Kafka部署与代码实例
kafka作为分布式日志收集或系统监控服务，我们有必要在合适的场合使用它。kafka的部署包括zookeeper环境/kafka环境，同时还需要进行一些配置操作.接下来介绍如何使用kafka. 我们使用3个zookeeper实例构建zk集群，使用2个kafka broker构建kafka集群. 其中kafka为0.8V，zookeeper为3.4.5V
·
转自：http://ee.riaos.com/?p=20006353
kafka作为分布式日志收集或系统监控服务，我们有必要在合适的场合使用它。kafka的部署包括zookeeper环境/kafka环境，同时还需要进行一些配置操作.接下来介绍如何使用kafka.
我们使用3个zookeeper实例构建zk集群，使用2个kafka broker构建kafka集群.
其中kafka为0.8V，zookeeper为3.4.5V
一.Zookeeper集群构建
我们有3个zk实例，分别为zk-0,zk-1,zk-2;如果你仅仅是测试使用，可以使用1个zk实例.
1) zk-0
调整配置文件：
clientPort=2181 server.0=127.0.0.1:2888:3888 server.1=127.0.0.1:2889:3889 server.2=127.0.0.1:2890:3890 ##只需要修改上述配置，其他配置保留默认值
启动zookeeper
./zkServer.sh start
2) zk-1
调整配置文件(其他配置和zk-0一只)：
clientPort=2182 ##只需要修改上述配置，其他配置保留默认值
启动zookeeper
./zkServer.sh start
3) zk-2
调整配置文件(其他配置和zk-0一只)：
clientPort=2183 ##只需要修改上述配置，其他配置保留默认值
启动zookeeper
./zkServer.sh start
二. Kafka集群构建
因为Broker配置文件涉及到zookeeper的相关约定，因此我们先展示broker配置文件.我们使用2个kafka broker来构建这个集群环境，分别为kafka-0,kafka-1.
1) kafka-0
在config目录下修改配置文件为：
broker.id=0 port=9092 num.network.threads=2 num.io.threads=2 socket.send.buffer.bytes=1048576 socket.receive.buffer.bytes=1048576 socket.request.max.bytes=104857600 log.dir=./logs num.partitions=2 log.flush.interval.messages=10000 log.flush.interval.ms=1000 log.retention.hours=168 #log.retention.bytes=1073741824 log.segment.bytes=536870912 log.cleanup.interval.mins=10 zookeeper.connect=127.0.0.1:2181,127.0.0.1:2182,127.0.0.1:2183 zookeeper.connection.timeout.ms=1000000 kafka.metrics.polling.interval.secs=5 kafka.metrics.reporters=kafka.metrics.KafkaCSVMetricsReporter kafka.csv.metrics.dir=/tmp/kafka_metrics kafka.csv.metrics.reporter.enabled=false
因为kafka用scala语言编写，因此运行kafka需要首先准备scala相关环境。
> cd kafka-0 > ./sbt update > ./sbt package > ./sbt assembly-package-dependency
其中最后一条指令执行有可能出现异常，暂且不管。 启动kafka broker：
> JMS_PORT=9997 bin/kafka-server-start.sh config/server.properties &
因为zookeeper环境已经正常运行了，我们无需通过kafka来挂载启动zookeeper.如果你的一台机器上部署了多个kafka broker，你需要声明JMS_PORT.
2) kafka-1
broker.id=1 port=9093 ##其他配置和kafka-0保持一致
然后和kafka-0一样执行打包命令，然后启动此broker.
> JMS_PORT=9998 bin/kafka-server-start.sh config/server.properties &
到目前为止环境已经OK了,那我们就开始展示编程实例吧。
三.项目准备
项目基于maven构建，不得不说kafka java客户端实在是太糟糕了；构建环境会遇到很多麻烦。建议参考如下pom.xml;其中各个依赖包必须版本协调一致。
<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.test</groupId>
<artifactId>test-kafka</artifactId>
<packaging>jar</packaging>
<name>test-kafka</name>
<url>http://maven.apache.org</url>
<version>1.0.0</version>
<dependencies>
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.14</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.8.0</artifactId>
<version>0.8.0-beta1</version>
<exclusions>
<exclusion>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.8.1</version>
</dependency>
<dependency>
<groupId>com.yammer.metrics</groupId>
<artifactId>metrics-core</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>com.101tec</groupId>
<artifactId>zkclient</artifactId>
<version>0.3</version>
</dependency>
</dependencies>
<build>
<finalName>test-kafka-1.0</finalName>
<resources>
<resource>
<directory>src/main/resources</directory>
<filtering>true</filtering>
</resource>
</resources>
<plugins>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>2.3.2</version>
<configuration>
<source>1.5</source>
<target>1.5</target>
<encoding>gb2312</encoding>
</configuration>
</plugin>
<plugin>
<artifactId>maven-resources-plugin</artifactId>
<version>2.2</version>
<configuration>
<encoding>gbk</encoding>
</configuration>
</plugin>
</plugins>
</build>
</project>
四.Producer端代码
1) producer.properties文件：此文件放在/resources目录下
#partitioner.class= metadata.broker.list=127.0.0.1:9092,127.0.0.1:9093 ##,127.0.0.1:9093 producer.type=sync compression.codec=0 serializer.class=kafka.serializer.StringEncoder ##在producer.type=async时有效 #batch.num.messages=100
2) LogProducer.java代码样例
package com.test.kafka;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.Properties;
import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;
public class LogProducer {
private Producer<String,String> inner;
