Caffenie配合Redis做两级缓存，Redis发布订阅实现缓存一致更新

一、什么是两级缓存

在项目中。一级缓存用Caffeine，二级缓存用Redis，查询数据时首先查本地的Caffeine缓存，没有命中再通过网络去访问Redis缓存，还是没有命中再查数据库。具体流程如下

二、简单的二级缓存实现-v1

目录结构

2.1 double-cache模块主要文件

pom文件

<?xml version="1.0" encoding="UTF-8"?>
<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>org.example</groupId>
    <artifactId>double-cache</artifactId>
    <version>1.0-SNAPSHOT</version>

    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.7.2</version>
        <relativePath/>
    </parent>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
        </dependency>

        <dependency>
            <groupId>com.github.ben-manes.caffeine</groupId>
            <artifactId>caffeine</artifactId>
            <version>2.9.2</version>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-redis</artifactId>
        </dependency>
    </dependencies>

</project>

2.2 测试模块的主要文件

OrderServiceImpl

@Slf4j
@Service
@RequiredArgsConstructor
public class OrderServiceImpl extends ServiceImpl<OrderMapper, Order> implements OrderService {
    private final OrderMapper orderMapper;
    private final Cache cache;
    private final RedisTemplate redisTemplate;
    @Override
    public Order getOrderById(Long id) {
        String key = CacheConstant.ORDER + id;
        Order order = (Order) cache.get(key,
                k -> {
                    //先查询 Redis
                    Object obj = redisTemplate.opsForValue().get(k);
                    if (Objects.nonNull(obj)) {
                        log.info("get data from redis");
                        return obj;
                    }
                    // Redis没有则查询 DB
                    log.info("get data from database");
                    Order myOrder = orderMapper.selectOne(new LambdaQueryWrapper<Order>()
                            .eq(Order::getId, id));
                    redisTemplate.opsForValue().set(k, myOrder, 120, TimeUnit.SECONDS);
                    return myOrder;
                });
        return order;
    }
    @Override
    public void updateOrder(Order order) {
        log.info("update order data");
        String key = CacheConstant.ORDER + order.getId();
        orderMapper.updateById(order);
        //修改 Redis
        redisTemplate.opsForValue().set(key, order, 120, TimeUnit.SECONDS);
        // 修改本地缓存
        cache.put(key, order);
    }
    @Override
    public void deleteOrder(Long id) {
        log.info("delete order");
        orderMapper.deleteById(id);
        String key = CacheConstant.ORDER + id;
        redisTemplate.delete(key);
        cache.invalidate(key);
    }
}

application.yml

server:
  port: 8090

spring:
  application:
    name: test-demo
  datasource:
    url: jdbc:mysql://localhost:3306/ktl?useUnicode=true&characterEncoding=UTF-8&serverTimezone=UTC
    username: root
    password: root
    driver-class-name: com.mysql.cj.jdbc.Driver
  redis:
    host: 192.168.200.131
    port: 6379
    database: 0
    timeout: 10000ms
    lettuce:
      pool:
        max-active: 8
        max-wait: -1ms
        max-idle: 8
        min-idle: 0
    password: root

logging:
  level:
    com.cn.dc: debug
    org.springframework: warn

pom文件

<?xml version="1.0" encoding="UTF-8"?>
<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>org.example</groupId>
    <artifactId>testcache</artifactId>
    <version>1.0-SNAPSHOT</version>

    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.7.2</version>
        <relativePath/>
    </parent>
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <mybatis-plus.version>3.3.2</mybatis-plus.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.example</groupId>
            <artifactId>double-cache</artifactId>
            <version>1.0-SNAPSHOT</version>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-pool2</artifactId>
            <version>2.8.1</version>
        </dependency>
        <dependency>
            <groupId>com.baomidou</groupId>
            <artifactId>mybatis-plus-boot-starter</artifactId>
            <version>${mybatis-plus.version}</version>
        </dependency>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.12</version>
            <scope>provided</scope>
        </dependency>
    </dependencies>
</project>

2.3 测试

测试get/{id}接口的时候，会把从db查出来的数据放入到redis和Caffeine中，在有效期内不需要再次从数据库查询

三、二级缓存实现-v2

v1的代码入侵性很强，因此加入了注解@Cacheable，@CachePut，@CacheEvict

3.1 double-cache模块

3.2 测试模块

OrderServiceImpl

@Slf4j
@Service
@RequiredArgsConstructor
public class OrderServiceImpl extends ServiceImpl<OrderMapper, Order> implements OrderService {
    private final OrderMapper orderMapper;
    private final RedisTemplate redisTemplate;

    @Override
    @Cacheable(value = "order",key = "#id")
//@Cacheable(cacheNames = "order",key = "#p0")
    public Order getOrderById(Long id) {
        String key= CacheConstant.ORDER + id;
        //先查询 Redis
        Object obj = redisTemplate.opsForValue().get(key);
        if (Objects.nonNull(obj)){
            log.info("get data from redis");
            return (Order) obj;
        }
        // Redis没有则查询 DB
        log.info("get data from database");
        Order myOrder = orderMapper.selectOne(new LambdaQueryWrapper<Order>()
                .eq(Order::getId, id));
        redisTemplate.opsForValue().set(key,myOrder,120, TimeUnit.SECONDS);
        return myOrder;
    }


    @Override
    @CachePut(cacheNames = "order",key = "#order.id")
    public Order updateOrder(Order order) {
        log.info("update order data");
        orderMapper.updateById(order);
        //修改 Redis
        redisTemplate.opsForValue().set(CacheConstant.ORDER + order.getId(),
                order, 120, TimeUnit.SECONDS);
        return order;
    }

    @Override
    @CacheEvict(cacheNames = "order",key = "#id")
    public void deleteOrder(Long id) {
        log.info("delete order");
        orderMapper.deleteById(id);
        redisTemplate.delete(CacheConstant.ORDER + id);
    }
}

四、二级缓存实现-v3-aop结合自定义注解

模仿spring通过注解管理缓存的方式，我们也可以选择自定义注解，然后在切面中处理缓存，从而将对业务代码的入侵降到最低。

首先定义一个注解，用于添加在需要操作缓存的方法上：

@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
@Documented
public @interface DoubleCache {
    String cacheName();
    String key();	//支持springEl表达式
    long l2TimeOut() default 120;
    CacheType type() default CacheType.FULL;
}

我们使用cacheName + key作为缓存的真正key（仅存在一个Cache中，不做CacheName隔离），l2TimeOut为可以设置的二级缓存Redis的过期时间，type是一个枚举类型的变量，表示操作缓存的类型，枚举类型定义如下：

public enum CacheType {
    FULL,   //存取
    PUT,    //只存
    DELETE  //删除
}

因为要使key支持springEl表达式，所以需要写一个方法，使用表达式解析器解析参数：

public class ElParser {
    public static String parse(String elString, TreeMap<String,Object> map){
        elString=String.format("#{%s}",elString);
        //创建表达式解析器
        ExpressionParser parser = new SpelExpressionParser();
        //通过evaluationContext.setVariable可以在上下文中设定变量。
        EvaluationContext context = new StandardEvaluationContext();
        map.entrySet().forEach(entry->
                context.setVariable(entry.getKey(),entry.getValue())
        );

        //解析表达式
        Expression expression = parser.parseExpression(elString, new TemplateParserContext());
        //使用Expression.getValue()获取表达式的值，这里传入了Evaluation上下文
        String value = expression.getValue(context, String.class);
        return value;
    }
}

