Timehash: Hierarchical Time Indexing for Efficient Business Hours Search

Temporal range filtering is critical in large-scale search systems, particularly location-based services filtering businesses by operating hours. Traditional approaches suffer from poor query performance (scope filtering), index size explosion (minute-level indexing), or reduced precision (coarse-grained indexing). PostgreSQL TSRANGE with GiST indexing offers exact semantics but imposes P50 latencies of 15-224 ms at 100K-1M scale, prohibitive for interactive search, and cannot embed within inverted index pipelines. We present Timehash, a hierarchical time indexing algorithm achieving over 97% reduction in index size versus minute-level indexing while maintaining 100% precision. Timehash uses a flexible multi-resolution strategy that integrates seamlessly into inverted index infrastructure. Through analysis of 12.6 million records from a production location search service deployed for 18 months, we demonstrate a domain-informed hierarchy-selection methodology via boundary-distribution analysis, with cross-dataset validation on the Yelp Open Dataset (127K US/CA businesses), where the same 5-level hierarchy reduces total terms to 0.77% of the 1-minute baseline (vs. 2.17% on the production dataset). We evaluate Timehash against naive inverted approaches, PostgreSQL GiST, and a within-Elasticsearch BKD baseline. On Yelp within a single Elasticsearch deployment with matched indexing, Timehash achieves 1.14-2.17x lower P50 latency than native BKD on production-typical multi-predicate top-K workloads (K