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：

12345<dependency><groupId>org.apache.kafka</groupId><artifactId>kafka_2.10</artifactId><version>0.8.0</version></dependency>

我们用的版本是0.8， 下面我们看下生产消息的代码：

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354packagecn.outofmemory.kafka;importjava.util.Properties;importkafka.javaapi.producer.Producer;importkafka.producer.KeyedMessage;importkafka.producer.ProducerConfig;/*** Hello world!**/publicclassKafkaProducer {privatefinalProducer<String, String> producer;publicfinalstaticString TOPIC = "TEST-TOPIC";privateKafkaProducer(){Properties props = newProperties();//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 = newProducer<String, String>(newProducerConfig(props));}voidproduce() {intmessageNo = 1000;finalintCOUNT = 10000;while(messageNo < COUNT) {String key = String.valueOf(messageNo);String data = "hello kafka message "+ key;producer.send(newKeyedMessage<String, String>(TOPIC, key ,data));System.out.println(data);messageNo ++;}}publicstaticvoidmain( String[] args ){newKafkaProducer().produce();}}

下面是消费端的代码实现：

12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758packagecn.outofmemory.kafka;importjava.util.HashMap;importjava.util.List;importjava.util.Map;importjava.util.Properties;importkafka.consumer.ConsumerConfig;importkafka.consumer.ConsumerIterator;importkafka.consumer.KafkaStream;importkafka.javaapi.consumer.ConsumerConnector;importkafka.serializer.StringDecoder;importkafka.utils.VerifiableProperties;publicclassKafkaConsumer {privatefinalConsumerConnector consumer;privateKafkaConsumer() {Properties props = newProperties();//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 = newConsumerConfig(props);consumer = kafka.consumer.Consumer.createJavaConsumerConnector(config);}voidconsume() {Map<String, Integer> topicCountMap = newHashMap<String, Integer>();topicCountMap.put(KafkaProducer.TOPIC, newInteger(1));StringDecoder keyDecoder = newStringDecoder(newVerifiableProperties());StringDecoder valueDecoder = newStringDecoder(newVerifiableProperties());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());}publicstaticvoidmain(String[] args) {newKafkaConsumer().consume();}}

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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