PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees
Gespeichert in:
| Titel: | PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees |
|---|---|
| Autoren: | Yuxuan Zhu, Tengjun Jin, Stefanos Baziotis, Chengsong Zhang, Charith Mendis, Daniel Kang |
| Quelle: | Proceedings of the ACM on Management of Data. 3:1-28 |
| Verlagsinformationen: | Association for Computing Machinery (ACM), 2025. |
| Publikationsjahr: | 2025 |
| Beschreibung: | After decades of research in approximate query processing (AQP), its adoption in the industry remains limited. Existing methods struggle to simultaneously provide user-specified error guarantees, eliminate maintenance overheads, and avoid modifications to database management systems. To address these challenges, we introduce two novel techniques, TAQA and BSAP. TAQA is a two-stage online AQP algorithm that achieves all three properties for arbitrary queries. However, it can be slower than exact queries if we use standard row-level sampling. BSAP resolves this by enabling block-level sampling with statistical guarantees in TAQA. We implement TAQA and BSAP in a prototype middleware system, PilotDB, that is compatible with all DBMSs supporting efficient block-level sampling. We evaluate PilotDB on PostgreSQL, SQL Server, and DuckDB over real-world benchmarks, demonstrating up to 126X speedups when running with a 5% guaranteed error. |
| Publikationsart: | Article |
| Sprache: | English |
| ISSN: | 2836-6573 |
| DOI: | 10.1145/3725335 |
| Dokumentencode: | edsair.doi...........3ffa3bb1ace10cd50116dc8dfd3b8797 |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science Schreiben Sie den ersten Kommentar!