Near data processing within column-oriented DBMSs for high performance analysis
- Rapidly growing data volumes push today's analytical systems close to the feasible processing limit. Massive parallelism is one possible solution to reduce the computational time of analytical algorithms. However, data transfer becomes a significant bottleneck since it blocks system resources moving data-to-code. Technological advances allow to economically place compute units close to storage and perform data processing operations close to data, minimizing data transfers and increasing scalability. Hence the principle of Near Data Processing (NDP) and the shift towards code-to-data. In the present paper we claim that the development of NDP-system architectures becomes an inevitable task in the future. Analytical DBMS like HPE Vertica have multiple points of impact with major advantages which are presented within this paper.
| Author of HS Reutlingen | Petrov, Ilia |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-9362 |
| URL: | http://subs.emis.de/LNI/Proceedings/Proceedings258/article5.html |
| ISBN: | 978-3-88579-652-7 |
| Erschienen in: | Digital Enterprise Computing (DEC 2016) : Böblingen, Germany, June 14 - 15, 2016 |
| Publisher: | Gesellschaft für Informatik e.V |
| Place of publication: | Bonn |
| Editor: | Dieter Hertweck |
| Document Type: | Book chapter |
| Language: | English |
| Publication year: | 2016 |
| Tag: | big data; high performance analysis; modern hardware technology; near data processing |
| Page Number: | 5 |
| First Page: | 235 |
| Last Page: | 239 |
| PPN: | Im Katalog der Hochschule Reutlingen ansehen |
| DDC classes: | 004 Informatik |
| Open access?: | Ja |
| Licence (German): |  Open Access |
