Unraveling the Impact of Window Semantics: Optimizing Join Order for Efficient Stream Processing
Summary: Introduces three semantics-preserving rewrite rules for reordering sliding, session, and interval window joins despite weakened algebraic properties. WJR enumerates equivalent multiway join orders, yielding up to 10× speedups over fixed-order stream plans. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Ariane Ziehn (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 2. Jan Szlang (Snowflake)
- 3. Steffen Zeuch (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 4. Volker Markl (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
BibTeX Citation
@article{ziehn_vldb25,
title = {{Unraveling the Impact of Window Semantics: Optimizing Join Order for Efficient Stream Processing}},
author = {Ziehn, Ariane and Szlang, Jan and Zeuch, Steffen and Markl, Volker},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {8},
pages = {2468--2481},
doi = {10.14778/3742728.3742741},
url = {https://doi.org/10.14778/3742728.3742741},
year = {2025}
}
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