Chapter 5. Deploying AMQ Streams using installation artifacts

As an alternative to using the OperatorHub to deploy AMQ Streams using the AMQ Streams Operator, you can use the installation artifacts. Having prepared your environment for a deployment of AMQ Streams, this section shows:

The procedures assume an OpenShift cluster is available and running.

AMQ Streams is based on AMQ Streams Strimzi 0.24.x. This section describes the procedures to deploy AMQ Streams on OpenShift 4.6 and 4.8.

Note

To run the commands in this guide, your cluster user must have the rights to manage role-based access control (RBAC) and CRDs.

5.1. Create the Kafka cluster

To be able to manage a Kafka cluster with the Cluster Operator, you must deploy it as a Kafka resource. AMQ Streams provides example deployment files to do this. You can use these files to deploy the Topic Operator and User Operator at the same time.

If you haven’t deployed a Kafka cluster as a Kafka resource, you can’t use the Cluster Operator to manage it. This applies, for example, to a Kafka cluster running outside of OpenShift. But you can deploy and use the Topic Operator and User Operator as standalone components.

Note

The Cluster Operator can watch one, multiple, or all namespaces in an OpenShift cluster. The Topic Operator and User Operator watch for KafkaTopics and KafkaUsers in the single namespace of the Kafka cluster deployment.

Deploying a Kafka cluster with the Topic Operator and User Operator

Perform these deployment steps if you want to use the Topic Operator and User Operator with a Kafka cluster managed by AMQ Streams.

  1. Deploy the Cluster Operator
  2. Use the Cluster Operator to deploy the:

Deploying a standalone Topic Operator and User Operator

Perform these deployment steps if you want to use the Topic Operator and User Operator with a Kafka cluster that is not managed by AMQ Streams.

5.1.1. Deploying the Cluster Operator

The Cluster Operator is responsible for deploying and managing Apache Kafka clusters within an OpenShift cluster.

The procedures in this section show:

5.1.1.1. Watch options for a Cluster Operator deployment

When the Cluster Operator is running, it starts to watch for updates of Kafka resources.

You can choose to deploy the Cluster Operator to watch Kafka resources from:

  • A single namespace (the same namespace containing the Cluster Operator)
  • Multiple namespaces
  • All namespaces
Note

AMQ Streams provides example YAML files to make the deployment process easier.

The Cluster Operator watches for changes to the following resources:

  • Kafka for the Kafka cluster.
  • KafkaConnect for the Kafka Connect cluster.
  • KafkaConnectS2I for the Kafka Connect cluster with Source2Image support.
  • KafkaConnector for creating and managing connectors in a Kafka Connect cluster.
  • KafkaMirrorMaker for the Kafka MirrorMaker instance.
  • KafkaBridge for the Kafka Bridge instance

When one of these resources is created in the OpenShift cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary OpenShift resources, such as StatefulSets, Services and ConfigMaps.

Each time a Kafka resource is updated, the operator performs corresponding updates on the OpenShift resources that make up the cluster for the resource.

Resources are either patched or deleted, and then recreated in order to make the cluster for the resource reflect the desired state of the cluster. This operation might cause a rolling update that might lead to service disruption.

When a resource is deleted, the operator undeploys the cluster and deletes all related OpenShift resources.

5.1.1.2. Deploying the Cluster Operator to watch a single namespace

This procedure shows how to deploy the Cluster Operator to watch AMQ Streams resources in a single namespace in your OpenShift cluster.

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    On Linux, use:

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

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
  2. Deploy the Cluster Operator:

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  3. Verify that the Cluster Operator was successfully deployed:

    oc get deployments

5.1.1.3. Deploying the Cluster Operator to watch multiple namespaces

This procedure shows how to deploy the Cluster Operator to watch AMQ Streams resources across multiple namespaces in your OpenShift cluster.

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    On Linux, use:

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

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
  2. Edit the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file to add a list of all the namespaces the Cluster Operator will watch to the STRIMZI_NAMESPACE environment variable.

    For example, in this procedure the Cluster Operator will watch the namespaces watched-namespace-1, watched-namespace-2, watched-namespace-3.

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq7/amq-streams-rhel8-operator:1.8.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: watched-namespace-1,watched-namespace-2,watched-namespace-3
  3. For each namespace listed, install the RoleBindings.

    In this example, we replace watched-namespace in these commands with the namespaces listed in the previous step, repeating them for watched-namespace-1, watched-namespace-2, watched-namespace-3:

    oc create -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace
    oc create -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace
  4. Deploy the Cluster Operator:

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  5. Verify that the Cluster Operator was successfully deployed:

    oc get deployments

5.1.1.4. Deploying the Cluster Operator to watch all namespaces

This procedure shows how to deploy the Cluster Operator to watch AMQ Streams resources across all namespaces in your OpenShift cluster.

When running in this mode, the Cluster Operator automatically manages clusters in any new namespaces that are created.

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    On Linux, use:

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

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
  2. Edit the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file to set the value of the STRIMZI_NAMESPACE environment variable to *.

