Apache Kafka & Stream Processing Fundamentals · Pelajaran

Mengintegrasikan Schema Registry dengan Kafka

Terapkan Schema Registry dalam aplikasi Kafka Anda untuk mengelola dan menegakkan skema data secara otomatis.

Mengintegrasikan Schema Registry dengan Kafka adalah pelajaran Apache Kafka & Stream Processing Fundamentals gratis di CoddyKit. Ini adalah pelajaran 3 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Apache Kafka & Stream Processing Fundamentals, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Apache Kafka & Stream Processing Fundamentals mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

Why Integrate Schema Registry?

You've learned about Kafka and Schema Registry. Now, let's connect them! Integrating Schema Registry into your Kafka applications is vital for ensuring data quality and compatibility.

It acts as a central repository for schemas, allowing producers and consumers to validate and evolve data formats safely.

How it Works: Serializers

To integrate, Kafka clients use special serializers and deserializers that communicate with the Schema Registry.

When a producer sends data, the KafkaAvroSerializer (or Protobuf/JSON Schema equivalent) takes your data, registers its schema (if new), and then prefixes the data with a schema ID before sending it to Kafka.

Producer Configuration Essentials

To make your Kafka producer work with Schema Registry, you need to set specific properties. These tell the producer where the Schema Registry is and which serializer to use.

key.serializer: Often StringSerializer or KafkaAvroSerializer.
value.serializer: Set this to io.confluent.kafka.serializers.KafkaAvroSerializer.
schema.registry.url: The URL of your Schema Registry instance (e.g., http://localhost:8081).

Producer Code: Defining an Avro Schema

Before we send data, we need to define its structure using an Avro schema. For simplicity, we'll create a basic 'User' schema with a name and age field.

This schema will be used to create a GenericRecord.

import org.apache.avro.Schema;

public class AvroSchemaDef {
  public static final String USER_SCHEMA_JSON = 
    "{\"namespace\": \"com.coddykit\", " +
    "\"type\": \"record\", " +
    "\"name\": \"User\", " +
    "\"fields\": [" +
    "{\"name\": \"name\", \"type\": \"string\"}," +
    "{\"name\": \"age\", \"type\": \"int\"}]}";

  public static final Schema USER_SCHEMA = 
    new Schema.Parser().parse(USER_SCHEMA_JSON);

  public static void main(String[] args) {
    System.out.println("User Schema Defined!");
  }
}

Producer Code: Sending Avro Data

Here's a complete Java producer application. Notice how we configure the serializers and the Schema Registry URL. We then create a GenericRecord based on our USER_SCHEMA and send it.

Try running this example!

import org.apache.kafka.clients.producer.*;
import io.confluent.kafka.serializers.KafkaAvroSerializer;
import org.apache.avro.Schema;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;

import java.util.Properties;

public class AvroProducer {

  public static final String USER_SCHEMA_JSON = 
    "{\"namespace\": \"com.coddykit\", " +
    "\"type\": \"record\", " +
    "\"name\": \"User\", " +
    "\"fields\": [" +
    "{\"name\": \"name\", \"type\": \"string\"}," +
    "{\"name\": \"age\", \"type\": \"int\"}]}";

  public static final Schema USER_SCHEMA = 
    new Schema.Parser().parse(USER_SCHEMA_JSON);

  public static void main(String[] args) {
    Properties props = new Properties();
    props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
    props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, KafkaAvroSerializer.class.getName());
    props.put("schema.registry.url", "http://localhost:8081");

    Producer<String, GenericRecord> producer = new KafkaProducer<>(props);
    String topic = "avro-users";

    GenericRecord user = new GenericData.Record(USER_SCHEMA);
    user.put("name", "Coddy");
    user.put("age", 5);

    ProducerRecord<String, GenericRecord> record = new ProducerRecord<>(topic, "user-1", user);

    try {
      producer.send(record, (metadata, exception) -> {
        if (exception == null) {
          System.out.println("Sent record to topic " + metadata.topic() + " partition " + metadata.partition() + " offset " + metadata.offset());
        } else {
          exception.printStackTrace();
        }
      });
    } finally {
      producer.flush();
      producer.close();
    }
  }
}

How it Works: Deserializers

On the consumer side, the KafkaAvroDeserializer (or equivalent) plays the opposite role.

