SpringCache缓存抽象层

GC 日志看起来乱，关键是找准几个核心指标。很多开发者面对 GC 日志不知道该关注什么。本文从实际调优经验出发，讲需要关注什么、忽略什么，帮你快速定位问题。

启用缓存

@Configuration
@EnableCaching
public class CacheConfig {
}

核心注解

注解	作用
@Cacheable	有缓存则返回，无缓存则执行方法并缓存
@CachePut	执行方法并更新缓存
@CacheEvict	清除缓存
@Caching	组合多个缓存操作
@CacheConfig	类级别共享缓存配置

基本使用

#

@Cacheable

@Service
public class UserService {
    
    @Cacheable(value = "users", key = "#id")
    public User getUser(Long id) {
        // 只会在缓存不存在时执行
        return userDao.findById(id);
    }
    
    @Cacheable(value = "users", key = "#username")
    public User getUserByUsername(String username) {
        return userDao.findByUsername(username);
    }
    
    // 多参数
    @Cacheable(value = "users", key = "#id + '_' + #type")
    public User getUser(Long id, String type) {
        return userDao.findByIdAndType(id, type);
    }
    
    // 使用 root 对象
    @Cacheable(value = "users", key = "#root.methodName + '_' + #id")
    public User getUserV2(Long id) {
        return userDao.findById(id);
    }
    
    // 条件缓存
    @Cacheable(value = "users", key = "#id", condition = "#id > 0")
    public User getUserIfPositive(Long id) {
        return userDao.findById(id);
    }
    
    //  unless：结果不满足条件时缓存
    @Cacheable(value = "users", key = "#id", unless = "#result == null")
    public User getUserUnlessNull(Long id) {
        return userDao.findById(id);
    }
}

#

@CachePut

@CachePut(value = "users", key = "#user.id")
public User updateUser(User user) {
    return userDao.save(user);
}

#

@CacheEvict

@CacheEvict(value = "users", key = "#id")
public void deleteUser(Long id) {
    userDao.deleteById(id);
}

// 清空整个缓存
@CacheEvict(value = "users", allEntries = true)
public void clearCache() {
}

// 在方法前清除
@CacheEvict(value = "users", key = "#id", beforeInvocation = true)
public void deleteUserBefore(Long id) {
    userDao.deleteById(id);
}

#

@Caching

@Caching(
    put = {
        @CachePut(value = "users", key = "#user.id"),
        @CachePut(value = "users", key = "#user.username")
    },
    evict = {
        @CacheEvict(value = "userList", allEntries = true)
    }
)
public User saveUser(User user) {
    return userDao.save(user);
}

#

@CacheConfig

@CacheConfig(cacheNames = "users")
@Service
public class UserService {
    
    @Cacheable(key = "#id")  // 不需要写 value
    public User getUser(Long id) {
        return userDao.findById(id);
    }
    
    @CacheEvict(key = "#id")
    public void deleteUser(Long id) {
        userDao.deleteById(id);
    }
}

缓存配置

#

Caffeine 本地缓存

<dependency>
    <groupId>com.github.ben-manes.caffeine</groupId>
    <artifactId>caffeine</artifactId>
</dependency>

@Configuration
@EnableCaching
public class CacheConfig {
    
    @Bean
    public CacheManager cacheManager() {
        CaffeineCacheManager cacheManager = new CaffeineCacheManager();
        cacheManager.setCaffeine(Caffeine.newBuilder()
            .maximumSize(1000)
            .expireAfterWrite(10, TimeUnit.MINUTES)
            .recordStats());
        return cacheManager;
    }
}

#

Redis 缓存

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-redis</artifactId>
</dependency>