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          # ...
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq7/amq-streams-rhel8-operator:1.8.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: "*"
            # ...
  3. Create ClusterRoleBindings that grant cluster-wide access for all namespaces to the Cluster Operator.

    oc create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
    oc create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator

    Replace my-cluster-operator-namespace with the namespace you want to install the Cluster Operator into.

  4. Deploy the Cluster Operator to your OpenShift cluster.

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  5. Verify that the Cluster Operator was successfully deployed:

    oc get deployments

5.1.2. Deploying Kafka

Apache Kafka is an open-source distributed publish-subscribe messaging system for fault-tolerant real-time data feeds.

The procedures in this section show:

When installing Kafka, AMQ Streams also installs a ZooKeeper cluster and adds the necessary configuration to connect Kafka with ZooKeeper.

5.1.2.1. Deploying the Kafka cluster

This procedure shows how to deploy a Kafka cluster to your OpenShift using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a Kafka resource.

AMQ Streams provides example YAMLs files for deployment in examples/kafka/:

kafka-persistent.yaml
Deploys a persistent cluster with three ZooKeeper and three Kafka nodes.
kafka-jbod.yaml
Deploys a persistent cluster with three ZooKeeper and three Kafka nodes (each using multiple persistent volumes).
kafka-persistent-single.yaml
Deploys a persistent cluster with a single ZooKeeper node and a single Kafka node.
kafka-ephemeral.yaml
Deploys an ephemeral cluster with three ZooKeeper and three Kafka nodes.
kafka-ephemeral-single.yaml
Deploys an ephemeral cluster with three ZooKeeper nodes and a single Kafka node.

In this procedure, we use the examples for an ephemeral and persistent Kafka cluster deployment:

Ephemeral cluster
In general, an ephemeral (or temporary) Kafka cluster is suitable for development and testing purposes, not for production. This deployment uses emptyDir volumes for storing broker information (for ZooKeeper) and topics or partitions (for Kafka). Using an emptyDir volume means that its content is strictly related to the pod life cycle and is deleted when the pod goes down.
Persistent cluster
A persistent Kafka cluster uses PersistentVolumes to store ZooKeeper and Kafka data. The PersistentVolume is acquired using a PersistentVolumeClaim to make it independent of the actual type of the PersistentVolume. For example, it can use Amazon EBS volumes in Amazon AWS deployments without any changes in the YAML files. The PersistentVolumeClaim can use a StorageClass to trigger automatic volume provisioning.

The example YAML files specify the latest supported Kafka version, and configuration for its supported log message format version and inter-broker protocol version. Updates to these properties are required when upgrading Kafka.

The example clusters are named my-cluster by default. The cluster name is defined by the name of the resource and cannot be changed after the cluster has been deployed. To change the cluster name before you deploy the cluster, edit the Kafka.metadata.name property of the Kafka resource in the relevant YAML file.

Default cluster name and specified Kafka versions

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    version: 2.8.0
    #...
    config:
      #...
      log.message.format.version: 2.8
      inter.broker.protocol.version: 2.8
  # ...

For more information about configuring the Kafka resource, see Kafka cluster configuration in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Create and deploy an ephemeral or persistent cluster.

    For development or testing, you might prefer to use an ephemeral cluster. You can use a persistent cluster in any situation.

    • To create and deploy an ephemeral cluster:

      oc apply -f examples/kafka/kafka-ephemeral.yaml
    • To create and deploy a persistent cluster:

      oc apply -f examples/kafka/kafka-persistent.yaml
  2. Verify that the Kafka cluster was successfully deployed:

    oc get deployments

5.1.2.2. Deploying the Topic Operator using the Cluster Operator

This procedure describes how to deploy the Topic Operator using the Cluster Operator.

You configure the entityOperator property of the Kafka resource to include the topicOperator. By default, the Topic Operator watches for KafkaTopics in the namespace of the Kafka cluster deployment.

If you want to use the Topic Operator with a Kafka cluster that is not managed by AMQ Streams, you must deploy the Topic Operator as a standalone component.

For more information about configuring the entityOperator and topicOperator properties, see Configuring the Entity Operator in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Edit the entityOperator properties of the Kafka resource to include topicOperator:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the Topic Operator spec using the properties described in EntityTopicOperatorSpec schema reference.

    Use an empty object ({}) if you want all properties to use their default values.

  3. Create or update the resource:

    Use oc apply:

    oc apply -f <your-file>

5.1.2.3. Deploying the User Operator using the Cluster Operator

This procedure describes how to deploy the User Operator using the Cluster Operator.

You configure the entityOperator property of the Kafka resource to include the userOperator. By default, the User Operator watches for KafkaUsers in the namespace of the Kafka cluster deployment.

If you want to use the User Operator with a Kafka cluster that is not managed by AMQ Streams, you must deploy the User Operator as a standalone component.

For more information about configuring the entityOperator and userOperator properties, see Configuring the Entity Operator in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Edit the entityOperator properties of the Kafka resource to include userOperator:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the User Operator spec using the properties described in EntityUserOperatorSpec schema reference in the Using AMQ Streams on OpenShift guide.

