Spring Boot + Redis Cache in Practice: From Annotations to Distributed Locks

Caching is the first line of defense for backend performance optimization.

Spring Cache Annotations

// The simplest way
@Cacheable(value = "users", key = "#id")
public User getUser(Long id) {
    return userMapper.selectById(id);
}

@CacheEvict(value = "users", key = "#user.id")
public void updateUser(User user) {
    userMapper.updateById(user);
}

@CachePut(value = "users", key = "#user.id")
public User saveUser(User user) {
    userMapper.insert(user);
    return user;
}

Redis Serialization Configuration

The default JDK serialization has poor readability, so switch to JSON:

@Bean
public RedisCacheConfiguration cacheConfiguration() {
    return RedisCacheConfiguration.defaultCacheConfig()
        .serializeValuesWith(
            RedisSerializationContext.SerializationPair
                .fromSerializer(new GenericJackson2JsonRedisSerializer())
        )
        .entryTtl(Duration.ofMinutes(30));
}

Three Major Cache Problems

Distributed Lock Implementation

public String deductStock(String productId) {
    String lockKey = "lock:stock:" + productId;
    String lockValue = UUID.randomUUID().toString();

    // SET NX EX: acquire the lock, 30-second expiration
    Boolean locked = redisTemplate.opsForValue()
        .setIfAbsent(lockKey, lockValue, 30, TimeUnit.SECONDS);

    if (Boolean.FALSE.equals(locked)) {
        throw new BusyException("System is busy, please try again later");
    }
    try {
        // Stock deduction logic
        return "success";
    } finally {
        // Lua script ensures atomic lock release
        String script = "if redis.call('get', KEYS[1]) == ARGV[1] " +
            "then return redis.call('del', KEYS[1]) else return 0 end";
        redisTemplate.execute(new DefaultRedisScript<>(script, Long.class),
            Collections.singletonList(lockKey), lockValue);
    }
}

Caching is not hard; what's hard is the moment a cache expires.