Caching Strategies for Search Results
Where and how long to cache search results without serving stale data.
What it means
Caching search results trades freshness for cost and speed, and the right tradeoff point depends entirely on how fast the underlying information changes — a static reference fact can be cached for days, while a stock price or breaking-news query is stale within minutes. A good caching layer sets TTL per query type or category rather than applying one blanket duration to every cached response.
In practice
A documentation-lookup query like Python requests library timeout parameter can safely cache for a day or more since the answer rarely changes; a query like current Bitcoin price cached for even five minutes can already be materially wrong by the time it's served, so it needs either a very short TTL or no caching at all.
Tradeoffs
Caching at the exact-query-string level is simple but misses near-duplicate queries with different phrasing but the same intent, leaving real savings on the table; caching at a normalized or semantic level catches more redundancy but adds implementation complexity and a small risk of conflating queries that were subtly different for a reason.