.config object should be fixed and the Cluster Operator will roll out the new configuration for Kafka MirrorMaker.
Property Description

bootstrapServers

A list of host:port pairs for establishing the initial connection to the Kafka cluster.

string

abortOnSendFailure

Flag to set the MirrorMaker to exit on a failed send. Default value is true.

boolean

authentication

Authentication configuration for connecting to the cluster. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

config

The MirrorMaker producer config. Properties with the following prefixes cannot be set: ssl., bootstrap.servers, sasl., security., interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

tls

TLS configuration for connecting MirrorMaker to the cluster.

KafkaMirrorMakerTls

B.123. KafkaMirrorMakerTemplate schema reference

Used in: KafkaMirrorMakerSpec

Property Description

deployment

Template for Kafka MirrorMaker Deployment.

ResourceTemplate

pod

Template for Kafka MirrorMaker Pods.

PodTemplate

mirrorMakerContainer

Template for Kafka MirrorMaker container.

ContainerTemplate

podDisruptionBudget

Template for Kafka MirrorMaker PodDisruptionBudget.

PodDisruptionBudgetTemplate

B.124. KafkaMirrorMakerStatus schema reference

Used in: KafkaMirrorMaker

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

labelSelector

Label selector for pods providing this resource.

string

replicas

The current number of pods being used to provide this resource.

integer

B.125. KafkaBridge schema reference

Property Description

spec

The specification of the Kafka Bridge.

KafkaBridgeSpec

status

The status of the Kafka Bridge.

KafkaBridgeStatus

B.126. KafkaBridgeSpec schema reference

Used in: KafkaBridge

Configures a Kafka Bridge cluster.

Configuration options relate to:

  • Kafka cluster bootstrap address

  • Security (Encryption, Authentication, and Authorization)

  • Consumer configuration

  • Producer configuration

  • HTTP configuration

B.126.1. logging

Kafka Bridge has its own configurable loggers:

  • logger.bridge

  • logger.<operation-id>

You can replace <operation-id> in the logger.<operation-id> logger to set log levels for specific operations:

  • createConsumer

  • deleteConsumer

  • subscribe

  • unsubscribe

  • poll

  • assign

  • commit

  • send

  • sendToPartition

  • seekToBeginning

  • seekToEnd

  • seek

  • healthy

  • ready

  • openapi

Each operation is defined according OpenAPI specification, and has a corresponding API endpoint through which the bridge receives requests from HTTP clients. You can change the log level on each endpoint to create fine-grained logging information about the incoming and outgoing HTTP requests.

Each logger has to be configured assigning it a name as http.openapi.operation.<operation-id>. For example, configuring the logging level for the send operation logger means defining the following:

logger.send.name = http.openapi.operation.send
logger.send.level = DEBUG

Kafka Bridge uses the Apache log4j2 logger implementation. Loggers are defined in the log4j2.properties file, which has the following default configuration for healthy and ready endpoints:

logger.healthy.name = http.openapi.operation.healthy
logger.healthy.level = WARN
logger.ready.name = http.openapi.operation.ready
logger.ready.level = WARN

The log level of all other operations is set to INFO by default.

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.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties. For more information about log levels, see Apache logging services.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaBridge
spec:
  # ...
  logging:
    type: inline
    loggers:
      logger.bridge.level: "INFO"
      # enabling DEBUG just for send operation
      logger.send.name: "http.openapi.operation.send"
      logger.send.level: "DEBUG"
  # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaBridge
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...

Any available loggers that are not configured have their level set to OFF.

If the Kafka Bridge was deployed using the Cluster Operator, changes to Kafka Bridge logging levels are applied dynamically.

If you use external logging, a rolling update is triggered when logging appenders are changed.

Garbage collector (GC)

Garbage collector logging can also be enabled (or disabled) using the jvmOptions property.

Property Description

replicas

The number of pods in the Deployment.

integer

image

The docker image for the pods.

string

bootstrapServers

A list of host:port pairs for establishing the initial connection to the Kafka cluster.

string

tls

TLS configuration for connecting Kafka Bridge to the cluster.

KafkaBridgeTls

authentication

Authentication configuration for connecting to the cluster. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

http

The HTTP related configuration.

KafkaBridgeHttpConfig

consumer

Kafka consumer related configuration.

