A catalog of stream processing optimizations
Open accessAbstract
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, and complex event processing. Since each community faces applications with challenging performance requirements, each of them has developed some of the same optimizations, but often with conflicting terminology and unstated assumptions. This article presents a survey of optimizations for stream processing. It is aimed both at users who need to understand and guide the system’s optimizer and at implementers who need to make engineering tradeoffs. To consolidate terminology, this article is organized as a catalog, in a style similar to catalogs of design patterns or refactorings. To make assumptions explicit and help understand tradeoffs, each optimization is presented with its safety constraints (when does it preserve correctness?) and a profitability experiment (when does it improve performance?). We hope that this survey will help future streaming system builders to stand on the shoulders of giants from not just their own community.
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Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing.
“Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing (Hirzel, Soulé, Schneider, Gedik, & Grimm, 2014).”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Hirzel, Soulé, Schneider, Gedik, & Grimm, 2014
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Self-citationDLACEP: A Deep-Learning Based Framework for Approximate Complex Event Processing
Amir, A., Kolchinsky, I., & Schuster, A. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3526136 · Cited by 12 · see citing works
Ku, M., Choi, E., & Min, D. (2014) · International Journal of Communication Systems · doi:10.1002/dac.2734 · Cited by 12 · see citing works
- Authors
- Hirzel, M., Soulé, R., Schneider, S., Gedik, B., & Grimm, R.
- Affiliations
- IBM (United States) (US)
- Università della Svizzera italiana (CH)
- Bilkent University (TR)
- New York University (US)
- Venue
- ACM Computing Surveys
- Published
- 2014-03
- Volume
- 46
- Issue
- 4
- Pages
- 1-34
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2148222209
- Cited by
- 299
- References
- 97
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 12 · 2023: 17 · 2022: 23 · 2021: 30 · 2020: 24 · 2019: 44 · 2018: 31 · 2017: 32 · 2016: 37 · 2015: 21 · 2014: 10 · 2013: 3 · 2012: 1
- FWCI
- 38.31
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM Computing Surveys (publishedVersion)
- Bilkent University Institutional Repository (Bilkent University) (submittedVersion)
- Funders
- National Science Foundation (1162444, 1111698)
- Office of Naval Research (N00014-12-1-0757, N00014-12-1, N00014-12, N00014)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Keywords
- stream processing, stream processing systems, correctness preservation, performance trade-offs, parallel computing, digital signal processing, complex event processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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