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Chapter 2. Deployment configuration

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

  • Kafka clusters
  • Kafka Connect clusters
  • Kafka Connect clusters with Source2Image support
  • Kafka Mirror Maker
  • Kafka Bridge
  • OAuth 2.0 token-based authentication
  • OAuth 2.0 token-based authorization

2.1. Kafka cluster configuration

The full schema of the Kafka resource is described in the Section B.2, “Kafka schema reference”. All labels that are applied to the desired Kafka resource will also be applied to the OpenShift 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: 1.6 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 OpenShift 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 OpenShift, 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 AMQ Streams.
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 AMQ Streams 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 AMQ Streams.

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

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

Note

AMQ Streams does not require OpenShift raw block volumes.

2.1.2.1. File systems

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

2.1.2.2. Apache Kafka and ZooKeeper storage

Use separate disks for Apache Kafka and ZooKeeper.

Three types of data storage are supported:

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

For more information, see Kafka and ZooKeeper storage.

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

Note

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

2.1.3. Kafka and ZooKeeper storage types

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

  • Ephemeral
  • Persistent
  • JBOD storage
Note

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

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

  • Kafka.spec.kafka
  • Kafka.spec.zookeeper

The storage type is configured in the type field.

Warning

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

Additional resources

2.1.3.1. 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
    # ...

2.1.3.1.1. 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.

2.1.3.2. Persistent storage

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

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

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

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

Example fragment of persistent storage configuration with 1000Gi size

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

The following example demonstrates the use of a storage class.

Example fragment of persistent storage configuration with specific Storage Class

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

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

Example fragment of persistent storage configuration with selector

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

2.1.3.2.1. Storage class overrides

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

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

Example AMQ Streams cluster using storage class overrides

apiVersion: kafka.strimzi.io/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.

2.1.3.2.2. Persistent Volume Claim naming

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

data-cluster-name-kafka-idx
Persistent Volume Claim for the volume used for storing data for the Kafka broker pod idx.
data-cluster-name-zookeeper-idx
Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod idx.
2.1.3.2.3. 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.

2.1.3.3. Resizing persistent volumes

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

Note

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

Prerequisites

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

Procedure

  1. In a Kafka resource, increase the size of the persistent volume allocated to the Kafka cluster, the ZooKeeper cluster, or both.

    • To increase the volume size allocated to the Kafka cluster, edit the spec.kafka.storage property.

    • To increase the volume size allocated to the ZooKeeper cluster, edit the spec.zookeeper.storage property.

      For example, to increase the volume size from 1000Gi to 2000Gi: 

      apiVersion: kafka.strimzi.io/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 oc apply: 

    oc apply -f your-file

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

Additional resources

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

2.1.3.4. JBOD storage overview

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

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

2.1.3.4.1. JBOD configuration

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

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

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

Users can add or remove volumes from the JBOD configuration.

2.1.3.4.2. JBOD and Persistent Volume Claims

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

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

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

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

2.1.3.5. Adding volumes to JBOD storage

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

Note

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

Prerequisites

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

Procedure

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

    apiVersion: kafka.strimzi.io/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 oc apply: 

    oc apply -f KAFKA-CONFIG-FILE
  3. Create new topics or reassign existing partitions to the new disks.

Additional resources

For more information about reassigning topics, see Section 2.1.24.2, “Partition reassignment”.

2.1.3.6. Removing volumes from JBOD storage

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

Important

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

Prerequisites

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

Procedure

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

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

    apiVersion: kafka.strimzi.io/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 oc apply: 

    oc apply -f your-file

Additional resources

For more information about reassigning topics, see Section 2.1.24.2, “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.

2.1.4.1. 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 Section 2.1.24, “Scaling clusters”.

Prerequisites

  • An OpenShift 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 oc apply: 

    oc apply -f your-file

Additional resources

If your cluster already has topics defined, see Section 2.1.24, “Scaling clusters”.

2.1.5. Kafka broker configuration

AMQ Streams 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 AMQ Streams.

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.

2.1.5.1. Configuring Kafka brokers

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

Prerequisites

  • An OpenShift 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 oc apply: 

    oc 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.

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

The GenericKafkaListener provides a flexible approach to listener configuration.

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

Generic listener configuration

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

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

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

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

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

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

Configuring listeners to secure access to Kafka brokers

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

Configuring external listeners for client access outside OpenShift

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

Listener certificates

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

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 AMQ Streams.

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.

2.1.7.1. 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
    # ...

