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.
Author of HS Reutlingen | Vinçon, Tobias; Knödler, Christian; Bernhardt, Arthur; Petrov, Ilia |
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URN: | urn:nbn:de:bsz:rt2-opus4-37780 |
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: | Journal article |
Language: | English |
Publication year: | 2022 |
Volume: | 15 |
Issue: | 10 |
Page Number: | 14 |
First Page: | 1991 |
Last Page: | 2004 |
DDC classes: | 004 Informatik |
Open access?: | Ja |
Licence (German): | Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |