Caching is often viewed as a simple tactical tool for performance, but it fundamentally alters system properties such as data consistency, failure behavior, and availability. System analysts must treat caching as a distributed system component rather than an isolated auxiliary feature.
The primary challenge lies in managing data consistency between the source of truth and the cache. Analysts are required to document specific strategies for cache invalidation and define system behavior for scenarios where the cache and the primary database temporarily diverge.
Caching introduces a trade-off between speed and data accuracy. Analysts must define acceptable data age, set TTL (Time-to-Live) parameters, and specify which scenarios prohibit the use of cached data, particularly for sensitive information like financial balances or medical records.
Cache failure can trigger a 'thundering herd' problem, where a surge of concurrent requests overwhelms the primary database. To prevent cascading failures, analysts should specify requirements for request deduplication mechanisms and define predictable degradation patterns for critical user flows.
In some cases, such as user feeds or catalogs, providing slightly stale data is preferable to showing nothing at all. Analysts can implement fallback TTL settings to allow the system to serve cached data even when the primary source is temporarily unavailable.
Effective caching requires selecting appropriate data rather than just the most expensive queries. Analysts must document the specific types of data to be cached and justify these choices to avoid unnecessary overhead from frequent invalidations.