When a consumer receives a message, the deserializer extracts the schema ID, fetches the corresponding schema from Schema Registry, and then uses that schema to correctly deserialize the message back into your application's data type (e.g., a GenericRecord or a specific Avro object).

Consumer Configuration Essentials

Similar to producers, Kafka consumers also need specific properties to work with Schema Registry:

key.deserializer: Often StringDeserializer or KafkaAvroDeserializer.
value.deserializer: Set this to io.confluent.kafka.serializers.KafkaAvroDeserializer.
schema.registry.url: The URL of your Schema Registry instance.
group.id: A unique ID for your consumer group.
auto.offset.reset: Defines behavior when no initial offset is found (e.g., earliest or latest).

Consumer Code: Receiving Avro Data

This consumer application is configured to read Avro messages from the 'avro-users' topic. It uses KafkaAvroDeserializer to automatically handle schema resolution.

Run this example AFTER running the producer to see the data!

import org.apache.kafka.clients.consumer.*;
import io.confluent.kafka.serializers.KafkaAvroDeserializer;
import org.apache.avro.generic.GenericRecord;

import java.time.Duration;
import java.util.Collections;
import java.util.Properties;

public class AvroConsumer {
  public static void main(String[] args) {
    Properties props = new Properties();
    props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    props.put(ConsumerConfig.GROUP_ID_CONFIG, "avro-consumer-group");
    props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");
    props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, KafkaAvroDeserializer.class.getName());
    props.put("schema.registry.url", "http://localhost:8081");
    props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");

    Consumer<String, GenericRecord> consumer = new KafkaConsumer<>(props);
    String topic = "avro-users";
    consumer.subscribe(Collections.singletonList(topic));

    System.out.println("Listening for messages on topic: " + topic);

    try {
      while (true) {
        ConsumerRecords<String, GenericRecord> records = consumer.poll(Duration.ofMillis(100));
        for (ConsumerRecord<String, GenericRecord> record : records) {
          System.out.printf("Received record (key=%s, value=%s, partition=%d, offset=%d)\n",
                            record.key(), record.value(), record.partition(), record.offset());
          GenericRecord user = record.value();
          System.out.println("  User Name: " + user.get("name") + ", Age: " + user.get("age"));
        }
      }
    } finally {
      consumer.close();
    }
  }
}

Benefits of Seamless Integration

Integrating Schema Registry with your Kafka clients offers significant advantages:

Data Compatibility: Ensures producers and consumers always understand each other's data formats.
Schema Evolution: Safely update schemas over time without breaking existing applications.
Data Governance: Centralized schema management provides a single source of truth for your data structures.
Reduced Boilerplate: Serializers/deserializers handle schema management automatically.

Quick Check: Schema Registry Setup

Which of the following properties are essential for a Kafka client (producer or consumer) to integrate with Confluent Schema Registry using Avro?

Recap: Integrating Schema Registry

In this lesson, you learned how to integrate Confluent Schema Registry with your Kafka applications.

We configured Kafka producers and consumers with `schema.registry.url`.
We used `KafkaAvroSerializer` and `KafkaAvroDeserializer` to handle Avro data automatically.
You saw practical examples of sending and receiving `GenericRecord`s.

This integration is key for robust, schema-driven data pipelines!

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Mengintegrasikan Schema Registry dengan Kafka” gratis?

Ya — teks lengkap “Mengintegrasikan Schema Registry dengan Kafka” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Apache Kafka & Stream Processing Fundamentals, upgrade ke CoddyKit PRO. Kursus Apache Kafka & Stream Processing Fundamentals mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Mengintegrasikan Schema Registry dengan Kafka”?

Terapkan Schema Registry dalam aplikasi Kafka Anda untuk mengelola dan menegakkan skema data secara otomatis. Kamu berlatih Apache Kafka & Stream Processing Fundamentals dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai Apache Kafka & Stream Processing Fundamentals?

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Semua pelajaran dalam kursus ini

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