至于Cache相关参数的配置，我们沿用V1版本中的配置即可。准备工作做完了，下面我们定义切面，在切面中操作Cache来读写Caffeine的缓存，操作RedisTemplate读写Redis缓存。

@Slf4j
@Component
@Aspect
@AllArgsConstructor
public class CacheAspect {
    private final Cache cache;
    private final RedisTemplate redisTemplate;
    private final String COLON = ":";

    @Pointcut("@annotation(org.example.doublecache.annotation.DoubleCache)")
    public void cacheAspect() {
    }

    @Around("cacheAspect()")
    public Object doAround(ProceedingJoinPoint point) throws Throwable {
        MethodSignature signature = (MethodSignature) point.getSignature();
        Method method = signature.getMethod();

//        if (!method.isAnnotationPresent(DoubleCache.class))
//            return null;

        //拼接解析springEl表达式的map
        String[] paramNames = signature.getParameterNames();
        Object[] args = point.getArgs();
        TreeMap<String, Object> treeMap = new TreeMap<>();
        for (int i = 0; i < paramNames.length; i++) {
            treeMap.put(paramNames[i],args[i]);
        }

        DoubleCache annotation = method.getAnnotation(DoubleCache.class);
        String elResult = ElParser.parse(annotation.key(), treeMap);
        String realKey = annotation.cacheName() + COLON + elResult;

        //强制更新
        if (annotation.type()== CacheType.PUT){
            Object object = point.proceed();
            redisTemplate.opsForValue().set(realKey, object,annotation.l2TimeOut(), TimeUnit.SECONDS);
            cache.put(realKey, object);
            return object;
        }
        //删除
        else if (annotation.type()== CacheType.DELETE){
            redisTemplate.delete(realKey);
            cache.invalidate(realKey);
            return point.proceed();
        }

        //读写，查询Caffeine
        Object caffeineCache = cache.getIfPresent(realKey);
        if (Objects.nonNull(caffeineCache)) {
            log.info("get data from caffeine");
            return caffeineCache;
        }

        //查询Redis
        Object redisCache = redisTemplate.opsForValue().get(realKey);
        if (Objects.nonNull(redisCache)) {
            log.info("get data from redis");
            cache.put(realKey, redisCache);
            return redisCache;
        }

        log.info("get data from database");
        Object object = point.proceed();
        if (Objects.nonNull(object)){
            //写回Redis
            redisTemplate.opsForValue().set(realKey, object,annotation.l2TimeOut(), TimeUnit.SECONDS);
            //写入Caffeine
            cache.put(realKey, object);
        }
        return object;
    }
}

4.1 double-cache模块

4.2 测试模块

OrderServiceImpl修改如下

@Slf4j
@Service
@RequiredArgsConstructor
public class OrderServiceImpl extends ServiceImpl<OrderMapper, Order> implements OrderService {
    private final OrderMapper orderMapper;
    @Override
    @DoubleCache(cacheName = "order", key = "#id",
            type = CacheType.FULL)
    public Order getOrderById(Long id) {
        Order myOrder = orderMapper.selectOne(new LambdaQueryWrapper<Order>()
                .eq(Order::getId, id));
        return myOrder;
    }

    @Override
    @DoubleCache(cacheName = "order",key = "#order.id",
            type = CacheType.PUT)
    public Order updateOrder(Order order) {
        orderMapper.updateById(order);
        return order;
    }

    @Override
    @DoubleCache(cacheName = "order",key = "#id",
            type = CacheType.DELETE)
    public void deleteOrder(Long id) {
        orderMapper.deleteById(id);
    }

    @Override
    @DoubleCache(cacheName = "order",key = "#id")
    public Order getOrderByIdAndStatus(Long id,Integer status) {
        Order myOrder = orderMapper.selectOne(new LambdaQueryWrapper<Order>()
                .eq(Order::getId, id)
                .eq(Order::getStatus,status));
        return myOrder;
    }

在TestApplication上加@EnableCaching

4.3 测试

从数据库10ms+，生产中会走网络通信会更长。
从Caffeine平均4ms

五、两级缓存-改造版本（符合JSR107规范+保证缓存数据一致性）

上面v3虽然说能够实现功能，但实现手法还是太粗糙了，并且遗留了一些问题没有处理。下面围绕两个方面进行进一步的改造：

• JSR107定义了缓存使用规范，spring中提供了基于这个规范的接口，所以我们可以直接使用spring中的接口进行Caffeine和Redis两级缓存的整合改造
• 在分布式环境下，如果一台主机的本地缓存进行修改，需要通知其他主机修改本地缓存，解决分布式环境下本地缓存一致性问题

5.1 JSR107规范

文档：JSR107中文版（非官方）
在JSR107缓存规范中定义了5个核心接口，分别是CachingProvider,CacheManager,Cache, Entry和Expiry，参考下面这张图，可以看到除了Entry和Expiry以外，从上到下都是一对多的包含关系。

从上面这张图我们可以看出，一个应用可以创建并管理多个CachingProvider，同样一个CachingProvider也可以管理多个CacheManager，缓存管理器CacheManager中则维护了多个Cache。

Cache是一个类似Map的数据结构，Entry就是其中存储的每一个key-value数据对，并且每个Entry都有一个过期时间Expiry。而我们在使用spring集成第三方的缓存时，只需要实现Cache和CacheManager这两个接口就可以了，下面分别具体来看一下。

5.2 根据JSR107规范改造

5.2.1 Cache

spring中的Cache接口规范了缓存组件的定义，包含了缓存的各种操作，实现具体缓存操作的管理。例如我们熟悉的RedisCache、EhCacheCache等，都实现了这个接口。
在Cache接口中，定义了get、put、evict、clear等方法，分别对应缓存的存入、取出、删除、清空操作。不过我们这里不直接使用Cache接口，AbstractValueAdaptingCache是一个抽象类，它已经实现了Cache接口，是spring在Cache接口的基础上帮助我们进行了一层封装，所以我们直接继承这个类就可以。

继承AbstractValueAdaptingCache抽象类后，除了创建Cache的构造方法外，还需要实现下面的几个方法：

// 在缓存中实际执行查找的操作，父类的get()方法会调用这个方法
protected abstract Object lookup(Object key);

// 通过key获取缓存值，如果没有找到，会调用valueLoader的call()方法
public <T> T get(Object key, Callable<T> valueLoader);

// 将数据放入缓存中
public void put(Object key, Object value);

// 删除缓存
public void evict(Object key);

// 清空缓存中所有数据
public void clear();

// 获取缓存名称，一般在CacheManager创建时指定
String getName();

// 获取实际使用的缓存
Object getNativeCache();

因为要整合RedisTemplate和Caffeine的Cache，所以这些都需要在缓存的构造方法中传入，除此之外构造方法中还需要再传出缓存名称cacheName，以及在配置文件中实际配置的一些缓存参数。先看一下构造方法的实现：

public class DoubleCache extends AbstractValueAdaptingCache {
    private String cacheName;
    private RedisTemplate<Object, Object> redisTemplate;
    private Cache<Object, Object> caffeineCache;
    private DoubleCacheConfig doubleCacheConfig;

    protected DoubleCache(boolean allowNullValues) {
        super(allowNullValues);
    }

    public DoubleCache(String cacheName,RedisTemplate<Object, Object> redisTemplate,
                       Cache<Object, Object> caffeineCache,
                       DoubleCacheConfig doubleCacheConfig){
        super(doubleCacheConfig.getAllowNull());
        this.cacheName=cacheName;
        this.redisTemplate=redisTemplate;
        this.caffeineCache=caffeineCache;
        this.doubleCacheConfig=doubleCacheConfig;
    }
    //...
}

抽象父类的构造方法中只有一个boolean类型的参数allowNullValues，表示是否允许缓存对象为null。除此之外，AbstractValueAdaptingCache中还定义了两个包装方法来配合这个参数进行使用，分别是toStoreValue()和fromStoreValue()，特殊用途是用于在缓存null对象时进行包装、以及在获取时进行解析并返回。

我们之后会在CacheManager中调用后面这个自己实现的构造方法，来实例化Cache对象，参数中DoubleCacheConfig是使用@ConfigurationProperties读取的yml配置文件封装的数据对象，会在后面使用。

当一个方法添加了@Cacheable注解时，执行时会先调用父类AbstractValueAdaptingCache中的get(key)方法，它会再调用我们自己实现的lookup方法。在实际执行查找操作的lookup方法中，我们的逻辑仍然是先查找Caffeine、没有找到时再查找Redis：

@Override
protected Object lookup(Object key) {
    // 先从caffeine中查找
    Object obj = caffeineCache.getIfPresent(key);
    if (Objects.nonNull(obj)){
        log.info("get data from caffeine");
        return obj;
    }

    //再从redis中查找
    String redisKey=this.name+":"+ key;
    obj = redisTemplate.opsForValue().get(redisKey);
    if (Objects.nonNull(obj)){
        log.info("get data from redis");
        caffeineCache.put(key,obj);
    }
    return obj;
}

如果lookup方法的返回结果不为null，那么就会直接返回结果给调用方。如果返回为null时，就会执行原方法，执行完成后调用put方法，将数据放入缓存中。接下来我们实现put方法：

@Override
public void put(Object key, Object value) {
    if(!isAllowNullValues() && Objects.isNull(value)){
        log.error("the value NULL will not be cached");
        return;
    }

    //使用 toStoreValue(value) 包装，解决caffeine不能存null的问题
    caffeineCache.put(key,toStoreValue(value));

    // null对象只存在caffeine中一份就够了，不用存redis了
    if (Objects.isNull(value))
        return;

    String redisKey=this.cacheName +":"+ key;
    Optional<Long> expireOpt = Optional.ofNullable(doubleCacheConfig)
            .map(DoubleCacheConfig::getRedisExpire);
    if (expireOpt.isPresent()){
        redisTemplate.opsForValue().set(redisKey,toStoreValue(value),
                expireOpt.get(), TimeUnit.SECONDS);
    }else{
        redisTemplate.opsForValue().set(redisKey,toStoreValue(value));
    }
}

上面我们对于是否允许缓存空对象进行了判断，能够缓存空对象的好处之一就是可以避免缓存穿透。需要注意的是，Caffeine中是不能直接缓存null的，因此可以使用父类提供的toStoreValue()方法，将它包装成一个NullValue类型。在取出对象时，如果是NullValue，也不用我们自己再去调用fromStoreValue()将这个包装类型还原，父类的get方法中已经帮我们做好了。

另外，上面在put方法中缓存空对象时，只在Caffeine缓存中一份即可，可以不用在Redis中再存一份。

缓存的删除方法evict()和清空方法clear()的实现就比较简单了，直接删除一跳或全部数据即可：

@Override
public void evict(Object key) {
    redisTemplate.delete(this.cacheName +":"+ key);
    caffeineCache.invalidate(key);
}

@Override
public void clear() {
    Set<Object> keys = redisTemplate.keys(this.cacheName.concat(":*"));
    for (Object key : keys) {
        redisTemplate.delete(String.valueOf(key));
    }
    caffeineCache.invalidateAll();
}

获取缓存cacheName和实际缓存的方法实现：

@Override
public String getName() {
    return this.cacheName;
}
@Override
public Object getNativeCache() {
    return this;
}

最后，我们再来看一下带有两个参数的get方法，为什么把这个方法放到最后来说呢，因为如果我们只是使用注解来管理缓存的话，那么这个方法不会被调用到，简单看一下实现：

@Override
public <T> T get(Object key, Callable<T> valueLoader) {
    ReentrantLock lock=new ReentrantLock();
    try{
        lock.lock();//加锁
        Object obj = lookup(key);
        if (Objects.nonNull(obj)){
            return (T)obj;
        }
        //没有找到
        obj = valueLoader.call();
        put(key,obj);//放入缓存
        return (T)obj;
    }catch (Exception e){
        log.error(e.getMessage());
    }finally {
        lock.unlock();
    }
    return null;
}

方法的实现比较容易理解，还是先调用lookup方法寻找是否已经缓存了对象，如果没有找到那么就调用Callable中的call方法进行获取，并在获取完成后存入到缓存中去。至于这个方法如何使用，具体代码我们放在后面使用这一块再看。

需要注意的是，这个方法的接口注释中强调了需要我们自己来保证方法同步，因此这里使用了ReentrantLock进行了加锁操作。到这里，Cache的实现就完成了。
DoubleCache类文件代码如下：

/**
 * 这个是没有加消息的最初版本
 **/
import com.github.benmanes.caffeine.cache.Cache;
import lombok.extern.slf4j.Slf4j;
import org.example.doublecache.config.DoubleCacheConfig;
import org.springframework.cache.support.AbstractValueAdaptingCache;
import org.springframework.data.redis.core.RedisTemplate;

import java.util.Objects;
import java.util.Optional;
import java.util.Set;
import java.util.concurrent.Callable;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.locks.ReentrantLock;

@Slf4j
public class DoubleCacheV0 extends AbstractValueAdaptingCache {
    private String cacheName;
    private RedisTemplate<Object, Object> redisTemplate;
    private Cache<Object, Object> caffeineCache;
    private DoubleCacheConfig doubleCacheConfig;

    protected DoubleCacheV0(boolean allowNullValues) {
        super(allowNullValues);
    }

    public DoubleCacheV0(String cacheName, RedisTemplate<Object, Object> redisTemplate,
                         Cache<Object, Object> caffeineCache,
                         DoubleCacheConfig doubleCacheConfig){
        super(doubleCacheConfig.getAllowNull());
        this.cacheName =cacheName;
        this.redisTemplate=redisTemplate;
        this.caffeineCache=caffeineCache;
        this.doubleCacheConfig=doubleCacheConfig;
    }

    //使用注解时不走这个方法，实际走父类的get方法
    @Override
    public <T> T get(Object key, Callable<T> valueLoader) {
        ReentrantLock lock=new ReentrantLock();
        try{
            lock.lock();//加锁

            Object obj = lookup(key);
            if (Objects.nonNull(obj)){
                return (T)obj;
            }
            //没有找到
            obj = valueLoader.call();
            //放入缓存
            put(key,obj);
            return (T)obj;
        }catch (Exception e){
            log.error(e.getMessage());
        }finally {
            lock.unlock();
        }
        return null;
    }

    @Override
    protected Object lookup(Object key) {
        // 先从caffeine中查找
        Object obj = caffeineCache.getIfPresent(key);
        if (Objects.nonNull(obj)){
            log.info("get data from caffeine");
            return obj; //不用fromStoreValue，否则返回的是null，会再查数据库
        }

