kafka-rest和schema registry服务注册

配置

一，下载confluent安装包，解压到linux目录，进入etc/kafka-rest/kafka-rest.properties
kafka-rest.properties配置
id=kafka-rest-test-server
schema.registry.url=http://192.168.237.136:8081
zookeeper.connect=192.168.237.136:2181
bootstrap.servers=PLAINTEXT://192.168.237.136:9092
二，进入etc/schema-registry/schema-registry.properties
schema-registry.properties配置
listeners=http://192.168.237.136:8081
kafkastore.connection.url=192.168.237.136:2181
kafkastore.topic=_schemas01
debug=false

注册服务

三，注册schema服务
curl -X POST -H “Content-Type: application/vnd.schemaregistry.v1+json”
–data ‘{“schema”:"{“type”: “record”, “name”: “myrecord”, “fields”: [{“name”: “id”, “type”: “int”},{“name”: “name”, “type”: “string”}]}
"}’
http://192.168.0.102:8081/subjects/ms02/versions
其中ms02为主题名
查看所有已注册的schema
终端输入 ：curl -X GET http://192.168.237.136:8081/subjects或者浏览器直接访问 http://192.168.237.136:8081/subjects
查看某一个主题对应的注册schema
curl -X GET http://192.168.0.102:8081/subjects/topics/versions
删除已经注册的schema
curl -X DELETE http://192.168.0.102:8081/subjects/topics/versions/1
其中1为版本号，要删哪个把1换为那个就好。

代码

package com.hrtj.service;
/*
 * 注册了两个服务
 * */
import java.util.Date;
import java.util.Properties;
import org.apache.avro.Schema;
import org.apache.avro.Schema.Parser;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericData.Record;
import org.apache.avro.generic.GenericRecord;
import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
import org.springframework.stereotype.Service;

@Service
public class AvroProducer {

	public static final String STU_SCHEMA1="{"+
			"\"type\": \"record\","+
			"\"name\": \"myrecord\","+
			"\"fields\":"+
				"["+
					"{\"name\": \"id\", \"type\": \"int\"},"+
					"{\"name\":\"name\",\"type\":\"string\"}"+
				"]"+
		"}";
	public static final String STU_SCHEMA2="{"+
			"\"type\": \"record\","+
			"\"name\": \"myrecord\","+
			"\"fields\":"+
				"["+
					"{\"name\": \"id\", \"type\": \"int\"},"+
					"{\"name\":\"name\",\"type\":\"string\"},"+
					"{\"name\":\"age\",\"type\":\"int\"}"+
				"]"+
		"}";
	
	private static final String bird_schema = "{\"type\":\"record\",\"name\":\"bird4\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"name\",\"type\":\"string\"},{\"name\":\"age\",\"type\":\"int\"},"
	+"{\"name\":\"timeid\",\"type\":{\"type\":\"long\",\"connect.version\":1,\"connect.name\":\"org.apache.kafka.connect.data.Timestamp\",\"logicalType\":\"timestamp-millis\"}}]}";
	
	public void sends1() throws Exception{
		Properties p = new Properties();
		p.put("bootstrap.servers", "192.168.0.102:9092");
		p.put(ProducerConfig.ACKS_CONFIG, "all");
		p.put(ProducerConfig.RETRIES_CONFIG, 0);
		p.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        p.put("value.serializer","io.confluent.kafka.serializers.KafkaAvroSerializer");
		p.put("schema.registry.url", "http://192.168.0.102:8081");
		
		
		Parser parser = new Schema.Parser();
		Schema schema1 = parser.parse(STU_SCHEMA1);
		
		@SuppressWarnings("resource")
		Producer producer = new KafkaProducer<>(p);
		for(int i=1;i<20000;i++){
			Record r1 = new GenericData.Record(schema1);
			r1.put("id", i);
			r1.put("name", "studt");
			
			ProducerRecord record1= new ProducerRecord("hr02","key",r1);
			producer.send(record1);
		}
	}
	
	public void sends2() throws Exception{
		Properties p = new Properties();
		p.put("bootstrap.servers", "hadoop4:9092");
		p.put(ProducerConfig.ACKS_CONFIG, "all");
		p.put(ProducerConfig.RETRIES_CONFIG, 0);
		p.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        p.put("value.serializer","io.confluent.kafka.serializers.KafkaAvroSerializer");
		p.put("schema.registry.url", "http://192.168.237.136:8081");
		Parser parser = new Schema.Parser();
		Schema schema2 = parser.parse(STU_SCHEMA2);
		@SuppressWarnings("resource")
		Producer producer = new KafkaProducer<>(p);
		for(int i=1;i<900;i++){
			Record r2 = new GenericData.Record(schema2);
			r2.put("id", (long)i);
			r2.put("name", "stud");
			r2.put("age", 19+i);
			ProducerRecord record2 = new ProducerRecord("ms01","key"+i,r2);
			producer.send(record2,new Callback() {
				@Override
				public void onCompletion(RecordMetadata metadata, Exception exception) {
					System.out.println("acks");
				}
			});
			Thread.sleep(3000);
			
		}
	}
	
	public void sends3() throws Exception{
		Properties p = new Properties();
		p.put("bootstrap.servers", "hadoop4:9092");
		p.put(ProducerConfig.ACKS_CONFIG, "all");
		p.put(ProducerConfig.RETRIES_CONFIG, 0);
		p.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        p.put("value.serializer","io.confluent.kafka.serializers.KafkaAvroSerializer");
		p.put("schema.registry.url", "http://192.168.237.136:8081");
		Parser parser = new Schema.Parser();
		Schema schema3 = parser.parse(bird_schema);
		@SuppressWarnings("resource")
		Producer producer = new KafkaProducer<>(p);
		for(int i=1;i<20;i++){
			Record r3 = new GenericData.Record(schema3);
			r3.put("id", i);
			r3.put("name", "stud");
			r3.put("age", 19+i);
			Date date = new Date();
			r3.put("timeid", date);
			ProducerRecord record3 = new ProducerRecord("test-mysql-bird",r3);
			producer.send(record3);
			Thread.sleep(100);
		}
	}
}

通过生产者把数据以对象的格式打进topic，通过ksql可以使用类sql语句查询。

package com.hrtj.service;
/*
 * 从topic消费数据
 * */
import java.util.Collections;
import java.util.Properties;
import org.apache.avro.generic.GenericRecord;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.springframework.stereotype.Service;

@Service
public class AvroConsumer {
	public void receive(){
		Properties p = new Properties();
		p.put("bootstrap.servers", "hadoop4:9092");
		p.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        // 使用Confluent实现的KafkaAvroDeserializer
        p.put("value.deserializer", "io.confluent.kafka.serializers.KafkaAvroDeserializer");
		p.put("group.id", "schema_test");
		p.put("schema.registry.url", "http://192.168.237.136:8081");
		p.put("auto.offset.reset", "earliest");
		
		KafkaConsumer consumer = new KafkaConsumer(p);
		consumer.subscribe(Collections.singleton("schema03"));
		
		try{
			while(true){
				ConsumerRecords records = consumer.poll(1000);
				for (ConsumerRecord r : records) {
					GenericRecord stu = r.value();
					System.out.println(r.key()+" : "+stu.get("id")+"\t"+stu.get("name")+"\t"+stu.get("age"));
				}
			}			
		}catch(Exception e){
			e.printStackTrace();
		}finally{
			System.out.println("AvroConsumer.receive()");
			consumer.close();
		}
	}
}

通过record的value()获取对象，进而获取对象的每个属性值。