public LogProducer() throws Exception{
Properties properties = new Properties();
properties.load(ClassLoader.getSystemResourceAsStream("producer.properties"));
ProducerConfig config = new ProducerConfig(properties);
inner = new Producer<String, String>(config);
}
public void send(String topicName,String message) {
if(topicName == null || message == null){
return;
}
KeyedMessage<String, String> km = new KeyedMessage<String, String>(topicName,message);
inner.send(km);
}
public void send(String topicName,Collection<String> messages) {
if(topicName == null || messages == null){
return;
}
if(messages.isEmpty()){
return;
}
List<KeyedMessage<String, String>> kms = new ArrayList<KeyedMessage<String, String>>();
for(String entry : messages){
KeyedMessage<String, String> km = new KeyedMessage<String, String>(topicName,entry);
kms.add(km);
}
inner.send(kms);
}
public void close(){
inner.close();
}
/**
* @param args
*/
public static void main(String[] args) {
LogProducer producer = null;
try{
producer = new LogProducer();
int i=0;
while(true){
producer.send("test-topic", "this is a sample" + i);
i++;
Thread.sleep(2000);
}
}catch(Exception e){
e.printStackTrace();
}finally{
if(producer != null){
producer.close();
}
}
}
}
五.Consumer端
1) consumer.properties:文件位于/resources目录下
zookeeper.connect=127.0.0.1:2181,127.0.0.1:2182,127.0.0.1:2183 ##,127.0.0.1:2182,127.0.0.1:2183 # timeout in ms for connecting to zookeeper zookeeper.connectiontimeout.ms=1000000 #consumer group id group.id=test-group #consumer timeout #consumer.timeout.ms=5000
2) LogConsumer.java代码样例
package com.test.kafka;
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.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
import kafka.message.MessageAndMetadata;
public class LogConsumer {
private ConsumerConfig config;
private String topic;
private int partitionsNum;
private MessageExecutor executor;
private ConsumerConnector connector;
private ExecutorService threadPool;
public LogConsumer(String topic,int partitionsNum,MessageExecutor executor) throws Exception{
Properties properties = new Properties();
properties.load(ClassLoader.getSystemResourceAsStream("consumer.properties"));
config = new ConsumerConfig(properties);
this.topic = topic;
this.partitionsNum = partitionsNum;
this.executor = executor;
}
public void start() throws Exception{
connector = Consumer.createJavaConsumerConnector(config);
Map<String,Integer> topics = new HashMap<String,Integer>();
topics.put(topic, partitionsNum);
Map<String, List<KafkaStream<byte[], byte[]>>> streams = connector.createMessageStreams(topics);
List<KafkaStream<byte[], byte[]>> partitions = streams.get(topic);
threadPool = Executors.newFixedThreadPool(partitionsNum);
for(KafkaStream<byte[], byte[]> partition : partitions){
threadPool.execute(new MessageRunner(partition));
}
}
public void close(){
try{
threadPool.shutdownNow();
}catch(Exception e){
//
}finally{
connector.shutdown();
}
}
class MessageRunner implements Runnable{
private KafkaStream<byte[], byte[]> partition;
MessageRunner(KafkaStream<byte[], byte[]> partition) {
this.partition = partition;
}
public void run(){
ConsumerIterator<byte[], byte[]> it = partition.iterator();
while(it.hasNext()){
MessageAndMetadata<byte[],byte[]> item = it.next();
System.out.println("partiton:" + item.partition());
System.out.println("offset:" + item.offset());
executor.execute(new String(item.message()));//UTF-8
}
}
}
interface MessageExecutor {
public void execute(String message);
}
/**
* @param args
*/
public static void main(String[] args) {
LogConsumer consumer = null;
try{
MessageExecutor executor = new MessageExecutor() {
public void execute(String message) {
System.out.println(message);
}
};
consumer = new LogConsumer("test-topic", 2, executor);
consumer.start();
}catch(Exception e){
e.printStackTrace();
}finally{
// if(consumer != null){
// consumer.close();
// }
}
}
}
在测试时，建议优先启动consumer，然后再启动producer，这样可以实时的观测到最新的消息。
Kafka开源项目指南提供详尽教程，助开发者掌握其架构、配置和使用，实现高效数据流管理和实时处理。它高性能、可扩展，适合日志收集和实时数据处理，通过持久化保障数据安全，是企业大数据生态系统的核心。
更多推荐
0
0- 0
已为社区贡献7条内容
所有评论(0)