NoDB: Efficient Query Execution on Raw Data Files.
As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare and to load the data into the database before executing the desired queries. Many applications already avoid using database systems, for example, sci...
| Publicado en: | Communications of the ACM Vol. 58; no. 12; pp. 112 - 122 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Association for Computing Machinery
Dec2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=111186000&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 111186000 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Dec2015 vid: 58 iid: 12 pid: 68 pub: Association for Computing Machinery artinfo: ui: 111186000 10.1145/2830508 ppf: 112 ppct: 10 formats: tig: atl: NoDB: Efficient Query Execution on Raw Data Files. aug: au: Alagiannis, loannis Borovica-Gajic, Renata Branco, Miguel Idreos, Stratos Ailamaki, Anastasia affil: École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. Harvard University, Cambridge, MA. su: Database management Technological innovations Database management software Query languages (Computer science) Data analysis software Social network analysis sug: subj: Database management Technological innovations Database management software Query languages (Computer science) Data analysis software Social network analysis ab: As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare and to load the data into the database before executing the desired queries. Many applications already avoid using database systems, for example, scientific data analysis and social networks, due to the complexity and the increased data-to-query time, that is, the time between getting the data and retrieving its first useful results. For many applications data collections keep growing fast, even on a daily basis, and this data deluge will only increase in the future, where it is expected to have much more data than what we can move or store, let alone analyze. We here present the design and roadmap of a new paradigm in database systems, called NoDB, which do not require data loading while still maintaining the whole feature set of a modern database system. In particular, we show how to make raw data files a first-class citizen, fully integrated with the query engine. Through our design and lessons learned by implementing the NoDB philosophy over a modern Database Management Systems (DBMS), we discuss the fundamental limitations as well as the strong opportunities that such a research path brings. We identify performance bottlenecks specific for in situ processing, namely the repeated parsing and tokenizing overhead and the expensive data type conversion. To address these problems, we introduce an adaptive indexing mechanism that maintains positional information to provide efficient access to raw data files, together with a flexible caching structure. We conclude that NoDB systems are feasible to design and implement over modern DBMS, bringing an unprecedented positive effect in usability and performance. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2015 holdings: @attributes: islocal: N |
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