Bingshen He, Mao Yang, Zhenyu Guo, Rishan Chen, Bing Su, Wei Lin, and Lidong Zhou
19 May 2010
Batched stream processing is a new distributed data processing paradigm that models recurring batch computations on incrementally bulk-appended data streams. The model is inspired by our empirical study on a trace from a large-scale production data-processing cluster; it allows a set of effective query optimizations that are not possible in a traditional batch processing model.
We have developed a query processing system called Comet that embraces batched stream processing and integrates with DryadLINQ. We used two complementary methods to evaluate the effectiveness of optimizations that Comet enables. First, a prototype system deployed on a 40-node cluster shows an I/O reduction of over 40% using our benchmark. Second, when applied to a real production trace covering over 19 million machine-hours, our simulator shows an estimated I/O saving of over 50%.
In ACM Symposium on Cloud Computing 2010
Publisher Association for Computing Machinery, Inc.
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Bingsheng He, Mao Yang, Zhenyu Guo, Rishan Chen, Bing Su, Wei Lin, and Lidong Zhou. Comet: Batched Stream Processing in Data Intensive Distributed Computing, Microsoft Research, December 2009.