Chapter 4. Deploying AMQ Streams Having prepared your environment for a deployment of AMQ Streams , this section shows: How to create the Kafka cluster Optional procedures to deploy other Kafka components according to your requirements: Kafka Connect Kafka MirrorMaker Kafka Bridge The procedures assume an OpenShift cluster is available and running. AMQ Streams is based on AMQ Streams Strimzi 0.20.x. This section describes the procedures to deploy AMQ Streams on OpenShift 3.11 and later. Note To run the commands in this guide, your cluster user must have the rights to manage role-based access control (RBAC) and CRDs. 4.1. Create the Kafka cluster In order to create your Kafka cluster, you deploy the Cluster Operator to manage the Kafka cluster, then deploy the Kafka cluster. When deploying the Kafka cluster using the Kafka resource, you can deploy the Topic Operator and User Operator at the same time. Alternatively, if you are using a non-AMQ Streams Kafka cluster, you can deploy the Topic Operator and User Operator as standalone components. 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. Deploy the Cluster Operator Use the Cluster Operator to deploy the: Kafka cluster Topic Operator User Operator 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. Deploy the standalone Topic Operator Deploy the standalone User Operator 4.1.1. 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: How to deploy the Cluster Operator to watch : A single namespace Multiple namespaces All namespaces Alternative deployment options: How to deploy the Cluster Operator deployment from the OperatorHub . 4.1.1.1. 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. 4.1.1.2. 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 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: On MacOS, use: Deploy the Cluster Operator: oc apply -f install/cluster-operator -n my-cluster-operator-namespace Verify that the Cluster Operator was successfully deployed: oc get deployments 4.1.1.3. 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 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: On MacOS, use: 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-rhel7-operator:1.6.7 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: watched-namespace-1,watched-namespace-2,watched-namespace-3 For each namespace listed, install the RoleBindings . In this example, we replace watched-namespace in these commands with the namespaces listed in the step, repeating them for watched-namespace-1 , watched-namespace-2 , watched-namespace-3 : oc apply -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace oc apply -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace oc apply -f install/cluster-operator/032-RoleBinding-strimzi-cluster-operator-topic-operator-delegation.yaml -n watched-namespace Deploy the Cluster Operator: oc apply -f install/cluster-operator -n my-cluster-operator-namespace Verify that the Cluster Operator was successfully deployed: oc get deployments 4.1.1.4. 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 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: On MacOS, use: 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-rhel7-operator:1.6.7 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: "*" # ... 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 oc create clusterrolebinding strimzi-cluster-operator-topic-operator-delegation --clusterrole=strimzi-topic-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. Deploy the Cluster Operator to your OpenShift cluster. oc apply -f install/cluster-operator -n my-cluster-operator-namespace Verify that the Cluster Operator was successfully deployed: oc get deployments 4.1.1.5. Deploying the Cluster Operator from the OperatorHub You can deploy the Cluster Operator to your OpenShift cluster by installing the AMQ Streams Operator from the OperatorHub. The OperatorHub is available in OpenShift 4 only. Prerequisites The Red Hat Operators OperatorSource is enabled in your OpenShift cluster. If you can see Red Hat Operators in the OperatorHub, the correct OperatorSource is enabled. For more information, see the Operators guide. Installation requires a user with sufficient privileges to install Operators from the OperatorHub. Procedure In the OpenShift 4 web console, click Operators > OperatorHub . Search or browse for the AMQ Streams Operator, in the Streaming & Messaging category. Click the AMQ Streams tile and then, in the sidebar on the right, click Install . On the Create Operator Subscription screen, choose from the following installation and update options: Installation Mode : Choose to install the AMQ Streams Operator to all (projects) namespaces in the cluster (the default option) or a specific (project) namespace. It is good practice to use namespaces to separate functions. We recommend that you dedicate a specific namespace to the Kafka cluster and other AMQ Streams components. Approval Strategy : By default, the AMQ Streams Operator is automatically upgraded to the latest AMQ Streams version by the Operator Lifecycle Manager (OLM). Optionally, select Manual if you want to manually approve future upgrades. For more information, see the Operators guide in the OpenShift documentation. Click Subscribe ; the AMQ Streams Operator is installed to your OpenShift cluster. The AMQ Streams Operator deploys the Cluster Operator, CRDs, and role-based access control (RBAC) resources to the selected namespace, or to all namespaces. On the Installed Operators screen, check the progress of the installation. The AMQ Streams Operator is ready to use when its status changes to InstallSucceeded . , you can deploy the other components of AMQ Streams, starting with a Kafka cluster, using the YAML example files. Additional resources Section 1.4, "AMQ Streams installation methods" Section 4.1.2.1, "Deploying the Kafka cluster" 4.1.2. 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: How to use the Cluster Operator to deploy: An ephemeral or persistent Kafka cluster The Topic Operator and User Operator by configuring the Kafka custom resource: Topic Operator User Operator Alternative standalone deployment procedures for the Topic Operator and User Operator: Deploy the standalone Topic Operator Deploy the standalone User Operator When installing Kafka, AMQ Streams also installs a ZooKeeper cluster and adds the necessary configuration to connect Kafka with ZooKeeper. 4.1.2.1. 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 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. apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster # ... For more information about configuring the Kafka resource, see Kafka cluster configuration in the Using AMQ Streams on OpenShift guide. Prerequisites The Cluster Operator must be deployed. Procedure 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 Verify that the Kafka cluster was successfully deployed: oc get deployments 4.1.2.2. 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 . 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 Entity Operator in the Using AMQ Streams on OpenShift guide. Prerequisites The Cluster Operator must be deployed. Procedure Edit the entityOperator properties of the Kafka resource to include topicOperator : apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: #... entityOperator: topicOperator: {} userOperator: {} 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. Create or update the resource: Use oc apply : oc apply -f <your-file> 4.1.2.3. 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 . 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 Entity Operator in the Using AMQ Streams on OpenShift guide. Prerequisites The Cluster Operator must be deployed. Procedure Edit the entityOperator properties of the Kafka resource to include userOperator : apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: #... entityOperator: topicOperator: {} userOperator: {} 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. Create or update the resource: oc apply -f <your-file> 4.1.3. Alternative standalone deployment options for AMQ Streams Operators When deploying a Kafka cluster using the Cluster Operator, you can also deploy the Topic Operator and User Operator. Alternatively, you can perform a standalone deployment. A standalone deployment means the Topic Operator and User Operator can operate with a Kafka cluster that is not managed by AMQ Streams. 4.1.3.1. Deploying the standalone Topic Operator This procedure shows how to deploy the Topic Operator as a standalone component. A standalone deployment requires configuration of environment variables, and is more complicated than deploying the Topic Operator using the Cluster Operator . However, a standalone deployment is more flexible as the Topic Operator can operate with any Kafka cluster, not necessarily one deployed by the Cluster Operator. Prerequisites You need an existing Kafka cluster for the Topic Operator to connect to. Procedure Edit the Deployment.spec.template.spec.containers[0].env properties in the install/topic-operator/05-Deployment-strimzi-topic-operator.yaml file by setting: STRIMZI_KAFKA_BOOTSTRAP_SERVERS to list the bootstrap brokers in your Kafka cluster, given as a comma-separated list of hostname : port pairs. STRIMZI_ZOOKEEPER_CONNECT to list the ZooKeeper nodes, given as a comma-separated list of hostname : port pairs. This should be the same ZooKeeper cluster that your Kafka cluster is using. STRIMZI_NAMESPACE to the OpenShift namespace in which you want the operator to watch for KafkaTopic resources. STRIMZI_RESOURCE_LABELS to the label selector used to identify the KafkaTopic resources managed by the operator. STRIMZI_FULL_RECONCILIATION_INTERVAL_MS to specify the interval between periodic reconciliations, in milliseconds. STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS to specify 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 . STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS to the ZooKeeper session timeout, in milliseconds. For example, 