zookeeper+kafka集群的安装

时效性要求很高的数据，库存，采取的是数据库+缓存双写的技术方案，也解决了双写的一致性的问题

缓存数据生产服务，监听一个消息队列，然后数据源服务（商品信息管理服务）发生了数据变更之后，就将数据变更的消息推送到消息队列中

缓存数据生产服务可以去消费到这个数据变更的消息，然后根据消息的指示提取一些参数，然后调用对应的数据源服务的接口，拉去数据，这个时候一般是从mysql库中拉去的

1、zookeeper集群搭建

zookeeper-3.4.5.tar.gz使用WinSCP拷贝到/usr/local目录下。

对zookeeper-3.4.5.tar.gz进行解压缩：tar -zxvf zookeeper-3.4.5.tar.gz。

对zookeeper目录进行重命名：mv zookeeper-3.4.5 zk

配置zookeeper相关的环境变量

vi ~/.bashrc
export ZOOKEEPER_HOME=/usr/local/zk
export PATH=$ZOOKEEPER_HOME/bin
source ~/.bashrc

cd zk/conf
cp zoo_sample.cfg zoo.cfg

vi zoo.cfg

　　修改：

dataDir=/usr/local/zk/data

　　新增：

server.0=eshop-cache01:2888:3888  
server.1=eshop-cache02:2888:3888
server.2=eshop-cache03:2888:3888

　　

cd zk

mkdir data

cd data

vi myid

0

在另外两个节点上按照上述步骤配置ZooKeeper，使用scp将zk和.bashrc拷贝到eshop-cache02和eshop-cache03上即可。唯一的区别是标识号分别设置为1和2。

分别在三台机器上执行：zkServer.sh start。

检查ZooKeeper状态：zkServer.sh status，应该是一个leader，两个follower

jps：检查三个节点是否都有QuromPeerMain进程

 

2、kafka集群搭建

scala，就是一门编程语言，现在比较火，很多比如大数据领域里面的spark（计算引擎）就是用scala编写的

scala-2.11.4.tgz使用WinSCP拷贝到/usr/local目录下。

对scala-2.11.4.tgz进行解压缩：tar -zxvf scala-2.11.4.tgz。

对scala目录进行重命名：mv scala-2.11.4 scala

配置scala相关的环境变量

vi ~/.bashrc

export SCALA_HOME=/usr/local/scala

export PATH=$SCALA_HOME/bin

source ~/.bashrc

查看scala是否安装成功：scala -version

按照上述步骤在其他机器上都安装好scala。使用scp将scala和.bashrc拷贝到另外两台机器上即可。

kafka_2.9.2-0.8.1.tgz使用WinSCP拷贝到/usr/local目录下。

对kafka_2.9.2-0.8.1.tgz进行解压缩：tar -zxvf kafka_2.9.2-0.8.1.tgz。

对kafka目录进行改名：mv kafka_2.9.2-0.8.1 kafka

配置kafka

vi /usr/local/kafka/config/server.properties

broker.id：依次增长的整数，0、1、2，集群中Broker的唯一id

zookeeper.connect=192.168.31.187:2181,192.168.31.19:2181,192.168.31.227:2181

安装slf4j

slf4j-1.7.6.zip上传到/usr/local目录下

unzip slf4j-1.7.6.zip

把slf4j中的slf4j-nop-1.7.6.jar复制到kafka的libs目录下面

解决kafka Unrecognized VM option 'UseCompressedOops'问题

vi /usr/local/kafka/bin/kafka-run-class.sh

if [ -z "$KAFKA_JVM_PERFORMANCE_OPTS" ]; then

KAFKA_JVM_PERFORMANCE_OPTS="-server -XX:+UseCompressedOops -XX:+UseParNewGC -XX:+UseConcMarkSweepGC -XX:+CMSClassUnloadingEnabled -XX:+CMSScavengeBeforeRemark -XX:+DisableExplicitGC -Djava.awt.headless=true"

fi

去掉-XX:+UseCompressedOops即可

按照上述步骤在另外两台机器分别安装kafka。用scp把kafka拷贝到其他机器即可。

唯一区别的，就是server.properties中的broker.id，要设置为1和2

在三台机器上的kafka目录下，分别执行以下命令：nohup bin/kafka-server-start.sh config/server.properties &

