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dc.contributor.authorCetintemel, Ugur
dc.contributor.authorTufte, Kristin
dc.contributor.authorWang, Hao
dc.contributor.authorZdonik, Stanley
dc.contributor.authorDu, Jiang
dc.contributor.authorKraska, Tim
dc.contributor.authorMaier, David
dc.contributor.authorMeehan, John
dc.contributor.authorPavlo, Andrew
dc.contributor.authorStonebraker, Michael
dc.contributor.authorSutherland, Erik
dc.contributor.authorMadden, Samuel R.
dc.contributor.authorTatbul Bitim, Emine Nesime
dc.date.accessioned2016-01-19T01:48:04Z
dc.date.available2016-01-19T01:48:04Z
dc.date.issued2014-08
dc.identifier.issn21508097
dc.identifier.urihttp://hdl.handle.net/1721.1/100909
dc.description.abstractFirst-generation streaming systems did not pay much attention to state management via ACID transactions (e.g., [3, 4]). S-Store is a data management system that combines OLTP transactions with stream processing. To create S-Store, we begin with H-Store, a main-memory transaction processing engine, and add primitives to support streaming. This includes triggers and transaction workflows to implement push-based processing, windows to provide a way to bound the computation, and tables with hidden state to implement scoping for proper isolation. This demo explores the benefits of this approach by showing how a naïve implementation of our benchmarks using only H-Store can yield incorrect results. We also show that by exploiting push-based semantics and our implementation of triggers, we can achieve significant improvement in transaction throughput. We demo two modern applications: (i) leaderboard maintenance for a version of "American Idol", and (ii) a city-scale bicycle rental scenario.en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.14778/2733004.2733048en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleS-Store: a streaming NewSQL system for big velocity applicationsen_US
dc.typeArticleen_US
dc.identifier.citationUgur Cetintemel, Jiang Du, Tim Kraska, Samuel Madden, David Maier, John Meehan, Andrew Pavlo, Michael Stonebraker, Erik Sutherland, Nesime Tatbul, Kristin Tufte, Hao Wang, and Stanley Zdonik. 2014. S-Store: a streaming NewSQL system for big velocity applications. Proc. VLDB Endow. 7, 13 (August 2014), 1633-1636.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorMadden, Samuel R.en_US
dc.contributor.mitauthorStonebraker, Michaelen_US
dc.contributor.mitauthorTatbul Bitim, Emine Nesimeen_US
dc.contributor.mitauthorWang, Haoen_US
dc.relation.journalProceedings of the VLDB Endowmenten_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsCetintemel, Ugur; Tatbul, Nesime; Tufte, Kristin; Wang, Hao; Zdonik, Stanley; Du, Jiang; Kraska, Tim; Madden, Samuel; Maier, David; Meehan, John; Pavlo, Andrew; Stonebraker, Michael; Sutherland, Eriken_US
dc.identifier.orcidhttps://orcid.org/0000-0001-9184-9058
dc.identifier.orcidhttps://orcid.org/0000-0002-7470-3265
mit.licensePUBLISHER_CCen_US


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