    Use an empty object ({}) if you want all properties to use their default values.

  3. Create or update the resource:

    oc apply -f <your-file>

5.1.3. Alternative standalone deployment options for AMQ Streams Operators

You can perform a standalone deployment of the Topic Operator and the User Operator instead of deploying these operators using the Cluster Operator. Consider a standalone deployment if you want to use the Topic Operator and the User Operator with a Kafka cluster that is not managed by the Cluster Operator.

In a standalone deployment, Kafka can be running outside of OpenShift. For example, you might be using Kafka as a managed service. You adjust the deployment configuration for the standalone operators to match the address of your Kafka cluster.

Note

In a standalone deployment, the Topic Operator and User Operator will not connect to an AMQ Streams Kafka cluster (that is, a Kafka cluster deployed using the Cluster Operator). The operators will not work, even if a connection with the cluster is established.

5.1.3.1. Deploying the standalone Topic Operator

This procedure shows how to deploy the Topic Operator as a standalone component for topic management. You can use a standalone Topic Operator with a Kafka cluster that is not managed by the Cluster Operator.

A standalone deployment can operate with any Kafka cluster.

Standalone deployment files are provided. Configure the 05-Deployment-strimzi-topic-operator.yaml deployment file to add the environment variables that enable the Topic Operator to connect to a Kafka cluster.

Prerequisites

  • You are running a Kafka cluster for the Topic Operator to connect to.

Procedure

  1. Edit the env properties in the install/topic-operator/05-Deployment-strimzi-topic-operator.yaml standalone deployment file.

    Example standalone Topic Operator deployment configuration

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: strimzi-topic-operator
      labels:
        app: strimzi
    spec:
      # ...
      template:
        # ...
        spec:
          # ...
          containers:
            - name: strimzi-topic-operator
              # ...
              env:
                - name: STRIMZI_NAMESPACE 1
                  valueFrom:
                    fieldRef:
                      fieldPath: metadata.namespace
                - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS 2
                  value: my-kafka-bootstrap-address:9092
                - name: STRIMZI_RESOURCE_LABELS 3
                  value: "strimzi.io/cluster=my-cluster"
                - name: STRIMZI_ZOOKEEPER_CONNECT 4
                  value: my-cluster-zookeeper-client:2181
                - name: STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS 5
                  value: "18000"
                - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS 6
                  value: "120000"
                - name: STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS 7
                  value: "6"
                - name: STRIMZI_LOG_LEVEL 8
                  value: INFO
                - name: STRIMZI_TLS_ENABLED 9
                  value: "false"
                - name: STRIMZI_JAVA_OPTS 10
                  value: "-Xmx=512M -Xms=256M"
                - name: STRIMZI_JAVA_SYSTEM_PROPERTIES 11
                  value: "-Djavax.net.debug=verbose -DpropertyName=value"

    1
    The OpenShift namespace for the Topic Operator to watch for KafkaTopic resources. Specify the namespace of the Kafka cluster.
    2
    The host and port pair of the bootstrap broker address to discover and connect to all brokers in the Kafka cluster. Use a comma-separated list to specify two or three broker addresses in case a server is down.
    3
    The label selector to identify the KafkaTopic resources managed by the Topic Operator.
    4
    The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
    5
    The ZooKeeper session timeout, in milliseconds. The default is 18000 (18 seconds).
    6
    The interval between periodic reconciliations, in milliseconds. The default is 120000 (2 minutes).
    7
    The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential backoff. Consider increasing this value when topic creation takes more time due to the number of partitions or replicas. The default is 6 attempts.
    8
    The level for printing logging messages. You can set the level to ERROR, WARNING, INFO, DEBUG, or TRACE.
    9
    Enables TLS support for encrypted communication with the Kafka brokers.
    10
    (Optional) The Java options used by the JVM running the Topic Operator.
    11
    (Optional) The debugging (-D) options set for the Topic Operator.
  2. If you enabled TLS with the STRIMZI_TLS_ENABLED environment variable, specify the keystore and truststore used to authenticate connection to the Kafka cluster.

    Example TLS configuration

    # ....
    env:
      - name: STRIMZI_TRUSTSTORE_LOCATION 1
        value: "/path/to/truststore.p12"
      - name: STRIMZI_TRUSTSTORE_PASSWORD 2
        value: "TRUSTSTORE-PASSWORD"
      - name: STRIMZI_KEYSTORE_LOCATION 3
        value: "/path/to/keystore.p12"
      - name: STRIMZI_KEYSTORE_PASSWORD 4
        value: "KEYSTORE-PASSWORD"
    # ..."

    1
    The truststore contains the public key (ca.crt) value of the Certificate Authority used to sign new user certificates for TLS client authentication.
    2
    The password for accessing the truststore.
    3
    The keystore contains the private key (ca.key) of the Certificate Authority for signing new user certificates for TLS client authentication.
    4
    The password for accessing the keystore.
  3. Deploy the standalone Topic Operator.

    oc create -f install/topic-operator
  4. Verify that the standalone Topic Operator has been deployed successfully.

    oc describe deployment strimzi-topic-operator

    The standalone Topic Operator is deployed when the Replicas entry shows 1 available.

