What caching strategy is recommended for hot data before external caches?

What caching strategy is recommended for hot data before external caches?

• A. Cache all data sets unconditionally.
• B. Cache only write operations.
• C. Rely solely on the database for all reads.
• D. Use local in-process caching for hot reference data before distributed caches.

Answer: (D)

The idea here is to layer caches so hot data is served as quickly as possible by the fastest path, then fall back to other caches as needed. Keeping hot reference data in a local in-process cache gives the absolute lowest latency reads because it avoids any network round trips. This makes access to frequently used items almost instant, which is exactly what you want for hot data. If the local cache misses, you then consult the distributed cache, which still provides fast access and data sharing across multiple app instances, but without forcing every read to go over the network. This combination gives you the best of both worlds: ultra-fast access for the things that matter most, plus scalable, cross-process caching for the rest. To make this work well, you need good eviction and invalidation strategies so updates keep both caches in sync and you don’t exhaust memory in the local layer. It’s better than trying to cache everything unconditionally, which can exhaust memory and degrade performance, or focusing only on writes, which ignores the primary benefit of caching reads. Relying solely on the database eliminates the gains caching provides.

The idea here is to layer caches so hot data is served as quickly as possible by the fastest path, then fall back to other caches as needed. Keeping hot reference data in a local in-process cache gives the absolute lowest latency reads because it avoids any network round trips. This makes access to frequently used items almost instant, which is exactly what you want for hot data. If the local cache misses, you then consult the distributed cache, which still provides fast access and data sharing across multiple app instances, but without forcing every read to go over the network. This combination gives you the best of both worlds: ultra-fast access for the things that matter most, plus scalable, cross-process caching for the rest.

To make this work well, you need good eviction and invalidation strategies so updates keep both caches in sync and you don’t exhaust memory in the local layer. It’s better than trying to cache everything unconditionally, which can exhaust memory and degrade performance, or focusing only on writes, which ignores the primary benefit of caching reads. Relying solely on the database eliminates the gains caching provides.