KafkaBridgeConsumerSpec

producer

Kafka producer related configuration.

KafkaBridgeProducerSpec

resources

CPU and memory resources to reserve. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

jvmOptions

Currently not supported JVM Options for pods.

JvmOptions

logging

Logging configuration for Kafka Bridge. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

enableMetrics

Enable the metrics for the Kafka Bridge. Default is false.

boolean

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

template

Template for Kafka Bridge resources. The template allows users to specify how is the Deployment and Pods generated.

KafkaBridgeTemplate

tracing

The configuration of tracing in Kafka Bridge. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

B.127. KafkaBridgeTls schema reference

Used in: KafkaBridgeSpec

Property Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

B.128. KafkaBridgeHttpConfig schema reference

Used in: KafkaBridgeSpec

Configures HTTP access to a Kafka cluster for the Kafka Bridge.

The default HTTP configuration is for the Kafka Bridge to listen on port 8080.

B.128.1. cors

As well as enabling HTTP access to a Kafka cluster, HTTP properties provide the capability to enable and define access control for the Kafka Bridge through Cross-Origin Resource Sharing (CORS). CORS is a HTTP mechanism that allows browser access to selected resources from more than one origin. To configure CORS, you define a list of allowed resource origins and HTTP access methods. For the origins, you can use a URL or a Java regular expression.

Example Kafka Bridge HTTP configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  http:
    port: 8080
    cors:
      allowedOrigins: "https://strimzi.io"
      allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  # ...
Property Description

port

The port which is the server listening on.

integer

cors

CORS configuration for the HTTP Bridge.

KafkaBridgeHttpCors

B.129. KafkaBridgeHttpCors schema reference

Used in: KafkaBridgeHttpConfig

Property Description

allowedOrigins

List of allowed origins. Java regular expressions can be used.

string array

allowedMethods

List of allowed HTTP methods.

string array

B.130. KafkaBridgeConsumerSpec schema reference

Used in: KafkaBridgeSpec

Configures consumer options for the Kafka Bridge as keys.

The values can be one of the following JSON types:

  • String

  • Number

  • Boolean

You can specify and configure the options listed in the Apache Kafka configuration documentation for consumers with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.

  • sasl.

  • security.

  • bootstrap.servers

  • group.id

When one of the forbidden options is present in the config property, it is ignored and a warning message will be printed to the Cluster Operator log file. All other options will be passed to Kafka

Important
 The Cluster Operator does not validate keys or values in the config object. If an invalid configuration is provided, the Kafka Bridge cluster might not start or might become unstable. Fix the configuration so that the Cluster Operator can roll out the new configuration to all Kafka Bridge nodes.

There are exceptions to the forbidden options. For client connection using a specific cipher suite for a TLS version, you can configure allowed ssl properties.

Example Kafka Bridge consumer configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  consumer:
    config:
      auto.offset.reset: earliest
      enable.auto.commit: true
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384"
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      ssl.endpoint.identification.algorithm: HTTPS
    # ...
Property Description

config

The Kafka consumer configuration used for consumer instances created by the bridge. Properties with the following prefixes cannot be set: ssl., bootstrap.servers, group.id, sasl., security. (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

B.131. KafkaBridgeProducerSpec schema reference

Used in: KafkaBridgeSpec

Configures producer options for the Kafka Bridge as keys.

The values can be one of the following JSON types:

  • String

  • Number

  • Boolean

You can specify and configure the options listed in the Apache Kafka configuration documentation for producers with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.

  • sasl.

  • security.

  • bootstrap.servers

When one of the forbidden options is present in the config property, it is ignored and a warning message will be printed to the Cluster Operator log file. All other options will be passed to Kafka

Important
 The Cluster Operator does not validate keys or values in the config object. If an invalid configuration is provided, the Kafka Bridge cluster might not start or might become unstable. Fix the configuration so that the Cluster Operator can roll out the new configuration to all Kafka Bridge nodes.

There are exceptions to the forbidden options. For client connection using a specific cipher suite for a TLS version, you can configure allowed ssl properties.

