Adaptive Compression For Databases

Leon Windheuser, Christoph Anneser, Huanchen Zhang, Thomas Neumann, Alfons Kemper

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

Efficient utilization of dynamic random access memory (DRAM) is crucial for achieving high-performance query processing in database systems, especially as data volumes continue to grow. Unfortunately, the cost of DRAM is unlikely to decrease in the coming years, and it is already the dominating cost factor in modern data centers. Consequently, lightweight in-memory compression techniques can reduce the memory footprint and maximize the data stored in memory. However, compressing all data, regardless of the compression algorithm’s efficiency, causes additional CPU overhead during query execution. To address this challenge, we introduce AdaCom, a novel framework that selectively applies lightweight succinct encodings only to infrequently accessed data. By doing so, we mitigate the performance overhead associated with compression. In our experimental evaluation, we demonstrate that AdaCom reduces the memory footprint by up to 40% while retaining most of the performance (≈ 95%).

OriginalspracheEnglisch
TitelAdvances in Database Technology - EDBT
Herausgeber (Verlag)OpenProceedings.org
Seiten143-149
Seitenumfang7
Auflage2
ISBN (elektronisch)9783893180912, 9783893180943, 9783893180950
DOIs
PublikationsstatusVeröffentlicht - 22 Nov. 2023
Veranstaltung27th International Conference on Extending Database Technology, EDBT 2024 - Paestum, Italien
Dauer: 25 März 202428 März 2024

Publikationsreihe

NameAdvances in Database Technology - EDBT
Nummer2
Band27
ISSN (elektronisch)2367-2005

Konferenz

Konferenz27th International Conference on Extending Database Technology, EDBT 2024
Land/GebietItalien
OrtPaestum
Zeitraum25/03/2428/03/24

Fingerprint

Untersuchen Sie die Forschungsthemen von „Adaptive Compression For Databases“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren