Can I set a TTL for @Cacheable

java

cache-management

ttl

spring-cache

byAlex Kataev·Jan 31, 2025

To enable a TTL for the annotation @Cacheable in Spring, you can use Caffeine and set it like this:

@Bean
public CacheManager caffeineCacheManager() {
    // TTL setup: Caffeine and coffee! ☕ Both have a 'brew' connection, don't they? 😄
    return new CaffeineCacheManager("cacheName", 
        Caffeine.newBuilder().expireAfterWrite(5, TimeUnit.MINUTES)::build);
}

The code snippet sets a 5-minute TTL for the "cacheName" cache — objects will be automatically removed post-write after this timeframe.

Deep Dive into TTL

Time-to-Live (TTL) in caching is a strategic mechanism that sets the expiration timeframe for cache data. Upon the completion of this set duration, the cache gets rid of the stale items automatically. It's essentially a self-clean routine that ensures your cache doesn’t serve out-of-date information.

Implementing TTL into caching relieves you from the need to manually control cache invalidation, which could be prone to errors and inefficient.

Tailoring Cache TTL

For more complex requirements, cache TTL behavior might need further customization:

TTL at Method Level

To apply TTL at the method level, use the @Cacheable annotation.

@Cacheable(value = "products", key = "#id", cacheManager = "customTtlCacheManager")
public Product getProductById(String id) { /* Who's got the product ID? 🛍 */ }

CacheManager Configuratıon

To further customize the caching process, use third-party libraries like Guava or EhCache. These libraries provide advanced functionalities which you can use to create diverse cache implementations.

Planned Cache Eviction

Don't want to manually remove stale cache entries? Use Spring's scheduling capabilities. Simply annotate the desired method with @Scheduled and Spring will do the cleaning for you.

Programmatic Cache Eviction

Need to get rid of specific cache entries due to certain application events? CacheManager.getCache("cacheName").evict(key); to the rescue.

Cache Performance Pitfalls

Don’t forget to closely monitor the maximumSize parameter when configuring cache. A home without a space limit might sound rad, but a cache without a bound can grow indefinitely and lead to memory issues. Setting sensible limits based on the requirements and available resources comes to the rescue here.

Application and Maintenance of TTL

The magic happens when cache configuration and TTL settings are properly aligned, validating your caching logic by leaps and bounds. Cache provider features like automatic eviction policies based on TTL can be employed to make caching a breeze. This way, your cache becomes less reliant on manual maintenance and aligns much better with easy-to-understand declarative patterns.

Nitty-Gritty: TTL Alternatives and Best Practices

TTL, TTI, and Size-Based Eviction

Did you know? Eviction policies come in multiple flavors! TTL (Time to Live), TTI (Time to Idle), and size-based eviction are the ones to note. Tune these to fit your application.

Troubleshooting Common TTL Issues

Uneven Cache Distribution: Ever experienced when some cache entries are visited frequently while others rarely? This is it.
Cache Stampede: Imagine a sudden rush of requests when numerous cache entries expire at the same time causing a spike in database load. Scary, isn't it? Solution: Randomize the TTL.
Cold Start: When cache eviction results in a performance drop, it's a cold start. Pre-warm your cache or use steady warm-up patterns to prevent this.

Monitoring & Logging

Remember to check the pulse of your cache once in a while. Monitor the performance and setup proper logging. The hit and miss rates help you adjust the TTL settings to perfection ratio.

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