How to Reduce API Latency Under Load Without Adding Another Cache Tier

Rising API traffic can expose database bottlenecks as queries accumulate, connection pools fill, and latency increases, prompting common remedies such as vertical database scaling, read replicas, or distributed caches like Redis. A Harper-built house-listings reference application explores an alternative for read-heavy workloads by keeping structured data, application logic, API handling, blob storage, and a worker-local in-memory result cache in one runtime. Its SearchListings resource performs filtering, sorting, and pagination on a cache miss, then reuses normalized query results for 30 seconds; in testing, uncached searches took about 5–6 milliseconds of server time, while cached requests took roughly 0.03–0.05 milliseconds because the underlying work was removed from the request path. The example does not argue that in-process caching replaces distributed caches universally, since shared state, independent scaling, and coordinated invalidation can still require an external cache, but it highlights that some architectures can reduce both database load and network dependencies without adding another service tier.

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