Caching as a Pattern
Caching isn’t just about making things faster—it’s about shaping the economics and behavior of a system.
When used intentionally, it lets us trade a bit of freshness for big wins in speed, cost, and reliability.
What This Pattern Looks Like
At its core, the caching pattern says:
“If the result of an expensive operation is likely to be needed again soon, store it close at hand and reuse it.”
- Inputs: deterministic request (query, filters, parameters)
- Operation: compute or fetch from a slower/expensive source
- Output: save the result with a defined lifetime (TTL or invalidation rules)
It’s a pattern you’ll see in databases, web APIs, ML inference, and even everyday apps.
Benefits
- Performance – Users see results faster.
- Cost Reduction – Expensive calls (to a DB, API, or model) happen less often.
- Resilience – If the source is down, cached responses can cover the gap.
- Scalability – Reduces load on systems that don’t scale linearly.
- User Experience – Feels more “instant,” even when upstream systems are slow.
Tradeoffs
- Freshness vs. Speed – Cached data can be stale if TTL is too long.
- Complexity – More moving parts: invalidation, keying, and cache tiers.
- Consistency – Multiple caches can get out of sync with the source of truth.
The famous line: “There are only two hard things in computer science: cache invalidation and naming things.”
When to Use
Caching is most valuable when:
- The same request is repeated by many users.
- The cost of computing/fetching is much higher than the cost of storing.
- Results don’t change constantly (or staleness is acceptable).
- You need a safety net for external dependencies.
Examples:
- Search results or autocomplete suggestions.
- Product catalogs or directory listings.
- API responses from partners with rate limits.
- Pre-rendered UI fragments in content-heavy sites.
When Not to Use
- Data changes every second and must be live (e.g., stock trading prices).
- Writes dominate reads (caches won’t be hit often).
- Cache invalidation adds more complexity than the performance it saves.
Levels of Caching
- Browser / Client-side: fastest, good for personalization.
- Edge / CDN: great for static or semi-static responses.
- Application-layer (memory, Redis): flexible, under your control.
- Database Query Caches: offload repeated queries.
Each layer is a different scope of reuse.
Why Capture This Pattern
Caching is easy to treat as an afterthought (“we’ll add it later”), but when you design with it from the start, you:
- Build cleaner, more predictable key structures.
- Give yourself knobs (TTLs, invalidation, prefetching) instead of hacks.
- Avoid scaling crises when traffic spikes.
Caching is a choice that pays dividends when treated as part of the architecture, not just an optimization.