Timehash: Hierarchical Time Indexing for Efficient Business Hours Search

Temporal range filtering is a critical operation in large-scale search systems, particularly for location-based services that need to filter businesses by operating hours. Traditional approaches either suffer from poor query performance (scope filtering) or index size explosion (minute-level indexing). We present Timehash, a novel hierarchical time indexing algorithm that achieves over 99% reduction in index size compared to minute-level indexing while maintaining 100% precision. Timehash employs a flexible multi-resolution strategy with customizable hierarchical levels. Through empirical analysis on distributions from 12.6 million business records of a production location search service, we demonstrate a data-driven methodology for selecting optimal hierarchies tailored to specific data distributions. We evaluated Timehash on up to 12.6 million synthetic POIs generated from production distributions. Experimental results show that a five-level hierarchy reduces index terms to 5.6 per document (99.1% reduction versus minute-level indexing), with zero false positives and zero false negatives. Scalability benchmarks confirm constant per-document cost from 100K to 12.6M POIs, while supporting complex scenarios such as break times and irregular schedules. Our approach is generalizable to various temporal filtering problems in search systems, e-commerce, and reservation platforms.