Chapter 2. Connecting to Kafka with Kamelets Apache Kafka is an open-source, distributed, publish-subscribe messaging system for creating fault-tolerant, real-time data feeds. Kafka quickly stores and replicates data for a large number of consumers (external connections). Kafka can help you build solutions that process streaming events. A distributed, event-driven architecture requires a "backbone" that captures, communicates and helps process events. Kafka can serve as the communication backbone that connects your data sources and events to applications. You can use Kamelets to configure communication between Kafka and external resources. Kamelets allow you to configure how data moves from one endpoint to another in a Kafka stream-processing framework without writing code. Kamelets are route templates that you configure by specifying parameter values. For example, Kafka stores data in a binary form. You can use Kamelets to serialize and deserialize the data for sending to, and receiving from, external connections. With Kamelets, you can validate the schema and make changes to the data, such as adding to it, filtering it, or masking it. Kamelets can also handle and process errors. 2.1. Overview of connecting to Kafka with Kamelets If you use an Apache Kafka stream-processing framework, you can use Kamelets to connect services and applications to a Kafka topic. The Kamelet Catalog provides the following Kamelets specifically for making connections to a Kafka topic: kafka-sink - Moves events from a data producer to a Kafka topic. In a Kamelet Binding, specify the kafka-sink Kamelet as the sink. kafka-source - Moves events from a Kafka topic to a data consumer. In a Kamelet Binding, specify the kafka-source Kamelet as the source. Figure 2.1 illustrates the flow of connecting source and sink Kamelets to a Kafka topic. Figure 2.1: Data flow with Kamelets and a Kafka topic Here is an overview of the basic steps for using Kamelets and Kamelet Bindings to connect applications and services to a Kafka topic: Set up Kafka: Install the needed OpenShift operators. For AMQ streams, install the Camel K and AMQ streams operators and the Camel K CLI. Create a Kafka instance. A Kafka instance operates as a message broker. A broker contains topics and orchestrates the storage and passing of messages. Create a Kafka topic. A topic provides a destination for the storage of data. Obtain Kafka authentication credentials. Determine which services or applications you want to connect to your Kafka topic. View the Kamelet Catalog to find the Kamelets for the source and sink components that you want to add to your integration. Also, determine the required configuration parameters for each Kamelet that you want to use. Create Kamelet Bindings: Create a Kamelet Binding that connects a data source (a component that produces data) to the Kafka topic (by using the kafka-sink Kamelet). Create a Kamelet Binding that connects the kafka topic (by using kafka-source Kamelet) to a data sink (a component that consumes data). Optionally, manipulate the data that passes between the Kafka topic and the data source or sink by adding one or more action Kamelets as intermediary steps within a Kamelet Binding. Optionally, define how to handle errors within a Kamelet Binding. Apply the Kamelet Bindings as resources to the project. The Camel K operator generates a separate Camel K integration for each Kamelet Binding. 2.2. Setting up Kafka To set up Kafka, you must: Install the required OpenShift operators Create a Kafka instance Create a Kafka topic Use the Red Hat product mentioned below to set up Kafka: Red Hat Advanced Message Queuing (AMQ) streams - A self-managed Apache Kafka offering. AMQ Streams is based on open source Strimzi and is included as part of Red Hat Integration . AMQ Streams is a distributed and scalable streaming platform based on Apache Kafka that includes a publish/subscribe messaging broker. Kafka Connect provides a framework to integrate Kafka-based systems with external systems. Using Kafka Connect, you can configure source and sink connectors to stream data from external systems into and out of a Kafka broker. 2.2.1. Setting up Kafka by using AMQ streams AMQ Streams simplifies the process of running Apache Kafka in an OpenShift cluster. 