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Using Apache Kafka with Schema Registry and JSON Schema

This guide shows how your Quarkus application can use Apache Kafka, JSON Schema serialized records, and connect to a schema registry (such as the Confluent Schema Registry or Apicurio Registry).

If you are not familiar with Kafka and Kafka in Quarkus in particular, consider first going through the Using Apache Kafka with Reactive Messaging guide.

Requisitos previos

To complete this guide, you need:

Roughly 30 minutes
An IDE
JDK 17+ installed with JAVA_HOME configured appropriately
Apache Maven 3.9.6
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)

Arquitectura

In this guide we are going to implement a REST resource, namely MovieResource, that will consume movie DTOs and put them in a Kafka topic.

Then, we will implement a consumer that will consume and collect messages from the same topic. The collected messages will be then exposed by another resource, ConsumedMovieResource, via Server-Sent Events.

The Movies will be serialized and deserialized using JSON Schema. The schema, describing the Movie, is stored in Apicurio Registry. The same concept applies if you are using the Confluent JSON Schema serde and Confluent Schema Registry.

Solución

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

Clone el repositorio Git: git clone https://github.com/quarkusio/quarkus-quickstarts.git o descargue un archivo.

The solution is located in the kafka-json-schema-quickstart directory.

Creación del proyecto Maven

En primer lugar, necesitamos un nuevo proyecto. Cree un nuevo proyecto con el siguiente comando:

CLI

quarkus create app org.acme:kafka-json-schema-quickstart \
    --extension='rest-jackson,messaging-kafka,apicurio-registry-json-schema' \
    --no-code
cd kafka-json-schema-quickstart

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:3.9.5:create \
    -DprojectGroupId=org.acme \
    -DprojectArtifactId=kafka-json-schema-quickstart \
    -Dextensions='rest-jackson,messaging-kafka,apicurio-registry-json-schema' \
    -DnoCode
cd kafka-json-schema-quickstart

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-json-schema-quickstart"

If you use Confluent Schema Registry, you don’t need the quarkus-apicurio-registry-json-schema extension. Instead, you need the quarkus-confluent-registry-json-schema extension and a few more dependencies. See Using the Confluent Schema Registry for details.

Json Schema

Json Schema is a data serialization system. Data structures are described using schemas. The first thing we need to do is to create a schema describing the Movie structure. Create a file called src/main/resources/json-schema.json with the schema for our record (Kafka message):

{
  "$id": "https://example.com/person.schema.json",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Movie",
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "The movie's title."
    },
    "yeay": {
      "type": "integer",
      "description": "The movie's year."
    }
  }
}

Note that auto-generating the Java class from the JSON Schema definition is not possible. Therefore, you must define the Java class as follows so it can be used by the serialization process:

package org.acme.kafka;

public class Movie {

    private String title;
    private Integer year;

    public Movie() {
    }

    public Movie(String title, Integer year) {
        this.title = title;
        this.year = year;
    }

    public String getTitle() {
        return title;
    }

    public void setTitle(String title) {
        this.title = title;
    }

    public Integer getYear() {
        return year;
    }

    public void setYear(Integer year) {
        this.year = year;
    }
}

The `Movie` producer

Having defined the schema, we can now jump to implementing the MovieResource.

Let’s open the MovieResource, inject an Emitter of Movie DTO and implement a @POST method that consumes Movie and sends it through the Emitter:

package org.acme.kafka;

import org.acme.kafka.quarkus.Movie;
import org.eclipse.microprofile.reactive.messaging.Channel;
import org.eclipse.microprofile.reactive.messaging.Emitter;
import org.jboss.logging.Logger;

import jakarta.ws.rs.POST;
import jakarta.ws.rs.Path;
import jakarta.ws.rs.core.Response;

@Path("/movies")
public class MovieResource {
    private static final Logger LOGGER = Logger.getLogger(MovieResource.class);

    @Channel("movies")
    Emitter<Movie> emitter;

    @POST
    public Response enqueueMovie(Movie movie) {
        LOGGER.infof("Sending movie %s to Kafka", movie.getTitle());
        emitter.send(movie);
        return Response.accepted().build();
    }

