kafka是吞吐量巨大的一个消息系统，它是用scala写的，和普通的消息的生产消费还有所不同，写了个demo程序供大家参考。kafka的安装请参考官方文档。
首先我们需要新建一个maven项目，然后在pom中引用kafka jar包，引用依赖如下：
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<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.10</artifactId>
<version>0.8.0</version>
</dependency>
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我们用的版本是0.8， 下面我们看下生产消息的代码：
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package cn.outofmemory.kafka;
import java.util.Properties;
import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;
/**
* Hello world!
*
*/
public class KafkaProducer
{
private final Producer<String, String> producer;
public final static String TOPIC = "TEST-TOPIC";
private KafkaProducer(){
Properties props = new Properties();
//此处配置的是kafka的端口
props.put("metadata.broker.list", "192.168.193.148:9092");
//配置value的序列化类
props.put("serializer.class", "kafka.serializer.StringEncoder");
//配置key的序列化类
props.put("key.serializer.class", "kafka.serializer.StringEncoder");
//request.required.acks
//0, which means that the producer never waits for an acknowledgement from the broker (the same behavior as 0.7). This option provides the lowest latency but the weakest durability guarantees (some data will be lost when a server fails).
//1, which means that the producer gets an acknowledgement after the leader replica has received the data. This option provides better durability as the client waits until the server acknowledges the request as successful (only messages that were written to the now-dead leader but not yet replicated will be lost).
//-1, which means that the producer gets an acknowledgement after all in-sync replicas have received the data. This option provides the best durability, we guarantee that no messages will be lost as long as at least one in sync replica remains.
props.put("request.required.acks","-1");
producer = new Producer<String, String>(new ProducerConfig(props));
}
void produce() {
int messageNo = 1000;
final int COUNT = 10000;
while (messageNo < COUNT) {
String key = String.valueOf(messageNo);
String data = "hello kafka message " + key;
producer.send(new KeyedMessage<String, String>(TOPIC, key ,data));
System.out.println(data);
messageNo ++;
}
}
public static void main( String[] args )
{
new KafkaProducer().produce();
}
}
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下面是消费端的代码实现：
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package cn.outofmemory.kafka;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
import kafka.serializer.StringDecoder;
import kafka.utils.VerifiableProperties;
public class KafkaConsumer {
private final ConsumerConnector consumer;
private KafkaConsumer() {
Properties props = new Properties();
//zookeeper 配置
props.put("zookeeper.connect", "192.168.193.148:2181");
//group 代表一个消费组
props.put("group.id", "jd-group");
//zk连接超时
props.put("zookeeper.session.timeout.ms", "4000");
props.put("zookeeper.sync.time.ms", "200");
props.put("auto.commit.interval.ms", "1000");
props.put("auto.offset.reset", "smallest");
//序列化类
props.put("serializer.class", "kafka.serializer.StringEncoder");
ConsumerConfig config = new ConsumerConfig(props);
consumer = kafka.consumer.Consumer.createJavaConsumerConnector(config);
}
void consume() {
Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
topicCountMap.put(KafkaProducer.TOPIC, new Integer(1));
StringDecoder keyDecoder = new StringDecoder(new VerifiableProperties());
StringDecoder valueDecoder = new StringDecoder(new VerifiableProperties());
Map<String, List<KafkaStream<String, String>>> consumerMap =
consumer.createMessageStreams(topicCountMap,keyDecoder,valueDecoder);
KafkaStream<String, String> stream = consumerMap.get(KafkaProducer.TOPIC).get(0);
ConsumerIterator<String, String> it = stream.iterator();
while (it.hasNext())
System.out.println(it.next().message());
}
public static void main(String[] args) {
new KafkaConsumer().consume();
}
}
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注意消费端需要配置成zk的地址，而生产端配置的是kafka的ip和端口。
源码地址获取：mingli
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