2.2.1.1. Preparing your OpenShift cluster for AMQ Streams To use Camel K or Kamelets and Red Hat AMQ Streams, you must install the following operators and tools: Red Hat Integration - AMQ Streams operator - Manages the communication between your Openshift Cluster and AMQ Streams for Apache Kafka instances. Red Hat Integration - Camel K operator - Installs and manages Camel K - a lightweight integration framework that runs natively in the cloud on OpenShift. Camel K CLI tool - Allows you to access all Camel K features. Prerequisites You are familiar with Apache Kafka concepts. You can access an OpenShift 4.6 (or later) cluster with the correct access level, the ability to create projects and install operators, and the ability to install the OpenShift and the Camel K CLI on your local system. You installed the OpenShift CLI tool ( oc ) so that you can interact with the OpenShift cluster at the command line. Procedure To set up Kafka by using AMQ Streams: Log in to your OpenShift cluster's web console. Create or open a project in which you plan to create your integration, for example my-camel-k-kafka . Install the Camel K operator and Camel K CLI as described in Installing Camel K . Install the AMQ streams operator: From any project, select Operators > OperatorHub . In the Filter by Keyword field, type AMQ Streams . Click the Red Hat Integration - AMQ Streams card and then click Install . The Install Operator page opens. Accept the defaults and then click Install . Select Operators > Installed Operators to verify that the Camel K and AMQ Streams operators are installed. steps Setting up a Kafka topic with AMQ Streams 2.2.1.2. Setting up a Kafka topic with AMQ Streams A Kafka topic provides a destination for the storage of data in a Kafka instance. You must set up a Kafka topic before you can send data to it. Prerequisites You can access an OpenShift cluster. You installed the Red Hat Integration - Camel K and Red Hat Integration - AMQ Streams operators as described in Preparing your OpenShift cluster . You installed the OpenShift CLI ( oc ) and the Camel K CLI ( kamel ). Procedure To set up a Kafka topic by using AMQ Streams: Log in to your OpenShift cluster's web console. Select Projects and then click the project in which you installed the Red Hat Integration - AMQ Streams operator. For example, click the my-camel-k-kafka project. Select Operators > Installed Operators and then click Red Hat Integration - AMQ Streams . Create a Kafka cluster: Under Kafka , click Create instance . Type a name for the cluster, for example kafka-test . Accept the other defaults and then click Create . The process to create the Kafka instance might take a few minutes to complete. When the status is ready, continue to the step. Create a Kafka topic: Select Operators > Installed Operators and then click Red Hat Integration - AMQ Streams . Under Kafka Topic , click Create Kafka Topic . Type a name for the topic, for example test-topic . Accept the other defaults and then click Create . 2.2.2. Creating a secret by using the SASL/Plain authentication method You can create a secret with the credentials that you obtained (Kafka bootstrap URL, service account ID, and service account secret). Procedure Edit the application.properties file and add the Kafka credentials. application.properties file Run the following command to create a secret that contains the sensitive properties in the application.properties file: You use this secret when you run a Camel K integration. See Also The Camel K Kafka Basic Quickstart 2.2.3. Creating a secret by using the SASL/OAUTHBearer authentication method You can create a secret with the credentials that you obtained (Kafka bootstrap URL, service account ID, and service account secret). Procedure Edit the application-oauth.properties file and add the Kafka credentials. application-oauth.properties file Run the following command to create a secret that contains the sensitive properties in the application.properties file: You use this secret when you run a Camel K integration. See Also The Camel K Kafka Basic Quickstart 2.3. Connecting a data source to a Kafka topic in a Kamelet Binding To connect a data source to a Kafka topic, you create a Kamelet Binding as illustrated in Figure 2.2 . Figure 2.2 Connecting a data source to a Kafka topic Prerequisites You know the name of the Kafka topic to which you want to send events. The example in this procedure uses test-topic for receiving events. You know the values of the following parameters for your Kafka instance: bootstrapServers - A comma separated list of Kafka Broker URLs. password - The password to authenticate to Kafka. For an unauthenticated kafka instance on AMQ Streams, you can specify any non-empty string. user - The user name to authenticate to Kafka. For an unauthenticated kafka instance on AMQ Streams, you can specify any non-empty string. securityProtocol - You know the security protocol for communicating