}

Now, we need to map the movies channel (the Emitter emits to this channel) to a Kafka topic and also map the schema to be used on this channel. To achieve this, edit the application.properties file, and add the following content:

# set the connector for the outgoing channel to `smallrye-kafka`
mp.messaging.outgoing.movies.connector=smallrye-kafka

# disable automatic detection of the serializers
quarkus.messaging.kafka.serializer-autodetection.enabled=false

# Set the value serializer for the channel `movies`
mp.messaging.outgoing.movies.value.serializer=io.apicurio.registry.serde.jsonschema.JsonSchemaKafkaSerializer

# set the topic name for the channel to `movies`
mp.messaging.outgoing.movies.topic=movies

# set the schema to be used for the channel `movies`. Note that this property accepts just a name or a path and the serializer will look for the resource on the classpath.
mp.messaging.outgoing.movies.apicurio.registry.artifact.schema.location=json-schema.json

# automatically register the schema with the registry, if not present
mp.messaging.outgoing.movies.apicurio.registry.auto-register=true

Note that unlike in the avro serialization, autodetect can’t be used with JSON Schema, so we must define the value.serializer. Just like with avro, we still have to define the apicurio.registry.auto-register property.

If you use Confluent Schema Registry, in this case you must define the value.serializer as well with the value io.confluent.kafka.serializers.json.KafkaJsonSchemaSerializer. It is also detected automatically. The Confluent Schema Registry analogue of apicurio.registry.auto-register is called auto.register.schemas. It defaults to true, so it doesn’t have to be configured in this example. It can be explicitly set to false if you want to disable automatic schema registration.

The `Movie` consumer

So, we can write records into Kafka containing our Movie data. That data is serialized using JSON Schema. Now, it’s time to implement a consumer for them.

Let’s create ConsumedMovieResource that will consume Movie messages from the movies-from-kafka channel and will expose it via Server-Sent Events:

package org.acme.kafka;

import jakarta.enterprise.context.ApplicationScoped;
import jakarta.ws.rs.GET;
import jakarta.ws.rs.Path;
import jakarta.ws.rs.Produces;
import jakarta.ws.rs.core.MediaType;

import org.acme.kafka.quarkus.Movie;
import org.eclipse.microprofile.reactive.messaging.Channel;
import org.jboss.resteasy.reactive.RestStreamElementType;

import io.smallrye.mutiny.Multi;

@ApplicationScoped
@Path("/consumed-movies")
public class ConsumedMovieResource {

    @Channel("movies-from-kafka")
    Multi<Movie> movies;

    @GET
    @Produces(MediaType.SERVER_SENT_EVENTS)
    @RestStreamElementType(MediaType.TEXT_PLAIN)
    public Multi<String> stream() {
        return movies.map(movie -> String.format("'%s' from %s", movie.getTitle(), movie.getYear()));
    }
}

The last bit of the application’s code is the configuration of the movies-from-kafka channel in application.properties:

# set the connector for the incoming channel to `smallrye-kafka`
mp.messaging.incoming.movies-from-kafka.connector=smallrye-kafka

# set the topic name for the channel to `movies`
mp.messaging.incoming.movies-from-kafka.topic=movies

# set the deserializer for the incoming channel
mp.messaging.incoming.movies-from-kafka.value.deserializer=io.apicurio.registry.serde.jsonschema.JsonSchemaKafkaDeserializer

# disable auto-commit, Reactive Messaging handles it itself
mp.messaging.incoming.movies-from-kafka.enable.auto.commit=false

mp.messaging.incoming.movies-from-kafka.auto.offset.reset=earliest

Again, unlike with Avro, we have to define the value.deserializer.

If you use Confluent Schema Registry, you must configure value.deserializer as well with the value ´io.confluent.kafka.serializers.json.KafkaJsonSchemaDeserializer´. They are both detected automatically.

Ejecución de la aplicación

Start the application in dev mode:

CLI

quarkus dev

Maven

./mvnw quarkus:dev

Gradle

./gradlew --console=plain quarkusDev

Kafka broker and Apicurio Registry instance are started automatically thanks to Dev Services. See Dev Services for Kafka and Dev Services for Apicurio Registry for more details.

