Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads
Summary: RusKey pioneers online LSM-tree design and RL-guided LSM transformations for dynamic workloads. FLSM-tree enables efficient transitions between compaction policies without workload priors, delivering up to 4× end-to-end performance vs RocksDB. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dingheng Mo (Nanyang Technological University)
- 2. Fanchao Chen (Fudan University)
- 3. Siqiang Luo (Nanyang Technological University)
- 4. Caihua Shan (Microsoft)
BibTeX Citation
@inproceedings{mo_sigmod23,
title = {{Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads}},
author = {Mo, Dingheng and Chen, Fanchao and Luo, Siqiang and Shan, Caihua},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3617333},
url = {https://dl.acm.org/doi/10.1145/3617333},
year = {2023}
}
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