with the Kafka brokers. For a Kafka cluster on AMQ streams, it is PLAINTEXT . You know which Kamelets you want to add to your Camel K integration and the required instance parameters. The example Kamelets for this procedure are: The coffee-source Kamelet - It has an optional parameter, period , that specifies how often to send each event. You can copy the code from Example source Kamelet to a file named coffee-source.kamelet.yaml file and then run the following command to add it as a resource to your namespace: oc apply -f coffee-source.kamelet.yaml The kafka-sink Kamelet provided in the Kamelet Catalog. You use the kafka-sink Kamelet because the Kafka topic is receiving data (it is the data consumer) in this binding. Procedure To connect a data source to a Kafka topic, create a Kamelet Binding: In an editor of your choice, create a YAML file with the following basic structure: Add a name for the Kamelet Binding. For this example, the name is coffees-to-kafka because the binding connects the coffee-source Kamelet to the kafka-sink Kamelet. For the Kamelet Binding's source, specify a data source Kamelet (for example, the coffee-source Kamelet produces events that contain data about coffee) and configure any parameters for the Kamelet. For the Kamelet Binding's sink, specify the kafka-sink Kamelet and its required properties. For example, when the Kafka cluster is on AMQ Streams, set the securityProtocol property to "PLAINTEXT" : Save the YAML file (for example, coffees-to-kafka.yaml ). Log into your OpenShift project. Add the Kamelet Binding as a resource to your OpenShift namespace: oc apply -f <kamelet binding filename> For example: oc apply -f coffees-to-kafka.yaml The Camel K operator generates and runs a Camel K integration by using the KameletBinding resource. It might take a few minutes to build. To see the status of the KameletBinding resource: oc get kameletbindings To see the status of their integrations: oc get integrations To view the integration's log: kamel logs <integration> -n <project> For example: kamel logs coffees-to-kafka -n my-camel-k-kafka See Also Applying operations to data within a Kafka connection Handling errors within a connection Connecting a Kafka topic to a data sink in a Kamelet Binding 2.4. Connecting a Kafka topic to a data sink in a Kamelet Binding To connect a Kafka topic to a data sink, you create a Kamelet Binding as illustrated in Figure 2.3 . Figure 2.3 Connecting a Kafka topic to a data sink Prerequisites You know the name of the Kafka topic from which you want to send events. The example in this procedure uses test-topic for sending events. It is the same topic that you used to receive events from the coffee source in Connecting a data source to a Kafka topic in a Kamelet Binding . You know the values of the following parameters for your Kafka instance: bootstrapServers - A comma separated list of Kafka Broker URLs. password - The password to authenticate to Kafka. user - The user name to authenticate to Kafka. You know the security protocol for communicating with the Kafka brokers. For a Kafka cluster on AMQ streams, it is PLAINTEXT . You know which Kamelets you want to add to your Camel K integration and the required instance parameters. The example Kamelets for this procedure are provided in the Kamelet Catalog: The kafka-source Kamelet - Use the kafka-source Kamelet because the Kafka topic is sending data (it is the data producer) in this binding. The example values for the required parameters are: bootstrapServers - "broker.url:9092" password - "testpassword" user - "testuser" topic - "test-topic" securityProtocol - For a Kafka cluster on AMQ streams, this parameter value is "PLAINTEXT" . The log-sink Kamelet - Use the log-sink to log the data that it receives from the kafka-source Kamelet. Optionally, specify the showStreams parameter to show the message body of the data. The log-sink Kamelet is useful for debugging purposes. Procedure To connect a Kafka topic to a data sink, create a Kamelet Binding: In an editor of your choice, create a YAML file with the following basic structure: Add a name for the Kamelet Binding. For this example, the name is kafka-to-log because the binding connects the kafka-source Kamelet to the log-sink Kamelet. For the Kamelet Binding's source, specify the kafka-source Kamelet and configure its parameters. For example, when the Kafka cluster is on AMQ Streams you must set the securityProtocol parameter to "PLAINTEXT" : + For the Kamelet Binding's sink, specify the data consumer Kamelet (for example, the log-sink Kamelet) and configure any parameters for the