Example Kafka Bridge producer configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  producer:
    config:
      acks: 1
      delivery.timeout.ms: 300000
      ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384"
      ssl.enabled.protocols: "TLSv1.2"
      ssl.protocol: "TLSv1.2"
      ssl.endpoint.identification.algorithm: HTTPS
    # ...
Property Description

config

The Kafka producer configuration used for producer instances created by the bridge. Properties with the following prefixes cannot be set: ssl., bootstrap.servers, sasl., security. (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

B.132. KafkaBridgeTemplate schema reference

Used in: KafkaBridgeSpec

Property Description

deployment

Template for Kafka Bridge Deployment.

ResourceTemplate

pod

Template for Kafka Bridge Pods.

PodTemplate

apiService

Template for Kafka Bridge API Service.

ResourceTemplate

bridgeContainer

Template for the Kafka Bridge container.

ContainerTemplate

podDisruptionBudget

Template for Kafka Bridge PodDisruptionBudget.

PodDisruptionBudgetTemplate

B.133. KafkaBridgeStatus schema reference

Used in: KafkaBridge

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

url

The URL at which external client applications can access the Kafka Bridge.

string

labelSelector

Label selector for pods providing this resource.

string

replicas

The current number of pods being used to provide this resource.

integer

B.134. KafkaConnector schema reference

Property Description

spec

The specification of the Kafka Connector.

KafkaConnectorSpec

status

The status of the Kafka Connector.

KafkaConnectorStatus

B.135. KafkaConnectorSpec schema reference

Used in: KafkaConnector

Property Description

class

The Class for the Kafka Connector.

string

tasksMax

The maximum number of tasks for the Kafka Connector.

integer

config

The Kafka Connector configuration. The following properties cannot be set: connector.class, tasks.max.

map

pause

Whether the connector should be paused. Defaults to false.

boolean

B.136. KafkaConnectorStatus schema reference

Used in: KafkaConnector

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

connectorStatus

The connector status, as reported by the Kafka Connect REST API.

map

tasksMax

The maximum number of tasks for the Kafka Connector.

integer

B.137. KafkaMirrorMaker2 schema reference

Property Description

spec

The specification of the Kafka MirrorMaker 2.0 cluster.

KafkaMirrorMaker2Spec

status

The status of the Kafka MirrorMaker 2.0 cluster.

KafkaMirrorMaker2Status

B.138. KafkaMirrorMaker2Spec schema reference

Used in: KafkaMirrorMaker2

Property Description

replicas

The number of pods in the Kafka Connect group.

integer

version

The Kafka Connect version. Defaults to 2.6.0. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

image

The docker image for the pods.

string

connectCluster

The cluster alias used for Kafka Connect. The alias must match a cluster in the list at spec.clusters.

string

clusters

Kafka clusters for mirroring.

KafkaMirrorMaker2ClusterSpec array

mirrors

Configuration of the MirrorMaker 2.0 connectors.

KafkaMirrorMaker2MirrorSpec array

resources

The maximum limits for CPU and memory resources and the requested initial resources. See external documentation of core/v1 resourcerequirements.

ResourceRequirements

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

logging

Logging configuration for Kafka Connect. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

tracing

The configuration of tracing in Kafka Connect. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

template

Template for Kafka Connect and Kafka Connect S2I resources. The template allows users to specify how the Deployment, Pods and Service are generated.

KafkaConnectTemplate

externalConfiguration

Pass data from Secrets or ConfigMaps to the Kafka Connect pods and use them to configure connectors.

ExternalConfiguration

B.139. KafkaMirrorMaker2ClusterSpec schema reference

Used in: KafkaMirrorMaker2Spec

Configures Kafka clusters for mirroring.

B.139.1. config

Use the config properties to configure Kafka options.

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

For client connection using a specific cipher suite for a TLS version, you can configure allowed ssl properties. You can also configure the ssl.endpoint.identification.algorithm property to enable or disable hostname verification.

Property Description

alias

Alias used to reference the Kafka cluster.

string

bootstrapServers

A comma-separated list of host:port pairs for establishing the connection to the Kafka cluster.

string

config

The MirrorMaker 2.0 cluster config. Properties with the following prefixes cannot be set: ssl., sasl., security., listeners, plugin.path, rest., bootstrap.servers, consumer.interceptor.classes, producer.interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

tls

TLS configuration for connecting MirrorMaker 2.0 connectors to a cluster.