2.1.7.2. Changing the number of ZooKeeper replicas

Prerequisites

  • An OpenShift 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 oc apply: 

    oc 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

AMQ Streams 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 AMQ Streams.

2.1.8.1. 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 AMQ Streams. 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.

2.1.8.2. Configuring ZooKeeper

Prerequisites

  • An OpenShift 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 oc apply: 

    oc 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 AMQ Streams.

However, if you want to use Kafka CLI tools that require a connection to ZooKeeper, you can use a terminal inside a ZooKeeper container and connect to localhost:12181 as the ZooKeeper address.

2.1.9.1. 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

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

Procedure

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

    For example: 

    oc exec -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.

2.1.10.1. Entity Operator configuration properties

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

The entityOperator property supports several sub-properties:

  • tlsSidecar
  • topicOperator
  • userOperator
  • template

The tlsSidecar property contains the configuration of the TLS sidecar container, which is used to communicate with ZooKeeper. For more information on configuring the TLS sidecar, see Section 2.1.19, “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 Section 2.6, “Customizing OpenShift resources”.

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

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

For more information on the properties 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.

2.1.10.2. Topic Operator configuration properties

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

watchedNamespace
The OpenShift namespace in which the topic operator watches for KafkaTopics. Default is the namespace where the Kafka cluster is deployed.
reconciliationIntervalSeconds
The interval between periodic reconciliations in seconds. Default 90.
zookeeperSessionTimeoutSeconds
The ZooKeeper session timeout in seconds. Default 20.
topicMetadataMaxAttempts
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation 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 Section 2.1.18, “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 Section 2.1.11, “CPU and memory resources”.
logging
The logging property configures the logging of the Topic Operator. For more details, see Section 2.1.10.4, “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
    # ...

2.1.10.3. User Operator configuration properties

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

watchedNamespace
The OpenShift namespace in which the user operator watches for KafkaUsers. Default is the namespace where the Kafka cluster is deployed.
reconciliationIntervalSeconds
The interval between periodic reconciliations in seconds. Default 120.
zookeeperSessionTimeoutSeconds
The ZooKeeper session timeout in seconds. Default 6.
image
The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 2.1.18, “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 Section 2.1.11, “CPU and memory resources”.
logging
The logging property configures the logging of the User Operator. For more details, see Section 2.1.10.4, “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
    # ...

2.1.10.4. 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

2.1.10.5. Configuring the Entity Operator

Prerequisites

  • An OpenShift 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 oc apply: 

    oc apply -f your-file

2.1.11. CPU and memory resources

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

AMQ Streams supports two types of resources:

  • CPU
  • Memory

AMQ Streams uses the OpenShift syntax for specifying CPU and memory resources.

2.1.11.1. 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

2.1.11.1.1. 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 OpenShift cluster, the pod is not scheduled.

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

  • 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
# ...

2.1.11.1.2. 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 AMQ Streams:

  • cpu
  • memory

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

Example resource limits configuration

# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...

2.1.11.1.3. 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 OpenShift is deployed.

Additional resources

2.1.11.1.4. 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.

2.1.11.2. Configuring resource requests and limits

Prerequisites

  • An OpenShift 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 oc apply: 

    oc 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 Section 2.1.10.4, “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

2.1.13. Kafka rack awareness

The rack awareness feature in AMQ Streams 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.

2.1.13.1. 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 OpenShift cluster nodes. The label is used by OpenShift when scheduling the Kafka broker pods to nodes. If the OpenShift 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 OpenShift versions) that can be used as the topologyKey value. For more information about OpenShift 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

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Consult your OpenShift 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 oc apply: 

    oc apply -f your-file

Additional resources

2.1.14. Healthchecks

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

OpenShift 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 AMQ Streams components.

Users can configure selected options for liveness and readiness probes.

2.1.14.1. 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 Section B.45, “Probe schema reference”.

An example of liveness and readiness probe configuration

# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...

2.1.14.2. Configuring healthchecks

Prerequisites

  • An OpenShift 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 oc apply: 

    oc apply -f your-file

2.1.15. Prometheus metrics

AMQ Streams 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 and Upgrading AMQ Streams on OpenShift guide.

2.1.15.1. 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:
    # ...

2.1.15.2. Configuring Prometheus metrics

Prerequisites

  • An OpenShift 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 oc apply: 

    oc apply -f your-file

2.1.16. JMX Options

AMQ Streams 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. AMQ Streams supports opening a password and username protected JMX port or a non-protected JMX port.

