An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments & Analysis]
Summary: Production PostgreSQL study of filter-agnostic filtered vector search across selectivities and correlations, comparing post- versus inline-filtering. Shows page-access and filter-check overheads can outweigh distance costs: ScaNN may beat graph indexes, with choices determined by workload and system costs. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Duo Lu (Brown University)
- 2. Helena Caminal (Google)
- 3. Manos Chatzakis (Université Paris Cité)
- 4. Yannis Papakonstantinou (Google)
- 5. Yannis Chronis (ETH Zurich; Google)
- 6. Vaibhav Jain (Google)
- 7. Fatma Özcan (Google)
BibTeX Citation
@inproceedings{lu_sigmod26,
title = {{An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments \& Analysis]}},
author = {Lu, Duo and Caminal, Helena and Chatzakis, Manos and Papakonstantinou, Yannis and Chronis, Yannis and Jain, Vaibhav and Özcan, Fatma},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802011},
url = {https://dl.acm.org/doi/10.1145/3802011},
year = {2026}
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