使用jps检查启动是否成功

使用基本命令检查kafka是否搭建成功

bin/kafka-topics.sh --zookeeper 192.168.31.187:2181,192.168.31.19:2181,192.168.31.227:2181 --topic test --replication-factor 1 --partitions 1 --create

bin/kafka-console-producer.sh --broker-list 192.168.31.181:9092,192.168.31.19:9092,192.168.31.227:9092 --topic test

bin/kafka-console-consumer.sh --zookeeper 192.168.31.187:2181,192.168.31.19:2181,192.168.31.227:2181 --topic test --from-beginning

 

kafka+ehcache+redis完成缓存数据生产服务的开发：

import org.apache.ibatis.session.SqlSessionFactory;
import org.apache.tomcat.jdbc.pool.DataSource;
import org.mybatis.spring.SqlSessionFactoryBean;
import org.mybatis.spring.annotation.MapperScan;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.EnableAutoConfiguration;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.boot.context.embedded.ServletListenerRegistrationBean;
import org.springframework.boot.context.properties.ConfigurationProperties;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.ComponentScan;
import org.springframework.core.io.support.PathMatchingResourcePatternResolver;
import org.springframework.jdbc.datasource.DataSourceTransactionManager;
import org.springframework.transaction.PlatformTransactionManager;

import com.roncoo.eshop.cache.listener.InitListener;

import redis.clients.jedis.HostAndPort;
import redis.clients.jedis.JedisCluster;

@EnableAutoConfiguration
@SpringBootApplication
@ComponentScan
@MapperScan("com.roncoo.eshop.cache.mapper")
public class Application {
 
    @Bean
    @ConfigurationProperties(prefix="spring.datasource")
    public DataSource dataSource() {
        return new org.apache.tomcat.jdbc.pool.DataSource();
    }
    
    @Bean
    public SqlSessionFactory sqlSessionFactoryBean() throws Exception {
        SqlSessionFactoryBean sqlSessionFactoryBean = new SqlSessionFactoryBean();
        sqlSessionFactoryBean.setDataSource(dataSource());
        PathMatchingResourcePatternResolver resolver = new PathMatchingResourcePatternResolver();
        sqlSessionFactoryBean.setMapperLocations(resolver.getResources("classpath:/mybatis/*.xml"));
        return sqlSessionFactoryBean.getObject();
    }
 
    @Bean
    public PlatformTransactionManager transactionManager() {
        return new DataSourceTransactionManager(dataSource());
    }
    
    @Bean
    public JedisCluster JedisClusterFactory() {
        Set<HostAndPort> jedisClusterNodes = new HashSet<HostAndPort>();
        jedisClusterNodes.add(new HostAndPort("192.168.31.19", 7003));
        jedisClusterNodes.add(new HostAndPort("192.168.31.19", 7004));
        jedisClusterNodes.add(new HostAndPort("192.168.31.227", 7006));
        JedisCluster jedisCluster = new JedisCluster(jedisClusterNodes);
        return jedisCluster;
    }

    @SuppressWarnings({ "rawtypes", "unchecked" })
    @Bean
       public ServletListenerRegistrationBean servletListenerRegistrationBean() {
        ServletListenerRegistrationBean servletListenerRegistrationBean = 
                new ServletListenerRegistrationBean();
        servletListenerRegistrationBean.setListener(new InitListener());  
        return servletListenerRegistrationBean;
     }
    
    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }
}

　　

import javax.servlet.ServletContext;
import javax.servlet.ServletContextEvent;
import javax.servlet.ServletContextListener;

import org.springframework.context.ApplicationContext;
import org.springframework.web.context.support.WebApplicationContextUtils;

import com.roncoo.eshop.kafka.KafkaConsumer;
import com.roncoo.eshop.spring.SpringContext;


/**
 * 系统初始化的监听器
 */
public class InitListener implements ServletContextListener {
  
  public void contextInitialized(ServletContextEvent sce) {
    ServletContext sc = sce.getServletContext();
    ApplicationContext context = WebApplicationContextUtils.getWebApplicationContext(sc);
    SpringContext.setApplicationContext(context);  
    
    new Thread(new KafkaConsumer("cache-message")).start();
  }
  
  public void contextDestroyed(ServletContextEvent sce) {
    
  }

}

　　

import org.springframework.cache.annotation.EnableCaching;
import org.springframework.cache.ehcache.EhCacheCacheManager;
import org.springframework.cache.ehcache.EhCacheManagerFactoryBean;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.io.ClassPathResource;