You might have noticed that we didn’t configure the schema registry URL anywhere. This is because Dev Services for Apicurio Registry configures all Kafka channels in Quarkus Messaging to use the automatically started registry instance.

Apicurio Registry, in addition to its native API, also exposes an endpoint that is API-compatible with Confluent Schema Registry. Therefore, this automatic configuration works both for Apicurio Registry serde and Confluent Schema Registry serde.

However, note that there’s no Dev Services support for running Confluent Schema Registry itself. If you want to use a running instance of Confluent Schema Registry, configure its URL, together with the URL of a Kafka broker:

kafka.bootstrap.servers=PLAINTEXT://localhost:9092
mp.messaging.connector.smallrye-kafka.schema.registry.url=http://localhost:8081

In the second terminal, query the ConsumedMovieResource resource with curl:

curl -N http://localhost:8080/consumed-movies

In the third one, post a few movies:

curl --header "Content-Type: application/json" \
  --request POST \
  --data '{"title":"The Shawshank Redemption","year":1994}' \
  http://localhost:8080/movies

curl --header "Content-Type: application/json" \
  --request POST \
  --data '{"title":"The Godfather","year":1972}' \
  http://localhost:8080/movies

curl --header "Content-Type: application/json" \
  --request POST \
  --data '{"title":"The Dark Knight","year":2008}' \
  http://localhost:8080/movies

curl --header "Content-Type: application/json" \
  --request POST \
  --data '{"title":"12 Angry Men","year":1957}' \
  http://localhost:8080/movies

Observe what is printed in the second terminal. You should see something along the lines of:

data:'The Shawshank Redemption' from 1994

data:'The Godfather' from 1972

data:'The Dark Knight' from 2008

data:'12 Angry Men' from 1957

Ejecución en modo JVM o nativo

When not running in dev or test mode, you will need to start your own Kafka broker and Apicurio Registry. The easiest way to get them running is to use docker-compose to start the appropriate containers.

If you use Confluent Schema Registry, you already have a Kafka broker and Confluent Schema Registry instance running and configured. You can ignore the docker-compose instructions here, as well as the Apicurio Registry configuration.

Create a docker-compose.yaml file at the root of the project with the following content:

version: '2'

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

  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://localhost:9092
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181

  schema-registry:
    image: apicurio/apicurio-registry-mem:2.4.2.Final
    ports:
      - 8081:8080
    depends_on:
      - kafka
    environment:
      QUARKUS_PROFILE: prod

Before starting the application, let’s first start the Kafka broker and Apicurio Registry:

docker-compose up

To stop the containers, use docker-compose down. You can also clean up the containers with docker-compose rm

You can build the application with:

CLI

quarkus build

Maven

./mvnw install

Gradle

./gradlew build

And run it in JVM mode with:

java -Dmp.messaging.connector.smallrye-kafka.apicurio.registry.url=http://localhost:8081/apis/registry/v2 -jar target/quarkus-app/quarkus-run.jar

By default, the application tries to connect to a Kafka broker listening at localhost:9092. You can configure the bootstrap server using: java -Dkafka.bootstrap.servers=... -jar target/quarkus-app/quarkus-run.jar

Specifying the registry URL on the command line is not very convenient, so you can add a configuration property only for the prod profile:

%prod.mp.messaging.connector.smallrye-kafka.apicurio.registry.url=http://localhost:8081/apis/registry/v2

You can build a native executable with:

CLI

quarkus build --native

Maven

./mvnw install -Dnative

Gradle

./gradlew build -Dquarkus.package.type=native

and run it with:

./target/kafka-json-schema-schema-quickstart-1.0.0-SNAPSHOT-runner -Dkafka.bootstrap.servers=localhost:9092

Testing the application

As mentioned above, Dev Services for Kafka and Apicurio Registry automatically start and configure a Kafka broker and Apicurio Registry instance in dev mode and for tests. Hence, we don’t have to set up Kafka and Apicurio Registry ourselves. We can just focus on writing the test.

