FactorJoin: A New Cardinality Estimation Framework for Join Queries
Summary: FactorJoin blends histogram efficiency with learned correlations. Offline single-table distributions and a factor-graph join model enable cardinality estimates without denormalization or workloads; small footprint and 40x latency, 100x smaller model. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ziniu Wu (Massachusetts Institute of Technology)
- 2. Parimarjan Negi (Massachusetts Institute of Technology)
- 3. Mohammad Alizadeh (Massachusetts Institute of Technology)
- 4. Tim Kraska (Massachusetts Institute of Technology)
- 5. Samuel Madden (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{wu_sigmod23,
title = {{FactorJoin: A New Cardinality Estimation Framework for Join Queries}},
author = {Wu, Ziniu and Negi, Parimarjan and Alizadeh, Mohammad and Kraska, Tim and Madden, Samuel},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588721},
url = {https://dl.acm.org/doi/10.1145/3588721},
year = {2023}
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