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Getting Started to Quarkus Messaging with Apache Kafka

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



  • 大概15分钟

  • 编辑器

  • JDK 17+ installed with JAVA_HOME configured appropriately

  • Apache Maven 3.9.6

  • Docker and Docker Compose or Podman, and Docker Compose

  • 如果你愿意的话,还可以选择使用Quarkus CLI

  • 如果你想构建原生可执行程序,可以选择安装Mandrel或者GraalVM,并正确配置(或者使用Docker在容器中进行构建)


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.

Producer App UI

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.



克隆 Git 仓库可使用命令: git clone ,或者下载 压缩包

The solution is located in the kafka-quickstart directory.


首先,我们需要创建两个项目: producerprocessor

要创建 producer 项目,请在终端中运行:

quarkus create app org.acme:kafka-quickstart-producer \
    --extension='rest-jackson,messaging-kafka' \

创建Grade项目,请添加 --gradle 或者 --gradle-kotlin-dsl 参数。

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

mvn io.quarkus.platform:quarkus-maven-plugin:3.11.3:create \
    -DprojectGroupId=org.acme \
    -DprojectArtifactId=kafka-quickstart-producer \
    -Dextensions='rest-jackson,messaging-kafka' \

创建Grade项目,请添加 -DbuildTool=gradle 或者 -DbuildTool=gradle-kotlin-dsl 参数。

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

要创建 processor 项目,请在同一目录下运行:

quarkus create app org.acme:kafka-quickstart-processor \
    --extension='messaging-kafka' \

创建Grade项目,请添加 --gradle 或者 --gradle-kotlin-dsl 参数。

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

mvn io.quarkus.platform:quarkus-maven-plugin:3.11.3:create \
    -DprojectGroupId=org.acme \
    -DprojectArtifactId=kafka-quickstart-processor \
    -Dextensions='messaging-kafka' \

创建Grade项目,请添加 -DbuildTool=gradle 或者 -DbuildTool=gradle-kotlin-dsl 参数。

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
│  ├──
│  ├── mvnw
│  ├── mvnw.cmd
│  ├── pom.xml
│  └── src
│     └── main
│        ├── docker
│        ├── java
│        └── resources
│           └──
└── kafka-quickstart-producer
   ├── mvnw
   ├── mvnw.cmd
   ├── pom.xml
   └── src
      └── main
         ├── docker
         ├── java
         └── resources



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 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/ 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) { = id;
        this.price = price;

    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.


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

package org.acme.kafka.producer;

import java.util.UUID;


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

public class QuotesResource {

    Emitter<String> quoteRequestEmitter; (1)

     * Endpoint to generate a new quote request id and send it to "quote-requests" Kafka topic using the emitter.
    public String createRequest() {
        UUID uuid = UUID.randomUUID();
        quoteRequestEmitter.send(uuid.toString()); (2)
        return uuid.toString(); (3)
1 注入一个响应式消息 Emitter ,来向 quote-requests 通道发送消息。
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.



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/ 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.
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
        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 表示该处理是 blocking ,不能在调用者线程上运行。

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 file, to configures the requests and quotes channels:


# Configure the incoming `quote-requests` Kafka topic

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:


channel-name 片段必须与 @Incoming@Outgoing 注解中设定的值相匹配:

  • 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. 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.


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;


Multi<Quote> quotes; (1)

 * Endpoint retrieving the "quotes" Kafka topic and sending the items to a server sent event.
@Produces(MediaType.SERVER_SENT_EVENTS) (2)
public Multi<Quote> stream() {
    return quotes; (3)
1 使用 @Channel 修饰符注入 quotes 通道
2 表示内容是使用 Server Sent Events 发送的
3 返回流 (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..


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">
    <meta charset="UTF-8">

    <link rel="stylesheet" type="text/css"
    <link rel="stylesheet" type="text/css"
<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>
<script src=""></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>`);

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

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.


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

mvn -f producer quarkus:dev


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.

在你的浏览器中打开 http://localhost:8080/quotes.html ,点击按钮来请求一些报价。


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'


    command: [
      "sh", "-c",
      "bin/ config/"
      - "2181:2181"
      LOG_DIR: /tmp/logs
      - kafka-quickstart-network

    command: [
      "sh", "-c",
      "bin/ config/ --override listeners=$${KAFKA_LISTENERS} --override advertised.listeners=$${KAFKA_ADVERTISED_LISTENERS} --override zookeeper.connect=$${KAFKA_ZOOKEEPER_CONNECT}"
      - zookeeper
      - "9092:9092"
      LOG_DIR: "/tmp/logs"
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      - kafka-quickstart-network

    image: quarkus-quickstarts/kafka-quickstart-producer:1.0-${QUARKUS_MODE:-jvm}
      context: producer
      dockerfile: src/main/docker/Dockerfile.${QUARKUS_MODE:-jvm}
      - kafka
      - "8080:8080"
      - kafka-quickstart-network

    image: quarkus-quickstarts/kafka-quickstart-processor:1.0-${QUARKUS_MODE:-jvm}
      context: processor
      dockerfile: src/main/docker/Dockerfile.${QUARKUS_MODE:-jvm}
      - kafka
      - 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


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.

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