Kamelet, for example: Save the YAML file (for example, kafka-to-log.yaml ). Log into your OpenShift project. Add the Kamelet Binding as a resource to your OpenShift namespace: oc apply -f <kamelet binding filename> For example: oc apply -f kafka-to-log.yaml The Camel K operator generates and runs a Camel K integration by using the KameletBinding resource. It might take a few minutes to build. To see the status of the KameletBinding resource: oc get kameletbindings To see the status of their integrations: oc get integrations To view the integration's log: kamel logs <integration> -n <project> For example: kamel logs kafka-to-log -n my-camel-k-kafka In the output, you should see coffee events, for example: To stop a running integration, delete the associated Kamelet Binding resource: oc delete kameletbindings/<kameletbinding-name> For example: oc delete kameletbindings/kafka-to-log See also Applying operations to data within a Kafka connection Adding an error handler policy to a Kamelet Binding 2.5. Applying operations to data within a Kafka connection If you want to perform an operation on the data that passes between a Kamelet and a Kafka topic, use action Kamelets as intermediary steps within a Kamelet Binding. Applying operations to data within a connection Routing event data to different destination topics Filtering event data for a specific Kafka topic 2.5.1. Routing event data to different destination topics When you configure a connection to a Kafka instance, you can optionally transform the topic information from the event data so that the event is routed to a different Kafka topic. Use one of the following transformation action Kamelets: Regex Router - Modify the topic of a message by using a regular expression and a replacement string. For example, if you want to remove a topic prefix, add a prefix, or remove part of a topic name. Configure the Regex Router Action Kamelet ( regex-router-action ). TimeStamp - Modify the topic of a message based on the original topic and the message's timestamp. For example, when using a sink that needs to write to different tables or indexes based on timestamps. For example, when you want to write events from Kafka to Elasticsearch, but each event needs to go to a different index based on information in the event itself. Configure the Timestamp Router Action Kamelet ( timestamp-router-action ). Message TimeStamp - Modify the topic of a message based on the original topic value and the timestamp field coming from a message value field. Configure the Message Timestamp Router Action Kamelet ( message-timestamp-router-action ). Predicate - Filter events based on the given JSON path expression by configuring the Predicate Filter Action Kamelet ( predicate-filter-action ). Prerequisites You have created a Kamelet Binding in which the sink is a kafka-sink Kamelet, as described in Connecting a data source to a Kafka topic in a Kamelet Binding . You know which type of transformation you want to add to the Kamelet Binding. Procedure To transform the destination topic, use one of the transformation action Kamelets as an intermediary step within the Kamelet Binding. For details on how to add an action Kamelet to a Kamelet Binding, see Adding an operation to a Kamelet Binding . 2.5.2. Filtering event data for a specific Kafka topic If you use a source Kamelet that produces records to many different Kafka topics and you want to filter out the records to one Kafka topic, add the topic-name-matches-filter-action Kamelet as an intermediary step in the Kamelet Binding. Prerequisites You have created a Kamelet Binding in a YAML file. You know the name of the Kafka topic from which you want to filter out event data. Procedure Edit the Kamelet Binding to include the topic-name-matches-filter-action Kamelet as an intermediary step between the source and sink Kamelets. Typically, you use the kafka-source Kamelet, as the source Kamelet and you supply a topic as the value of the required topic parameter. In the following Kamelet Binding example, the kafka-source Kamelet specifies the test-topic, test-topic-2, and test-topic-3 Kafka topics and the topic-name-matches-filter-action Kamelet specifies to filter out the event data from the topic-test topic: If you want to filter topics coming from a source Kamelet other than the kafka-source Kamelet, you must supply the Kafka topic information. You can use the insert-header-action Kamelet to add a Kafka topic field as an intermediary step, before the topic-name-matches-filter-action step in the Kamelet Binding as shown in the following example: Save the Kamelet Binding YAML file.

