# Getting Started to Quarkus Messaging with Apache Kafka

This guide demonstrates how your Quarkus application can utilize Quarkus Messaging to interact with Apache Kafka.

## Prerequisites

To complete this guide, you need:

- Roughly 15 minutes

- An IDE

- JDK 17+ installed with `JAVA_HOME` configured appropriately

- Apache Maven 3.9.9

- Docker and Docker Compose or Podman, and Docker Compose

- Optionally the Quarkus CLI if you want to use it

- Optionally Mandrel or GraalVM installed and configured appropriately if you want to build a native executable (or Docker if you use a native container build)

## Architecture

In this guide, we are going to develop two applications communicating with Kafka. The first application sends a quote request to Kafka and consumes Kafka messages from the quote topic. The second application receives the quote request and sends a quote back.

The first application, the producer, will let the user request some quotes over an HTTP endpoint. For each quote request a random identifier is generated and returned to the user, to mark the quote request as pending. At the same time, the generated request id is sent over a Kafka topic `quote-requests`.

The second application, the processor, will read from the `quote-requests` topic, put a random price to the quote, and send it to a Kafka topic named `quotes`.

Lastly, the producer will read the quotes and send them to the browser using server-sent events. The user will therefore see the quote price updated from pending to the received price in real-time.

## Solution

We recommend that you follow the instructions in the next sections and create applications step by step. However, you can go right to the completed example.

Clone the Git repository: `git clone https://github.com/quarkusio/quarkus-quickstarts.git`, or download an archive.

The solution is located in the `kafka-quickstart` directory.

## Creating the Maven Project

First, we need to create two projects: the producer and the processor.

To create the producer project, in a terminal run:

CLI

    quarkus create app org.acme:kafka-quickstart-producer \
        --extension='rest-jackson,messaging-kafka' \
        --no-code

To create a Gradle project, add the `--gradle` or `--gradle-kotlin-dsl` option.

For more information about how to install and use the Quarkus CLI, see the Quarkus CLI guide.

Maven

    mvn io.quarkus.platform:quarkus-maven-plugin:3.26.0.CR1:create \
        -DprojectGroupId=org.acme \
        -DprojectArtifactId=kafka-quickstart-producer \
        -Dextensions='rest-jackson,messaging-kafka' \
        -DnoCode

To create a Gradle project, add the `-DbuildTool=gradle` or `-DbuildTool=gradle-kotlin-dsl` option.

For Windows users:

- If using cmd, (don’t use backward slash `\` and put everything on the same line)

- If using Powershell, wrap `-D` parameters in double quotes e.g. `"-DprojectArtifactId=kafka-quickstart-producer"`

This command creates the project structure and selects two Quarkus extensions we will be using:

1. Quarkus REST (formerly RESTEasy Reactive) and its Jackson support (to handle JSON) to serve the HTTP endpoint.

2. The Kafka connector for Reactive Messaging

To create the processor project, from the same directory, run:

CLI

    quarkus create app org.acme:kafka-quickstart-processor \
        --extension='messaging-kafka' \
        --no-code

To create a Gradle project, add the `--gradle` or `--gradle-kotlin-dsl` option.

For more information about how to install and use the Quarkus CLI, see the Quarkus CLI guide.

Maven

    mvn io.quarkus.platform:quarkus-maven-plugin:3.26.0.CR1:create \
        -DprojectGroupId=org.acme \
        -DprojectArtifactId=kafka-quickstart-processor \
        -Dextensions='messaging-kafka' \
        -DnoCode

To create a Gradle project, add the `-DbuildTool=gradle` or `-DbuildTool=gradle-kotlin-dsl` option.

For Windows users:

- If using cmd, (don’t use backward slash `\` and put everything on the same line)

- If using Powershell, wrap `-D` parameters in double quotes e.g. `"-DprojectArtifactId=kafka-quickstart-processor"`

At that point, you should have the following structure:

    .
    ├── kafka-quickstart-processor
    │  ├── README.md
    │  ├── mvnw
    │  ├── mvnw.cmd
    │  ├── pom.xml
    │  └── src
    │     └── main
    │        ├── docker
    │        ├── java
    │        └── resources
    │           └── application.properties
    └── kafka-quickstart-producer
       ├── README.md
       ├── mvnw
       ├── mvnw.cmd
       ├── pom.xml
       └── src
          └── main
             ├── docker
             ├── java
             └── resources
                └── application.properties

Open the two projects in your favorite IDE.

| Dev Services No need to start a Kafka broker when using the dev mode or for tests. Quarkus starts a broker for you automatically. See Dev Services for Kafka for details.