KafkaMirrorMaker2Tls

authentication

Authentication configuration for connecting to the cluster. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

B.140. KafkaMirrorMaker2Tls schema reference

Used in: KafkaMirrorMaker2ClusterSpec

Property Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

B.141. KafkaMirrorMaker2MirrorSpec schema reference

Used in: KafkaMirrorMaker2Spec

Property Description

sourceCluster

The alias of the source cluster used by the Kafka MirrorMaker 2.0 connectors. The alias must match a cluster in the list at spec.clusters.

string

targetCluster

The alias of the target cluster used by the Kafka MirrorMaker 2.0 connectors. The alias must match a cluster in the list at spec.clusters.

string

sourceConnector

The specification of the Kafka MirrorMaker 2.0 source connector.

KafkaMirrorMaker2ConnectorSpec

checkpointConnector

The specification of the Kafka MirrorMaker 2.0 checkpoint connector.

KafkaMirrorMaker2ConnectorSpec

heartbeatConnector

The specification of the Kafka MirrorMaker 2.0 heartbeat connector.

KafkaMirrorMaker2ConnectorSpec

topicsPattern

A regular expression matching the topics to be mirrored, for example, "topic1|topic2|topic3". Comma-separated lists are also supported.

string

topicsBlacklistPattern

A regular expression matching the topics to exclude from mirroring. Comma-separated lists are also supported.

string

groupsPattern

A regular expression matching the consumer groups to be mirrored. Comma-separated lists are also supported.

string

groupsBlacklistPattern

A regular expression matching the consumer groups to exclude from mirroring. Comma-separated lists are also supported.

string

B.142. KafkaMirrorMaker2ConnectorSpec schema reference

Used in: KafkaMirrorMaker2MirrorSpec

Property Description

tasksMax

The maximum number of tasks for the Kafka Connector.

integer

config

The Kafka Connector configuration. The following properties cannot be set: connector.class, tasks.max.

map

pause

Whether the connector should be paused. Defaults to false.

boolean

B.143. KafkaMirrorMaker2Status schema reference

Used in: KafkaMirrorMaker2

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

url

The URL of the REST API endpoint for managing and monitoring Kafka Connect connectors.

string

connectorPlugins

The list of connector plugins available in this Kafka Connect deployment.

ConnectorPlugin array

connectors

List of MirrorMaker 2.0 connector statuses, as reported by the Kafka Connect REST API.

map array

labelSelector

Label selector for pods providing this resource.

string

replicas

The current number of pods being used to provide this resource.

integer

B.144. KafkaRebalance schema reference

Property Description

spec

The specification of the Kafka rebalance.

KafkaRebalanceSpec

status

The status of the Kafka rebalance.

KafkaRebalanceStatus

B.145. KafkaRebalanceSpec schema reference

Used in: KafkaRebalance

Property Description

goals

A list of goals, ordered by decreasing priority, to use for generating and executing the rebalance proposal. The supported goals are available at https://github.com/linkedin/cruise-control#goals. If an empty goals list is provided, the goals declared in the default.goals Cruise Control configuration parameter are used.

string array

skipHardGoalCheck

Whether to allow the hard goals specified in the Kafka CR to be skipped in optimization proposal generation. This can be useful when some of those hard goals are preventing a balance solution being found. Default is false.

boolean

excludedTopics

A regular expression where any matching topics will be excluded from the calculation of optimization proposals. This expression will be parsed by the java.util.regex.Pattern class; for more information on the supported formar consult the documentation for that class.

string

concurrentPartitionMovementsPerBroker

The upper bound of ongoing partition replica movements going into/out of each broker. Default is 5.

integer

concurrentIntraBrokerPartitionMovements

The upper bound of ongoing partition replica movements between disks within each broker. Default is 2.

integer

concurrentLeaderMovements

The upper bound of ongoing partition leadership movements. Default is 1000.

integer

replicationThrottle

The upper bound, in bytes per second, on the bandwidth used to move replicas. There is no limit by default.

integer

replicaMovementStrategies

A list of strategy class names used to determine the execution order for the replica movements in the generated optimization proposal. By default BaseReplicaMovementStrategy is used, which will execute the replica movements in the order that they were generated.

string array

B.146. KafkaRebalanceStatus schema reference

Used in: KafkaRebalance

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

sessionId

The session identifier for requests to Cruise Control pertaining to this KafkaRebalance resource. This is used by the Kafka Rebalance operator to track the status of ongoing rebalancing operations.

string

optimizationResult

A JSON object describing the optimization result.

map