        //再从redis中查找
        String redisKey=this.cacheName +":"+ key;
        obj = redisTemplate.opsForValue().get(redisKey);
        if (Objects.nonNull(obj)){
            log.info("get data from redis");
            caffeineCache.put(key,obj);
        }
        return obj;
    }

    @Override
    public void put(Object key, Object value) {
        if(!isAllowNullValues() && Objects.isNull(value)){
            log.error("the value NULL will not be cached");
            return;
        }

        //使用 toStoreValue(value) 包装，解决caffeine不能存null的问题
        //caffeineCache.put(key,value);
        caffeineCache.put(key,toStoreValue(value));

        // null对象只存在caffeine中一份就够了，不用存redis了
        if (Objects.isNull(value))
            return;

        String redisKey=this.cacheName +":"+ key;
        Optional<Long> expireOpt = Optional.ofNullable(doubleCacheConfig)
                .map(DoubleCacheConfig::getRedisExpire);
        if (expireOpt.isPresent()){
            redisTemplate.opsForValue().set(redisKey,toStoreValue(value),
                    expireOpt.get(), TimeUnit.SECONDS);
        }else{
            redisTemplate.opsForValue().set(redisKey,toStoreValue(value));
        }
    }

    @Override
    public void evict(Object key) {
        redisTemplate.delete(this.cacheName +":"+ key);
        caffeineCache.invalidate(key);
    }

    @Override
    public void clear() {
        //如果是正式环境，避免使用keys命令
        Set<Object> keys = redisTemplate.keys(this.cacheName.concat(":*"));
        for (Object key : keys) {
            redisTemplate.delete(String.valueOf(key));
        }
        caffeineCache.invalidateAll();
    }

    @Override
    public String getName() {
        return this.cacheName;
    }

    @Override
    public Object getNativeCache() {
        return this;
    }

}

5.2.2 CacheManager

下面我们接着看另一个重要的接口CacheManager。
从名字就可以看出，CacheManager是一个缓存管理器，它可以被用来管理一组Cache。在上一篇文章的v2版本中，我们使用的CaffeineCacheManager就实现了这个接口，除此之外还有RedisCacheManager、EhCacheCacheManager等也都是通过这个接口实现。

下面我们要自定义一个类实现CacheManager接口，管理上面实现的DoubleCache作为spring中的缓存使用。接口中需要实现的方法只有下面两个：

//根据cacheName获取Cache实例，不存在时进行创建
Cache getCache(String name);

//返回管理的所有cacheName
Collection<String> getCacheNames();

在自定义的缓存管理器中，我们要使用ConcurrentHashMap维护一组不同的Cache，再定义一个构造方法，在参数中传入已经在spring中配置好的RedisTemplate，以及相关的缓存配置参数：

public class DoubleCacheManager implements CacheManager {
    Map<String, Cache> cacheMap = new ConcurrentHashMap<>();
    private RedisTemplate<Object, Object> redisTemplate;
    private DoubleCacheConfig dcConfig;

    public DoubleCacheManager(RedisTemplate<Object, Object> redisTemplate,
                              DoubleCacheConfig doubleCacheConfig) {
        this.redisTemplate = redisTemplate;
        this.dcConfig = doubleCacheConfig;
    }
    //...
}

然后实现getCache方法，逻辑很简单，先根据name从Map中查找对应的Cache，如果找到则直接返回，这个参数name就是上一篇文章中提到的cacheName，CacheManager根据它实现不同Cache的隔离。

如果没有根据名称找到缓存的话，那么新建一个DoubleCache对象，并放入Map中。这里使用的ConcurrentHashMap的putIfAbsent()方法放入，避免重复创建Cache以及造成Cache内数据的丢失。具体代码如下：

@Override
public Cache getCache(String name) {
    Cache cache = cacheMap.get(name);
    if (Objects.nonNull(cache)) {
        return cache;
    }

    cache = new DoubleCache(name, redisTemplate, createCaffeineCache(), dcConfig);
    Cache oldCache = cacheMap.putIfAbsent(name, cache);
    return oldCache == null ? cache : oldCache;
}

在上面创建DoubleCache对象的过程中，需要先创建一个Caffeine的Cache对象作为参数传入，这一过程主要是根据实际项目的配置文件中的具体参数进行初始化，代码如下：

private com.github.benmanes.caffeine.cache.Cache createCaffeineCache(){
    Caffeine<Object, Object> caffeineBuilder = Caffeine.newBuilder();
    Optional<DoubleCacheConfig> dcConfigOpt = Optional.ofNullable(this.dcConfig);
    dcConfigOpt.map(DoubleCacheConfig::getInit)
            .ifPresent(init->caffeineBuilder.initialCapacity(init));
    dcConfigOpt.map(DoubleCacheConfig::getMax)
            .ifPresent(max->caffeineBuilder.maximumSize(max));
    dcConfigOpt.map(DoubleCacheConfig::getExpireAfterWrite)
            .ifPresent(eaw->caffeineBuilder.expireAfterWrite(eaw,TimeUnit.SECONDS));
    dcConfigOpt.map(DoubleCacheConfig::getExpireAfterAccess)
            .ifPresent(eaa->caffeineBuilder.expireAfterAccess(eaa,TimeUnit.SECONDS));
    dcConfigOpt.map(DoubleCacheConfig::getRefreshAfterWrite)
            .ifPresent(raw->caffeineBuilder.refreshAfterWrite(raw,TimeUnit.SECONDS));
    return caffeineBuilder.build();
}

getCacheNames方法很简单，直接返回Map的keySet就可以了，代码如下：

@Override
public Collection<String> getCacheNames() {
    return cacheMap.keySet();
}

DoubleCacheManager总的代码如下

import org.example.doublecache.config.DoubleCacheConfig;
import com.github.benmanes.caffeine.cache.Caffeine;
import org.springframework.cache.Cache;
import org.springframework.cache.CacheManager;
import org.springframework.data.redis.core.RedisTemplate;

import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.TimeUnit;

public class DoubleCacheManager implements CacheManager {
    Map<String, Cache> cacheMap = new ConcurrentHashMap<>();
    private RedisTemplate<Object, Object> redisTemplate;
    private DoubleCacheConfig dcConfig;

    public DoubleCacheManager(RedisTemplate<Object, Object> redisTemplate,
                              DoubleCacheConfig doubleCacheConfig) {
        this.redisTemplate = redisTemplate;
        this.dcConfig = doubleCacheConfig;
    }

    @Override
    public Cache getCache(String name) {
        Cache cache = cacheMap.get(name);
        if (Objects.nonNull(cache)) {
            return cache;
        }

        cache = new DoubleCacheV0(name, redisTemplate, createCaffeineCache(), dcConfig);
        Cache oldCache = cacheMap.putIfAbsent(name, cache);
        return oldCache == null ? cache : oldCache;
    }

    @Override
    public Collection<String> getCacheNames() {
        return cacheMap.keySet();
    }

    private com.github.benmanes.caffeine.cache.Cache createCaffeineCache(){
        Caffeine<Object, Object> caffeineBuilder = Caffeine.newBuilder();
        Optional<DoubleCacheConfig> dcConfigOpt = Optional.ofNullable(this.dcConfig);
        dcConfigOpt.map(DoubleCacheConfig::getInit)
                .ifPresent(init->caffeineBuilder.initialCapacity(init));
        dcConfigOpt.map(DoubleCacheConfig::getMax)
                .ifPresent(max->caffeineBuilder.maximumSize(max));
        dcConfigOpt.map(DoubleCacheConfig::getExpireAfterWrite)
                .ifPresent(eaw->caffeineBuilder.expireAfterWrite(eaw,TimeUnit.SECONDS));
        dcConfigOpt.map(DoubleCacheConfig::getExpireAfterAccess)
                .ifPresent(eaa->caffeineBuilder.expireAfterAccess(eaa,TimeUnit.SECONDS));
        dcConfigOpt.map(DoubleCacheConfig::getRefreshAfterWrite)
                .ifPresent(raw->caffeineBuilder.refreshAfterWrite(raw,TimeUnit.SECONDS));
        return caffeineBuilder.build();
    }
}

