Implementing a Two‑Level Cache with Caffeine and Redis in Spring Boot

In high‑performance service architectures, caching is essential; hot data is first stored in a remote cache such as Redis or MemCache , and only when a cache miss occurs is the database queried.

When remote caches become insufficient, a local cache (e.g., Guava or Caffeine ) can be added as a first‑level cache, forming a two‑level cache architecture that further reduces latency.

V1.0 – Manual implementation – Define a Caffeine bean, configure dependencies, and write explicit cache logic in the service layer. Example configuration:

@Configuration
public class CaffeineConfig {
    @Bean
    public Cache
caffeineCache() {
        return Caffeine.newBuilder()
                .initialCapacity(128)
                .maximumSize(1024)
                .expireAfterWrite(60, TimeUnit.SECONDS)
                .build();
    }
}

POM dependencies:

<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.springframework.boot</groupId>
    <artifactId>spring-boot-starter-cache</artifactId>
</dependency>

Application.yml Redis connection:

spring:
  redis:
    host: 127.0.0.1
    port: 6379
    database: 0
    timeout: 10000ms
    lettuce:
      pool:
        max-active: 8
        max-wait: -1ms
        max-idle: 8
        min-idle: 0

Service method with manual two‑level cache handling:

public Order getOrderById(Long id) {
    String key = CacheConstant.ORDER + id;
    Order order = (Order) cache.get(key, k -> {
        Object obj = redisTemplate.opsForValue().get(k);
        if (obj != null) {
            log.info("get data from redis");
            return obj;
        }
        log.info("get data from database");
        Order dbOrder = orderMapper.selectOne(new LambdaQueryWrapper
().eq(Order::getId, id));
        redisTemplate.opsForValue().set(k, dbOrder, 120, TimeUnit.SECONDS);
        return dbOrder;
    });
    return order;
}

V2.0 – Spring Cache annotations – Enable caching and use @Cacheable , @CachePut , and @CacheEvict to let Spring manage the cache, reducing manual code. Example:

@Cacheable(value = "order", key = "#id")
public Order getOrderById(Long id) { /* business logic */ }

@CachePut(cacheNames = "order", key = "#order.id")
public Order updateOrder(Order order) { /* business logic */ }

@CacheEvict(cacheNames = "order", key = "#id")
public void deleteOrder(Long id) { /* business logic */ }

Cache manager configuration for Caffeine:

@Configuration
public class CacheManagerConfig {
    @Bean
    public CacheManager cacheManager() {
        CaffeineCacheManager manager = new CaffeineCacheManager();
        manager.setCaffeine(Caffeine.newBuilder()
                .initialCapacity(128)
                .maximumSize(1024)
                .expireAfterWrite(60, TimeUnit.SECONDS));
        return manager;
    }
}

V3.0 – Custom annotation with AOP – Define @DoubleCache to describe cache name, key (SpringEL), timeout, and operation type, then implement an aspect that parses the key, checks Caffeine first, then Redis, and finally the database.

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

Aspect handling the annotation:

@Component
@Aspect
@AllArgsConstructor
public class CacheAspect {
    private final Cache cache;
    private final RedisTemplate redisTemplate;

    @Pointcut("@annotation(com.cn.dc.annotation.DoubleCache)")
    public void cacheAspect() {}

    @Around("cacheAspect()")
    public Object doAround(ProceedingJoinPoint point) throws Throwable {
        MethodSignature sig = (MethodSignature) point.getSignature();
        Method method = sig.getMethod();
        String[] paramNames = sig.getParameterNames();
        Object[] args = point.getArgs();
        TreeMap
map = new TreeMap<>();
        for (int i = 0; i < paramNames.length; i++) {
            map.put(paramNames[i], args[i]);
        }
        DoubleCache ann = method.getAnnotation(DoubleCache.class);
        String realKey = ann.cacheName() + ":" + ElParser.parse(ann.key(), map);
        if (ann.type() == CacheType.PUT) {
            Object obj = point.proceed();
            redisTemplate.opsForValue().set(realKey, obj, ann.l2TimeOut(), TimeUnit.SECONDS);
            cache.put(realKey, obj);
            return obj;
        }
        if (ann.type() == CacheType.DELETE) {
            redisTemplate.delete(realKey);
            cache.invalidate(realKey);
            return point.proceed();
        }
        Object local = cache.getIfPresent(realKey);
        if (local != null) {
            log.info("get data from caffeine");
            return local;
        }
        Object remote = redisTemplate.opsForValue().get(realKey);
        if (remote != null) {
            log.info("get data from redis");
            cache.put(realKey, remote);
            return remote;
        }
        log.info("get data from database");
        Object result = point.proceed();
        if (result != null) {
            redisTemplate.opsForValue().set(realKey, result, ann.l2TimeOut(), TimeUnit.SECONDS);
            cache.put(realKey, result);
        }
        return result;
    }
}

Applying the custom annotation makes service code concise:

@DoubleCache(cacheName = "order", key = "#id", type = CacheType.FULL)
public Order getOrderById(Long id) { return orderMapper.selectOne(new LambdaQueryWrapper
().eq(Order::getId, id)); }

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

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

The article concludes that two‑level caching can improve latency and reduce database load, but it introduces complexity such as consistency, expiration policies, and concurrency handling; therefore, developers should evaluate whether the added local cache is truly needed for their specific workload.