Tutorial: Stream processing optimizations
DEBS 2013 - Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems
249 - 258
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This tutorial starts with a survey of optimizations for streaming applications. The survey is organized as a catalog that introduces uniform terminology and a common categorization of optimizations across disciplines, such as data management, programming languages, and operating systems. After this survey, the tutorial continues with a deep-dive into the fission optimization, which automatically transforms streaming applications for data-parallelism. Fis-sion helps an application improve its throughput by taking advantage of multiple cores in a machine, or, in the case of a distributed streaming engine, multiple machines in a cluster. While the survey of optimizations covers a wide range of work from the literature, the in-depth discussion of ission relies more heavily on the presenters' own research and experience in the area. The tutorial concludes with a discussion of open research challenges in the field of stream processing optimizations. Copyright © 2013 ACM.
Published Version (Please cite this version)http://dx.doi.org/10.1145/2488222.2488268
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Schneider, S.; Hirzel, M.; Gedik, B.G.; Wu, K.-L. (2012)Streaming applications transform possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. The streaming ...
CAPSULE: Language and system support for efficient state sharing in distributed stream processing systems Losa G.; Kumar V.; Andrade H.; Gedik, B.; Hirzel, M.; Soulé, R.; Wu, K.-L. (2012)Data stream processing applications are often expressed as data flow graphs, composed of operators connected via streams. This structured representation provides a simple yet powerful paradigm for building large-scale, ...
Hirzel M.; Soulé R.; Schneider S.; Gedik, B.; Grimm, R. (Association for Computing Machinery, 2014)Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing. These communities include digital signal processing, databases, operating systems, ...