5.2.3 配置&使用

@Data
@ConfigurationProperties(prefix = "doublecache")
public class DoubleCacheConfig {
    private Boolean allowNull = true;
    private Integer init = 100;
    private Integer max = 1000;
    private Long expireAfterWrite ;
    private Long expireAfterAccess;
    private Long refreshAfterWrite;
    private Long redisExpire;
}

在application.yml文件中配置缓存的参数，代码中使用@ConfigurationProperties接收到DoubleCacheConfig类中：

doublecache:
  allowNull: true
  init: 128
  max: 1024
  expireAfterWrite: 30  #Caffeine过期时间
  redisExpire: 60      #Redis缓存过期时间

配置自定义的DoubleCacheManager作为默认的缓存管理器：

@Configuration
@EnableConfigurationProperties({DoubleCacheConfig.class})
public class CacheConfig {
    @Autowired
    DoubleCacheConfig doubleCacheConfig;

    @Bean
    public DoubleCacheManager cacheManager(RedisTemplate<Object,Object> redisTemplate,
                                           DoubleCacheConfig doubleCacheConfig){
        return new DoubleCacheManager(redisTemplate,doubleCacheConfig);
    }
}

Service中的代码还是老样子，不需要在代码中手动操作缓存，只要直接在方法上使用@Cache相关注解即可：

@Service @Slf4j
@AllArgsConstructor
public class OrderServiceImpl implements OrderService {
    private final OrderMapper orderMapper;

    @Cacheable(value = "order",key = "#id")
    public Order getOrderById(Long id) {
        Order myOrder = orderMapper.selectOne(new LambdaQueryWrapper<Order>()
                .eq(Order::getId, id));
        return myOrder;
    }

    @CachePut(cacheNames = "order",key = "#order.id")
    public Order updateOrder(Order order) {
        orderMapper.updateById(order);
        return order;
    }

    @CacheEvict(cacheNames = "order",key = "#id")
    public void deleteOrder(Long id) {
        orderMapper.deleteById(id);
    }
    
    //没有注解,使用get(key,callable)方法
    public Order getOrderById2(Long id) {
        DoubleCacheManager cacheManager = SpringContextUtil.getBean(DoubleCacheManager.class);
        Cache cache = cacheManager.getCache("order");
        Order order =(Order) cache.get(id, (Callable<Object>) () -> {
            log.info("get data from database");
            Order myOrder = orderMapper.selectOne(new LambdaQueryWrapper<Order>()
                    .eq(Order::getId, id));
            return myOrder;
        });
        return order;
    }    
}

5.2.4 测试

ps：只要添加了spring-boot-starter-data-redis依赖，并且application.yml中配置了redis的连接相关的配置，且@EnableCaching，那么使用@Cacheable等注解，spring就会自动使用Redis进行缓存（pojo类需要实现Serializable），并且不需要RestTemplate（就算配置了RestTemplate）也没用。
上面是我项目中测试错误的情况下发现的，原因是spring.factories文件里并没有添加CacheConfig，而且RestTemplate配置的json序列化器没有生效。

1. 目录
   

如果出现java: 方法引用无效 找不到符号类似错误，可能是lombok以来笨笨过低，与idea版本不匹配。

5.3 分布式环境改造-保证缓存数据一致性

在分布式环境下，可能会存在各个主机上一级缓存不一致的问题。当一台主机修改了本地缓存后，其他主机是没有感知的，仍然保持了之前的缓存，那么这种情况下就可能取到脏数据。既然我们在项目中已经使用了Redis，那么就可以使用它的发布/订阅功能来使各个节点的缓存进行同步。

5.3.1 定义消息体

在使用Redis发送消息前，需要先定义一个消息对象。其中的数据包括消息要作用于的Cache名称、操作类型、数据以及发出消息的源主机标识：

@Data
@NoArgsConstructor
@AllArgsConstructor
public class CacheMassage implements Serializable {
    private static final long serialVersionUID = -3574997636829868400L;

    private String cacheName;
    private CacheMsgType type;  //标识更新或删除操作
    private Object key;			
    private Object value;
    private String msgSource;   //源主机标识，用来避免重复操作
}

定义一个枚举来标识消息的类型，是要进行更新还是删除操作：

public enum CacheMsgType {
    UPDATE,
    DELETE;
}

消息体中的msgSource是添加的一个消息源主机的标识，添加这个是为了避免收到当前主机发送的消息后，再进行重复操作，也就是说收到本机发出的消息直接丢掉什么都不做就可以了。源主机标识这里使用的是主机ip加项目端口的方式，获取方法如下：

public static String getMsgSource() throws UnknownHostException {
    String host = InetAddress.getLocalHost().getHostAddress();
    Environment env = SpringContextUtil.getBean(Environment.class);
    String port = env.getProperty("server.port");
    return host+":"+port;
}

这样消息体的定义就完成了，之后只要调用redisTemplate的convertAndSend方法就可以把这个对象发布到指定的主题上了。

5.3.2 Redis消息配置

要使用Redis的消息监听功能，需要配置两项内容：

• MessageListenerAdapter：消息监听适配器，可以在其中指定自定义的监听代理类，并且可以自定义使用哪个方法处理监听逻辑
• RedisMessageListenerContainer： 一个可以为消息监听器提供异步行为的容器，并且提供消息转换和分派等底层功能

@Configuration
public class MessageConfig {
    public static final String TOPIC="cache.msg";
    
    @Bean
    RedisMessageListenerContainer container(MessageListenerAdapter listenerAdapter,
                                            RedisConnectionFactory redisConnectionFactory){
        RedisMessageListenerContainer container = new RedisMessageListenerContainer();
        container.setConnectionFactory(redisConnectionFactory);
        container.addMessageListener(listenerAdapter, new PatternTopic(TOPIC));
        return container;
    }
    
    @Bean
    MessageListenerAdapter adapter(RedisMessageReceiver receiver){
        return new MessageListenerAdapter(receiver,"receive");
    }   
}

在上面的监听适配器MessageListenerAdapter中，我们传入了一个自定义的RedisMessageReceiver接收并处理消息，并指定使用它的receive方法来处理监听到的消息，下面我们就来看看它如何接收消息并消费。

5.3.3 消息消费逻辑

定义一个类RedisMessageReceiver来接收并消费消息，需要在它的方法中实现以下功能：

• 反序列化接收到的消息，转换为前面定义的CacheMassage类型对象
• 根据消息的主机标识判断这条消息是不是本机发出的，如果是那么直接丢弃，只有接收到其他主机发出的消息才进行处理
• 使用cacheName得到具体使用的那一个DoubleCache实例
• 根据消息的类型判断要执行的是更新还是删除操作，调用对应的方法

@Slf4j @Component
@AllArgsConstructor
public class RedisMessageReceiver {
    private final RedisTemplate redisTemplate;
    private final DoubleCacheManager manager;

