分布式发布订阅消息系统Kafka JAVA客户端代码示例
jopen
10年前
介绍
http://kafka.apache.orgkafka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
来自：http://my.oschina.net/cloudcoder/blog/299215