    Note

    You might experience a delay with the deployment if you have a slow connection to the OpenShift cluster and the Topic Operator images have not been downloaded before.

5.1.3.2. Deploying the standalone User Operator

This procedure shows how to deploy the User Operator as a standalone component for user management. You can use a standalone User Operator with a Kafka cluster that is not managed by the Cluster Operator.

A standalone deployment can operate with any Kafka cluster.

Standalone deployment files are provided. Edit the 05-Deployment-strimzi-user-operator.yaml deployment file to add the environment variables that enable the User Operator to connect to a Kafka cluster.

Prerequisites

  • You are running a Kafka cluster for the User Operator to connect to.

Procedure

  1. Edit the following env properties in the install/user-operator/05-Deployment-strimzi-user-operator.yaml standalone deployment file.

    Example standalone User Operator deployment configuration

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: strimzi-user-operator
      labels:
        app: strimzi
    spec:
      # ...
      template:
        # ...
        spec:
          # ...
          containers:
            - name: strimzi-user-operator
              # ...
              env:
                - name: STRIMZI_NAMESPACE 1
                  valueFrom:
                    fieldRef:
                      fieldPath: metadata.namespace
                - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS 2
                  value: my-kafka-bootstrap-address:9092
                - name: STRIMZI_CA_CERT_NAME 3
                  value: my-cluster-clients-ca-cert
                - name: STRIMZI_CA_KEY_NAME 4
                  value: my-cluster-clients-ca
                - name: STRIMZI_ZOOKEEPER_CONNECT 5
                  value: my-cluster-zookeeper-client:2181
                - name: STRIMZI_LABELS 6
                  value: "strimzi.io/cluster=my-cluster"
                - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS 7
                  value: "120000"
                - name: STRIMZI_ZOOKEEPER_CONNECT 8
                  value: my-cluster-zookeeper-client:2181
                - name: STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS 9
                  value: "18000"
                - name: STRIMZI_LOG_LEVEL 10
                  value: INFO
                - name: STRIMZI_GC_LOG_ENABLED 11
                  value: "true"
                - name: STRIMZI_CA_VALIDITY 12
                  value: "365"
                - name: STRIMZI_CA_RENEWAL 13
                  value: "30"
                - name: STRIMZI_JAVA_OPTS 14
                  value: "-Xmx=512M -Xms=256M"
                - name: STRIMZI_JAVA_SYSTEM_PROPERTIES 15
                  value: "-Djavax.net.debug=verbose -DpropertyName=value"

    1
    The OpenShift namespace for the User Operator to watch for KafkaUser resources. Only one namespace can be specified.
    2
    The host and port pair of the bootstrap broker address to discover and connect to all brokers in the Kafka cluster. Use a comma-separated list to specify two or three broker addresses in case a server is down.
    3
    The OpenShift Secret that contains the public key (ca.crt) value of the Certificate Authority that signs new user certificates for TLS client authentication.
    4
    The OpenShift Secret that contains the private key (ca.key) value of the Certificate Authority that signs new user certificates for TLS client authentication.
    5
    The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
    6
    The label selector used to identify the KafkaUser resources managed by the User Operator.
    7
    The interval between periodic reconciliations, in milliseconds. The default is 120000 (2 minutes).
    8
    The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
    9
    The ZooKeeper session timeout, in milliseconds. The default is 18000 (18 seconds).
    10
    The level for printing logging messages. You can set the level to ERROR, WARNING, INFO, DEBUG, or TRACE.
    11
    Enables garbage collection (GC) logging. The default is true.
    12
    The validity period for the Certificate Authority. The default is 365 days.
    13
    The renewal period for the Certificate Authority. The renewal period is measured backwards from the expiry date of the current certificate. The default is 30 days to initiate certificate renewal before the old certificates expire.
    14
    (Optional) The Java options used by the JVM running the User Operator
    15
    (Optional) The debugging (-D) options set for the User Operator
  2. If you are using TLS to connect to the Kafka cluster, specify the secrets used to authenticate the connection. Otherwise, go to the next step.

    Example TLS configuration

    # ....
    env:
      - name: STRIMZI_CLUSTER_CA_CERT_SECRET_NAME 1
        value: my-cluster-cluster-cert
      - name: STRIMZI_EO_KEY_SECRET_NAME 2
        value: my-cluster-cluster-ca
    # ..."

    1
    The OpenShift Secret that contains the public key (ca.crt) value of the Certificate Authority that signs Kafka broker certificates for TLS client authentication.
    2
    The OpenShift Secret that contains the keystore (entity-operator_.p12) with the private key and certificate for TLS authentication against the Kafka cluster. The Secret must also contain the password (entity-operator_.password) for accessing the keystore.
  3. Deploy the standalone User Operator.

    oc create -f install/user-operator
  4. Verify that the standalone User Operator has been deployed successfully.

    oc describe deployment strimzi-user-operator

    The standalone User Operator is deployed when the Replicas entry shows 1 available.