First, let’s add test dependencies on REST Client and Awaitility to the build file:

pom.xml

<!-- we'll use Jakarta REST Client for talking to the SSE endpoint -->
<dependency>
    <groupId>io.quarkus</groupId>
    <artifactId>quarkus-rest-client</artifactId>
    <scope>test</scope>
</dependency>
<dependency>
    <groupId>org.awaitility</groupId>
    <artifactId>awaitility</artifactId>
    <scope>test</scope>
</dependency>

build.gradle

testImplementation("io.quarkus:quarkus-rest-client")
testImplementation("org.awaitility:awaitility")

In the test, we will send movies in a loop and check if the ConsumedMovieResource returns what we send.

package org.acme.kafka;

import io.quarkus.test.common.QuarkusTestResource;
import io.quarkus.test.common.http.TestHTTPResource;
import io.quarkus.test.junit.QuarkusTest;
import io.restassured.http.ContentType;
import org.hamcrest.Matchers;
import org.junit.jupiter.api.Test;

import jakarta.ws.rs.client.Client;
import jakarta.ws.rs.client.ClientBuilder;
import jakarta.ws.rs.client.WebTarget;
import jakarta.ws.rs.sse.SseEventSource;
import java.net.URI;
import java.util.List;
import java.util.concurrent.CopyOnWriteArrayList;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

import static io.restassured.RestAssured.given;
import static java.util.concurrent.TimeUnit.MILLISECONDS;
import static java.util.concurrent.TimeUnit.SECONDS;
import static org.awaitility.Awaitility.await;
import static org.hamcrest.MatcherAssert.assertThat;

@QuarkusTest
public class MovieResourceTest {

    @TestHTTPResource("/consumed-movies")
    URI consumedMovies;

    @Test
    public void testHelloEndpoint() throws InterruptedException {
        // create a client for `ConsumedMovieResource` and collect the consumed resources in a list
        Client client = ClientBuilder.newClient();
        WebTarget target = client.target(consumedMovies);

        List<String> received = new CopyOnWriteArrayList<>();

        SseEventSource source = SseEventSource.target(target).build();
        source.register(inboundSseEvent -> received.add(inboundSseEvent.readData()));

        // in a separate thread, feed the `MovieResource`
        ExecutorService movieSender = startSendingMovies();

        source.open();

        // check if, after at most 5 seconds, we have at least 2 items collected, and they are what we expect
        await().atMost(5, SECONDS).until(() -> received.size() >= 2);
        assertThat(received, Matchers.hasItems("'The Shawshank Redemption' from 1994",
                "'12 Angry Men' from 1957"));
        source.close();

        // shutdown the executor that is feeding the `MovieResource`
        movieSender.shutdownNow();
        movieSender.awaitTermination(5, SECONDS);
    }

    private ExecutorService startSendingMovies() {
        ExecutorService executorService = Executors.newSingleThreadExecutor();
        executorService.execute(() -> {
            while (true) {
                given()
                        .contentType(ContentType.JSON)
                        .body("{\"title\":\"The Shawshank Redemption\",\"year\":1994}")
                .when()
                        .post("/movies")
                .then()
                        .statusCode(202);

                given()
                        .contentType(ContentType.JSON)
                        .body("{\"title\":\"12 Angry Men\",\"year\":1957}")
                .when()
                        .post("/movies")
                .then()
                        .statusCode(202);

                try {
                    Thread.sleep(200L);
                } catch (InterruptedException e) {
                    break;
                }
            }
        });
        return executorService;
    }

}

We modified the MovieResourceTest that was generated together with the project. This test class has a subclass, NativeMovieResourceIT, that runs the same test against the native executable. To run it, execute:

CLI

quarkus build --native

Maven

./mvnw install -Dnative

Gradle

./gradlew build -Dquarkus.package.type=native

Manual setup

If we couldn’t use Dev Services and wanted to start a Kafka broker and Apicurio Registry instance manually, we would define a QuarkusTestResourceLifecycleManager.

