使用Schema Registry管理Schema_云消息队列 Kafka 版(Kafka)-阿里云帮助中心

云消息队列 Confluent 版使用Schema Registry管理Schema。本文将向您介绍Schema Registry的基本操作。

前提条件

购买云消息队列 Confluent 版，详情请参见购买和部署实例。
获取Kafka集群和Schema Registry集群相关的访问权限，详情请参见RBAC授权。
安装Java 1.8或者1.11环境。具体操作，请参见安装JDK。
安装Maven以编译客户端演示程序。具体操作，请参见安装Maven。

设置客户端环境

执行以下命令，安装maven example示例代码，并使用examples/clients/avro作为项目路径。

git clone https://github.com/confluentinc/examples.git
#使用该路径下的maven项目进行演示
cd examples/clients/avro
git checkout 7.1.0-post

在$HOME/.confluent/路径下创建客户端配置文件java.config。在配置文件中，配置如下配置项。

bootstrap.servers=<your broker access address>
security.protocol=SASL_SSL
#your user should have the authority to access the topic
sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username='<user>' password='<secret>';
sasl.mechanism=PLAIN
schema.registry.url=<your schema registry access address>
basic.auth.credentials.source=USER_INFO
#your user should have the authority to access the schema registry
basic.auth.user.info=<user>:<user-secret>

创建Topic

在主页的左侧导航栏，单击Topics，然后单击右上角的Add topic。
在New Topic页面，设置Topic名称和分区数，单击Create with defaults。
在Topics页面，找到创建好的Topic，单击Topic名称进入Topic详情页。
在Topic详情页，单击Messages页签，然后单击Produce a new message to this topic，向此Topic发送JSON格式的测试数据。

开启Schema格式校验

在目标Topic详情页面，单击Configuration页签。
依次单击Configuration > Edit settings > Switch to expert mode，将confluent_value_schema_validation字段设置为true，启用Schema验证消息内容格式。启用后发送和消费数据时将进行格式校验。

创建Schema

执行cat命令，查看maven example项目如下路径的文件内容。

cat src/main/resources/avro/io/confluent/examples/clients/basicavro/Payment.avsc

返回结果

{
 "namespace": "io.confluent.examples.clients.basicavro",
 "type": "record",
 "name": "Payment",
 "fields": [
     {"name": "id", "type": "string"},
     {"name": "amount", "type": "double"}
 ]
}

在Topic详情页面，单击Schema，然后单击Set a schema。
在Schema页签，单击Avro，将上述Payment.avsc文本填入文本框，单击Create。

Kafka Producer/Consumer Client程序

以下示例使用Maven来配置项目和管理依赖项。完整的pom.xml示例，请参见pom.xml。

创建Producer

在Producer客户端应用程序中，需要设置Kafka相关配置参数。具体的配置请参见上文设置客户端环境。

构造生产者时，需要将消息序列化方式指定为KafkaAvroSerializer类，并将消息值类配置为Payment类。

示例代码如下：

package io.confluent.examples.clients.basicavro;

import io.confluent.kafka.serializers.AbstractKafkaSchemaSerDeConfig;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;
import io.confluent.kafka.serializers.KafkaAvroSerializer;
import org.apache.kafka.common.errors.SerializationException;

import java.util.Properties;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.io.FileInputStream;
import java.io.InputStream;

public class ProducerExample {

    private static final String TOPIC = "transactions";
    private static final Properties props = new Properties();
    private static String configFile;

    @SuppressWarnings("InfiniteLoopStatement")
    public static void main(final String[] args) throws IOException {

        if (args.length < 1) {
          // Backwards compatibility, assume localhost
          props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
          props.put(AbstractKafkaSchemaSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, "http://localhost:8081");
        } else {
          // Load properties from a local configuration file
          // Create the configuration file (e.g. at '$HOME/.confluent/java.config') with configuration parameters
          // to connect to your Kafka cluster, which can be on your local host, Confluent Cloud, or any other cluster.
          // Documentation at https://docs.confluent.io/platform/current/tutorials/examples/clients/docs/java.html
          configFile = args[0];
          if (!Files.exists(Paths.get(configFile))) {
            throw new IOException(configFile + " not found.");
          } else {
            try (InputStream inputStream = new FileInputStream(configFile)) {
              props.load(inputStream);
            }
          }
        }

        props.put(ProducerConfig.ACKS_CONFIG, "all");
        props.put(ProducerConfig.RETRIES_CONFIG, 0);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, KafkaAvroSerializer.class);

        try (KafkaProducer<String, Payment> producer = new KafkaProducer<String, Payment>(props)) {

            for (long i = 0; i < 10; i++) {
                final String orderId = "id" + Long.toString(i);
                final Payment payment = new Payment(orderId, 1000.00d);
                final ProducerRecord<String, Payment> record = new ProducerRecord<String, Payment>(TOPIC, payment.getId().toString(), payment);
                producer.send(record);
                Thread.sleep(1000L);
            }

            producer.flush();
            System.out.printf("Successfully produced 10 messages to a topic called %s%n", TOPIC);

