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StreamScope: Continuous Reliable Distributed Processing of Big Data Streams
Wei Lin and Haochuan Fan, Microsoft; Zhengping Qian, Microsoft Research; Junwei Xu, Sen Yang, and Jingren Zhou, Microsoft; Lidong Zhou, Microsoft Research
This paper is part of the Operational Systems Track
STREAMSCOPE
(or STREAMS
) is a reliable distributed stream computation engine that has been deployed in shared 20,000-server production clusters at Microsoft. STREAMS provides a continuous temporal stream model that allows users to express complex stream processing logic naturally and declaratively. STREAMS
supports business-critical streaming applications that can process tens of billions (or tens of terabytes) of input events per day continuously with complex logic involving tens of temporal joins, aggregations, and sophisticated userdefined functions, while maintaining tens of terabytes in-memory computation states on thousands of machines.
STREAMS
introduces two abstractions, rVertex
and rStream
, to manage the complexity in distributed stream computation systems. The abstractions allow efficient and flexible distributed execution and failure recovery, make it easy to reason about correctness even with failures, and facilitate the development, debugging, and deployment of complex multi-stage streaming applications.
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author = {Wei Lin and Zhengping Qian and Junwei Xu and Sen Yang and Jingren Zhou and Lidong Zhou},
title = {{StreamScope}: Continuous Reliable Distributed Processing of Big Data Streams},
booktitle = {13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16)},
year = {2016},
isbn = {978-1-931971-29-4},
address = {Santa Clara, CA},
pages = {439--453},
url = {https://www.usenix.org/conference/nsdi16/technical-sessions/presentation/lin},
publisher = {USENIX Association},
month = mar
}
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