@Configuration
@EnableCaching
public class CacheConfig {
    
    @Bean
    public CacheManager cacheManager(RedisConnectionFactory factory) {
        RedisCacheConfiguration config = RedisCacheConfiguration.defaultCacheConfig()
            .entryTtl(Duration.ofMinutes(30))
            .serializeKeysWith(
                RedisSerializationContext.SerializationPair.fromSerializer(
                    new StringRedisSerializer()))
            .serializeValuesWith(
                RedisSerializationContext.SerializationPair.fromSerializer(
                    new GenericJackson2JsonRedisSerializer()));
        
        return RedisCacheManager.builder(factory)
            .cacheDefaults(config)
            .build();
    }
}

#

多级缓存

@Configuration
@EnableCaching
public class CacheConfig {
    
    @Primary
    @Bean
    public CacheManager cacheManager() {
        // 本地缓存 Caffeine
        CaffeineCacheManager localCache = new CaffeineCacheManager();
        localCache.setCaffeine(Caffeine.newBuilder()
            .maximumSize(100)
            .expireAfterWrite(1, TimeUnit.MINUTES));
        
        return localCache;
    }
    
    @Bean
    public CacheManager redisCacheManager(RedisConnectionFactory factory) {
        // Redis 缓存
        return RedisCacheManager.builder(factory).build();
    }
}

@Service
public class UserService {
    
    @Cacheable(value = "users", cacheManager = "cacheManager")
    public User getUserFromLocal(Long id) {
        return userDao.findById(id);
    }
    
    @Cacheable(value = "users:redis", cacheManager = "redisCacheManager")
    public User getUserFromRedis(Long id) {
        return userDao.findById(id);
    }
}

SpEL 表达式

@Cacheable(value = "users", key = "#id")
@Cacheable(value = "users", key = "#user.id")
@Cacheable(value = "users", key = "#root.args[0]")
@Cacheable(value = "users", key = "T(java.util.Objects).hash(#id, #name)")
@Cacheable(value = "users", key = "'user_' + #id")
@Cacheable(value = "users", keyGenerator = "customKeyGenerator")

自定义 KeyGenerator

@Component
public class CustomKeyGenerator implements KeyGenerator {
    
    @Override
    public Object generate(Object target, Method method, Object... params) {
        return target.getClass().getSimpleName() + "_" 
            + method.getName() + "_" 
            + StringUtils.arrayToDelimitedString(params, "_");
    }
}

缓存问题及解决

#

缓存穿透

问题：查询不存在的数据，每次都会打到数据库。

解决：

@Cacheable(value = "users", key = "#id", unless = "#result == null")
public User getUser(Long id) {
    return userDao.findById(id);
}

// 或使用空值缓存
@Cacheable(value = "users", key = "#id")
public Optional<User> getUserOptional(Long id) {
    return Optional.ofNullable(userDao.findById(id));
}

#

缓存击穿

问题：热点 key 过期，大量请求同时打到数据库。

解决：

@Cacheable(value = "users", key = "#id")
@DistributedLock(key = "'lock:user:' + #id")
public User getUser(Long id) {
    return userDao.findById(id);
}

#

缓存雪崩

问题：大量 key 同时过期。

解决：

// 随机过期时间
@Bean
public CacheManager cacheManager() {
    CaffeineCacheManager manager = new CaffeineCacheManager();
    manager.setCaffeine(Caffeine.newBuilder()
        .expireAfterWrite(10 + (int)(Math.random() * 5), TimeUnit.MINUTES));
    return manager;
}

总结

Spring Cache 简化了缓存的使用：

注解	场景
@Cacheable	读缓存
@CachePut	写缓存
@CacheEvict	删缓存
@CacheConfig	类级配置

结合 Caffeine（本地）和 Redis（分布式），可以构建高效的缓存层。

核心要点

1. 关注 Minor GC 和 Full GC 的频率和耗时

2. 年轻代晋升到老年代的对象大小和频率

3. GC 前后的内存使用变化

4. 使用 jstat、jmap、jvisualvm 等工具辅助分析

总结

GC 调优是一个持续的过程，没有一劳永逸的方案。需要结合业务特点、数据量、响应时间要求来调整。理解 GC 日志是调优的第一步。