Kafka入門例項

摘要:本文主要講了Kafka的一個簡單入門例項

原始碼下載：https://github.com/appleappleapple/BigDataLearning

kafka安裝過程看這裡：Kafka在Windows安裝執行

整個工程目錄如下：

1、pom檔案

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>
	<groupId>com.lin</groupId>
	<artifactId>Kafka-Demo</artifactId>
	<version>0.0.1-SNAPSHOT</version>

	<dependencies>
		<dependency>
			<groupId>org.apache.kafka</groupId>
			<artifactId>kafka_2.10</artifactId>
			<version>0.9.0.0</version>
		</dependency>

		<dependency>
			<groupId>org.opentsdb</groupId>
			<artifactId>java-client</artifactId>
			<version>2.1.0-SNAPSHOT</version>
			<exclusions>
				<exclusion>
					<groupId>org.slf4j</groupId>
					<artifactId>slf4j-log4j12</artifactId>
				</exclusion>
				<exclusion>
					<groupId>log4j</groupId>
					<artifactId>log4j</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.slf4j</groupId>
					<artifactId>jcl-over-slf4j</artifactId>
				</exclusion>
			</exclusions>
		</dependency>

		<dependency>
			<groupId>com.alibaba</groupId>
			<artifactId>fastjson</artifactId>
			<version>1.2.4</version>
		</dependency>


	</dependencies>
</project>

2、生產者

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<String, String> producer;
	public final static String TOPIC = "linlin";

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

		// 配置value的序列化類
		props.put("serializer.class", "kafka.serializer.StringEncoder");
		// 配置key的序列化類
		props.put("key.serializer.class", "kafka.serializer.StringEncoder");

		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();
	}
}

右鍵：run as java application

執行結果：

3、消費者

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 代表一個消費組
		props.put("group.id", "lingroup");

		// zk連線超時
		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");
		// 序列化類
		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();
	}
}

執行結果：

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原始碼下載：https://github.com/appleappleapple/BigDataLearning