2.1.16.1. Configuring JMX options

Prerequisites

  • An OpenShift 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. JVM Options

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

  • Apache Kafka
  • Apache ZooKeeper
  • Apache Kafka Connect
  • Apache Kafka MirrorMaker
  • AMQ Streams Kafka Bridge

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

2.1.17.1. 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.

2.1.17.2. Configuring JVM options

Prerequisites

  • An OpenShift 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 oc apply: 

    oc apply -f your-file

2.1.18. Container images

AMQ Streams 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 AMQ Streams. In such a case, you should either copy the AMQ Streams images or build them from the source. If the configured image is not compatible with AMQ Streams images, it might not work properly.

2.1.18.1. Container image configurations

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

Warning

Overriding container images is recommended only in special situations.

2.1.18.2. Configuring container images

Prerequisites

  • An OpenShift 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 oc apply: 

    oc apply -f your-file

2.1.19. TLS sidecar

A sidecar is a container that runs in a pod but serves a supporting purpose. In AMQ Streams, 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

2.1.19.1. 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 Section 2.1.18, “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 Section 2.1.14.1, “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:
    # ...

2.1.19.2. Configuring TLS sidecar

Prerequisites

  • An OpenShift 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 oc apply: 

    oc apply -f your-file

2.1.20. Configuring pod scheduling

Important

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

2.1.20.1. Scheduling pods based on other applications

2.1.20.1.1. 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.

2.1.20.1.2. 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 OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

2.1.20.1.3. Configuring pod anti-affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/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 oc apply: 

    oc apply -f your-file

2.1.20.2. Scheduling pods to specific nodes

2.1.20.2.1. Node scheduling

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

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

2.1.20.2.2. 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 OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

2.1.20.2.3. Configuring node affinity in Kafka components

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Label the nodes where AMQ Streams components should be scheduled.

    This can be done using oc label: 

    oc label node 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 oc apply: 

    oc apply -f your-file

2.1.20.3. Using dedicated nodes

2.1.20.3.1. Dedicated nodes

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

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.

2.1.20.3.2. 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 OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.

2.1.20.3.3. 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 OpenShift specification. For more details, see the Kubernetes taints and tolerations.

2.1.20.3.4. Setting up dedicated nodes and scheduling pods on them

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

  3. Set the taints on the selected nodes:

    This can be done using oc adm taint: 

    oc adm taint node your-node dedicated=Kafka:NoSchedule

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

    This can be done using oc label: 

    oc label node 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 oc apply: 

    oc apply -f your-file

2.1.21. 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 and Upgrading AMQ Streams on OpenShift guide.

2.1.22. 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 an OpenShift annotation.

Prerequisites

See the Deploying and Upgrading AMQ Streams on OpenShift 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 OpenShift. For example, using oc annotate: 

    oc 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.

2.1.23. 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 an OpenShift annotation.

Prerequisites

See the Deploying and Upgrading AMQ Streams on OpenShift 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 OpenShift. For example, using oc annotate: 

    oc 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.

2.1.24. Scaling clusters

2.1.24.1. Scaling Kafka clusters

2.1.24.1.1. Adding brokers to a cluster

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

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

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

2.1.24.1.2. Removing brokers from a cluster

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

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

2.1.24.2. Partition reassignment

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

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

It has three different modes:

--generate
Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you 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.

2.1.24.2.1. Reassignment JSON file

The reassignment JSON file has a specific structure: 

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

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

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

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

The following is an example reassignment JSON file that assigns 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.

2.1.24.2.2. Reassigning partitions between JBOD volumes

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

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

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

For example: 

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

2.1.24.3. Generating reassignment JSON files

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

Prerequisites

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

Procedure

  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 | oc 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. 

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

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

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

2.1.24.4. Creating reassignment JSON files manually

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

2.1.24.5. Reassignment throttles

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

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

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

2.1.24.6. Scaling up a Kafka cluster

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

Prerequisites

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

Procedure

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

    For example: 

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

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

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

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

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

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

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

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

  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. 

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

    For example, 

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

2.1.24.7. 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 | \
  oc exec broker-pod -c kafka -i -- /bin/bash -c \
  'cat > /tmp/reassignment.json'

    For example: 

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

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

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

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

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

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

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

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

  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. 

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

    For example, 

    oc exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify
  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-z0-9]-delete$ then the broker still has live partitions and it should not be stopped.