        } catch (final SerializationException e) {
            e.printStackTrace();
        } catch (final InterruptedException e) {
            e.printStackTrace();
        }

    }

}

更多信息，请参见ProducerExample。

说明

pom.xml包含avro-maven-plugin，Payment类二进制文件是在编译期间自动生成的。

运行Producer

进入example项目的examples/clients/avro目录下，执行以下步骤。

执行以下命令，编译项目。
```
mvn clean compile package
```
查看Topic详情页面的Messages页签。此时应该没有消息记录。

执行Producer代码。

mvn exec:java -Dexec.mainClass=io.confluent.examples.clients.basicavro.ProducerExample \
  -Dexec.args="$HOME/.confluent/java.config"

该命令使用了之前准备的客户端配置文件$HOME/.confluent/java.config。命令执行完成后，会输出如下结果：

...
Successfully produced 10 messages to a topic called transactions
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
...

查看结果。此时，您应该可以在Messages页面查看到上一步骤写入到消息。

创建Consumer

在Consumer客户端应用程序中，同样需要设置Kafka相关配置参数。具体的配置详情请参见上文设置客户端环境。构造消费者时，需要将消息反序列化方式指定为KafkaAvroDeSerializer类，并将消息值类配置为Payment类。示例代码如下：

package io.confluent.examples.clients.basicavro;

import io.confluent.kafka.serializers.AbstractKafkaSchemaSerDeConfig;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import io.confluent.kafka.serializers.KafkaAvroDeserializer;
import io.confluent.kafka.serializers.KafkaAvroDeserializerConfig;
import org.apache.kafka.common.serialization.StringDeserializer;

import java.time.Duration;
import java.util.Collections;
import java.util.Properties;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.io.FileInputStream;
import java.io.InputStream;

public class ConsumerExample {

    private static final String TOPIC = "transactions";
    private static final Properties props = new Properties();
    private static String configFile;

    @SuppressWarnings("InfiniteLoopStatement")
    public static void main(final String[] args) throws IOException {

        if (args.length < 1) {
          // Backwards compatibility, assume localhost
          props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
          props.put(AbstractKafkaSchemaSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, "http://localhost:8081");
        } else {
          // Load properties from a local configuration file
          // Create the configuration file (e.g. at '$HOME/.confluent/java.config') with configuration parameters
          // to connect to your Kafka cluster, which can be on your local host, Confluent Cloud, or any other cluster.
          // Documentation at https://docs.confluent.io/platform/current/tutorials/examples/clients/docs/java.html
          configFile = args[0];
          if (!Files.exists(Paths.get(configFile))) {
            throw new IOException(configFile + " not found.");
          } else {
            try (InputStream inputStream = new FileInputStream(configFile)) {
              props.load(inputStream);
            }
          }
        }

        props.put(ConsumerConfig.GROUP_ID_CONFIG, "test-payments");
        props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "true");
        props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "1000");
        props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
        props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, KafkaAvroDeserializer.class);
        props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, true); 

        try (final KafkaConsumer<String, Payment> consumer = new KafkaConsumer<>(props)) {
            consumer.subscribe(Collections.singletonList(TOPIC));

            while (true) {
                final ConsumerRecords<String, Payment> records = consumer.poll(Duration.ofMillis(100));
                for (final ConsumerRecord<String, Payment> record : records) {
                    final String key = record.key();
                    final Payment value = record.value();
                    System.out.printf("key = %s, value = %s%n", key, value);
                }
            }

        }
    }

}

更多信息，请参见ConsumerExample.。

运行Consumer代码

执行以下命令，编译项目。
```
mvn clean compile package
```

运行ConsumerExample。

mvn exec:java -Dexec.mainClass=io.confluent.examples.clients.basicavro.ConsumerExample \
  -Dexec.args="$HOME/.confluent/java.config"

执行成功后，查看输出：

...
key = id0, value = {"id": "id0", "amount": 1000.0}
key = id1, value = {"id": "id1", "amount": 1000.0}
key = id2, value = {"id": "id2", "amount": 1000.0}
key = id3, value = {"id": "id3", "amount": 1000.0}
key = id4, value = {"id": "id4", "amount": 1000.0}
key = id5, value = {"id": "id5", "amount": 1000.0}
key = id6, value = {"id": "id6", "amount": 1000.0}
key = id7, value = {"id": "id7", "amount": 1000.0}
key = id8, value = {"id": "id8", "amount": 1000.0}
key = id9, value = {"id": "id9", "amount": 1000.0}
...