分布式消息系统Kafka Java客户端代码

介绍

kafka是一种高吞吐量的分布式发布订阅消息系统。

kafka是linkedin用于日志处理的分布式消息队列，linkedin的日志数据容量大，但对可靠性要求不高，其日志数据主要包括用户行为（登录、浏览、点击、分享、喜欢）以及系统运行日志（CPU、内存、磁盘、网络、系统及进程状态）

当前很多的消息队列服务提供可靠交付保证，并默认是即时消费（不适合离线）。

高可靠交付对linkedin的日志不是必须的，故可通过降低可靠性来提高性能，同时通过构建分布式的集群，允许消息在系统中累积，使得kafka同时支持离线和在线日志处理

测试环境

kafka_2.10-0.8.1.1 3个节点做的集群

zookeeper-3.4.5 一个实例节点

代码示例

消息生产者代码示例

import java.util.Collections;
import java.util.Date;
import java.util.Properties;
import java.util.Random;

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

/**
* 详细可以参考：https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+Producer+Example
* @author Fung
*
*/
public class ProducerDemo {
public static void main(String[] args) {
Random rnd = new Random();
int events=100;

// 设置配置属性
Properties props = new Properties();
props.put(“metadata.broker.list”,”172.168.63.221:9092,172.168.63.233:9092,172.168.63.234:9092″);
props.put(“serializer.class”, ”kafka.serializer.StringEncoder”);
// key.serializer.class默认为serializer.class
props.put(“key.serializer.class”, ”kafka.serializer.StringEncoder”);
// 可选配置，如果不配置，则使用默认的partitioner
props.put(“partitioner.class”, ”com.catt.kafka.demo.PartitionerDemo”);
// 触发acknowledgement机制，否则是fire and forget，可能会引起数据丢失
// 值为0,1,-1,可以参考
// http://kafka.apache.org/08/configuration.html
props.put(“request.required.acks”, ”1″);
ProducerConfig config = new ProducerConfig(props);

// 创建producer
Producer<String, String> producer = new Producer<String, String>(config);
// 产生并发送消息
long start=System.currentTimeMillis();
for (long i = 0; i < events; i++) {
long runtime = new Date().getTime();
String ip = ”192.168.2.” + i;//rnd.nextInt(255);
String msg = runtime + ”,www.example.com,” + ip;
//如果topic不存在，则会自动创建，默认replication-factor为1，partitions为0
KeyedMessage<String, String> data = new KeyedMessage<String, String>(
“page_visits”, ip, msg);
producer.send(data);
}
System.out.println(“耗时:” + (System.currentTimeMillis() - start));
// 关闭producer
producer.close();
}
}

消息消费者代码示例

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

import kafka.consumer.Consumer;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;

/**
* 详细可以参考：https://cwiki.apache.org/confluence/display/KAFKA/Consumer+Group+Example
*
* @author Fung
*
*/
public class ConsumerDemo {
private final ConsumerConnector consumer;
private final String topic;
private ExecutorService executor;

public ConsumerDemo(String a_zookeeper, String a_groupId, String a_topic) {
consumer = Consumer.createJavaConsumerConnector(createConsumerConfig(a_zookeeper,a_groupId));
this.topic = a_topic;
}

public void shutdown() {
if (consumer != null)
consumer.shutdown();
if (executor != null)
executor.shutdown();
}

public void run(int numThreads) {
Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
topicCountMap.put(topic, new Integer(numThreads));
Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = consumer
.createMessageStreams(topicCountMap);
List<KafkaStream<byte[], byte[]>> streams = consumerMap.get(topic);

// now launch all the threads
executor = Executors.newFixedThreadPool(numThreads);

// now create an object to consume the messages
//
int threadNumber = 0;
for (final KafkaStream stream : streams) {
executor.submit(new ConsumerMsgTask(stream, threadNumber));
threadNumber++;
}
}

private static ConsumerConfig createConsumerConfig(String a_zookeeper,
String a_groupId) {
Properties props = new Properties();
props.put(“zookeeper.connect”, a_zookeeper);
props.put(“group.id”, a_groupId);
props.put(“zookeeper.session.timeout.ms”, ”400″);
props.put(“zookeeper.sync.time.ms”, ”200″);
props.put(“auto.commit.interval.ms”, ”1000″);

return new ConsumerConfig(props);
}

public static void main(String[] arg) {
String[] args = { ”172.168.63.221:2188″, ”group-1″, ”page_visits”, ”12″ };
String zooKeeper = args[0];
String groupId = args[1];
String topic = args[2];
int threads = Integer.parseInt(args[3]);

ConsumerDemo demo = new ConsumerDemo(zooKeeper, groupId, topic);
demo.run(threads);

try {
Thread.sleep(10000);
} catch (InterruptedException ie) {

}
demo.shutdown();
}
}

消息处理类

import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;

public class ConsumerMsgTask implements Runnable {
private KafkaStream m_stream;
private int m_threadNumber;

public ConsumerMsgTask(KafkaStream stream, int threadNumber) {
m_threadNumber = threadNumber;
m_stream = stream;
}

public void run() {
ConsumerIterator<byte[], byte[]> it = m_stream.iterator();
while (it.hasNext())
System.out.println(“Thread ” + m_threadNumber + ”: ”
+ new String(it.next().message()));
System.out.println(“Shutting down Thread: ” + m_threadNumber);
}
}

Partitioner类示例

import kafka.producer.Partitioner;
import kafka.utils.VerifiableProperties;

public class PartitionerDemo implements Partitioner {
public PartitionerDemo(VerifiableProperties props) {

}

@Override
public int partition(Object obj, int numPartitions) {
int partition = 0;
if (obj instanceof String) {
String key=(String)obj;
int offset = key.lastIndexOf(‘.’);
if (offset > 0) {
partition = Integer.parseInt(key.substring(offset + 1)) % numPartitions;
}
}else{
partition = obj.toString().length() % numPartitions;
}

return partition;
}

}

参考

https://cwiki.apache.org/confluence/display/KAFKA/Index

https://kafka.apache.org/

来自：开源中国