Pushing Data-Induced Predicates Through Joins in Big-Data Clusters
Summary: Introduces data-induced predicates that propagate filters across joins, enabling optimizer-only data skipping without execution overhead. Using existing zone maps—and modestly richer statistics—substantially reduces input and roughly doubles median production-cluster query speed. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Srikanth Kandula (Microsoft)
- 2. Laurel Orr (University of Washington)
- 3. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@article{kandula_vldb20,
title = {{Pushing Data-Induced Predicates Through Joins in Big-Data Clusters}},
author = {Kandula, Srikanth and Orr, Laurel and Chaudhuri, Surajit},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {3},
pages = {252--265},
doi = {10.14778/3368289.3368292},
url = {https://doi.org/10.14778/3368289.3368292},
year = {2020}
}
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