10000 . Default 20000 (20 seconds). STRIMZI_TOPICS_PATH to the Zookeeper node path where the Topic Operator stores its metadata. Default /strimzi/topics . STRIMZI_TLS_ENABLED to enable TLS support for encrypting the communication with Kafka brokers. Default true . STRIMZI_TRUSTSTORE_LOCATION to the path to the truststore containing certificates for enabling TLS based communication. Mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED . STRIMZI_TRUSTSTORE_PASSWORD to the password for accessing the truststore defined by STRIMZI_TRUSTSTORE_LOCATION . Mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED . STRIMZI_KEYSTORE_LOCATION to the path to the keystore containing private keys for enabling TLS based communication. Mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED . STRIMZI_KEYSTORE_PASSWORD to the password for accessing the keystore defined by STRIMZI_KEYSTORE_LOCATION . Mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED . STRIMZI_LOG_LEVEL to the level for printing logging messages. The value can be set to: ERROR , WARNING , INFO , DEBUG , and TRACE . Default INFO . STRIMZI_JAVA_OPTS (optional) to the Java options used for the JVM running the Topic Operator. An example is -Xmx=512M -Xms=256M . STRIMZI_JAVA_SYSTEM_PROPERTIES (optional) to list the -D options which are set to the Topic Operator. An example is -Djavax.net.debug=verbose -DpropertyName=value . Deploy the Topic Operator: oc apply -f install/topic-operator Verify that the Topic Operator has been deployed successfully: oc describe deployment strimzi-topic-operator The Topic Operator is deployed when the Replicas: entry shows 1 available . Note You may experience a delay with the deployment if you have a slow connection to the OpenShift cluster and the images have not been downloaded before. 4.1.3.2. Deploying the standalone User Operator This procedure shows how to deploy the User Operator as a standalone component. A standalone deployment requires configuration of environment variables, and is more complicated than deploying the User Operator using the Cluster Operator . However, a standalone deployment is more flexible as the User Operator can operate with any Kafka cluster, not necessarily one deployed by the Cluster Operator. Prerequisites You need an existing Kafka cluster for the User Operator to connect to. Procedure Edit the following Deployment.spec.template.spec.containers[0].env properties in the install/user-operator/05-Deployment-strimzi-user-operator.yaml file by setting: STRIMZI_KAFKA_BOOTSTRAP_SERVERS to list the Kafka brokers, given as a comma-separated list of hostname : port pairs. STRIMZI_ZOOKEEPER_CONNECT to list the ZooKeeper nodes, given as a comma-separated list of hostname : port pairs. This must be the same ZooKeeper cluster that your Kafka cluster is using. Connecting to ZooKeeper nodes with TLS encryption is not supported. STRIMZI_NAMESPACE to the OpenShift namespace in which you want the operator to watch for KafkaUser resources. STRIMZI_LABELS to the label selector used to identify the KafkaUser resources managed by the operator. STRIMZI_FULL_RECONCILIATION_INTERVAL_MS to specify the interval between periodic reconciliations, in milliseconds. STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS to the ZooKeeper session timeout, in milliseconds. For example, 10000 . Default 20000 (20 seconds). STRIMZI_CA_CERT_NAME to point to an OpenShift Secret that contains the public key of the Certificate Authority for signing new user certificates for TLS client authentication. The Secret must contain the public key of the Certificate Authority under the key ca.crt . STRIMZI_CA_KEY_NAME to point to an OpenShift Secret that contains the private key of the Certificate Authority for signing new user certificates for TLS client authentication. The Secret must contain the private key of the Certificate Authority under the key ca.key . STRIMZI_CLUSTER_CA_CERT_SECRET_NAME to point to an OpenShift Secret containing the public key of the Certificate Authority used for signing Kafka brokers certificates for enabling TLS-based communication. The Secret must contain the public key of the Certificate Authority under the key ca.crt . This environment variable is optional and should be set only if the communication with the Kafka cluster is TLS based. STRIMZI_EO_KEY_SECRET_NAME to point to an OpenShift Secret containing the private key and related certificate for TLS client authentication against the Kafka cluster. The Secret must contain the keystore with the private key and certificate under the key entity-operator.p12 , and the related password under the key entity-operator.password . This environment variable is optional and should be set only if TLS client authentication is needed when the communication with the Kafka cluster is TLS based. STRIMZI_CA_VALIDITY the validity period for the Certificate Authority. Default is 365 days. STRIMZI_CA_RENEWAL the renewal period for the Certificate Authority. STRIMZI_LOG_LEVEL to the level for printing