论坛 › kafka › kafka_2.9.2-0.8.1.1分布式集群搭建代码开发实例

meimei10251314 发表于 2017-5-23 18:38:58

kafka_2.9.2-0.8.1.1分布式集群搭建代码开发实例

　　准备3台虚拟机, 系统是RHEL64服务版.
1) 每台机器配置如下:

$ cat /etc/hosts

    # zookeeper hostnames: 
    192.168.8.182       zk1 
    192.168.8.183       zk2 
    192.168.8.184       zk3 


2) 每台机器上安装jdk, zookeeper, kafka, 配置如下:

$ vi /etc/profile
     
    # jdk, zookeeper, kafka 
    export KAFKA_HOME=/usr/local/lib/kafka/kafka_2.9.2-0.8.11 
    export ZK_HOME=/usr/local/lib/zookeeper/zookeeper-3.4.6 
    export CLASSPATH=.:$JAVA_HOME/lib/tools.jar:$JAVA_HOME/lib/dt.jar 
    export PATH=$JAVA_HOME/bin:$JAVA_HOME/jre/bin:$KAFKA_HOME/bin:$ZK_HOME/bin:$PATH 

3) 每台机器上运行:

$ source /etc/profile

$ mkdir -p /var/lib/zookeeper

$ cd $ZK_HOME/conf

$ cp zoo_sample.cfg zoo.cfg

$ vi zoo.cfg
     
    dataDir=/var/lib/zookeeper 
     
    # the port at which the clients will connect 
    clientPort=2181 
     
    # zookeeper cluster 
    server.1=zk1:2888:3888 
    server.2=zk2:2888:3888 
    server.3=zk3:2888:3888 

4) 每台机器上生成myid:

zk1:

$ echo "1" > /var/lib/zookeeper/myid

zk2:

$ echo "2" > /var/lib/zookeeper/myid

zk3:

$ echo "3" > /var/lib/zookeeper/myid
5) 每台机器上运行setup关闭防火墙

Firewall:

[   ] enabled
6) 每台机器上启动zookeeper:

$ zkServer.sh start

查看状态:

$ zkServer.sh status


1)下载KAFKA

    $ wget http://apache.fayea.com/apache-mirror/kafka/0.8.1.1/kafka_2.9.2-0.8.1.1.tgz

安装和配置参考上一篇文章:

http://blog.csdn.net/ubuntu64fan/article/details/26678877

2)配置$KAFKA_HOME/config/server.properties

我们安装3个broker，分别在3个vm上：zk1，zk2，zk3:

zk1:

$ vi /etc/sysconfig/network

    NETWORKING=yes 
    HOSTNAME=zk1 


$ vi $KAFKA_HOME/config/server.properties

    broker.id=0 
    port=9092 
    host.name=zk1 
    advertised.host.name=zk1 
    ... 
    num.partitions=2 
    ... 
    zookeeper.contact=zk1:2181,zk2:2181,zk3:2181 

zk2:

$ vi /etc/sysconfig/network

    NETWORKING=yes 
    HOSTNAME=zk2 


$ vi $KAFKA_HOME/config/server.properties

    broker.id=1 
    port=9092 
    host.name=zk2 
    advertised.host.name=zk2 
    ... 
    num.partitions=2 
    ... 
    zookeeper.contact=zk1:2181,zk2:2181,zk3:2181 


zk3:

$ vi /etc/sysconfig/network

    NETWORKING=yes 
    HOSTNAME=zk3 


$ vi $KAFKA_HOME/config/server.properties

    broker.id=2 
    port=9092 
    host.name=zk3 
    advertised.host.name=zk3 
    ... 
    num.partitions=2 
    ... 
    zookeeper.contact=zk1:2181,zk2:2181,zk3:2181 


3)启动zookeeper服务, 在zk1,zk2,zk3上分别运行：

$ zkServer.sh start
4)启动kafka服务, 在zk1,zk2,zk3上分别运行：

$ kafka-server-start.sh $KAFKA_HOME/config/server.properties
5) 新建一个TOPIC(replication-factor=num of brokers)

$ kafka-topics.sh --create --topic test --replication-factor 3 --partitions 2 --zookeeper zk1:2181
6)假设我们在zk2上，开一个终端，发送消息至kafka（zk2模拟producer）

$ kafka-console-producer.sh --broker-list zk1:9092 --sync --topic test

在发送消息的终端输入：Hello Kafka

7)假设我们在zk3上，开一个终端，显示消息的消费（zk3模拟consumer）

$ kafka-console-consumer.sh --zookeeper zk1:2181 --topic test --from-beginning
在消费消息的终端显示：Hello Kafka

项目准备开发

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。如果kafka client的版

本和kafka server的版本不一致,将会有很多异常,比如"broker id not exists"等;因为kafka从0.7升级到0.8之后(正名为2.8.0),client与server通讯的protocol已经改变.

