SAHARA: Memory footprint reduction of cloud databases with automated table partitioning

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

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URN: urn:nbn:de:bsz:180-madoc-623669
Document Type: Conference or workshop publication
Year of publication: 2022
Book title: Proceedings of the 25th International Conference on Extending Database Technology, EDBT 2022. Edinburgh, UK, March 29 - April 1
The title of a journal, publication series: Advances in Database Technology
Volume: 25,1
Page range: 13-26
Conference title: EDBT 2022
Location of the conference venue: Edinburgh, UK
Date of the conference: 29.03.-01.04.2022
Place of publication: Konstanz
Publishing house:
ISBN: 978-3-89318-086-8
ISSN: 2367-2005
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science III (Moerkotte 1996-)
Pre-existing license: Creative Commons Attribution, Non-Commercial, No Derivatives 4.0 International (CC BY-NC-ND 4.0)
Subject: 004 Computer science, internet
Abstract: 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.
Additional information: Online-Ressource

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