logging messages. The value can be set to: ERROR , WARNING , INFO , DEBUG , and TRACE . Default INFO . STRIMZI_GC_LOG_ENABLED to enable garbage collection (GC) logging. Default true . Default is 30 days to initiate certificate renewal before the old certificates expire. STRIMZI_JAVA_OPTS (optional) to the Java options used for the JVM running User Operator. An example is -Xmx=512M -Xms=256M . STRIMZI_JAVA_SYSTEM_PROPERTIES (optional) to list the -D options which are set to the User Operator. An example is -Djavax.net.debug=verbose -DpropertyName=value . Deploy the User Operator: oc apply -f install/user-operator Verify that the User Operator has been deployed successfully: oc describe deployment strimzi-user-operator The User Operator is deployed when the Replicas: entry shows 1 available . Note You may experience a delay with the deployment if you have a slow connection to the OpenShift cluster and the images have not been downloaded before. 4.2. 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: Deploy a Kafka Connect cluster using a KafkaConnect resource Create a Kafka Connect image containing the connectors you need to make your connection Create and manage connectors using a KafkaConnector resource or the Kafka Connect REST API Deploy a KafkaConnector resource to Kafka Connect 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. 4.2.1. 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. Prerequisites The Cluster Operator must be deployed. Running Kafka cluster. Procedure Deploy Kafka Connect to your OpenShift cluster. For a Kafka cluster with 3 or more brokers, use the examples/connect/kafka-connect.yaml file. For a Kafka cluster with less than 3 brokers, use the examples/connect/kafka-connect-single-node-kafka.yaml file. oc apply -f examples/connect/kafka-connect.yaml Verify that Kafka Connect was successfully deployed: oc get deployments 4.2.2. 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 4.1. File connectors File Connector Description 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 Cluster Operator can also use images that you have created to deploy a Kafka Connect cluster to your OpenShift cluster. The procedures in this section show how to add your own connector classes to connector images by: Creating a container image from the Kafka Connect base image (manually or using continuous integration) Creating a container image using OpenShift builds and Source-to-Image (S2I) (available only on OpenShift) Important You create the configuration for connectors directly using the Kafka Connect REST API or KafkaConnector custom resources . 4.2.2.1. 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. Prerequisites The Cluster Operator must be deployed. Procedure Create a new Dockerfile using registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.7 as the base image: Example plug-in file Build the container image. Push your custom image to your container registry. 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/v1beta1 kind: KafkaConnect metadata: name: my-connect-cluster spec: 1 #... image: my-new-container-image 2 config: 3 #... 1 The specification for the Kafka Connect cluster . 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: Container image configuration and the KafkaConnect.spec.image property Cluster Operator configuration and the STRIMZI_KAFKA_CONNECT_IMAGES variable 4.2.2.2. 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-26-rhel7:1.6.7 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. Procedure 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 Create a directory with Kafka Connect plug-ins: 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. When the build has finished, the new image is used automatically by the Kafka Connect deployment. 4.2.3. 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 tasks for a connector instance Restart failed tasks (not supported by KafkaConnector resource) Pause a connector instance Resume a previously paused connector instance Delete a connector instance 4.2.3.1. 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. You manage a running connector instance by updating its corresponding KafkaConnector , and then applying the updates. 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 . 4.2.3.2. 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 . 4.2.4. Deploying a KafkaConnector resource to Kafka Connect This procedure describes how to deploy the example KafkaConnector to a Kafka Connect cluster. The example YAML will create a FileStreamSourceConnector to send each line of the license file to Kafka as a message in a topic named my-topic . Prerequisites A Kafka Connect deployment in which KafkaConnectors are enabled A running Cluster Operator Procedure Edit the examples/connect/source-connector.yaml file: apiVersion: kafka.strimzi.io/v1alpha1 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" topic: my-topic # ... 1 Enter a name for the KafkaConnector resource. This will be used as the name of the connector within Kafka Connect. You can choose any name that is valid for an OpenShift resource. 