## The Quote object

The `Quote` class will be used in both producer and processor projects. For the sake of simplicity, we will duplicate the class. In both projects, create the `src/main/java/org/acme/kafka/model/Quote.java` file, with the following content:

    package org.acme.kafka.model;

    public class Quote {

        public String id;
        public int price;

        /**
        * Default constructor required for Jackson serializer
        */
        public Quote() { }

        public Quote(String id, int price) {
            this.id = id;
            this.price = price;
        }

        @Override
        public String toString() {
            return "Quote{" +
                    "id='" + id + '\'' +
                    ", price=" + price +
                    '}';
        }
    }

JSON representation of `Quote` objects will be used in messages sent to the Kafka topic and also in the server-sent events sent to web browsers.

Quarkus has built-in capabilities to deal with JSON Kafka messages. In a following section, we will create serializer/deserializer classes for Jackson.

## Sending quote request

Inside the producer project, create the `src/main/java/org/acme/kafka/producer/QuotesResource.java` file and add the following content:

    package org.acme.kafka.producer;

    import java.util.UUID;

    import jakarta.ws.rs.GET;
    import jakarta.ws.rs.POST;
    import jakarta.ws.rs.Path;
    import jakarta.ws.rs.Produces;
    import jakarta.ws.rs.core.MediaType;

    import org.acme.kafka.model.Quote;
    import org.eclipse.microprofile.reactive.messaging.Channel;
    import org.eclipse.microprofile.reactive.messaging.Emitter;

    @Path("/quotes")
    public class QuotesResource {

        @Channel("quote-requests")
        Emitter<String> quoteRequestEmitter; (1)

        /**
         * Endpoint to generate a new quote request id and send it to "quote-requests" Kafka topic using the emitter.
         */
        @POST
        @Path("/request")
        @Produces(MediaType.TEXT_PLAIN)
        public String createRequest() {
            UUID uuid = UUID.randomUUID();
            quoteRequestEmitter.send(uuid.toString()); (2)
            return uuid.toString(); (3)
        }
    }

1 | Inject a Reactive Messaging `Emitter` to send messages to the `quote-requests` channel.
2 | On a post request, generate a random UUID and send it to the Kafka topic using the emitter.
3 | Return the same UUID to the client.

The `quote-requests` channel is going to be managed as a Kafka topic, as that’s the only connector on the classpath. If not indicated otherwise, like in this example, Quarkus uses the channel name as topic name. So, in this example, the application writes into the `quote-requests` topic. Quarkus also configures the serializer automatically, because it finds that the `Emitter` produces `String` values.

| When you have multiple connectors, you would need to indicate which connector you want to use in the application configuration.

## Processing quote requests

Now let’s consume the quote request and give out a price. Inside the processor project, create the `src/main/java/org/acme/kafka/processor/QuotesProcessor.java` file and add the following content:

    package org.acme.kafka.processor;

    import java.util.Random;

    import jakarta.enterprise.context.ApplicationScoped;

    import org.acme.kafka.model.Quote;
    import org.eclipse.microprofile.reactive.messaging.Incoming;
    import org.eclipse.microprofile.reactive.messaging.Outgoing;

    import io.smallrye.reactive.messaging.annotations.Blocking;

    /**
     * A bean consuming data from the "quote-requests" Kafka topic (mapped to "requests" channel) and giving out a random quote.
     * The result is pushed to the "quotes" Kafka topic.
     */
    @ApplicationScoped
    public class QuotesProcessor {

        private Random random = new Random();

        @Incoming("requests") (1)
        @Outgoing("quotes")   (2)
        @Blocking             (3)
        public Quote process(String quoteRequest) throws InterruptedException {
            // simulate some hard working task
            Thread.sleep(200);
            return new Quote(quoteRequest, random.nextInt(100));
        }
    }

1 | Indicates that the method consumes the items from the `requests` channel.
2 | Indicates that the objects returned by the method are sent to the `quotes` channel.
3 | Indicates that the processing is blocking and cannot be run on the caller thread.

