zookeeper+kafka集群的安装

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 ~/.bashrcexport ZOOKEEPER_HOME=/usr/local/zkexport PATH=$ZOOKEEPER_HOME/binsource ~/.bashrccd zk/confcp zoo_sample.cfg zoo.cfgvi zoo.cfg

修改：

dataDir=/usr/local/zk/data

新增：

server.0=eshop-cache01:2888:3888 server.1=eshop-cache02:2888:3888server.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 jedisClusterNodes = new HashSet(); 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@EnableCachingpublic 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 topicCountMap = new HashMap(); topicCountMap.put(topic, 1); Map>> consumerMap = consumerConnector.createMessageStreams(topicCountMap); List> 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 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;@Controllerpublic 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：

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