//缓存配置管理类
@Configuration
@EnableCaching
public class CacheConfiguration {
    @Bean
    public EhCacheManagerFactoryBean ehCacheManagerFactoryBean(){
        EhCacheManagerFactoryBean ehCacheManagerFactoryBean=
                new EhCacheManagerFactoryBean();
        ehCacheManagerFactoryBean.setConfigLocation(new ClassPathResource("ehcache.xml")); 
        ehCacheManagerFactoryBean.setShared(true);
        return ehCacheManagerFactoryBean;
    }
    @Bean
    public EhCacheCacheManager ehCacheCacheManager(EhCacheManagerFactoryBean ehCacheManagerFactoryBean){
        return new EhCacheCacheManager(ehCacheManagerFactoryBean.getObject());
    }
}

　　

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;

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

// kafka消费者
public class KafkaConsumer implements Runnable   {
    private ConsumerConnector consumerConnector;
    private String topic;
    
    public KafkaConsumer(String topic){
        this.consumerConnector=Consumer.createJavaConsumerConnector(
                createConsumerConfig());
        this.topic=topic;
    }
    
    @SuppressWarnings("rawtypes")
    public void run(){
        Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
        topicCountMap.put(topic, 1);
        
        Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = 
                consumerConnector.createMessageStreams(topicCountMap);
        List<KafkaStream<byte[], byte[]>> streams = consumerMap.get(topic);
        
        for (KafkaStream stream : streams) {
            new Thread(new KafkaMessageProcessor(stream)).start();
        }
    }
    
    //创建kafka cosumer config
    private static ConsumerConfig createConsumerConfig() {
        Properties props = new Properties();
        props.put("zookeeper.connect", "192.168.31.187:2181,192.168.31.19:2181,192.168.31.227:2181");
        props.put("group.id", "eshop-cache-group");
        props.put("zookeeper.session.timeout.ms", "40000");
        props.put("zookeeper.sync.time.ms", "200");
        props.put("auto.commit.interval.ms", "1000");
        return new ConsumerConfig(props);
    }
}

　　

import com.alibaba.fastjson.JSONObject;
import com.roncoo.eshop.model.ProductInfo;
import com.roncoo.eshop.model.ShopInfo;
import com.roncoo.eshop.service.CacheService;
import com.roncoo.eshop.spring.SpringContext;

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

//kafka消息处理线程
@SuppressWarnings("rawtypes")
public class KafkaMessageProcessor implements Runnable {

  private KafkaStream  kafkaStream;
  private CacheService cacheService;
  
  public KafkaMessageProcessor(KafkaStream kafkaStream) {
    this.kafkaStream = kafkaStream;
    this.cacheService = (CacheService) SpringContext.getApplicationContext()
        .getBean("cacheService"); 
  }
  
  @SuppressWarnings("unchecked")
  public void run(){
    ConsumerIterator<byte[],byte[]> it= kafkaStream.iterator();
    while(it.hasNext()){
      String message =new String(it.next().message());
      // 首先将message转换成json对象
      JSONObject messageJSONObject =JSONObject.parseObject(message);
      
      // 从这里提取出消息对应的服务的标识
      String serviceId =messageJSONObject.getString("serviceId");  
      
      // 如果是商品信息服务
      if("productInfoService".equals(serviceId)){
        processProductInfoChangeMessage(messageJSONObject);
      }else if("shopInfoService".equals(serviceId)) {
            processShopInfoChangeMessage(messageJSONObject);  
          }
    }
  }

  //处理商品信息变更的消息
  private void processProductInfoChangeMessage(JSONObject messageJSONObject){
    // 提取出商品id
    Long productId = messageJSONObject.getLong("productId");
    
    // 调用商品信息服务的接口
    // 直接用注释模拟：getProductInfo?productId=1，传递过去
    // 商品信息服务，一般来说就会去查询数据库，去获取productId=1的商品信息，然后返回回来
    