For every Kafka record from the `quote-requests` topic, Reactive Messaging calls the `process` method, and sends the returned `Quote` object to the `quotes` channel. In this case, we need to configure the channel in the `application.properties` file, to configures the `requests` and `quotes` channels:

    %dev.quarkus.http.port=8081

    # Configure the incoming `quote-requests` Kafka topic
    mp.messaging.incoming.requests.topic=quote-requests
    mp.messaging.incoming.requests.auto.offset.reset=earliest

Note that in this case we have one incoming and one outgoing connector configuration, each one distinctly named. The configuration properties are structured as follows:

`mp.messaging.[outgoing|incoming].{channel-name}.property=value`

The `channel-name` segment must match the value set in the `@Incoming` and `@Outgoing` annotation:

- `quote-requests` → Kafka topic from which we read the quote requests

- `quotes` → Kafka topic in which we write the quotes

| More details about this configuration is available on the Producer configuration and Consumer configuration section from the Kafka documentation. These properties are configured with the prefix `kafka`. An exhaustive list of configuration properties is available in Kafka Reference Guide - Configuration.

`mp.messaging.incoming.requests.auto.offset.reset=earliest` instructs the application to start reading the topics from the first offset, when there is no committed offset for the consumer group. In other words, it will also process messages sent before we start the processor application.

There is no need to set serializers or deserializers. Quarkus detects them, and if none are found, generates them using JSON serialization.

## Receiving quotes

Back to our producer project. Let’s modify the `QuotesResource` to consume quotes from Kafka and send them back to the client via Server-Sent Events:

    import io.smallrye.mutiny.Multi;

    ...

    @Channel("quotes")
    Multi<Quote> quotes; (1)

    /**
     * Endpoint retrieving the "quotes" Kafka topic and sending the items to a server sent event.
     */
    @GET
    @Produces(MediaType.SERVER_SENT_EVENTS) (2)
    public Multi<Quote> stream() {
        return quotes; (3)
    }

1 | Injects the `quotes` channel using the `@Channel` qualifier
2 | Indicates that the content is sent using `Server Sent Events`
3 | Returns the stream (Reactive Stream)

No need to configure anything, as Quarkus will automatically associate the `quotes` channel to the `quotes` Kafka topic. It will also generate a deserializer for the `Quote` class.

| Message serialization in Kafka In this example we used Jackson to serialize/deserialize Kafka messages. For more options on message serialization, see Kafka Reference Guide - Serialization. We strongly suggest adopting a contract-first approach using a schema registry. To learn more about how to use Apache Kafka with the schema registry and Avro, follow the Using Apache Kafka with Schema Registry and Avro guide for Avro or you can follow the Using Apache Kafka with Schema Registry and JSON Schema guide..

## The HTML page

Final touch, the HTML page requesting quotes and displaying the prices obtained over SSE.

Inside the producer project, create the `src/main/resources/META-INF/resources/quotes.html` file with the following content:

    <!DOCTYPE html>
    <html lang="en">
    <head>
        <meta charset="UTF-8">
        <title>Prices</title>

        <link rel="stylesheet" type="text/css"
              href="https://cdnjs.cloudflare.com/ajax/libs/patternfly/3.24.0/css/patternfly.min.css">
        <link rel="stylesheet" type="text/css"
              href="https://cdnjs.cloudflare.com/ajax/libs/patternfly/3.24.0/css/patternfly-additions.min.css">
    </head>
    <body>
    <div class="container">
        <div class="card">
            <div class="card-body">
                <h2 class="card-title">Quotes</h2>
                <button class="btn btn-info" id="request-quote">Request Quote</button>
                <div class="quotes"></div>
            </div>
        </div>
    </div>
    </body>
    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
    <script>
        $("#request-quote").click((event) => {
            fetch("/quotes/request", {method: "POST"})
            .then(res => res.text())
            .then(qid => {
                var row = $(`<h4 class='col-md-12' id='${qid}'>Quote # <i>${qid}</i> | <strong>Pending</strong></h4>`);
                $(".quotes").prepend(row);
            });
        });

        var source = new EventSource("/quotes");
        source.onmessage = (event) => {
          var json = JSON.parse(event.data);
          $(`#${json.id}`).html((index, html) => {
            return html.replace("Pending", `\$\xA0${json.price}`);
          });
        };
    </script>
    </html>

