S-Store: Streaming Meets Transaction Processing
John Meehan, Nesime Tatbul, Stan Zdonik, Cansu Aslantas, Ugur, Cetintemel, Jiang Du, Tim Kraska, Samuel Madden, David Maier, Andrew Pavlo,, Michael Stonebraker, Kristin Tufte, Hao Wang

TL;DR
S-Store integrates streaming and transaction processing into a single system, enabling real-time, transactional data handling with higher throughput and stronger guarantees than existing systems.
Contribution
This work introduces S-Store, a system that combines stream processing with OLTP in a unified platform, extending H-Store to support both paradigms efficiently.
Findings
S-Store achieves higher throughput for streaming workloads than H-Store alone.
S-Store matches or exceeds Spark Streaming and Storm performance.
S-Store provides stronger transactional guarantees than existing streaming systems.
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. 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…
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Taxonomy
TopicsDigital Platforms and Economics · Customer churn and segmentation · Peer-to-Peer Network Technologies