    //接收通知，进行处理
    public void receive(String message) throws UnknownHostException {
        CacheMassage msg = (CacheMassage) redisTemplate
                .getValueSerializer().deserialize(message.getBytes());
        log.info(msg.toString());

        //如果是本机发出的消息，那么不进行处理
        if (msg.getMsgSource().equals(MessageSourceUtil.getMsgSource())){
            log.info("收到本机发出的消息，不做处理");
            return;
        }

        DoubleCache cache = (DoubleCache) manager.getCache(msg.getCacheName());
        if (msg.getType()== CacheMsgType.UPDATE) {
            cache.updateL1Cache(msg.getKey(),msg.getValue());
            log.info("更新本地缓存");
        }

        if (msg.getType()== CacheMsgType.DELETE) {
            log.info("删除本地缓存");
            cache.evictL1Cache(msg.getKey());
        }
    }
}

在上面的代码中，调用了DoubleCache中更新一级缓存方法updateL1Cache、删除一级缓存方法evictL1Cache，我们会后面在DoubleCache中进行添加。

5.3.4 修改DoubleCache

在DoubleCache中先添加上面提到的两个方法，由CacheManager获取到具体缓存后调用，进行一级缓存的更新或删除操作：

// 更新一级缓存
public void updateL1Cache(Object key,Object value){
    caffeineCache.put(key,value);
}

// 删除一级缓存
public void evictL1Cache(Object key){
    caffeineCache.invalidate(key);
}

好了，需要修改的都修改完了，那么我们要在什么场合发送消息呢？答案是在DoubleCache中存入缓存的put方法和移除缓存的evict方法中。首先修改put方法，方法中前面的逻辑不变，在最后添加发送消息通知其他节点更新一级缓存的逻辑：

public void put(Object key, Object value) {
	// 省略前面的不变代码...

    //发送信息通知其他节点更新一级缓存	
	CacheMassage cacheMassage
			= new CacheMassage(this.cacheName, CacheMsgType.UPDATE,
			key,value, MessageSourceUtil.getMsgSource());
	redisTemplate.convertAndSend(MessageConfig.TOPIC,cacheMassage);
}

然后修改evict方法，同样保持前面的逻辑不变，在最后添加发送消息的代码：

public void evict(Object key) {
	// 省略前面的不变代码...

    //发送信息通知其他节点删除一级缓存   
    CacheMassage cacheMassage
            = new CacheMassage(this.cacheName, CacheMsgType.DELETE,
            key,null, MessageSourceUtil.getMsgSource());
    redisTemplate.convertAndSend(MessageConfig.TOPIC,cacheMassage);
}

适配分布式环境的改造工作到此结束，最终版如下。

六、最终版代码-v4.release

6.1 double-cache模块

目录

6.2 代码

DoubleCache

import com.github.benmanes.caffeine.cache.Cache;
import lombok.extern.slf4j.Slf4j;
import org.example.doublecache.config.DoubleCacheConfig;
import org.example.doublecache.config.MessageConfig;
import org.example.doublecache.msg.CacheMassage;
import org.example.doublecache.msg.CacheMsgType;
import org.example.doublecache.msg.MessageSourceUtil;
import org.springframework.cache.support.AbstractValueAdaptingCache;
import org.springframework.data.redis.core.RedisTemplate;

import java.net.UnknownHostException;
import java.util.Objects;
import java.util.Optional;
import java.util.Set;
import java.util.concurrent.Callable;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.locks.ReentrantLock;

@Slf4j
public class DoubleCache extends AbstractValueAdaptingCache {
    private String cacheName;
    private RedisTemplate<Object, Object> redisTemplate;
    private Cache<Object, Object> caffeineCache;
    private DoubleCacheConfig doubleCacheConfig;

    protected DoubleCache(boolean allowNullValues) {
        super(allowNullValues);
    }

    public DoubleCache(String cacheName, RedisTemplate<Object, Object> redisTemplate,
                       Cache<Object, Object> caffeineCache,
                       DoubleCacheConfig doubleCacheConfig) {
        super(doubleCacheConfig.getAllowNull());
        this.cacheName = cacheName;
        this.redisTemplate = redisTemplate;
        this.caffeineCache = caffeineCache;
        this.doubleCacheConfig = doubleCacheConfig;
    }

    //使用注解时不走这个方法，实际走父类的get方法
    @Override
    public <T> T get(Object key, Callable<T> valueLoader) {
        ReentrantLock lock = new ReentrantLock();
        try {
            lock.lock();//加锁

            Object obj = lookup(key);
            if (Objects.nonNull(obj)) {
                return (T) obj;
            }
            //没有找到
            obj = valueLoader.call();
            //放入缓存
            put(key, obj);
            return (T) obj;
        } catch (Exception e) {
            log.error(e.getMessage());
        } finally {
            lock.unlock();
        }
        return null;
    }

    @Override
    protected Object lookup(Object key) {
        // 先从caffeine中查找
        Object obj = caffeineCache.getIfPresent(key);
        if (Objects.nonNull(obj)) {
            log.info("get data from caffeine");
            return obj; //不用fromStoreValue，否则返回的是null，会再查数据库
        }

        //再从redis中查找
        String redisKey = this.cacheName + ":" + key;
        obj = redisTemplate.opsForValue().get(redisKey);
        if (Objects.nonNull(obj)) {
            log.info("get data from redis");
            caffeineCache.put(key, obj);
        }
        return obj;
    }

    @Override
    public void put(Object key, Object value) {
        if (!isAllowNullValues() && Objects.isNull(value)) {
            log.error("the value NULL will not be cached");
            return;
        }

        //使用 toStoreValue(value) 包装，解决caffeine不能存null的问题
        //caffeineCache.put(key,value);
        caffeineCache.put(key, toStoreValue(value));

        // null对象只存在caffeine中一份就够了，不用存redis了
        if (Objects.isNull(value))
            return;

        String redisKey = this.cacheName + ":" + key;
        Optional<Long> expireOpt = Optional.ofNullable(doubleCacheConfig)
                .map(DoubleCacheConfig::getRedisExpire);
        if (expireOpt.isPresent()) {
            redisTemplate.opsForValue().set(redisKey, toStoreValue(value),
                    expireOpt.get(), TimeUnit.SECONDS);
        } else {
            redisTemplate.opsForValue().set(redisKey, toStoreValue(value));
        }

        //发送信息通知其他节点更新一级缓存
        //同样，空对象不会给其他节点发送信息
        try {
            CacheMessage cacheMessage
                    = new CacheMessage(this.cacheName, CacheMsgType.UPDATE,
                    key, value, MessageSourceUtil.getMsgSource());
            redisTemplate.convertAndSend(MessageConfig.TOPIC, cacheMassage);
        } catch (UnknownHostException e) {
            log.error(e.getMessage());
        }
    }

    @Override
    public void evict(Object key) {
        redisTemplate.delete(this.cacheName + ":" + key);
        caffeineCache.invalidate(key);

        //发送信息通知其他节点删除一级缓存
        try {
            CacheMessage cacheMessage
                    = new CacheMessage(this.cacheName, CacheMsgType.DELETE,
                    key, null, MessageSourceUtil.getMsgSource());
            redisTemplate.convertAndSend(MessageConfig.TOPIC, cacheMassage);
        } catch (UnknownHostException e) {
            log.error(e.getMessage());
        }
    }

