关于 avro 的 maven 工程的搭建以及 avro 的入门知识，可以参考： Apache Avro 入门
1. 定义 schema 文件，并编译 maven 工程生成实体类
schema 文件名称为：stock.avsc，内容如下：
{
"namespace": "com.bonc.rdpe.kafka110.beans",
"type": "record",
"name": "Stock",
"fields": [
{"name": "stockCode", "type": "string"},
{"name": "stockName", "type": "string"},
{"name": "tradeTime", "type": "long"},
{"name": "preClosePrice", "type": "float"},
{"name": "openPrice", "type": "float"},
{"name": "currentPrice", "type": "float"},
{"name": "highPrice", "type": "float"},
{"name": "lowPrice", "type": "float"}
]
}
编译 maven 工程生成实体类：
2. 自定义序列化类和反序列化类
(1) 序列化类
package com.bonc.rdpe.kafka110.serializer;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.util.Map;
import org.apache.avro.io.BinaryEncoder;
import org.apache.avro.io.DatumWriter;
import org.apache.avro.io.EncoderFactory;
import org.apache.avro.specific.SpecificDatumWriter;
import org.apache.kafka.common.errors.SerializationException;
import org.apache.kafka.common.serialization.Serializer;
import com.bonc.rdpe.kafka110.beans.Stock;
/**
* @Title AvroSerializer.java
* @Description 使用传统的 Avro API 自定义序列化类
* @Author YangYunhe
* @Date 2018-06-21 16:40:35
*/
public class AvroSerializer implements Serializer<Stock> {
@Override
public void close() {}
@Override
public void configure(Map<String, ?> arg0, boolean arg1) {}
@Override
public byte[] serialize(String topic, Stock data) {
if(data == null) {
return null;
}
DatumWriter<Stock> writer = new SpecificDatumWriter<>(data.getSchema());
ByteArrayOutputStream out = new ByteArrayOutputStream();
BinaryEncoder encoder = EncoderFactory.get().directBinaryEncoder(out, null);
try {
writer.write(data, encoder);
}catch (IOException e) {
throw new SerializationException(e.getMessage());
}
return out.toByteArray();
}
}
(2) 反序列化类
package com.bonc.rdpe.kafka110.deserializer;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.util.Map;
import org.apache.avro.io.BinaryDecoder;
import org.apache.avro.io.DatumReader;
import org.apache.avro.io.DecoderFactory;
import org.apache.avro.specific.SpecificDatumReader;
import org.apache.kafka.common.serialization.Deserializer;
import com.bonc.rdpe.kafka110.beans.Stock;
/**
* @Title AvroDeserializer.java
* @Description 使用传统的 Avro API 自定义反序列类
* @Author YangYunhe
* @Date 2018-06-21 17:19:40
*/
public class AvroDeserializer implements Deserializer<Stock> {
@Override
public void close() {}
@Override
public void configure(Map<String, ?> arg0, boolean arg1) {}
@Override
public Stock deserialize(String topic, byte[] data) {
if(data == null) {
return null;
}
Stock stock = new Stock();
ByteArrayInputStream in = new ByteArrayInputStream(data);
DatumReader<Stock> userDatumReader = new SpecificDatumReader<>(stock.getSchema());
BinaryDecoder decoder = DecoderFactory.get().directBinaryDecoder(in, null);
try {
stock = userDatumReader.read(null, decoder);
} catch (IOException e) {
e.printStackTrace();
}
return stock;
}
}
3. KafkaProducer使用自定义的序列化类发送消息
package com.bonc.rdpe.kafka110.producer;
import java.util.Properties;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
import com.bonc.rdpe.kafka110.beans.Stock;
/**
* @Title TraditionalAvroProducer.java
* @Description Kafka Producer 发送avro序列化后的Stock对象
* @Author YangYunhe
* @Date 2018-06-21 17:41:59
*/
public class TraditionalAvroProducer {
public static void main(String[] args) throws Exception {
Stock[] stocks = new Stock[100];
for(int i = 0; i < 100; i++) {
stocks[i] = new Stock();
stocks[i].setStockCode(String.valueOf(i));
stocks[i].setStockName("stock" + i);
stocks[i].setTradeTime(System.currentTimeMillis());
stocks[i].setPreClosePrice(100.0F);