    Note

    You might experience a delay with the deployment if you have a slow connection to the OpenShift cluster and the User Operator images have not been downloaded before.

5.2. Deploy Kafka Connect

Kafka Connect is a tool for streaming data between Apache Kafka and external systems.

In AMQ Streams, Kafka Connect is deployed in distributed mode. Kafka Connect can also work in standalone mode, but this is not supported by AMQ Streams.

Using the concept of connectors, Kafka Connect provides a framework for moving large amounts of data into and out of your Kafka cluster while maintaining scalability and reliability.

Kafka Connect is typically used to integrate Kafka with external databases and storage and messaging systems.

The procedures in this section show how to:

Note

The term connector is used interchangeably to mean a connector instance running within a Kafka Connect cluster, or a connector class. In this guide, the term connector is used when the meaning is clear from the context.

5.2.1. Deploying Kafka Connect to your OpenShift cluster

This procedure shows how to deploy a Kafka Connect cluster to your OpenShift cluster using the Cluster Operator.

A Kafka Connect cluster is implemented as a Deployment with a configurable number of nodes (also called workers) that distribute the workload of connectors as tasks so that the message flow is highly scalable and reliable.

The deployment uses a YAML file to provide the specification to create a KafkaConnect resource.

In this procedure, we use the example file provided with AMQ Streams:

  • examples/connect/kafka-connect.yaml

For information about configuring the KafkaConnect resource (or the KafkaConnectS2I resource with Source-to-Image (S2I) support), see Kafka Connect cluster configuration in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Deploy Kafka Connect to your OpenShift cluster. Use the examples/connect/kafka-connect.yaml file to deploy Kafka Connect.

    oc apply -f examples/connect/kafka-connect.yaml
  2. Verify that Kafka Connect was successfully deployed:

    oc get deployments

5.2.2. Kafka Connect configuration for multiple instances

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

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: connect-cluster 1
    offset.storage.topic: connect-cluster-offsets 2
    config.storage.topic: connect-cluster-configs 3
    status.storage.topic: connect-cluster-status  4
    # ...
# ...
1
Kafka Connect cluster group that the instance belongs to.
2
Kafka topic that stores connector offsets.
3
Kafka topic that stores connector and task status configurations.
4
Kafka topic that stores connector and task status updates.
Note

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

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

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

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

5.2.3. Extending Kafka Connect with connector plug-ins

The AMQ Streams container images for Kafka Connect include two built-in file connectors for moving file-based data into and out of your Kafka cluster.

Table 5.1. File connectors

File ConnectorDescription

FileStreamSourceConnector

Transfers data to your Kafka cluster from a file (the source).

FileStreamSinkConnector

Transfers data from your Kafka cluster to a file (the sink).

The procedures in this section show how to add your own connector classes to connector images by:

Important

You create the configuration for connectors directly using the Kafka Connect REST API or KafkaConnector custom resources.

5.2.3.1. Creating a new container image automatically using AMQ Streams

This procedure shows how to configure Kafka Connect so that AMQ Streams automatically builds a new container image with additional connectors. You define the connector plugins using the .spec.build.plugins property of the KafkaConnect custom resource. AMQ Streams will automatically download and add the connector plugins into a new container image. The container is pushed into the container repository specified in .spec.build.output and automatically used in the Kafka Connect deployment.

Prerequisites

You need to provide your own container registry where images can be pushed to, stored, and pulled from. AMQ Streams supports private container registries as well as public registries such as Quay or Docker Hub.

Procedure

  1. Configure the KafkaConnect custom resource by specifying the container registry in .spec.build.output, and additional connectors in .spec.build.plugins:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: my-connect-cluster
    spec: 1
      #...
      build:
        output: 2
          type: docker
          image: my-registry.io/my-org/my-connect-cluster:latest
          pushSecret: my-registry-credentials
        plugins: 3
          - name: debezium-postgres-connector
            artifacts:
              - type: tgz
                url: https://repo1.maven.org/maven2/io/debezium/debezium-connector-postgres/1.3.1.Final/debezium-connector-postgres-1.3.1.Final-plugin.tar.gz
                sha512sum: 962a12151bdf9a5a30627eebac739955a4fd95a08d373b86bdcea2b4d0c27dd6e1edd5cb548045e115e33a9e69b1b2a352bee24df035a0447cb820077af00c03
          - name: camel-telegram
            artifacts:
              - type: tgz
                url: https://repo.maven.apache.org/maven2/org/apache/camel/kafkaconnector/camel-telegram-kafka-connector/0.7.0/camel-telegram-kafka-connector-0.7.0-package.tar.gz
                sha512sum: a9b1ac63e3284bea7836d7d24d84208c49cdf5600070e6bd1535de654f6920b74ad950d51733e8020bf4187870699819f54ef5859c7846ee4081507f48873479
      #...
    1
    2
    (Required) Configuration of the container registry where new images are pushed.
    3
    (Required) List of connector plugins and their artifacts to add to the new container image. Each plugin must be configured with at least one artifact.
  2. Create or update the resource:

    $ oc apply -f KAFKA-CONNECT-CONFIG-FILE
  3. Wait for the new container image to build, and for the Kafka Connect cluster to be deployed.
  4. Use the Kafka Connect REST API or the KafkaConnector custom resources to use the connector plugins you added.

