Advanced Spring Boot 4: Event-Driven Architecture (Kafka) · レッスン

Spring BootとSchema Registryの統合

Confluent Schema RegistryをSpring BootのKafkaアプリケーションに統合し、Avroのシリアライズとデシリアライズを自動的に処理します。

「Spring BootとSchema Registryの統合」はCoddyKit上の無料Advanced Spring Boot 4: Event-Driven Architecture (Kafka)レッスンです。これはレッスン3/4です。下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。これはAdvanced Spring Boot 4: Event-Driven Architecture (Kafka)学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Advanced Spring Boot 4: Event-Driven Architecture (Kafka)コースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

Welcome to Schema Registry Integration

In previous lessons, you learned about the importance of schema management and how to define schemas using Apache Avro. Now, it's time to bring it all together!

This lesson will guide you through integrating the Confluent Schema Registry with your Spring Boot Kafka applications. This integration automates Avro serialization and deserialization, making your data pipelines robust and easy to manage.

What is Confluent Schema Registry?

The Confluent Schema Registry is a standalone service that provides a centralized repository for Avro schemas. It acts as a gatekeeper, ensuring that all data flowing through your Kafka topics conforms to predefined schemas.

It stores a versioned history of all your schemas.
It provides a RESTful interface for registering and retrieving schemas.
It assigns a unique ID to each registered schema.

This central management is key for evolving schemas without breaking older applications.

How It Works with Kafka

When using the Schema Registry with Kafka:

Producers send messages, first registering their Avro schema with the Registry if it's new. They then include a small schema ID in the message payload (or header) along with the serialized Avro data.
Consumers receive messages, extract the schema ID, and fetch the corresponding schema from the Registry. They then use this schema to correctly deserialize the Avro data.

This process is largely transparent to your application code once configured correctly.

Adding Dependencies for Avro

To enable Avro serialization/deserialization with Spring Kafka and the Schema Registry, you need to add specific dependencies to your project. For Maven, this typically involves the kafka-avro-serializer library.

Here are the key dependencies:

spring-kafka: Core Spring Kafka functionality.
kafka-avro-serializer: Confluent's Avro serializer/deserializer.
avro: Apache Avro core library.

You'll also need to configure a plugin (like avro-maven-plugin) to generate Java classes from your .avsc schema files.

Configuring Schema Registry Client

Spring Boot needs to know where your Confluent Schema Registry is running. You specify this in your application.properties or application.yml file.

The property spring.kafka.properties.schema.registry.url is crucial. Let's look at an example:

spring.kafka.bootstrap-servers=localhost:9092
spring.kafka.producer.key-serializer=org.apache.kafka.common.serialization.StringSerializer
spring.kafka.producer.value-serializer=io.confluent.kafka.serializers.KafkaAvroSerializer
spring.kafka.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer
spring.kafka.consumer.value-deserializer=io.confluent.kafka.serializers.KafkaAvroDeserializer
spring.kafka.properties.schema.registry.url=http://localhost:8081

Defining Our Avro Message Class

Before we send or receive Avro messages, we need an Avro schema and a corresponding Java class. Typically, you'd define a .avsc file and use a build plugin to generate the Java class.

For our example, let's imagine we have a simple User Avro schema. This would generate a Java class like com.coddykit.User with fields like name and age. We'll use this generated class directly in our Spring Boot code.

Producer-Side Avro Serialization

Once your Spring Boot application is configured with the Avro serializer and the Schema Registry URL, sending Avro messages is straightforward. The KafkaTemplate automatically handles the serialization and interaction with the Schema Registry.