    You can check this by executing the command: 

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

    where N is the number of the Pod(s) being deleted.

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

  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).

2.1.25. Deleting Kafka nodes manually

Additional resources

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

Warning

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

Prerequisites

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

Procedure

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

    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 OpenShift.

    Use oc annotate: 

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

2.1.26. Deleting ZooKeeper nodes manually

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

Warning

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

Prerequisites

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

Procedure

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

    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 OpenShift.

    Use oc annotate: 

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

2.1.27. Maintenance time windows for rolling updates

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

2.1.27.1. Maintenance time windows overview

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

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

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

2.1.27.2. Maintenance time window definition

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

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

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

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

Note

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

Additional resources

2.1.27.3. Configuring a maintenance time window

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

Prerequisites

  • An OpenShift cluster.
  • The Cluster Operator is running.

Procedure

  1. Add or edit the maintenanceTimeWindows property in the Kafka resource. For example to allow maintenance between 0800 and 1059 and between 1400 and 1559 you would set the maintenanceTimeWindows as shown below: 

    apiVersion: kafka.strimzi.io/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 oc apply: 

    oc apply -f your-file

Additional resources

2.1.28. 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 2.1. Annotation for the Secret that forces renewal of certificates
    CertificateSecretAnnotate command

    Cluster CA

    KAFKA-CLUSTER-NAME-cluster-ca-cert

    oc annotate secret KAFKA-CLUSTER-NAME-cluster-ca-cert strimzi.io/force-renew=true

    Clients CA

    KAFKA-CLUSTER-NAME-clients-ca-cert

    oc 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: 

    oc 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

2.1.29. 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 2.2. Commands for replacing private keys
    Private key forSecretAnnotate command

    Cluster CA

    <cluster-name>-cluster-ca

    oc annotate secret <cluster-name>-cluster-ca strimzi.io/force-replace=true

    Clients CA

    <cluster-name>-clients-ca

    oc 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

2.1.30. List of resources created as part of Kafka cluster

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

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

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

Additional resources

2.2.1. Configuring Kafka Connect

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

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

Kafka connector configuration

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

In 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 and Upgrading AMQ Streams on OpenShift 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 OpenShift resources used for storing configurations and confidential data. You can use ConfigMaps and Secrets to configure certain elements of a connector. You can then reference the configuration values in HTTP REST commands (this keeps the configuration separate and more secure, if needed). This method applies especially to confidential data, such as usernames, passwords, or certificates.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

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

Procedure

  1. Edit the spec properties 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 AMQ Streams.
    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: 

    oc apply -f KAFKA-CONNECT-CONFIG-FILE
  3. If authorization is enabled for Kafka Connect, configure Kafka Connect users to enable access to the Kafka Connect consumer group and topics.

2.2.2. Kafka Connect configuration for multiple instances

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

apiVersion: kafka.strimzi.io/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 AMQ Streams, or they can be specified explicitly in the spec of the KafkaConnect or KafkaConnectS2I resource.

Example configuration properties in the KafkaConnect resource

apiVersion: kafka.strimzi.io/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

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

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

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

    PropertyName

    offset.storage.topic

    connect-cluster-offsets

    status.storage.topic

    connect-cluster-status

    config.storage.topic

    connect-cluster-configs

    group

    connect-cluster 

    apiVersion: kafka.strimzi.io/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. 

    oc 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 OpenShift cluster:

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

2.2.5. List of Kafka Connect (S2I) cluster resources

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

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

2.2.6. Integrating with Debezium for change data capture

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

Debezium has multiple uses, including:

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

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

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

2.3. Kafka MirrorMaker cluster configuration

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

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

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

The following procedure shows how the resource is configured:

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

2.3.1. Configuring Kafka MirrorMaker

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

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

Prerequisites

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

  • Source and target Kafka clusters must be available

Procedure

  1. Edit the spec properties for the KafkaMirrorMaker resource.

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

    apiVersion: kafka.strimzi.io/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: 

    oc apply -f <your-file>

2.3.2. List of Kafka MirrorMaker cluster resources

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

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

2.4. Kafka MirrorMaker 2.0 cluster configuration

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

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

Data replication across clusters supports scenarios that require:

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

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

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

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

How MirrorMaker 2.0 replicates data is described here:

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

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

2.4.1. MirrorMaker 2.0 data replication

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

MirrorMaker 2.0 uses:

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

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

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

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

Figure 2.1. Replication across two clusters

MirrorMaker 2.0 replication

2.4.2. Cluster configuration

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

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

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

A MirrorMaker 2.0 cluster is required at each target destination.

