Why Caching Is a Consistency Problem

The starting point

Caching is often introduced as a performance optimization, but the deeper problem is consistency. The moment a value exists in both a primary database and a cache, the system has two representations of the same fact. A read can now be fast while being stale. The engineering question is not simply how to make the cache fast. It is what freshness guarantee the application actually needs.

A cache creates a second state

Suppose a blog article is cached for five minutes. An editor publishes a correction, but readers may continue seeing the previous value until the cache expires. That may be perfectly acceptable for a public blog. The same strategy could be unacceptable for account balances or permissions.

This is why cache design starts with semantics. How stale can the data be? Who invalidates it? What happens if invalidation fails? Can the application tolerate a miss and fall back to the source? These questions define the design more than the choice of caching product.

Do not cache problems that do not exist

A small portfolio database is unlikely to need a distributed cache merely because caching is considered a production technique. PostgreSQL can serve a modest dataset efficiently, and Next.js already has server-side rendering and caching primitives. Adding another system introduces operational complexity and another consistency boundary.

The right time to introduce a cache is when measurements show that repeated computation or database access is a real bottleneck and the freshness tradeoff is understood.