Integer compression in NVRAM-centric data stores: Comparative experimental analysis to DRAM
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Lightweight integer compression algorithms play an important role in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. Thus, there is a large number of algorithms to choose from, while different algorithms are tailored to different data characteristics. As we show in this paper, with the availability of byte-addressable non-volatile random-access memory (NVRAM), a novel type of main memory with specific characteristics increases the overall complexity in this domain. In particular, we provide a detailed evaluation of state-of-the-art lightweight integer compression schemes and database operations on NVRAM and compare it with DRAM. Furthermore, we reason about possible deployments of middle- and heavyweight approaches for better adaptation to NVRAM characteristics. Finally, we investigate a combined approach where both volatile and non-volatile memories are used in a cooperative fashion that is likely to be the case for hybrid and NVRAM-centric database systems.
Details
Original language | English |
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Title of host publication | DaMoN'19: Proceedings of the 15th International Workshop on Data Management on New Hardware |
Publisher | Association for Computing Machinery (ACM), New York |
Pages | 1-11 |
ISBN (print) | 978-1-4503-6801-8 |
Publication status | Published - 1 Jul 2019 |
Peer-reviewed | Yes |
Publication series
Series | MOD: International Conference on Management of Data (DaMoN) |
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ISSN | 0730-8078 |
Conference
Title | 15th International Workshop on Data Management on New Hardware, DaMoN 2019, Held with ACM SIGMOD/PODS 2019 |
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Duration | 1 July 2019 |
City | Amsterdam |
Country | Netherlands |
External IDs
dblp | conf/damon/ZarubinDKHLW19 |
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ORCID | /0000-0001-8107-2775/work/142253577 |
Keywords
ASJC Scopus subject areas
Keywords
- Analysis, Data compression, Database systems, In-memory, NVRAM