Additional resources

See the Using AMQ Streams on OpenShift guide for more information on:

5.2.3.2. Creating a Docker image from the Kafka Connect base image

This procedure shows how to create a custom image and add it to the /opt/kafka/plugins directory.

You can use the Kafka container image on Red Hat Ecosystem Catalog as a base image for creating your own custom image with additional connector plug-ins.

At startup, the AMQ Streams version of Kafka Connect loads any third-party connector plug-ins contained in the /opt/kafka/plugins directory.

Procedure

  1. Create a new Dockerfile using registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0 as the base image:

    FROM registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0
    USER root:root
    COPY ./my-plugins/ /opt/kafka/plugins/
    USER 1001

    Example plug-in file

    $ tree ./my-plugins/
    ./my-plugins/
    ├── debezium-connector-mongodb
    │   ├── bson-3.4.2.jar
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mongodb-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mongodb-driver-3.4.2.jar
    │   ├── mongodb-driver-core-3.4.2.jar
    │   └── README.md
    ├── debezium-connector-mysql
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mysql-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mysql-binlog-connector-java-0.13.0.jar
    │   ├── mysql-connector-java-5.1.40.jar
    │   ├── README.md
    │   └── wkb-1.0.2.jar
    └── debezium-connector-postgres
        ├── CHANGELOG.md
        ├── CONTRIBUTE.md
        ├── COPYRIGHT.txt
        ├── debezium-connector-postgres-0.7.1.jar
        ├── debezium-core-0.7.1.jar
        ├── LICENSE.txt
        ├── postgresql-42.0.0.jar
        ├── protobuf-java-2.6.1.jar
        └── README.md

  2. Build the container image.
  3. Push your custom image to your container registry.
  4. Point to the new container image.

    You can either:

    • Edit the KafkaConnect.spec.image property of the KafkaConnect custom resource.

      If set, this property overrides the STRIMZI_KAFKA_CONNECT_IMAGES variable in the Cluster Operator.

      apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
      spec: 1
        #...
        image: my-new-container-image 2
        config: 3
          #...
      1
      2
      The docker image for the pods.
      3
      Configuration of the Kafka Connect workers (not connectors).

      or

    • In the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file, edit the STRIMZI_KAFKA_CONNECT_IMAGES variable to point to the new container image, and then reinstall the Cluster Operator.

Additional resources

See the Using AMQ Streams on OpenShift guide for more information on:

5.2.3.3. Creating a container image using OpenShift builds and Source-to-Image

This procedure shows how to use OpenShift builds and the Source-to-Image (S2I) framework to create a new container image.

An OpenShift build takes a builder image with S2I support, together with source code and binaries provided by the user, and uses them to build a new container image. Once built, container images are stored in OpenShift’s local container image repository and are available for use in deployments.

A Kafka Connect builder image with S2I support is provided on the Red Hat Ecosystem Catalog as part of the registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0 image. This S2I image takes your binaries (with plug-ins and connectors) and stores them in the /tmp/kafka-plugins/s2i directory. It creates a new Kafka Connect image from this directory, which can then be used with the Kafka Connect deployment. When started using the enhanced image, Kafka Connect loads any third-party plug-ins from the /tmp/kafka-plugins/s2i directory.

Important

With the introduction of build configuration to the KafkaConnect resource, AMQ Streams can now automatically build a container image with the connector plugins you require for your data connections. As a result, support for Kafka Connect with Source-to-Image (S2I) is deprecated and will be removed after AMQ Streams 1.8. To prepare for this change, you can migrate Kafka Connect S2I instances to Kafka Connect instances.

Procedure

  1. On the command line, use the oc apply command to create and deploy a Kafka Connect S2I cluster:

    oc apply -f examples/connect/kafka-connect-s2i.yaml
  2. Create a directory with Kafka Connect plug-ins:

    $ tree ./my-plugins/
    ./my-plugins/
    ├── debezium-connector-mongodb
    │   ├── bson-3.4.2.jar
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mongodb-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mongodb-driver-3.4.2.jar
    │   ├── mongodb-driver-core-3.4.2.jar
    │   └── README.md
    ├── debezium-connector-mysql
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mysql-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mysql-binlog-connector-java-0.13.0.jar
    │   ├── mysql-connector-java-5.1.40.jar
    │   ├── README.md
    │   └── wkb-1.0.2.jar
    └── debezium-connector-postgres
        ├── CHANGELOG.md
        ├── CONTRIBUTE.md
        ├── COPYRIGHT.txt
        ├── debezium-connector-postgres-0.7.1.jar
        ├── debezium-core-0.7.1.jar
        ├── LICENSE.txt
        ├── postgresql-42.0.0.jar
        ├── protobuf-java-2.6.1.jar
        └── README.md
  3. Use the oc start-build command to start a new build of the image using the prepared directory:

    oc start-build my-connect-cluster-connect --from-dir ./my-plugins/
    Note

    The name of the build is the same as the name of the deployed Kafka Connect cluster.

