Tutorial: Stream processing optimizations
Author
Schneider, S.
Hirzel, M.
Gedik, Buğra
Date
2013Source Title
DEBS 2013 - Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems
Publisher
ACM
Pages
249 - 258
Language
English
Type
Conference PaperItem Usage Stats
123
views
views
195
downloads
downloads
Abstract
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.
Keywords
Data parallelismFission
Optimizaition
Stream processing
Data parallelism
Distributed streaming
Fission
Multiple machine
Optimizaition
Research challenges
Stream processing
Streaming applications
Information management
Software architecture
Surveys
Optimization
Permalink
http://hdl.handle.net/11693/27986Published Version (Please cite this version)
http://dx.doi.org/10.1145/2488222.2488268Collections
Related items
Showing items related by title, author, creator and subject.
-
Auto-parallelizing stateful distributed streaming applications
Schneider, S.; Hirzel, M.; Gedik, Buğra; 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, Buğra; Hirzel, M.; Soulé, R.; Wu, K. -L. (ACM, 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, ... -
Generic windowing support for extensible stream processing systems
Gedik, B. (John Wiley & Sons Ltd., 2014)Stream processing applications process high volume, continuous feeds from live data sources, employ data-in-motion analytics to analyze these feeds, and produce near real-time insights with low latency. One of the fundamental ...