pom.xml

<dependency>
    <groupId>io.strimzi</groupId>
    <artifactId>strimzi-test-container</artifactId>
    <version>0.105.0</version>
    <scope>test</scope>
    <exclusions>
        <exclusion>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
        </exclusion>
    </exclusions>
</dependency>

build.gradle

testImplementation("io.strimzi:strimzi-test-container:0.105.0") {
    exclude group: "org.apache.logging.log4j", module: "log4j-core"
}

package org.acme.kafka;

import java.util.HashMap;
import java.util.Map;

import org.testcontainers.containers.GenericContainer;

import io.quarkus.test.common.QuarkusTestResourceLifecycleManager;
import io.strimzi.StrimziKafkaContainer;

public class KafkaAndSchemaRegistryTestResource implements QuarkusTestResourceLifecycleManager {

    private final StrimziKafkaContainer kafka = new StrimziKafkaContainer();

    private GenericContainer<?> registry;

    @Override
    public Map<String, String> start() {
        kafka.start();
        registry = new GenericContainer<>("apicurio/apicurio-registry-mem:2.4.2.Final")
                .withExposedPorts(8080)
                .withEnv("QUARKUS_PROFILE", "prod");
        registry.start();
        Map<String, String> properties = new HashMap<>();
        properties.put("mp.messaging.connector.smallrye-kafka.apicurio.registry.url",
                "http://" + registry.getHost() + ":" + registry.getMappedPort(8080) + "/apis/registry/v2");
        properties.put("kafka.bootstrap.servers", kafka.getBootstrapServers());
        return properties;
    }

    @Override
    public void stop() {
        registry.stop();
        kafka.stop();
    }
}

@QuarkusTest
@QuarkusTestResource(KafkaAndSchemaRegistryTestResource.class)
public class MovieResourceTest {
    ...
}

Using compatible versions of the Apicurio Registry

The quarkus-apicurio-registry-json-schema extension depends on recent versions of Apicurio Registry client, and most versions of Apicurio Registry server and client are backwards compatible. For some you need to make sure that the client used by Serdes is compatible with the server.

For example, with Apicurio dev service if you set the image name to use version 2.1.5.Final:

quarkus.apicurio-registry.devservices.image-name=quay.io/apicurio/apicurio-registry-mem:2.1.5.Final

You need to make sure that apicurio-registry-serdes-json-schema-serde dependency and the REST client apicurio-common-rest-client-vertx dependency are set to compatible versions:

pom.xml

<dependency>
    <groupId>io.quarkus</groupId>
    <artifactId>quarkus-apicurio-registry-json-schema</artifactId>
    <exclusions>
        <exclusion>
            <groupId>io.apicurio</groupId>
            <artifactId>apicurio-common-rest-client-vertx</artifactId>
        </exclusion>
        <exclusion>
            <groupId>io.apicurio</groupId>
            <artifactId>apicurio-registry-serdes-json-schema-serde</artifactId>
        </exclusion>
    </exclusions>
</dependency>
<dependency>
    <groupId>io.apicurio</groupId>
    <artifactId>apicurio-registry-client</artifactId>
    <version>2.1.5.Final</version>
</dependency>
<dependency>
    <groupId>io.apicurio</groupId>
    <artifactId>apicurio-registry-common</artifactId>
    <version>2.1.5.Final</version>
</dependency>
<dependency>
    <groupId>io.apicurio</groupId>
    <artifactId>apicurio-registry-serdes-json-schema-serde</artifactId>
    <version>2.1.5.Final</version>
    <exclusions>
        <exclusion>
            <groupId>io.apicurio</groupId>
            <artifactId>apicurio-common-rest-client-jdk</artifactId>
        </exclusion>
        <exclusion>
            <groupId>io.apicurio</groupId>
            <artifactId>apicurio-registry-client</artifactId>
        </exclusion>
        <exclusion>
            <groupId>io.apicurio</groupId>
            <artifactId>apicurio-registry-common</artifactId>
        </exclusion>
    </exclusions>
</dependency>
<dependency>
    <groupId>io.apicurio</groupId>
    <artifactId>apicurio-common-rest-client-vertx</artifactId>
    <version>0.1.5.Final</version>
</dependency>

build.gradle

dependencies {
    implementation(platform("io.quarkus.platform:quarkus-bom:2.12.3.Final"))

    ...