Nothing spectacular here. When the user clicks the button, HTTP request is made to request a quote, and a pending quote is added to the list. On each quote received over SSE, the corresponding item in the list is updated.

## Get it running

You just need to run both applications. In one terminal, run:

    mvn -f producer quarkus:dev

In another terminal, run:

    mvn -f processor quarkus:dev

Quarkus starts a Kafka broker automatically, configures the application and shares the Kafka broker instance between different applications. See Dev Services for Kafka for more details.

Open `http://localhost:8080/quotes.html` in your browser and request some quotes by clicking the button.

## Running in JVM or Native mode

When not running in dev or test mode, you will need to start your Kafka broker. You can follow the instructions from the Apache Kafka website or create a `docker-compose.yaml` file with the following content:

    version: '3.5'

    services:

      zookeeper:
        image: quay.io/strimzi/kafka:0.41.0-kafka-3.7.0
        command: [
          "sh", "-c",
          "bin/zookeeper-server-start.sh config/zookeeper.properties"
        ]
        ports:
          - "2181:2181"
        environment:
          LOG_DIR: /tmp/logs
        networks:
          - kafka-quickstart-network

      kafka:
        image: quay.io/strimzi/kafka:0.41.0-kafka-3.7.0
        command: [
          "sh", "-c",
          "bin/kafka-server-start.sh config/server.properties --override listeners=$${KAFKA_LISTENERS} --override advertised.listeners=$${KAFKA_ADVERTISED_LISTENERS} --override zookeeper.connect=$${KAFKA_ZOOKEEPER_CONNECT}"
        ]
        depends_on:
          - zookeeper
        ports:
          - "9092:9092"
        environment:
          LOG_DIR: "/tmp/logs"
          KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
          KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092
          KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
        networks:
          - kafka-quickstart-network

      producer:
        image: quarkus-quickstarts/kafka-quickstart-producer:1.0-${QUARKUS_MODE:-jvm}
        build:
          context: producer
          dockerfile: src/main/docker/Dockerfile.${QUARKUS_MODE:-jvm}
        depends_on:
          - kafka
        environment:
          KAFKA_BOOTSTRAP_SERVERS: kafka:9092
        ports:
          - "8080:8080"
        networks:
          - kafka-quickstart-network

      processor:
        image: quarkus-quickstarts/kafka-quickstart-processor:1.0-${QUARKUS_MODE:-jvm}
        build:
          context: processor
          dockerfile: src/main/docker/Dockerfile.${QUARKUS_MODE:-jvm}
        depends_on:
          - kafka
        environment:
          KAFKA_BOOTSTRAP_SERVERS: kafka:9092
        networks:
          - kafka-quickstart-network

    networks:
      kafka-quickstart-network:
        name: kafkaquickstart

Make sure you first build both applications in JVM mode with:

    mvn -f producer package
    mvn -f processor package

Once packaged, run `docker-compose up`.

| This is a development cluster, do not use in production.

You can also build and run our applications as native executables. First, compile both applications as native:

    mvn -f producer package -Dnative -Dquarkus.native.container-build=true
    mvn -f processor package -Dnative -Dquarkus.native.container-build=true

Run the system with:

    export QUARKUS_MODE=native
    docker-compose up --build

## Going further

This guide has shown how you can interact with Kafka using Quarkus. It utilizes SmallRye Reactive Messaging to build data streaming applications.

For the exhaustive list of features and configuration options, check the Reference guide for Apache Kafka Extension.

| In this guide we explore how we can interact with Apache Kafka using the Quarkus Messaging extensions. Quarkus extension for Kafka also allows using Kafka clients directly.