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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.

Pré-requisitos

Para concluir este guia, você precisa:

Cerca de 15 minutos
Um IDE
JDK 17+ installed with JAVA_HOME configured appropriately
Apache Maven 3.9.6
Docker e Docker Compose ou Podman e Docker Compose
Opcionalmente, o Quarkus CLI se você quiser usá-lo
Opcionalmente, Mandrel ou GraalVM instalado e configurado apropriadamente se você quiser criar um executável nativo (ou Docker se você usar uma compilação de contêiner nativo)

Arquitetura

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.

Solução

Recomendamos que siga as instruções nas próximas seções e crie aplicativos passo a passo. No entanto, você pode ir direto para o exemplo concluído.

Clone o repositório Git: git clone https://github.com/quarkusio/quarkus-quickstarts.git, ou baixe um arquivo.

The solution is located in the kafka-quickstart directory.

Criando o projeto Maven

Em primeiro lugar, temos de criar dois projetos: o producer e o processor.

Para criar o projeto producer, em um terminal, execute:

CLI

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

Para criar um projeto Gradle, adicione a opção --gradle ou --gradle-kotlin-dsl.

Para obter mais informações sobre como instalar e usar a CLI do Quarkus, consulte o guia Quarkus CLI.

Maven

mvn io.quarkus.platform:quarkus-maven-plugin:3.10.0:create \
    -DprojectGroupId=org.acme \
    -DprojectArtifactId=kafka-quickstart-producer \
    -Dextensions='rest-jackson,messaging-kafka' \
    -DnoCode

Para criar um projeto Gradle, adicione a opção '-DbuildTool=gradle' ou '-DbuildTool=gradle-kotlin-dsl'.

Para usuários do Windows:

Se estiver usando cmd, (não use barra invertida '\' e coloque tudo na mesma linha)
Se estiver usando o Powershell, envolva os parâmetros '-D' entre aspas duplas, por exemplo, '"-DprojectArtifactId=kafka-quickstart-producer"'

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

Quarkus REST (formerly RESTEasy Reactive) and its Jackson support (to handle JSON) to serve the HTTP endpoint.
The Kafka connector for Reactive Messaging

Para criar o projeto do processor, a partir do mesmo diretório, execute:

CLI

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

Para criar um projeto Gradle, adicione a opção --gradle ou --gradle-kotlin-dsl.

Para obter mais informações sobre como instalar e usar a CLI do Quarkus, consulte o guia Quarkus CLI.

Maven

mvn io.quarkus.platform:quarkus-maven-plugin:3.10.0:create \
    -DprojectGroupId=org.acme \
    -DprojectArtifactId=kafka-quickstart-processor \
    -Dextensions='messaging-kafka' \
    -DnoCode

Para criar um projeto Gradle, adicione a opção '-DbuildTool=gradle' ou '-DbuildTool=gradle-kotlin-dsl'.

Para usuários do Windows:

Se estiver usando cmd, (não use barra invertida '\' e coloque tudo na mesma linha)
Se estiver usando o Powershell, envolva os parâmetros '-D' entre aspas duplas, por exemplo, '"-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

Abra os dois projetos no seu IDE preferido.

Serviços de Desenvolvimento

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.

O objeto Quote

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.

Enviando pedido de cotação

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	Injete um serviço de mensagens reativas `Emitter` para enviar mensagens para o canal `quote-requests`.
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.

Processamento de pedidos de cotação

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	Indica que o processamento está a bloquear e não pode ser executado na thread do chamador.

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

O segmento channel-name deve corresponder ao valor definido na anotação @Incoming e @Outgoing:

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.

Recebendo cotações

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	Injecta o canal `quotes` utilizando o qualificador `@Channel`
2	Indica que o conteúdo é enviado utilizando `Server Sent Events`
3	Devolve o fluxo (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..

A página HTML

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.

Executando a aplicação

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.

Abra http://localhost:8080/quotes.html no seu browser e peça algumas cotações clicando no botão.

Execução em modo JVM ou nativo

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:0.39.0-kafka-3.6.1
    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:0.39.0-kafka-3.6.1
    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

Indo mais longe

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.

Pré-requisitos
Arquitetura
Solução
Criando o projeto Maven
O objeto Quote
Enviando pedido de cotação
Processamento de pedidos de cotação
Recebendo cotações
A página HTML
Executando a aplicação
Execução em modo JVM ou nativo
Indo mais longe

Getting Started to Quarkus Messaging with Apache Kafka

Pré-requisitos

Arquitetura

Solução

Criando o projeto Maven

O objeto Quote

Enviando pedido de cotação

Processamento de pedidos de cotação

Recebendo cotações

A página HTML

Executando a aplicação

Execução em modo JVM ou nativo

Indo mais longe

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