    implementation("io.quarkus:quarkus-apicurio-registry-json-schema")
    implementation("io.apicurio:apicurio-registry-serdes-json-schema-serde") {
        exclude group: "io.apicurio", module: "apicurio-common-rest-client-jdk"
        exclude group: "io.apicurio", module: "apicurio-registry-client"
        exclude group: "io.apicurio", module: "apicurio-registry-common"
        version {
            strictly "2.1.5.Final"
        }
    }
    implementation("io.apicurio:apicurio-registry-client") {
        version {
            strictly "2.1.5.Final"
        }
    }
    implementation("io.apicurio:apicurio-registry-common") {
        version {
            strictly "2.1.5.Final"
        }
    }
    implementation("io.apicurio:apicurio-common-rest-client-vertx") {
        version {
            strictly "0.1.5.Final"
        }
    }
}

Known previous compatible versions for apicurio-registry-client and apicurio-common-rest-client-vertx are the following

apicurio-registry-client 2.1.5.Final with apicurio-common-rest-client-vertx 0.1.5.Final
apicurio-registry-client 2.3.1.Final with apicurio-common-rest-client-vertx 0.1.13.Final

Using the Confluent Schema Registry

If you want to use the Confluent Schema Registry, you need the quarkus-confluent-registry-json-schema extension, instead of the quarkus-apicurio-registry-json-schema extension. Also, you need to add a few dependencies and a custom Maven repository to your pom.xml / build.gradle file:

pom.xml

<dependencies>
    ...
    <!-- the extension -->
    <dependency>
        <groupId>io.quarkus</groupId>
        <artifactId>quarkus-confluent-registry-json-schema</artifactId>
    </dependency>
    <!-- Confluent registry libraries use Jakarta REST client -->
    <dependency>
        <groupId>io.quarkus</groupId>
        <artifactId>quarkus-rest-client</artifactId>
    </dependency>
    <dependency>
        <groupId>io.confluent</groupId>
        <artifactId>kafka-json-schema-serializer</artifactId>
        <version>7.2.0</version>
        <exclusions>
            <exclusion>
                <groupId>jakarta.ws.rs</groupId>
                <artifactId>jakarta.ws.rs-api</artifactId>
            </exclusion>
        </exclusions>
    </dependency>
</dependencies>

<repositories>
    <!-- io.confluent:kafka-json-schema-serializer is only available from this repository: -->
    <repository>
        <id>confluent</id>
        <url>https://packages.confluent.io/maven/</url>
        <snapshots>
            <enabled>false</enabled>
        </snapshots>
    </repository>
</repositories>

build.gradle

repositories {
    ...

    maven {
        url "https://packages.confluent.io/maven/"
    }
}

dependencies {
    ...

    implementation("io.quarkus:quarkus-confluent-registry-json-schema")

    // Confluent registry libraries use Jakarta REST client
    implementation("io.quarkus:quarkus-rest-client")

    implementation("io.confluent:kafka-json-schema-serializer:7.2.0") {
        exclude group: "jakarta.ws.rs", module: "jakarta.ws.rs-api"
    }
}

In JVM mode, any version of io.confluent:kafka-json-schema-serializer can be used. In native mode, Quarkus supports the following versions: 6.2.x, 7.0.x, 7.1.x, 7.2.x, 7.3.x.

For version 7.4.x and 7.5.x, due to an issue with the Confluent Schema Serializer, you need to add another dependency:

pom.xml

<dependency>
    <groupId>com.fasterxml.jackson.dataformat</groupId>
    <artifactId>jackson-dataformat-csv</artifactId>
</dependency>

build.gradle

dependencies {
    implementation("com.fasterxml.jackson.dataformat:jackson-dataformat-csv")
}

For any other versions, the native configuration may need to be adjusted.

Using Apache Kafka with Schema Registry and JSON Schema

Requisitos previos

Arquitectura

Solución

Creación del proyecto Maven

Json Schema

The Movie producer

The Movie consumer

Ejecución de la aplicación

Ejecución en modo JVM o nativo

Testing the application

Manual setup

Using compatible versions of the Apicurio Registry

Using the Confluent Schema Registry

Further reading

The `Movie` producer

The `Movie` consumer