    @Override
    public void clear() {
        //如果是正式环境，避免使用keys命令
        Set<Object> keys = redisTemplate.keys(this.cacheName.concat(":*"));
        for (Object key : keys) {
            redisTemplate.delete(String.valueOf(key));
        }
        caffeineCache.invalidateAll();
    }

    @Override
    public String getName() {
        return this.cacheName;
    }

    @Override
    public Object getNativeCache() {
        return this;
    }

    // 更新一级缓存
    public void updateL1Cache(Object key, Object value) {
        caffeineCache.put(key, value);
    }

    // 删除一级缓存
    public void evictL1Cache(Object key) {
        caffeineCache.invalidate(key);
    }
}

DoubleCacheManager

import org.example.doublecache.config.DoubleCacheConfig;
import com.github.benmanes.caffeine.cache.Caffeine;
import org.springframework.cache.Cache;
import org.springframework.cache.CacheManager;
import org.springframework.data.redis.core.RedisTemplate;

import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.TimeUnit;

public class DoubleCacheManager implements CacheManager {
    Map<String, Cache> cacheMap = new ConcurrentHashMap<>();
    private RedisTemplate<Object, Object> redisTemplate;
    private DoubleCacheConfig dcConfig;

    public DoubleCacheManager(RedisTemplate<Object, Object> redisTemplate,
                              DoubleCacheConfig doubleCacheConfig) {
        this.redisTemplate = redisTemplate;
        this.dcConfig = doubleCacheConfig;
    }

    @Override
    public Cache getCache(String name) {
        Cache cache = cacheMap.get(name);
        if (Objects.nonNull(cache)) {
            return cache;
        }

        cache = new DoubleCache(name, redisTemplate, createCaffeineCache(), dcConfig);
        Cache oldCache = cacheMap.putIfAbsent(name, cache);
        return oldCache == null ? cache : oldCache;
    }

    @Override
    public Collection<String> getCacheNames() {
        return cacheMap.keySet();
    }

    private com.github.benmanes.caffeine.cache.Cache createCaffeineCache(){
        Caffeine<Object, Object> caffeineBuilder = Caffeine.newBuilder();
        Optional<DoubleCacheConfig> dcConfigOpt = Optional.ofNullable(this.dcConfig);
        dcConfigOpt.map(DoubleCacheConfig::getInit)
                .ifPresent(init->caffeineBuilder.initialCapacity(init));
        dcConfigOpt.map(DoubleCacheConfig::getMax)
                .ifPresent(max->caffeineBuilder.maximumSize(max));
        dcConfigOpt.map(DoubleCacheConfig::getExpireAfterWrite)
                .ifPresent(eaw->caffeineBuilder.expireAfterWrite(eaw,TimeUnit.SECONDS));
        dcConfigOpt.map(DoubleCacheConfig::getExpireAfterAccess)
                .ifPresent(eaa->caffeineBuilder.expireAfterAccess(eaa,TimeUnit.SECONDS));
        dcConfigOpt.map(DoubleCacheConfig::getRefreshAfterWrite)
                .ifPresent(raw->caffeineBuilder.refreshAfterWrite(raw,TimeUnit.SECONDS));
        return caffeineBuilder.build();
    }
}

CacheConfig 把@EnableCaching注解移动到这里来了

import org.example.doublecache.cache.DoubleCacheManager;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.context.properties.EnableConfigurationProperties;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.data.redis.core.RedisTemplate;

@Configuration
@EnableCaching
@EnableConfigurationProperties({DoubleCacheConfig.class})
public class CacheConfig {
    @Autowired
    DoubleCacheConfig doubleCacheConfig;

    @Bean
    public DoubleCacheManager cacheManager(RedisTemplate<Object,Object> redisTemplate,
                                           DoubleCacheConfig doubleCacheConfig){
        return new DoubleCacheManager(redisTemplate,doubleCacheConfig);
    }
}

DoubleCacheConfig

import lombok.Data;
import org.springframework.boot.context.properties.ConfigurationProperties;

@Data
@ConfigurationProperties(prefix = "doublecache")
public class DoubleCacheConfig {
    private Boolean allowNull = true;
    private Integer init = 100;
    private Integer max = 1000;
    private Long expireAfterWrite ;
    private Long expireAfterAccess;
    private Long refreshAfterWrite;
    private Long redisExpire;
}

MessageConfig

import org.example.doublecache.msg.RedisMessageReceiver;
import org.example.doublecache.util.SpringContextUtil;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.Import;
import org.springframework.data.redis.connection.RedisConnectionFactory;
import org.springframework.data.redis.listener.PatternTopic;
import org.springframework.data.redis.listener.RedisMessageListenerContainer;
import org.springframework.data.redis.listener.adapter.MessageListenerAdapter;

@Configuration
@Import({RedisMessageReceiver.class, SpringContextUtil.class})
public class MessageConfig {
    public static final String TOPIC="cache.msg";

    @Bean
    RedisMessageListenerContainer container(MessageListenerAdapter listenerAdapter,
                                            RedisConnectionFactory redisConnectionFactory){
        RedisMessageListenerContainer container = new RedisMessageListenerContainer();
        container.setConnectionFactory(redisConnectionFactory);
        container.addMessageListener(listenerAdapter, new PatternTopic(TOPIC));
        return container;
    }

    @Bean
    MessageListenerAdapter adapter(RedisMessageReceiver receiver){
        return new MessageListenerAdapter(receiver,"receive");
    }
}

RedisConfig

import com.fasterxml.jackson.annotation.JsonAutoDetect;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import com.fasterxml.jackson.annotation.PropertyAccessor;
import com.fasterxml.jackson.databind.ObjectMapper;
import org.springframework.cache.annotation.CachingConfigurerSupport;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.data.redis.connection.RedisConnectionFactory;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.serializer.Jackson2JsonRedisSerializer;
import org.springframework.data.redis.serializer.RedisSerializer;
import org.springframework.data.redis.serializer.StringRedisSerializer;

@Configuration
public class RedisConfig extends CachingConfigurerSupport {
    @Bean
    public RedisTemplate redisTemplate(RedisConnectionFactory redisConnectionFactory){
        RedisTemplate<Object, Object> redisTemplate=new RedisTemplate<>();
        redisTemplate.setConnectionFactory(redisConnectionFactory);

        // json序列化设置
        Jackson2JsonRedisSerializer<Object> jackson2JsonRedisSerializer
                = new Jackson2JsonRedisSerializer<>(Object.class);
        ObjectMapper om = new ObjectMapper();
        om.setVisibility(PropertyAccessor.ALL, JsonAutoDetect.Visibility.ANY);

        om.activateDefaultTyping(om.getPolymorphicTypeValidator(),
                ObjectMapper.DefaultTyping.NON_FINAL,//类名序列化到json串中
                JsonTypeInfo.As.WRAPPER_ARRAY);
        jackson2JsonRedisSerializer.setObjectMapper(om);

        //String类型的序列化
        RedisSerializer<?> stringSerializer = new StringRedisSerializer();

        redisTemplate.setKeySerializer(stringSerializer);// key采用String序列化方式
        redisTemplate.setValueSerializer(jackson2JsonRedisSerializer);// value序列化
        redisTemplate.setHashKeySerializer(stringSerializer);// Hash key采用String序列化方式
        redisTemplate.setHashValueSerializer(jackson2JsonRedisSerializer);// Hash value序列化
        redisTemplate.afterPropertiesSet();
        return redisTemplate;
    }
}