stocks[i].setOpenPrice(88.8F);
stocks[i].setCurrentPrice(120.5F);
stocks[i].setHighPrice(300.0F);
stocks[i].setLowPrice(12.4F);
}
Properties props = new Properties();
props.put("bootstrap.servers", "192.168.42.89:9092,192.168.42.89:9093,192.168.42.89:9094");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
// 设置序列化类为自定义的 avro 序列化类
props.put("value.serializer", "com.bonc.rdpe.kafka110.serializer.AvroSerializer");
Producer<String, Stock> producer = new KafkaProducer<>(props);
for(Stock stock : stocks) {
ProducerRecord<String, Stock> record = new ProducerRecord<>("dev3-yangyunhe-topic001", stock);
RecordMetadata metadata = producer.send(record).get();
StringBuilder sb = new StringBuilder();
sb.append("stock: ").append(stock.toString()).append(" has been sent successfully!").append("\n")
.append("send to partition ").append(metadata.partition())
.append(", offset = ").append(metadata.offset());
System.out.println(sb.toString());
Thread.sleep(100);
}
producer.close();
}
}
4. KafkaConsumer使用自定义的反序列化类接收消息
package com.bonc.rdpe.kafka110.consumer;
import java.util.Collections;
import java.util.Properties;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import com.bonc.rdpe.kafka110.beans.Stock;
/**
* @Title TraditionalAvroConsumer.java
* @Description Kafka Consumer 解析avro序列化后的Stock对象
* @Author YangYunhe
* @Date 2018-06-21 17:43:03
*/
public class TraditionalAvroConsumer {
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "192.168.42.89:9092,192.168.42.89:9093,192.168.42.89:9094");
props.put("group.id", "dev3-yangyunhe-group001");
props.put("key.deserializer","org.apache.kafka.common.serialization.StringDeserializer");
// 设置反序列化类为自定义的avro反序列化类
props.put("value.deserializer","com.bonc.rdpe.kafka110.deserializer.AvroDeserializer");
KafkaConsumer<String, Stock> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Collections.singletonList("dev3-yangyunhe-topic001"));
try {
while(true) {
ConsumerRecords<String, Stock> records = consumer.poll(100);
for(ConsumerRecord<String, Stock> record : records) {
Stock stock = record.value();
System.out.println(stock.toString());
}
}
}finally {
consumer.close();
}
}
}
5. 测试结果
运行生产者代码后控制台输出：
stock: {"stockCode": "0", "stockName": "stock0", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4} has been sent successfully!
send to partition 0, offset = 552
stock: {"stockCode": "1", "stockName": "stock1", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4} has been sent successfully!
send to partition 2, offset = 551
stock: {"stockCode": "2", "stockName": "stock2", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4} has been sent successfully!
send to partition 1, offset = 551
stock: {"stockCode": "3", "stockName": "stock3", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4} has been sent successfully!
send to partition 0, offset = 553
stock: {"stockCode": "4", "stockName": "stock4", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4} has been sent successfully!
send to partition 2, offset = 552
......
运行消费者代码后控制台输出：
{"stockCode": "0", "stockName": "stock0", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4}
{"stockCode": "1", "stockName": "stock1", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4}
{"stockCode": "2", "stockName": "stock2", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4}
{"stockCode": "3", "stockName": "stock3", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4}
{"stockCode": "4", "stockName": "stock4", "tradeTime": 1529631848353, "preClosePrice": 100.0, "openPrice": 88.8, "currentPrice": 120.5, "highPrice": 300.0, "lowPrice": 12.4}
......
