Timehash: Hierarchical Time Indexing for Efficient Business Hours Search
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913160938651648 |
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| author | Kim, Jinoh Son, Jaewon |
| author_facet | Kim, Jinoh Son, Jaewon |
| contents | 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 <= 100), with methods converging at large K where document materialization dominates. A five-level hierarchy (4h, 1h, 15m, 5m, 1m) reduces index terms to 9.6 per document, a 97.8% reduction and 46x compaction, with zero false positives and zero false negatives. Per-doc cost stays constant from 100K to 12.6M POIs while supporting break times, irregular schedules, and midnight-spanning ranges |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02941 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Timehash: Hierarchical Time Indexing for Efficient Business Hours Search Kim, Jinoh Son, Jaewon Databases Information Retrieval H.3.1; H.3.3 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 <= 100), with methods converging at large K where document materialization dominates. A five-level hierarchy (4h, 1h, 15m, 5m, 1m) reduces index terms to 9.6 per document, a 97.8% reduction and 46x compaction, with zero false positives and zero false negatives. Per-doc cost stays constant from 100K to 12.6M POIs while supporting break times, irregular schedules, and midnight-spanning ranges |
| title | Timehash: Hierarchical Time Indexing for Efficient Business Hours Search |
| topic | Databases Information Retrieval H.3.1; H.3.3 |
| url | https://arxiv.org/abs/2603.02941 |