Apache Kafka & Stream Processing Fundamentals · 课时

将 Schema Registry 与 Kafka 集成

在 Kafka 应用中实施 Schema Registry，自动管理和强制执行数据模式

将 Schema Registry 与 Kafka 集成 是 CoddyKit 上的免费 Apache Kafka & Stream Processing Fundamentals 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Apache Kafka & Stream Processing Fundamentals 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Apache Kafka & Stream Processing Fundamentals 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

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!

常见问题解答

「将 Schema Registry 与 Kafka 集成」课时是免费的吗？

是的 — 「将 Schema Registry 与 Kafka 集成」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Apache Kafka & Stream Processing Fundamentals 课程的其余内容，请升级到 CoddyKit PRO。 Apache Kafka & Stream Processing Fundamentals 课程共包含 4 节课。

「将 Schema Registry 与 Kafka 集成」这节课中我会学到什么？

在 Kafka 应用中实施 Schema Registry，自动管理和强制执行数据模式你通过在浏览器中直接运行的动手代码来练习 Apache Kafka & Stream Processing Fundamentals，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Apache Kafka & Stream Processing Fundamentals 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Apache Kafka & Stream Processing Fundamentals 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「将 Schema Registry 与 Kafka 集成」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Apache Kafka & Stream Processing Fundamentals 课中编写并运行代码吗？

能。每节 Apache Kafka & Stream Processing Fundamentals 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

← 返回 Apache Kafka & Stream Processing Fundamentals