CacheMassage

import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import java.io.Serializable;

@Data
@NoArgsConstructor
@AllArgsConstructor
public class CacheMessage implements Serializable {
    private static final long serialVersionUID = -3574997636829868400L;

    private String cacheName;
    private CacheMsgType type;  //标识更新或删除操作
    private Object key;
    private Object value;
    private String msgSource;   //源主机标识，用来避免重复操作
}

CacheMsgType

public enum CacheMsgType {
    UPDATE,
    DELETE;
}

MessageSourceUtil

import org.springframework.core.env.Environment;

import java.net.InetAddress;
import java.net.UnknownHostException;

public class MessageSourceUtil {
    public static String getMsgSource() throws UnknownHostException {
        String host = InetAddress.getLocalHost().getHostAddress();
        Environment env = SpringContextUtil.getBean(Environment.class);
        String port = env.getProperty("server.port");
        return host+":"+port;
    }
}

RedisMessageReceiver

import lombok.AllArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.example.doublecache.cache.DoubleCache;
import org.example.doublecache.cache.DoubleCacheManager;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.stereotype.Component;
import java.net.UnknownHostException;

@Slf4j
@Component
@AllArgsConstructor
public class RedisMessageReceiver {
    private final RedisTemplate redisTemplate;
    private final DoubleCacheManager manager;

    //接收通知，进行处理
    public void receive(String message) throws UnknownHostException {
        CacheMessage msg = (CacheMessage) redisTemplate
                .getValueSerializer().deserialize(message.getBytes());
        log.info(msg.toString());

        //如果是本机发出的消息，那么不进行处理
        if (msg.getMsgSource().equals(MessageSourceUtil.getMsgSource())){
            log.info("收到本机发出的消息，不做处理");
            return;
        }

        DoubleCache cache = (DoubleCache) manager.getCache(msg.getCacheName());
        if (msg.getType()== CacheMsgType.UPDATE) {
            cache.updateL1Cache(msg.getKey(),msg.getValue());
            log.info("更新本地缓存");
        }

        if (msg.getType()== CacheMsgType.DELETE) {
            log.info("删除本地缓存");
            cache.evictL1Cache(msg.getKey());
        }
    }
}

SpringContextUtil

import org.springframework.beans.BeansException;
import org.springframework.context.ApplicationContext;
import org.springframework.context.ApplicationContextAware;
import org.springframework.stereotype.Component;

@Component
public class SpringContextUtil implements ApplicationContextAware {

    private static ApplicationContext applicationContext;

    @Override
    public void setApplicationContext(ApplicationContext applicationContext) throws BeansException {
        this.applicationContext = applicationContext;
    }

    public static ApplicationContext getApplicationContext() {
        return applicationContext;
    }

    public static Object getBean(String name) {
        return applicationContext.getBean(name);
    }

    public static <T> T getBean(Class<T> t) {
        return applicationContext.getBean(t);
    }

}

spring.factories

org.springframework.boot.autoconfigure.EnableAutoConfiguration = \
  org.example.doublecache.config.RedisConfig, \
  org.example.doublecache.config.CacheConfig, \
  org.example.doublecache.config.MessageConfig

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<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>org.example</groupId>
    <artifactId>double-cache</artifactId>
    <version>1.0-SNAPSHOT</version>

    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.7.2</version>
        <relativePath/>
    </parent>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
        </dependency>

        <dependency>
            <groupId>com.github.ben-manes.caffeine</groupId>
            <artifactId>caffeine</artifactId>
            <version>2.9.2</version>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-redis</artifactId>
        </dependency>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.16</version>
            <scope>provided</scope>
        </dependency>
    </dependencies>

</project>

6.3 测试模块主要代码

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<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>org.example</groupId>
    <artifactId>testcache</artifactId>
    <version>1.0-SNAPSHOT</version>

    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.7.2</version>
        <relativePath/>
    </parent>
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <mybatis-plus.version>3.3.2</mybatis-plus.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.example</groupId>
            <artifactId>double-cache</artifactId>
            <version>1.0-SNAPSHOT</version>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>com.baomidou</groupId>
            <artifactId>mybatis-plus-boot-starter</artifactId>
            <version>${mybatis-plus.version}</version>
        </dependency>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.16</version>
            <scope>provided</scope>
        </dependency>
    </dependencies>
</project>

application.yml

server:
  port: 8090

spring:
  application:
    name: test-demo
  datasource:
    url: jdbc:mysql://localhost:3306/ktl?useUnicode=true&characterEncoding=UTF-8&serverTimezone=UTC
    username: root
    password: root
    driver-class-name: com.mysql.cj.jdbc.Driver
    hikari:
      minimum-idle: 5
      connection-test-query: SELECT 1 FROM DUAL
      maximum-pool-size: 20
      auto-commit: true
      idle-timeout: 30000
      pool-name: MyHikariCP
      max-lifetime: 60000
      connection-timeout: 30000
  redis:
    host: 192.168.200.131
    port: 6379
    database: 0
    timeout: 10000ms
    lettuce:
      pool:
        max-active: 8
        max-wait: -1ms
        max-idle: 8
        min-idle: 0
    password: root
doublecache:
  allowNull: true
  init: 128 #Caffeine初始容量
  max: 1024 #Caffeine最大容量
  expireAfterWrite: 30  #Caffeine过期时间
  redisExpire: 60      #Redis缓存过期时间

OrderServiceImpl

@Service @Slf4j
@AllArgsConstructor
public class OrderServiceImpl extends ServiceImpl<OrderMapper,Order> implements OrderService{
    private final OrderMapper orderMapper;

    @Cacheable(value = "order",key = "#id")
    public Order getOrderById(Long id) {
        Order myOrder = orderMapper.selectOne(new LambdaQueryWrapper<Order>()
                .eq(Order::getId, id));
        return myOrder;
    }

    @CachePut(cacheNames = "order",key = "#order.id")
    public Order updateOrder(Order order) {
        orderMapper.updateById(order);
        return order;
    }

    @CacheEvict(cacheNames = "order",key = "#id")
    public void deleteOrder(Long id) {
        orderMapper.deleteById(id);
    }

    //没有注解,使用get(key,callable)方法
    public Order getOrderById2(Long id) {
//        DoubleCacheManager cacheManager = SpringContextUtil.getBean(DoubleCacheManager.class);
//        Cache cache = cacheManager.getCache("order");
//        Order order =(Order) cache.get(id, (Callable<Object>) () -> {
//            log.info("get data from database");
//            Order myOrder = orderMapper.selectOne(new LambdaQueryWrapper<Order>()
//                    .eq(Order::getId, id));
//            return myOrder;
//        });
        return null;
    }
}

6.4 测试结果

CacheMessage中端口都是8090，是因为实际占用的端口是8090.

发现CacheMessage写成Massage了，自己改一下

启动3个测试模块的服务（端口分别为8090,8091,8092），在配置里VM Options 修改 -Dserver.port=8091。
发送请求:http://127.0.0.1:8090/get/2

在30秒内发送请求http://127.0.0.1:8091/get/2，发现8091端口的应用本地也有缓存。

测试update，在30秒内发送请求127.0.0.1:8092/update

{
    "id": 2,
    "orderNumber": "10002",
    "money": 99.0,
    "status": 1
}

其它两个服务都打印出了“更新本地缓存”的日志

测试delete，发送请求127.0.0.1:8090/del?id=2
发现8091端口如下

通过8090get

到此，OK