SAHARA : Memory Footprint Reduction of Cloud Databases with Automated Table Partitioning

Brendle, Michael; Weber, Nick; Valiyev, Mahammad; May, Norman; Schulze, Robert; Böhm, Alexander; Moerkotte, Guido; Grossniklaus, Michael

SAHARA : Memory Footprint Reduction of Cloud Databases with Automated Table Partitioning

Dateien

Brendle_2-1wjvu7v8187120.pdfGröße: 1.01 MBDownloads: 104

Datum

2022

Autor:innen

Brendle, Michael

Weber, Nick

Valiyev, Mahammad

May, Norman

Schulze, Robert

Böhm, Alexander

Moerkotte, Guido

Grossniklaus, Michael

Open Access-Veröffentlichung

Open Access Bookpart

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

Proceedings 25th International Conference on Extending Database Technology (EDBT 2022). Konstanz: University of Konstanz, 2022, pp. 13-26. Advances in Database Technology. 25,1. eISSN 2367-2005. ISBN 978-3-89318-086-8. Available under: doi: 10.5441/002/edbt.2022.02

Zusammenfassung

Enterprises increasingly move their databases into the cloud. As a result, database-as-a-service providers are challenged to meet the performance guarantees assured in service-level agreements (SLAs) while keeping hardware costs as low as possible. Being cost-effective is particularly crucial for cloud databases where the provisioned amount of DRAM dominates the hardware costs. A way to decrease the memory footprint is to leverage access skew in the workload by moving rarely accessed cold data to cheaper storage layers and retaining only frequently accessed hot data in main memory. In this paper, we present SAHARA, an advisor that proposes a table partitioning for column stores with minimal memory footprint while still adhering to all performance SLAs. SAHARA collects lightweight workload statistics, classifies data as hot and cold, and calculates optimal or near-optimal range partitioning layouts with low optimization time using a novel cost model. We integrated SAHARA into a commercial cloud database and show in our experiments for real-world and synthetic benchmarks a memory footprint reduction of 2.5× while still fulfilling all performance SLAs provided by the customer or advertised by the DBaaS provider.

Fachgebiet (DDC)

004 Informatik

Konferenz

25th International Conference on Extending Database Technology (EDBT 2022), 29. März 2022 - 1. Apr. 2022, Edinburgh, UK

Zitieren

ISO 690

BRENDLE, Michael, Nick WEBER, Mahammad VALIYEV, Norman MAY, Robert SCHULZE, Alexander BÖHM, Guido MOERKOTTE, Michael GROSSNIKLAUS, 2022. SAHARA : Memory Footprint Reduction of Cloud Databases with Automated Table Partitioning. 25th International Conference on Extending Database Technology (EDBT 2022). Edinburgh, UK, 29. März 2022 - 1. Apr. 2022. In: Proceedings 25th International Conference on Extending Database Technology (EDBT 2022). Konstanz: University of Konstanz, 2022, pp. 13-26. Advances in Database Technology. 25,1. eISSN 2367-2005. ISBN 978-3-89318-086-8. Available under: doi: 10.5441/002/edbt.2022.02

BibTex

@inproceedings{Brendle2022SAHAR-59633,
  year={2022},
  doi={10.5441/002/edbt.2022.02},
  title={SAHARA : Memory Footprint Reduction of Cloud Databases with Automated Table Partitioning},
  number={25,1},
  isbn={978-3-89318-086-8},
  publisher={University of Konstanz},
  address={Konstanz},
  series={Advances in Database Technology},
  booktitle={Proceedings 25th International Conference on Extending Database Technology (EDBT 2022)},
  pages={13--26},
  author={Brendle, Michael and Weber, Nick and Valiyev, Mahammad and May, Norman and Schulze, Robert and Böhm, Alexander and Moerkotte, Guido and Grossniklaus, Michael}
}

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Universitätsbibliographie

Ja