    String productInfoJSON = "{\"id\": 1, \"name\": \"iphone7手机\", \"price\": 5599, \"pictureList\":\"a.jpg,b.jpg\", \"specification\": \"iphone7的规格\", \"service\": \"iphone7的售后服务\", \"color\": \"红色,白色,黑色\", \"size\": \"5.5\", \"shopId\": 1}";
    ProductInfo productInfo = JSONObject.parseObject(productInfoJSON, ProductInfo.class);
    cacheService.saveProductInfo2LocalCache(productInfo);
    System.out.println("===================获取刚保存到本地缓存的商品信息：" + cacheService.getProductInfoFromLocalCache(productId));  
    cacheService.saveProductInfo2ReidsCache(productInfo);  
  }
  
  //处理店铺信息变更的消息
  private void processShopInfoChangeMessage(JSONObject messageJSONObject){
    // 提取出商品id
    Long productId = messageJSONObject.getLong("productId");
    Long shopId = messageJSONObject.getLong("shopId"); 
    
    String shopInfoJSON = "{\"id\": 1, \"name\": \"小王的手机店\", \"level\": 5, \"goodCommentRate\":0.99}";
    ShopInfo shopInfo = JSONObject.parseObject(shopInfoJSON, ShopInfo.class);
    cacheService.saveShopInfo2LocalCache(shopInfo);
    System.out.println("===================获取刚保存到本地缓存的店铺信息：" + cacheService.getShopInfoFromLocalCache(shopId));   
    cacheService.saveShopInfo2ReidsCache(shopInfo);  
  }
}

　　

public class ProductInfo {
  private Long id;
  private String name;
  private Double price;
  
  public ProductInfo() {
    
  }
  
  public ProductInfo(Long id, String name, Double price) {
    this.id = id;
    this.name = name;
    this.price = price;
  }
  
  public Long getId() {
    return id;
  }
  public void setId(Long id) {
    this.id = id;
  }
  public String getName() {
    return name;
  }
  public void setName(String name) {
    this.name = name;
  }
  public Double getPrice() {
    return price;
  }
  public void setPrice(Double price) {
    this.price = price;
  }
}

　　

//店铺信息
public class ShopInfo {
  private Long id;
  private String name;
  private Integer level;
  private Double goodCommentRate;

  public Long getId() {
    return id;
  }

  public void setId(Long id) {
    this.id = id;
  }

  public String getName() {
    return name;
  }

  public void setName(String name) {
    this.name = name;
  }

  public Integer getLevel() {
    return level;
  }

  public void setLevel(Integer level) {
    this.level = level;
  }

  public Double getGoodCommentRate() {
    return goodCommentRate;
  }

  public void setGoodCommentRate(Double goodCommentRate) {
    this.goodCommentRate = goodCommentRate;
  }

  @Override
  public String toString() {
    return "ShopInfo [id=" + id + ", name=" + name + ", level=" + level
        + ", goodCommentRate=" + goodCommentRate + "]";
  }
}

　　

import com.roncoo.eshop.model.ProductInfo;
import com.roncoo.eshop.model.ShopInfo;

//缓存service接口
public interface CacheService {
   //将商品信息保存到本地缓存中
   public ProductInfo saveLocalCache(ProductInfo productInfo);
   
   //从本地缓存中获取商品信息
   public ProductInfo getLocalCache(Long id);
   
   //将商品信息保存到本地的ehcache缓存中
   public ProductInfo saveProductInfo2LocalCache(ProductInfo productInfo);
   
   //从本地ehcache缓存中获取商品信息
   public ProductInfo getProductInfoFromLocalCache(Long productId);
   
   // 将店铺信息保存到本地的ehcache缓存中
   public ShopInfo saveShopInfo2LocalCache(ShopInfo shopInfo);
   
   //从本地ehcache缓存中获取店铺信息
   public ShopInfo getShopInfoFromLocalCache(Long shopId);
   
   //将商品信息保存到redis中
   public void saveProductInfo2ReidsCache(ProductInfo productInfo);
   
   //将店铺信息保存到redis中
   public void saveShopInfo2ReidsCache(ShopInfo shopInfo);
   
