Sub-optimal Join Order Identification with L1-error
Summary: Introduces L1-error, a permutation distance over subplan cardinalities with the same join count, weighting errors by magnitude and prioritizing small multi-way joins. Used within a standard decision tree, L1-error accurately identifies sub-optimal plans across four benchmarks, with gains when combined with Q-error as a low-overhead composite feature. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yesdaulet Izenov (University of California Merced)
- 2. Asoke Datta (University of California Merced)
- 3. Brian Tsan (University of California Merced)
- 4. Florin Rusu (University of California Merced)
BibTeX Citation
@inproceedings{izenov_sigmod24,
title = {{Sub-optimal Join Order Identification with L1-error}},
author = {Izenov, Yesdaulet and Datta, Asoke and Tsan, Brian and Rusu, Florin},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639272},
url = {https://dl.acm.org/doi/10.1145/3639272},
year = {2024}
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