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Near-data processing in database systems on native computational storage under HTAP workloads

  • Today’s Hybrid Transactional and Analytical Processing (HTAP) systems, tackle the ever-growing data in combination with a mixture of transactional and analytical workloads. While optimizing for aspects such as data freshness and performance isolation, they build on the traditional data-to-code principle and may trigger massive cold data transfers that impair the overall performance and scalability. Firstly, in this paper we show that Near-Data Processing (NDP) naturally fits in the HTAP design space. Secondly, we propose an NDP database architecture, allowing transactionally consistent in-situ executions of analytical operations in HTAP settings. We evaluate the proposed architecture in state-of-the-art key/value-stores and multi-versioned DBMS. In contrast to traditional setups, our approach yields robust, resource- and cost-effcient performance.

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Metadaten
Author of HS ReutlingenVinçon, Tobias; Knödler, Christian; Bernhardt, Arthur; Petrov, Ilia
DOI:https://doi.org/10.14778/3547305.3547307
ISSN:2150-8097
Erschienen in:Proceedings of the VLDB Endowment
Publisher:Association of Computing Machinery
Place of publication:New York
Document Type:Article
Language:English
Year of Publication:2022
Volume:15
Issue:10
Page Number:14
First Page:1991
Last Page:2004
DDC classes:004 Informatik
Open Access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International