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HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search

Summary: Harmony: distributed ANNS with a multi-gran partition (dimension-based + vector-based) for balanced load, reduced comms. Early-stop pruning leverages distance monotonicity to prune, delivering 4.63x throughput on 4 nodes and 58% gains on skewed workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7514
Venue
SIGMOD
Year
2026
Pagerank
5.173224e-05
Overall Rank
10,034 | 31.16%
DOI
10.1145/3749167

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{xu_sigmod26,
        title = {{HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search}},
        author = {Xu, Qian and Zhang, Feng and Li, Chengxi and Cao, Lei and Chen, Zheng and Zhai, Jidong and Du, Xiaoyong},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3749167},
        url = {https://dl.acm.org/doi/10.1145/3749167},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,525 Quantization Meets Projection: A Happy Marriage for Approximate k-Nearest Neighbor Search 2026 VLDB 5.093636e-05
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