Cache Invalidation

Cache invalidation coordinates cached copies with the information they represent. A cache entry might contain a database query result, a web asset, an API response, or a generated answer. Before choosing an invalidation strategy, define how much staleness the application can tolerate. A product description might remain usable for several minutes, while a revoked permission or an inventory reservation may require an authoritative check rather than a cached answer.

Common approaches include time-based expiration, explicit invalidation after a change, and versioned cache keys. A time to live, or TTL, limits how long an entry is normally reusable, but does not detect source changes immediately. Explicit invalidation removes or marks affected entries when a write occurs. Versioned keys make old entries unreachable by directing readers to a new version; the old entries can expire later. Capacity eviction is different: it removes entries to free memory, not because their contents are known to be outdated.

In a cache-aside database application, the application reads the cache first and queries the database on a miss. After committing a database update, it can delete the affected cache entry so that a later read repopulates it. This sequence still has races: a concurrent reader can retrieve old database data and populate the cache after the deletion. Systems with tighter correctness requirements may need version checks, coordinated updates, or authoritative reads. Updating one record can also invalidate multiple derived entries, such as product details, category pages, and aggregate reports.

For content delivery, distinguish the origin, CDN cache, and browser cache. Replacing an origin object does not automatically replace copies already cached elsewhere. A CDN invalidation targets cached paths, whereas a versioned filename such as app.a81c.js lets clients request a distinct object. CloudFront invalidations do not clear browser caches, and propagation is not instantaneous. Use targeted invalidations for corrections and content-hashed filenames for routine immutable-asset deployments.

When troubleshooting CloudFront, inspect the requested URL, selected cache behavior, cache key, response headers, and origin response. Headers such as Age and X-Cache help distinguish cached responses from origin fetches. Check whether query strings, cookies, or headers that change the response are included in the cache key. Missing dimensions can serve the wrong variant; unnecessary dimensions reduce the hit ratio. Review Cache-Control, cache-policy minimum, default, and maximum TTLs, and error caching settings. A positive CloudFront minimum TTL can cause caching even when the origin sends no-cache, no-store, or private. Correct the underlying policy or origin problem before invalidating, or the cache can refill with the same incorrect response.

Caching improves dynamic scalability by absorbing repeated work rather than merely adding application instances. However, invalidating a popular entry can send many simultaneous requests to the database or origin, creating a cache stampede. Request coalescing, refresh-ahead, and randomized TTLs reduce this risk. Serving stale content during refresh can improve availability when the business permits it, but is inappropriate for data requiring strict freshness. Monitor hit ratio, miss latency, backend load, and invalidation failures, not just cache memory usage.

Generative AI caches need validity rules beyond the prompt text. Deterministic request hashing should use a canonical representation of relevant inputs, including model and prompt versions, inference settings, tenant scope, and retrieval context. A result fingerprint can associate an answer with the document or tool-result versions on which it depends. Changes to those dependencies should invalidate the answer or move requests into a new cache namespace. Hashing identifies matching inputs; it does not itself ensure that an answer remains valid.

Semantic caching reuses answers for sufficiently similar requests, so similarity thresholds must be tested against correctness and authorization requirements. A similar question may still require a different answer because of time, user permissions, or changed source documents. Model prompt caching is distinct from answer caching: it reuses processing of supported prompt prefixes and generally still involves a model invocation. Edge caching of generated responses requires the same tenant isolation and freshness controls as other personalized content. Evaluate avoided invocations, cache costs, latency, and incorrect-reuse rates together.