2 Enter the name of the Kafka Connect cluster in which to create the connector. 3 The name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster. 4 The maximum number of tasks that the connector can create. 5 Configuration settings for the connector. Available configuration options depend on the connector class. Create the KafkaConnector in your OpenShift cluster: oc apply -f examples/connect/source-connector.yaml Check that the resource was created: oc get kctr --selector strimzi.io/cluster=my-connect-cluster -o name 4.3. 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. 4.3.1. 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. Prerequisites The Cluster Operator must be deployed. Procedure 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 Verify that MirrorMaker was successfully deployed: oc get deployments 4.4. Deploy Kafka Bridge The Cluster Operator deploys one or more Kafka bridge replicas to send data between Kafka clusters and clients via HTTP API. 4.4.1. 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. Prerequisites The Cluster Operator must be deployed. Procedure Deploy Kafka Bridge to your OpenShift cluster: oc apply -f examples/bridge/kafka-bridge.yaml Verify that Kafka Bridge was successfully deployed: oc get deployments

Commands:
sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace /' install/cluster-operator/*RoleBinding*.yaml
sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace /' install/cluster-operator/*RoleBinding*.yaml
apply -f install/cluster-operator -n my-cluster-operator-namespace
get deployments
sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace /' install/cluster-operator/*RoleBinding*.yaml
sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace /' install/cluster-operator/*RoleBinding*.yaml
apiVersion: apps/v1 kind: Deployment spec: # template: spec: serviceAccountName: strimzi-cluster-operator containers: - name: strimzi-cluster-operator image: registry.redhat.io/amq7/amq-streams-rhel7-operator:1.6.7 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: watched-namespace-1,watched-namespace-2,watched-namespace-3
apply -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace apply -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace apply -f install/cluster-operator/032-RoleBinding-strimzi-cluster-operator-topic-operator-delegation.yaml -n watched-namespace
apply -f install/cluster-operator -n my-cluster-operator-namespace
get deployments
sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace /' install/cluster-operator/*RoleBinding*.yaml
sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace /' install/cluster-operator/*RoleBinding*.yaml
apiVersion: apps/v1 kind: Deployment spec: # template: spec: # serviceAccountName: strimzi-cluster-operator containers: - name: strimzi-cluster-operator image: registry.redhat.io/amq7/amq-streams-rhel7-operator:1.6.7 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: "*" #
create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-cluster-operator-namespace :strimzi-cluster-operator create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-cluster-operator-namespace :strimzi-cluster-operator create clusterrolebinding strimzi-cluster-operator-topic-operator-delegation --clusterrole=strimzi-topic-operator --serviceaccount my-cluster-operator-namespace :strimzi-cluster-operator
apply -f install/cluster-operator -n my-cluster-operator-namespace
get deployments
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster
apply -f examples/kafka/kafka-ephemeral.yaml
apply -f examples/kafka/kafka-persistent.yaml
get deployments
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: # entityOperator: topicOperator: {} userOperator: {}
apply -f <your-file>
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: # entityOperator: topicOperator: {} userOperator: {}
apply -f <your-file>
apply -f install/topic-operator
describe deployment strimzi-topic-operator
apply -f install/user-operator
describe deployment strimzi-user-operator
apply -f examples/connect/kafka-connect.yaml
get deployments
FROM registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.7 USER root:root COPY ./ my-plugins / /opt/kafka/plugins/ USER 1001
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
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect-cluster spec: 1 # image: my-new-container-image 2 config: 3 #
apply -f examples/connect/kafka-connect-s2i.yaml
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
start-build my-connect-cluster-connect --from-dir ./ my-plugins /
apiVersion: kafka.strimzi.io/v1alpha1 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" topic: my-topic #
apply -f examples/connect/source-connector.yaml
get kctr --selector strimzi.io/cluster=my-connect-cluster -o name
apply -f examples/mirror-maker/kafka-mirror-maker.yaml
apply -f examples/mirror-maker/kafka-mirror-maker-2.yaml
get deployments
apply -f examples/bridge/kafka-bridge.yaml
get deployments