Here's a simplified example of a Spring Boot producer sending a User object:

package com.coddykit.producer;

import com.coddykit.User;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.CommandLineRunner;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.kafka.core.KafkaTemplate;

@SpringBootApplication
public class AvroProducerApplication implements CommandLineRunner {

    @Autowired
    private KafkaTemplate<String, User> kafkaTemplate;

    public static void main(String[] args) {
        SpringApplication.run(AvroProducerApplication.class, args);
    }

    @Override
    public void run(String... args) throws Exception {
        String topic = "users-avro-topic";
        User user = new User("Alice", 30);
        kafkaTemplate.send(topic, "user-key-1", user);
        System.out.println("Sent Avro User: " + user);
    }
}

// Simplified User class for demonstration (generated from Avro schema)
// public class User { 
//    private String name; 
//    private int age; 
//    public User(String name, int age) { this.name = name; this.age = age; } 
//    public String getName() { return name; } 
//    public int getAge() { return age; } 
//    @Override public String toString() { return "User{" + "name='" + name + '\'' + ", age=" + age + '}'; } 
// }

Consumer-Side Avro Deserialization

Similarly, on the consumer side, Spring's @KafkaListener, combined with the Avro deserializer and Schema Registry configuration, automatically converts the incoming Avro message bytes into your Java Avro object.

You just specify the target Avro class type in your listener method signature.

package com.coddykit.consumer;

import com.coddykit.User;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.kafka.annotation.KafkaListener;

@SpringBootApplication
public class AvroConsumerApplication {

    public static void main(String[] args) {
        SpringApplication.run(AvroConsumerApplication.class, args);
    }

    @KafkaListener(topics = "users-avro-topic", groupId = "avro-group")
    public void listen(User user) {
        System.out.println("Received Avro User: " + user);
    }
}

Handling Schema Evolution

One of the most powerful benefits of using the Schema Registry with Avro is its support for schema evolution. As your application needs change, you might need to add, remove, or modify fields in your Avro schemas.

The Schema Registry helps ensure that even if a producer sends data with a newer schema version, older consumers (and vice-versa) can still process the messages without errors, provided the schema changes are compatible (e.g., adding a field with a default value).

Key Benefits of Integration

Integrating the Confluent Schema Registry with Spring Boot Kafka applications brings several significant advantages:

Data Compatibility: Ensures producers and consumers always agree on data format, preventing deserialization errors.
Schema Evolution: Gracefully handles schema changes over time.
Reduced Boilerplate: Automatic serialization/deserialization means less manual code.
Centralized Management: A single source of truth for all your event schemas.
Strong Typing: Leveraging Avro's strong typing for better data quality and developer experience.

Quick Check: Schema Registry Purpose

What is the primary role of the Confluent Schema Registry when integrated with Spring Boot Kafka applications using Avro?

Recap: Seamless Avro with Spring

Great job! You've learned how to integrate the Confluent Schema Registry with your Spring Boot Kafka applications. This powerful combination automates Avro serialization and deserialization, making your event-driven microservices more robust and maintainable.

We covered the role of the Schema Registry and how it works.
You saw how to configure Spring Boot for Avro and Schema Registry.
We explored producer and consumer examples for seamless Avro object handling.
We touched upon the benefits of schema evolution and data compatibility.

By leveraging the Schema Registry, you ensure that your data contracts are always honored, even as your applications evolve.

よくある質問

「Spring BootとSchema Registryの統合」レッスンは無料ですか？

はい。「Spring BootとSchema Registryの統合」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Advanced Spring Boot 4: Event-Driven Architecture (Kafka)コースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Advanced Spring Boot 4: Event-Driven Architecture (Kafka)コースには全4レッスンが含まれています。

「Spring BootとSchema Registryの統合」で何を学びますか？

Confluent Schema RegistryをSpring BootのKafkaアプリケーションに統合し、Avroのシリアライズとデシリアライズを自動的に処理します。ブラウザで直接実行するハンズオンコードでAdvanced Spring Boot 4: Event-Driven Architecture (Kafka)を演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

Advanced Spring Boot 4: Event-Driven Architecture (Kafka)を始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのAdvanced Spring Boot 4: Event-Driven Architecture (Kafka)は初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。これはレッスン3/4です。

「Spring BootとSchema Registryの統合」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このAdvanced Spring Boot 4: Event-Driven Architecture (Kafka)レッスンでコードを書いて実行できますか？

はい。すべてのAdvanced Spring Boot 4: Event-Driven Architecture (Kafka)レッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

← Advanced Spring Boot 4: Event-Driven Architecture (Kafka)に戻る