My team is looking to improve the response time of our Java REST APIs by implementing a distributed cache. We’ve settled on Redis, but we’re debating between using the Spring Cache abstraction (@Cacheable) or using the RedisTemplate directly for more control. What are the pros and cons of each approach when dealing with complex data objects and frequent cache invalidation requirements?
3 answers
For 90% of use cases, the @Cacheable abstraction is sufficient and much cleaner. It keeps your business logic decoupled from the caching infrastructure. However, the "magic" of AOP (Aspect-Oriented Programming) can be tricky—for instance, calling a cached method from within the same class won't trigger the cache. If you need fine-grained control, like setting different TTLs (Time-To-Live) dynamically or using Redis-specific data structures like Sets or Hashes, RedisTemplate is the way to go. In our high-scale retail app, we used @Cacheable for product details but switched to RedisTemplate for the shopping cart logic to handle frequent updates.
How are you handling the "cache stampede" problem where many requests hit the database simultaneously when a popular key expires?
Make sure your objects are properly Serializable; we wasted hours debugging 'SerializationException' when moving from local Caffeine cache to Redis.
That is so true, Rachel. Switching to JSON serialization via Jackson2JsonRedisSerializer instead of standard Java serialization made our cache much more readable and easier to debug.
Samuel, we solved that by implementing "jitter" in our TTLs—basically adding a small random amount of time to the expiration so they don't all die at once. We also use a "locking" mechanism for very expensive queries. If the cache is empty, the first request acquires a lock to refresh the cache while the other requests wait or return a slightly stale value. This keeps our database load steady and prevents those dangerous spikes in latency that can happen during high-traffic events like sales or product launches.