2.4.2.1. Bidirectional replication (active/active)

The MirrorMaker 2.0 architecture supports bidirectional replication in an active/active cluster configuration.

Each cluster replicates the data of the other cluster using the concept of source and remote topics. As the same topics are stored in each cluster, remote topics are automatically renamed by MirrorMaker 2.0 to represent the source cluster. The name of the originating cluster is prepended to the name of the topic.

Figure 2.2. Topic renaming

MirrorMaker 2.0 bidirectional architecture

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

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

2.4.2.2. Unidirectional replication (active/passive)

The MirrorMaker 2.0 architecture supports unidirectional replication in an active/passive cluster configuration.

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

You can override automatic renaming by adding IdentityReplicationPolicy to the source connector configuration of the KafkaMirrorMaker2 resource. With this configuration applied, topics retain their original names.

2.4.2.3. Topic configuration synchronization

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

2.4.2.4. Data integrity

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

2.4.2.5. Offset tracking

MirrorMaker 2.0 tracks offsets for consumer groups using internal topics.

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

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

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

2.4.2.6. 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 and Upgrading AMQ Streams on OpenShift guide for instructions on running a:

  • Source and target Kafka clusters must be available

Procedure

  1. Edit the spec properties for the KafkaMirrorMaker2 resource.

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

    apiVersion: kafka.strimzi.io/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 AMQ Streams.
    13
    SSL properties for external listeners to run with a specific cipher suite for a TLS version.
    14
    Hostname verification is enabled by setting to HTTPS. An empty string disables the verification.
    15
    TLS encryption for the target Kafka cluster is configured in the same way as for the source Kafka cluster.
    16
    MirrorMaker 2.0 connectors.
    17
    Cluster alias for the source cluster used by the MirrorMaker 2.0 connectors.
    18
    Cluster alias for the target cluster used by the MirrorMaker 2.0 connectors.
    19
    Configuration for the MirrorSourceConnector that creates remote topics. The config overrides the default configuration options.
    20
    Replication factor for mirrored topics created at the target cluster.
    21
    Replication factor for the MirrorSourceConnector offset-syncs internal topic that maps the offsets of the source and target clusters.
    22
    When ACL rules synchronization is enabled, ACLs are applied to synchronized topics. The default is true.
    23
    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 an OpenShift Secret mounted to Kafka MirrorMaker as an environment variable.

  2. Create or update the resource: 

    oc apply -f <your-file>

2.5. Kafka Bridge cluster configuration

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

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

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

The full schema of the KafkaBridge resource is described in Section B.121, “KafkaBridge schema reference”.

2.5.1. Configuring the Kafka Bridge

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

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

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

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

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

Procedure

  1. Edit the spec properties for the KafkaBridge resource.

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

    apiVersion: kafka.strimzi.io/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: 

    oc apply -f KAFKA-BRIDGE-CONFIG-FILE

2.5.2. List of Kafka Bridge cluster resources

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

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

2.6. Customizing OpenShift resources

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

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

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

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

Kafka.spec.kafka
See Section B.53, “KafkaClusterTemplate schema reference”
Kafka.spec.zookeeper
See Section B.63, “ZookeeperClusterTemplate schema reference”
Kafka.spec.entityOperator
See Section B.68, “EntityOperatorTemplate schema reference”
Kafka.spec.kafkaExporter
See Section B.74, “KafkaExporterTemplate schema reference”
Kafka.spec.cruiseControl
See Section B.71, “CruiseControlTemplate schema reference”
KafkaConnect.spec
See Section B.88, “KafkaConnectTemplate schema reference”
KafkaConnectS2I.spec
See Section B.88, “KafkaConnectTemplate schema reference”
KafkaMirrorMaker.spec
See Section B.119, “KafkaMirrorMakerTemplate schema reference”
KafkaMirrorMaker2.spec
See Section B.88, “KafkaConnectTemplate schema reference”
KafkaBridge.spec
See Section B.128, “KafkaBridgeTemplate schema reference”
KafkaUser.spec
See Section B.112, “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

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

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

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

Additional resources

2.7. 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 oc 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: 

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

    The properties file defines the logging configuration: 

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

  2. Define external logging in the spec of the resource, setting the logging.name to the name of the ConfigMap. 

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

  3. Create or update the resource. 

    oc apply -f kafka.yaml
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