Perseus: Achieving Strong Consistency and High Data Freshness for Scalable Geo-distributed HTAP
Summary: Perseus enforces strong consistency for transactions and analytical queries in scalable geo-distributed HTAP by augmenting dependency graphs with data versions and full inter-version dependencies. A dynamic snapshot algorithm selectively includes updates to minimize analytical staleness, achieving up to ~90% lower visibility delay while remaining scalable and robust to network instability. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Haoze Song (University of Hong Kong)
- 2. Xusheng Chen (Huawei)
- 3. Ruijie Gong (University of Hong Kong)
- 4. Zekai Sun (University of Hong Kong)
- 5. Tianxiang Shen (University of Hong Kong)
- 6. Cheng Li (University of Science and Technology Beijing)
- 7. Hao Feng (Huawei)
- 8. Sen Wang (Huawei)
- 9. Heming Cui (University of Hong Kong)
BibTeX Citation
@inproceedings{song_sigmod26,
title = {{Perseus: Achieving Strong Consistency and High Data Freshness for Scalable Geo-distributed HTAP}},
author = {Song, Haoze and Chen, Xusheng and Gong, Ruijie and Sun, Zekai and Shen, Tianxiang and Li, Cheng and Feng, Hao and Wang, Sen and Cui, Heming},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3749178},
url = {https://dl.acm.org/doi/10.1145/3749178},
year = {2026}
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