Commands:
camel.component.kafka.brokers = <YOUR-KAFKA-BOOTSTRAP-URL-HERE> camel.component.kafka.security-protocol = SASL_SSL camel.component.kafka.sasl-mechanism = PLAIN camel.component.kafka.sasl-jaas-config=org.apache.kafka.common.security.plain.PlainLoginModule required username='<YOUR-SERVICE-ACCOUNT-ID-HERE>' password='<YOUR-SERVICE-ACCOUNT-SECRET-HERE>'; consumer.topic=<TOPIC-NAME> producer.topic=<TOPIC-NAME>
create secret generic kafka-props --from-file application.properties
camel.component.kafka.brokers = <YOUR-KAFKA-BOOTSTRAP-URL-HERE> camel.component.kafka.security-protocol = SASL_SSL camel.component.kafka.sasl-mechanism = OAUTHBEARER camel.component.kafka.sasl-jaas-config = org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required oauth.client.id='<YOUR-SERVICE-ACCOUNT-ID-HERE>' oauth.client.secret='<YOUR-SERVICE-ACCOUNT-SECRET-HERE>' oauth.token.endpoint.uri="https://identity.api.openshift.com/auth/realms/rhoas/protocol/openid-connect/token" ; camel.component.kafka.additional-properties[sasl.login.callback.handler.class]=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler consumer.topic=<TOPIC-NAME> producer.topic=<TOPIC-NAME>
create secret generic kafka-props --from-file application-oauth.properties
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: spec: source: sink:
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: coffees-to-kafka spec: source: sink:
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: coffees-to-kafka spec: source: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: coffee-source properties: period: 5000 sink:
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: coffees-to-kafka spec: source: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: coffee-source properties: period: 5000 sink: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: kafka-sink properties: bootstrapServers: "broker.url:9092" password: "testpassword" topic: "test-topic" user: "testuser" securityProtocol: "PLAINTEXT"
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: spec: source: sink:
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: kafka-to-log spec: source: sink:
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: kafka-to-log spec: source: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: kafka-source properties: bootstrapServers: "broker.url:9092" password: "testpassword" topic: "test-topic" user: "testuser" securityProtocol: "PLAINTEXT" sink:
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: kafka-to-log spec: source: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: kafka-source properties: bootstrapServers: "broker.url:9092" password: "testpassword" topic: "test-topic" user: "testuser" securityProtocol: "PLAINTEXT" // only for AMQ streams sink: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: log-sink properties: showStreams: true
INFO [log-sink-E80C5C904418150-0000000000000001] (Camel (camel-1) thread #0 - timer://tick) {"id":7259,"uid":"a4ecb7c2-05b8-4a49-b0d2-d1e8db5bc5e2","blend_name":"Postmodern Symphony","origin":"Huila, Colombia","variety":"Kona","notes":"delicate, chewy, black currant, red apple, star fruit","intensifier":"balanced"}
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: kafka-to-log-by-topic spec: source: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: kafka-source properties: bootstrapServers: "broker.url:9092" password: "testpassword" topic: "test-topic, test-topic-2, test-topic-3" user: "testuser" securityProtocol: "PLAINTEXT" // only for AMQ streams steps: - ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: topic-name-matches-filter-action properties: regex: "test-topic" sink: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: log-sink properties: showStreams: true
apiVersion: camel.apache.org/v1alpha1 kind: KameletBinding metadata: name: coffee-to-log-by-topic spec: source: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: coffee-source properties: period: 5000 steps: - ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: insert-header-action properties: name: "KAFKA.topic" value: "test-topic" - ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: topic-name-matches-filter-action properties: regex: "test-topic" sink: ref: kind: Kamelet apiVersion: camel.apache.org/v1alpha1 name: log-sink properties: showStreams: true