Lindorm-UWC: An Ultra-Wide-Column Database for Internet of Vehicles
Summary: Empirical study of real-world IoV workloads reveals extreme high-throughput, multi-metric writes that challenge conventional DBMS designs. Lindorm-UWC uses per-partition ultra-wide-column storage with cold/hot separation to handle multi-metric ingestion, achieving ~79% higher write throughput, competitive query performance, and tens of PB in production. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Qianyu Ouyang (Alibaba; Tsinghua University)
- 2. Chunhui Shen (Alibaba; Zhejiang University)
- 3. Wenlong Yang (Alibaba)
- 4. Peng Yu (Alibaba)
- 5. Qiang Xiao (Alibaba)
- 6. Jianhui Lei (Alibaba)
- 7. Yadong Chen (Alibaba)
- 8. Qilu Zhong (Alibaba)
- 9. Xiang Wang (Alibaba)
- 10. Yong Lin (Alibaba)
- 11. Qingyi Meng (Alibaba)
- 12. Zhicheng Ji (Alibaba; Tsinghua University)
- 13. Wei Meng (Alibaba)
- 14. Cen Zheng (Alibaba)
- 15. Sheng Wang (Alibaba)
- 16. Dan Pei (Tsinghua University)
- 17. Wei Zhang (Alibaba)
- 18. Feifei Li (Alibaba)
- 19. Jingren Zhou (Alibaba)
BibTeX Citation
@article{ouyang_vldb24,
title = {{Lindorm-UWC: An Ultra-Wide-Column Database for Internet of Vehicles}},
author = {Ouyang, Qianyu and Shen, Chunhui and Yang, Wenlong and Yu, Peng and Xiao, Qiang and Lei, Jianhui and Chen, Yadong and Zhong, Qilu and Wang, Xiang and Lin, Yong and Meng, Qingyi and Ji, Zhicheng and Meng, Wei and Zheng, Cen and Wang, Sheng and Pei, Dan and Zhang, Wei and Li, Feifei and Zhou, Jingren},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4117--4129},
doi = {10.14778/3685800.3685831},
url = {https://doi.org/10.14778/3685800.3685831},
year = {2024}
}
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,258 | Kangaroo: Efficient Lossless Floating-Point Compression via Dynamic Reference Selection | 2026 | SIGMOD | 5.093636e-05 |
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