DART: A Lock-free Two-layer Hashed ART Index for Disaggregated Memory
Summary: DART: lock-free, crash-consistent two-layer hashed ART for disaggregated memory. Uses a hashed upper skip table + in-node adaptive hash layout to cut remote round trips and confine search to small buckets, boosting concurrency/scalability up to 5.8x on YCSB. (summarized by gpt-5.4-mini on Apr 11 2026)
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Authors
- 1. Bowen Zhang (Shanghai Jiao Tong University)
- 2. Shengan Zheng (Shanghai Jiao Tong University)
- 3. Shi Shu (Shanghai Jiao Tong University)
- 4. Jingxiang Li (Shanghai Jiao Tong University)
- 5. Zhenlin Qi (Shanghai Jiao Tong University)
- 6. Weiquan Huang (Shanghai Jiao Tong University)
- 7. Jianguo Wang (Purdue University)
- 8. Linpeng Huang (Shanghai Jiao Tong University)
- 9. Hong Mei (Peking University)
BibTeX Citation
@inproceedings{zhang_sigmod26,
title = {{DART: A Lock-free Two-layer Hashed ART Index for Disaggregated Memory}},
author = {Zhang, Bowen and Zheng, Shengan and Shu, Shi and Li, Jingxiang and Qi, Zhenlin and Huang, Weiquan and Wang, Jianguo and Huang, Linpeng and Mei, Hong},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786636},
url = {https://dl.acm.org/doi/10.1145/3786636},
year = {2026}
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