S-Store: a streaming NewSQL system for big velocity applications
First-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, a...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery (ACM)
2016
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| Online Access: | http://hdl.handle.net/1721.1/100909 https://orcid.org/0000-0001-9184-9058 https://orcid.org/0000-0002-7470-3265 |
| _version_ | 1826211191837425664 |
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| author | Cetintemel, Ugur Tufte, Kristin Wang, Hao Zdonik, Stanley Du, Jiang Kraska, Tim Maier, David Meehan, John Pavlo, Andrew Stonebraker, Michael Sutherland, Erik Madden, Samuel R. Tatbul Bitim, Emine Nesime |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Cetintemel, Ugur Tufte, Kristin Wang, Hao Zdonik, Stanley Du, Jiang Kraska, Tim Maier, David Meehan, John Pavlo, Andrew Stonebraker, Michael Sutherland, Erik Madden, Samuel R. Tatbul Bitim, Emine Nesime |
| author_sort | Cetintemel, Ugur |
| collection | MIT |
| description | First-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. |
| first_indexed | 2024-09-23T15:02:01Z |
| format | Article |
| id | mit-1721.1/100909 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:02:01Z |
| publishDate | 2016 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1009092022-09-29T12:12:58Z S-Store: a streaming NewSQL system for big velocity applications Cetintemel, Ugur Tufte, Kristin Wang, Hao Zdonik, Stanley Du, Jiang Kraska, Tim Maier, David Meehan, John Pavlo, Andrew Stonebraker, Michael Sutherland, Erik Madden, Samuel R. Tatbul Bitim, Emine Nesime Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Madden, Samuel R. Stonebraker, Michael Tatbul Bitim, Emine Nesime Wang, Hao First-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. 2016-01-19T01:48:04Z 2016-01-19T01:48:04Z 2014-08 Article http://purl.org/eprint/type/ConferencePaper 21508097 http://hdl.handle.net/1721.1/100909 Ugur 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. https://orcid.org/0000-0001-9184-9058 https://orcid.org/0000-0002-7470-3265 en_US http://dx.doi.org/10.14778/2733004.2733048 Proceedings of the VLDB Endowment Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License http://creativecommons.org/licenses/by-nc-nd/3.0/ application/pdf Association for Computing Machinery (ACM) MIT web domain |
| spellingShingle | Cetintemel, Ugur Tufte, Kristin Wang, Hao Zdonik, Stanley Du, Jiang Kraska, Tim Maier, David Meehan, John Pavlo, Andrew Stonebraker, Michael Sutherland, Erik Madden, Samuel R. Tatbul Bitim, Emine Nesime S-Store: a streaming NewSQL system for big velocity applications |
| title | S-Store: a streaming NewSQL system for big velocity applications |
| title_full | S-Store: a streaming NewSQL system for big velocity applications |
| title_fullStr | S-Store: a streaming NewSQL system for big velocity applications |
| title_full_unstemmed | S-Store: a streaming NewSQL system for big velocity applications |
| title_short | S-Store: a streaming NewSQL system for big velocity applications |
| title_sort | s store a streaming newsql system for big velocity applications |
| url | http://hdl.handle.net/1721.1/100909 https://orcid.org/0000-0001-9184-9058 https://orcid.org/0000-0002-7470-3265 |
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