  

<dependencies>
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.14</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.8.2</artifactId>
<version>0.8.0</version>
<exclusions>
<exclusion>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.8.2</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>
　　


Producer端代码

    1) producer.properties文件：此文件放在/resources目录下


  

#partitioner.class=
##broker列表可以为kafka server的子集,因为producer需要从broker中获取metadata
##尽管每个broker都可以提供metadata,此处还是建议,将所有broker都列举出来
##此值,我们可以在spring中注入过来
##metadata.broker.list=127.0.0.1:9092,127.0.0.1:9093
##,127.0.0.1:9093
##同步,建议为async
producer.type=sync
compression.codec=0
serializer.class=kafka.serializer.StringEncoder
##在producer.type=async时有效
#batch.num.messages=100
　　

    2) KafkaProducerClient.java代码样例


  

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 KafkaProducerClient {
private Producer<String, String> inner;
private String brokerList;//for metadata discovery,spring setter
private String location = "kafka-producer.properties";//spring setter
private String defaultTopic;//spring setter
public void setBrokerList(String brokerList) {
this.brokerList = brokerList;
}
public void setLocation(String location) {
this.location = location;
}
public void setDefaultTopic(String defaultTopic) {
this.defaultTopic = defaultTopic;
}
public KafkaProducerClient(){}
public void init() throws Exception {
Properties properties = new Properties();
properties.load(Thread.currentThread().getContextClassLoader().getResourceAsStream(location));

if(brokerList != null) {
properties.put("metadata.broker.list", brokerList);
}
ProducerConfig config = new ProducerConfig(properties);
inner = new Producer<String, String>(config);
}
public void send(String message){
send(defaultTopic,message);
}
public void send(Collection<String> messages){
send(defaultTopic,messages);
}
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>>();
int i= 0;
for (String entry : messages) {
KeyedMessage<String, String> km = new KeyedMessage<String, String>(topicName,entry);
kms.add(km);
i++;
if(i % 20 == 0){
inner.send(kms);
kms.clear();
}
}
if(!kms.isEmpty()){
inner.send(kms);
}
}
public void close() {
inner.close();
}
/**
* @param args
*/
public static void main(String[] args) {
KafkaProducerClient producer = null;
try {
producer = new KafkaProducerClient();
//producer.setBrokerList("");
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();
}
}
}
}

　　
Consumer端

     1) consumer.properties:文件位于/resources目录下



## 此值可以配置,也可以通过spring注入
##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
auto.commit.enable=true
auto.commit.interval.ms=60000
　　

    2) KafkaConsumerClient.java代码样例


  

package com.test.kafka;
import java.nio.ByteBuffer;
import java.nio.CharBuffer;
import java.nio.charset.Charset;
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.Message;
import kafka.message.MessageAndMetadata;
public class KafkaConsumerClient {
private String groupid; //can be setting by spring
private String zkConnect;//can be setting by spring
private String location = "kafka-consumer.properties";//配置文件位置
private String topic;
private int partitionsNum = 1;
private MessageExecutor executor; //message listener
private ExecutorService threadPool;
private ConsumerConnector connector;
private Charset charset = Charset.forName("utf8");
public void setGroupid(String groupid) {
this.groupid = groupid;
}
public void setZkConnect(String zkConnect) {
this.zkConnect = zkConnect;
}
public void setLocation(String location) {
this.location = location;
}
public void setTopic(String topic) {
this.topic = topic;
}
public void setPartitionsNum(int partitionsNum) {
this.partitionsNum = partitionsNum;
}
public void setExecutor(MessageExecutor executor) {
this.executor = executor;
}
public KafkaConsumerClient() {}
//init consumer,and start connection and listener
public void init() throws Exception {
if(executor == null){
throw new RuntimeException("KafkaConsumer,exectuor cant be null!");
}
Properties properties = new Properties();
properties.load(Thread.currentThread().getContextClassLoader().getResourceAsStream(location));
if(groupid != null){
properties.put("groupid", groupid);
}
if(zkConnect != null){
properties.put("zookeeper.connect", zkConnect);
}
ConsumerConfig config = new ConsumerConfig(properties);
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 * 2);
//start
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()) {
// connector.commitOffsets();手动提交offset,当autocommit.enable=false时使用
MessageAndMetadata<byte[], byte[]> item = it.next();
try{
executor.execute(new String(item.message(),charset));// UTF-8,注意异常
}catch(Exception e){
//
}
}
}
public String getContent(Message message){
ByteBuffer buffer = message.payload();
if (buffer.remaining() == 0) {
return null;
}
CharBuffer charBuffer = charset.decode(buffer);
return charBuffer.toString();
}
}
public static interface MessageExecutor {
public void execute(String message);
}
/**
* @param args
*/
public static void main(String[] args) {
KafkaConsumerClient consumer = null;
try {
MessageExecutor executor = new MessageExecutor() {
public void execute(String message) {
System.out.println(message);
}
};
consumer = new KafkaConsumerClient();
consumer.setTopic("test-topic");
consumer.setPartitionsNum(2);
consumer.setExecutor(executor);
consumer.init();
} catch (Exception e) {
e.printStackTrace();
} finally {
if(consumer != null){
consumer.close();
}
}
}
}
　　

    需要提醒的是,上述LogConsumer类中,没有太多的关注异常情况,必须在MessageExecutor.execute()方法中抛出异常时的情况.

    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。
　　详细java api使用见：
　　https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+SimpleConsumer+Example
　　https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+Producer+Example
　　https://cwiki.apache.org/confluence/display/KAFKA/Consumer+Group+Example
　　推荐更详细的文章
　　http://blog.csdn.net/zhongwen7710/article/details/41252649
　　http://blog.csdn.net/lizhitao/article/details/39499283

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