  4. When the build has finished, the new image is used automatically by the Kafka Connect deployment.

5.2.4. Creating and managing connectors

When you have created a container image for your connector plug-in, you need to create a connector instance in your Kafka Connect cluster. You can then configure, monitor, and manage a running connector instance.

A connector is an instance of a particular connector class that knows how to communicate with the relevant external system in terms of messages. Connectors are available for many external systems, or you can create your own.

You can create source and sink types of connector.

Source connector
A source connector is a runtime entity that fetches data from an external system and feeds it to Kafka as messages.
Sink connector
A sink connector is a runtime entity that fetches messages from Kafka topics and feeds them to an external system.

AMQ Streams provides two APIs for creating and managing connectors:

  • KafkaConnector resources (referred to as KafkaConnectors)
  • Kafka Connect REST API

Using the APIs, you can:

  • Check the status of a connector instance
  • Reconfigure a running connector
  • Increase or decrease the number of connector tasks for a connector instance
  • Restart connectors
  • Restart connector tasks, including failed tasks
  • Pause a connector instance
  • Resume a previously paused connector instance
  • Delete a connector instance

5.2.4.1. KafkaConnector resources

KafkaConnectors allow you to create and manage connector instances for Kafka Connect in an OpenShift-native way, so an HTTP client such as cURL is not required. Like other Kafka resources, you declare a connector’s desired state in a KafkaConnector YAML file that is deployed to your OpenShift cluster to create the connector instance. KafkaConnector resources must be deployed to the same namespace as the Kafka Connect cluster they link to.

You manage a running connector instance by updating its corresponding KafkaConnector resource, and then applying the updates. Annotations are used to manually restart connector instances and connector tasks. You remove a connector by deleting its corresponding KafkaConnector.

To ensure compatibility with earlier versions of AMQ Streams, KafkaConnectors are disabled by default. To enable them for a Kafka Connect cluster, you must use annotations on the KafkaConnect resource. For instructions, see Configuring Kafka Connect in the Using AMQ Streams on OpenShift guide.

When KafkaConnectors are enabled, the Cluster Operator begins to watch for them. It updates the configurations of running connector instances to match the configurations defined in their KafkaConnectors.

AMQ Streams includes an example KafkaConnector, named examples/connect/source-connector.yaml. You can use this example to create and manage a FileStreamSourceConnector and a FileStreamSinkConnector as described in Section 5.2.5, “Deploying the example KafkaConnector resources”.

5.2.4.2. Availability of the Kafka Connect REST API

The Kafka Connect REST API is available on port 8083 as the <connect-cluster-name>-connect-api service.

If KafkaConnectors are enabled, manual changes made directly using the Kafka Connect REST API are reverted by the Cluster Operator.

The operations supported by the REST API are described in the Apache Kafka documentation.

5.2.5. Deploying the example KafkaConnector resources

AMQ Streams includes an example KafkaConnector in examples/connect/source-connector.yaml. This creates a basic FileStreamSourceConnector instance that sends each line of the Kafka license file (an example file source) to a single Kafka topic.

This procedure describes how to create:

  • A FileStreamSourceConnector that reads data from the Kafka license file (the source) and writes the data as messages to a Kafka topic.
  • A FileStreamSinkConnector that reads messages from the Kafka topic and writes the messages to a temporary file (the sink).
Note

In a production environment, you prepare container images containing your desired Kafka Connect connectors, as described in Section 5.2.3, “Extending Kafka Connect with connector plug-ins”.

The FileStreamSourceConnector and FileStreamSinkConnector are provided as examples. Running these connectors in containers as described here is unlikely to be suitable for production use cases.

Prerequisites

Procedure

  1. Edit the examples/connect/source-connector.yaml file:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-source-connector 1
      labels:
        strimzi.io/cluster: my-connect-cluster 2
    spec:
      class: org.apache.kafka.connect.file.FileStreamSourceConnector 3
      tasksMax: 2 4
      config: 5
        file: "/opt/kafka/LICENSE" 6
        topic: my-topic 7
        # ...
    1
    Name of the KafkaConnector resource, which is used as the name of the connector. Use any name that is valid for an OpenShift resource.
    2
    Name of the Kafka Connect cluster to create the connector instance in. Connectors must be deployed to the same namespace as the Kafka Connect cluster they link to.
    3
    Full name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
    4
    Maximum number of Kafka Connect Tasks that the connector can create.
    5
    Connector configuration as key-value pairs.
    6
    This example source connector configuration reads data from the /opt/kafka/LICENSE file.
    7
    Kafka topic to publish the source data to.
  2. Create the source KafkaConnector in your OpenShift cluster:

