Kafka入门实例

摘要:本文主要讲了Kafka的一个简单入门实例

源码下载：https://github.com/appleappleapple/BigDataLearning

kafka安装过程看这里：Kafka在Windows安装运行

整个工程目录如下：

1、pom文件

	4.0.0
	com.lin
	Kafka-Demo
	0.0.1-SNAPSHOT

	
		
			org.apache.kafka
			kafka_2.10
			0.9.0.0
		

		
			org.opentsdb
			java-client
			2.1.0-SNAPSHOT
			
				
					org.slf4j
					slf4j-log4j12
				
				
					log4j
					log4j
				
				
					org.slf4j
					jcl-over-slf4j
				
			
		

		
			com.alibaba
			fastjson
			1.2.4
		


	

2、生产者

package com.lin.demo.producer;

import java.util.Properties;

import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;

public class KafkaProducer {
	private final Producer producer;
	public final static String TOPIC = "linlin";

	private KafkaProducer() {
		Properties props = new Properties();
		// 此处配置的是kafka的端口
		props.put("metadata.broker.list", "127.0.0.1:9092");
		props.put("zk.connect", "127.0.0.1:2181");  

		// 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");

		props.put("request.required.acks", "-1");

		producer = new Producer(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(TOPIC, key, data));
			System.out.println(data);
			messageNo++;
		}
	}

	public static void main(String[] args) {
		new KafkaProducer().produce();
	}
}

右键：run as java application

运行结果：

3、消费者

package com.lin.demo.consumer;

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;

import com.lin.demo.producer.KafkaProducer;

public class KafkaConsumer {

	private final ConsumerConnector consumer;

	private KafkaConsumer() {
		Properties props = new Properties();
		// zookeeper 配置
		props.put("zookeeper.connect", "127.0.0.1:2181");

		// group 代表一个消费组
		props.put("group.id", "lingroup");

		// zk连接超时
		props.put("zookeeper.session.timeout.ms", "4000");
		props.put("zookeeper.sync.time.ms", "200");
		props.put("rebalance.max.retries", "5");
		props.put("rebalance.backoff.ms", "1200");
		
	
		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 topicCountMap = new HashMap();
		topicCountMap.put(KafkaProducer.TOPIC, new Integer(1));

		StringDecoder keyDecoder = new StringDecoder(new VerifiableProperties());
		StringDecoder valueDecoder = new StringDecoder(new VerifiableProperties());

		Map>> consumerMap = consumer.createMessageStreams(topicCountMap, keyDecoder, valueDecoder);
		KafkaStream stream = consumerMap.get(KafkaProducer.TOPIC).get(0);
		ConsumerIterator it = stream.iterator();
		while (it.hasNext())
			System.out.println("<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<" + it.next().message() + "<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<");
	}

	public static void main(String[] args) {
		new KafkaConsumer().consume();
	}
}

运行结果：

监控页面

源码下载：https://github.com/appleappleapple/BigDataLearning