使用Apache Kafka与Confluent Schema Registry一起的解决方案包括以下几个步骤:
首先,需要按照官方文档的指导安装和配置Apache Kafka和Confluent Schema Registry。可以从Confluent官方网站上下载并安装Confluent Platform,该平台包含了Kafka和Schema Registry。
在Kafka中,消息被发布到主题(topic)中。可以使用以下代码创建一个Kafka主题:
import kafka.admin.AdminUtils;
import kafka.utils.ZKStringSerializer$;
import org.I0Itec.zkclient.ZkClient;
import scala.collection.JavaConversions;
public class KafkaTopicCreator {
public static void createTopic(String topicName, int numPartitions, int replicationFactor, Properties zkProperties) {
ZkClient zkClient = new ZkClient(zkProperties.getProperty("zookeeper.connect"), 10000, 10000, ZKStringSerializer$.MODULE$);
AdminUtils.createTopic(zkClient, topicName, numPartitions, replicationFactor, new Properties(), null);
zkClient.close();
}
public static void main(String[] args) {
Properties zkProperties = new Properties();
zkProperties.setProperty("zookeeper.connect", "localhost:2181");
createTopic("my-topic", 1, 1, zkProperties);
}
}
在使用Confluent Schema Registry之前,需要定义和注册一个Schema。Schema是按照Avro格式定义的数据结构。以下是一个示例Avro Schema的定义:
{
"type": "record",
"name": "User",
"fields": [
{"name": "id", "type": "int"},
{"name": "name", "type": "string"},
{"name": "email", "type": "string"}
]
}
使用Confluent Schema Registry发送和接收消息需要使用Avro格式的数据,并使用Schema进行序列化和反序列化。以下是一个示例代码:
import io.confluent.kafka.serializers.KafkaAvroSerializer;
import org.apache.kafka.clients.producer.*;
import org.apache.kafka.common.serialization.StringSerializer;
import java.util.Properties;
public class KafkaAvroProducer {
public static void main(String[] args) {
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092");
properties.setProperty("key.serializer", StringSerializer.class.getName());
properties.setProperty("value.serializer", KafkaAvroSerializer.class.getName());
properties.setProperty("schema.registry.url", "http://localhost:8081");
String topic = "my-topic";
Producer producer = new KafkaProducer<>(properties);
User user = new User(1, "John Doe", "johndoe@example.com");
ProducerRecord record = new ProducerRecord<>(topic, user);
producer.send(record, new Callback() {
@Override
public void onCompletion(RecordMetadata metadata, Exception exception) {
if (exception != null) {
exception.printStackTrace();
} else {
System.out.println("Message sent successfully to topic " + metadata.topic());
}
}
});
producer.flush();
producer.close();
}
}
import io.confluent.kafka.serializers.KafkaAvroDeserializer;
import org.apache.kafka.clients.consumer.*;
import org.apache.kafka.common.serialization.StringDeserializer;
import java.time.Duration;
import java.util.Collections;
import java.util.Properties;
public class KafkaAvroConsumer {
public static void main(String[] args) {
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092");
properties.setProperty("key.deserializer", StringDeserializer.class.getName());
properties.setProperty("value.deserializer", KafkaAvroDeserializer.class.getName());
properties.setProperty("schema.registry.url", "http://localhost:8081");
properties.setProperty("group.id", "my-group");
String topic = "my-topic";
KafkaConsumer consumer = new KafkaConsumer<>(properties);
consumer.subscribe(Collections.singleton(topic));
while (true) {
ConsumerRecords records = consumer.poll(Duration.ofMillis(100));
for (ConsumerRecord record : records) {
User user = record.value();
System.out.println("Received message: " + user.toString());
}
}
}
}
在上述代码中,User
类是