Proceedings of the VLDB Endowment - Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii
database management, Transaction processing systems (Computer systems)
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. 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 supports, and we can concentrate on the additional implementation 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, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees.
Meehan, John; Tatbul, Nesime; Aslantas, Cansu; Cetintemel, Ugur; Du, Jiang; Kraska, Tim; Madden, Samuel; Maier, David; Pavlo, Andrew; Stonebraker, Michael; Tufte, Kristin A.; and Wang, Hao, "S-Store: Streaming Meets Transaction Processing" (2015). Computer Science Faculty Publications and Presentations. 143.