S-Store: streaming meets transaction processing
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Madden_S-store.pdf
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920.35 KB
Format
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Checksum (MD5)
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Author(s)
Meehan, John
Pavlo, Andrew
Tufte, Kristin
Zdonik, Stan
Aslantas, Cansu
Cetintemel, Ugur
Du, Jiang
Kraska, Tim
Maier, David
Tatbul Bitim, Emine Nesime
Date Issued
September 2015
Journal
Proceedings of the VLDB Endowment
Publisher
Association for Computing Machinery
Citation
Meehan, John, et al. “S-Store: Streaming Meets Transaction Processing.” Proceedings of the VLDB Endowment, vol. 8, no. 13, Sept. 2015, pp. 2134–45.
Version
Final published version
Abstract
Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system, and provides both ACID and stream-oriented guarantees. We chose to build S-Store as an extension of H-Store - an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already provides, and we can concentrate on the additional features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Esper and Apache Storm, and show how S-Store can sometimes exceed their performance while at the same time providing stronger correctness guarantees.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/2831360.2831367