}

　　

import javax.annotation.Resource;

import org.springframework.cache.annotation.CachePut;
import org.springframework.cache.annotation.Cacheable;
import org.springframework.stereotype.Service;

import com.alibaba.fastjson.JSONObject;
import com.roncoo.eshop.model.ProductInfo;
import com.roncoo.eshop.model.ShopInfo;
import com.roncoo.eshop.service.CacheService;

import redis.clients.jedis.JedisCluster;

//缓存Service实现类
@Service("cacheService")
public class CacheServiceImpl implements CacheService {
  public static final String CACHE_NAME ="local";
  
  @Resource
  private JedisCluster jedisCluster;
  
  //将商品信息保存到本地缓存中
  @CachePut(value = CACHE_NAME, key = "'key_'+#productInfo.getId()")
  public ProductInfo saveLocalCache(ProductInfo productInfo) {
    return productInfo;
  }
  
   // 从本地缓存中获取商品信息
  @Cacheable(value = CACHE_NAME, key = "'key_'+#id")
  public ProductInfo getLocalCache(Long id) {
    return null;
  }
  
  //将商品信息保存到本地的ehcache缓存中
  @CachePut(value=CACHE_NAME,key = "'product_info_'+#productInfo.getId()")
  public ProductInfo saveProductInfo2LocalCache(ProductInfo productInfo) {
    return productInfo;
  }
  
  //从本地ehcache缓存中获取商品信息
  @Cacheable(value = CACHE_NAME, key = "'product_info_'+#productId")
  public ProductInfo getProductInfoFromLocalCache(Long productId) {
    return null;
  }
  
  //将店铺信息保存到本地的ehcache缓存中
  @CachePut(value = CACHE_NAME, key = "'shop_info_'+#shopInfo.getId()")
  public ShopInfo saveShopInfo2LocalCache(ShopInfo shopInfo) {
    return shopInfo;
  }
  
  //从本地ehcache缓存中获取店铺信息
  @Cacheable(value = CACHE_NAME, key = "'shop_info_'+#shopId")
  public ShopInfo getShopInfoFromLocalCache(Long shopId) {
    return null;
  }
  
  //将商品信息保存到redis中
  public void saveProductInfo2ReidsCache(ProductInfo productInfo){
    String key="product_info_" + productInfo.getId();
    jedisCluster.set(key, JSONObject.toJSONString(productInfo));
  }
  
  //将店铺信息保存到redis中
  public void saveShopInfo2ReidsCache(ShopInfo shopInfo){
    String key="shop_info_" + shopInfo.getId();
    jedisCluster.set(key, JSONObject.toJSONString(shopInfo));  
  }
}

　　

import org.springframework.context.ApplicationContext;

/**
 * spring上下文
 */
public class SpringContext {

  private static ApplicationContext applicationContext;

  public static ApplicationContext getApplicationContext() {
    return applicationContext;
  }

  public static void setApplicationContext(ApplicationContext applicationContext) {
    SpringContext.applicationContext = applicationContext;
  }
  
}

　　

import javax.annotation.Resource;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.ResponseBody;
import com.roncoo.eshop.model.ProductInfo;
import com.roncoo.eshop.service.CacheService;

@Controller
public class CacheController {
  @Resource
  private CacheService cacheService;
  
  @RequestMapping("/testPutCache")
  @ResponseBody
  public String testPutCache(ProductInfo productInfo) {
    cacheService.saveLocalCache(productInfo);
    return "success";
  }
  
  @RequestMapping("/testGetCache")
  @ResponseBody
  public ProductInfo testGetCache(Long id) {
    return cacheService.getLocalCache(id);
  }
}

　　如何提升缓存命中率：

分发层+应用层，双层nginx

分发层nginx，负责流量分发的逻辑和策略，这个里面它可以根据你自己定义的一些规则，比如根据productId去进行hash，然后对后端的nginx数量取模

将某一个商品的访问的请求，就固定路由到一个nginx后端服务器上去，保证说只会从redis中获取一次缓存数据，后面全都是走nginx本地缓存了

后端的nginx服务器，就称之为应用服务器; 最前端的nginx服务器，被称之为分发服务器

大幅度提升你的nginx本地缓存这一层的命中率，大幅度减少redis后端的压力，提升性能

 缓存命中率低的原因：