    oc apply -f examples/connect/source-connector.yaml
  3. Create an examples/connect/sink-connector.yaml file:

    touch examples/connect/sink-connector.yaml
  4. Paste the following YAML into the sink-connector.yaml file:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-sink-connector
      labels:
        strimzi.io/cluster: my-connect
    spec:
      class: org.apache.kafka.connect.file.FileStreamSinkConnector 1
      tasksMax: 2
      config: 2
        file: "/tmp/my-file" 3
        topics: my-topic 4
    1
    Full name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
    2
    Connector configuration as key-value pairs.
    3
    Temporary file to publish the source data to.
    4
    Kafka topic to read the source data from.
  5. Create the sink KafkaConnector in your OpenShift cluster:

    oc apply -f examples/connect/sink-connector.yaml
  6. Check that the connector resources were created:

    oc get kctr --selector strimzi.io/cluster=MY-CONNECT-CLUSTER -o name
    
    my-source-connector
    my-sink-connector

    Replace MY-CONNECT-CLUSTER with your Kafka Connect cluster.

  7. In the container, execute kafka-console-consumer.sh to read the messages that were written to the topic by the source connector:

    oc exec MY-CLUSTER-kafka-0 -i -t -- bin/kafka-console-consumer.sh --bootstrap-server MY-CLUSTER-kafka-bootstrap.NAMESPACE.svc:9092 --topic my-topic --from-beginning
Source and sink connector configuration options

The connector configuration is defined in the spec.config property of the KafkaConnector resource.

The FileStreamSourceConnector and FileStreamSinkConnector classes support the same configuration options as the Kafka Connect REST API. Other connectors support different configuration options.

Table 5.2. Configuration options for the FileStreamSource connector class

NameTypeDefault valueDescription

file

String

Null

Source file to write messages to. If not specified, the standard input is used.

topic

List

Null

The Kafka topic to publish data to.

Table 5.3. Configuration options for FileStreamSinkConnector class

NameTypeDefault valueDescription

file

String

Null

Destination file to write messages to. If not specified, the standard output is used.

topics

List

Null

One or more Kafka topics to read data from.

topics.regex

String

Null

A regular expression matching one or more Kafka topics to read data from.

5.2.6. Performing a restart of a Kafka connector

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

Prerequisites

  • The Cluster Operator is running.

Procedure

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

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

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

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

Additional resources

5.2.7. Performing a restart of a Kafka connector task

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

Prerequisites

  • The Cluster Operator is running.

Procedure

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

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

    oc describe KafkaConnector KAFKACONNECTOR-NAME
  3. To restart the connector task, annotate the KafkaConnector resource in OpenShift. For example, using oc annotate to restart task 0:

    oc annotate KafkaConnector KAFKACONNECTOR-NAME strimzi.io/restart-task=0
  4. Wait for the next reconciliation to occur (every two minutes by default).

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

Additional resources

5.3. Deploy Kafka MirrorMaker

The Cluster Operator deploys one or more Kafka MirrorMaker replicas to replicate data between Kafka clusters. This process is called mirroring to avoid confusion with the Kafka partitions replication concept. MirrorMaker consumes messages from the source cluster and republishes those messages to the target cluster.

5.3.1. Deploying Kafka MirrorMaker to your OpenShift cluster

This procedure shows how to deploy a Kafka MirrorMaker cluster to your OpenShift cluster using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a KafkaMirrorMaker or KafkaMirrorMaker2 resource depending on the version of MirrorMaker deployed.

In this procedure, we use the example files provided with AMQ Streams:

  • examples/mirror-maker/kafka-mirror-maker.yaml
  • examples/mirror-maker/kafka-mirror-maker-2.yaml

For information about configuring KafkaMirrorMaker or KafkaMirrorMaker2 resources, see Kafka MirrorMaker cluster configuration in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Deploy Kafka MirrorMaker to your OpenShift cluster:

    For MirrorMaker:

    oc apply -f examples/mirror-maker/kafka-mirror-maker.yaml

    For MirrorMaker 2.0:

    oc apply -f examples/mirror-maker/kafka-mirror-maker-2.yaml
  2. Verify that MirrorMaker was successfully deployed:

    oc get deployments

5.4. Deploy Kafka Bridge

The Cluster Operator deploys one or more Kafka bridge replicas to send data between Kafka clusters and clients via HTTP API.

5.4.1. Deploying Kafka Bridge to your OpenShift cluster

This procedure shows how to deploy a Kafka Bridge cluster to your OpenShift cluster using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a KafkaBridge resource.

In this procedure, we use the example file provided with AMQ Streams:

  • examples/bridge/kafka-bridge.yaml

For information about configuring the KafkaBridge resource, see Kafka Bridge cluster configuration in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Deploy Kafka Bridge to your OpenShift cluster:

    oc apply -f examples/bridge/kafka-bridge.yaml
  2. Verify that Kafka Bridge was successfully deployed:

    oc get deployments