DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees
Summary: DobLIX: a dual-objective learned index for LSM trees that trains models to minimize both index lookup error and downstream on-disk data-access cost, addressing I/O-heavy lookups when index parts live on disk. Includes an RL tuner for online adaptation, yielding 1.19×–2.21× throughput gains in RocksDB while preserving write efficiency. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Alireza Heidari (Huawei)
- 2. Amirhossein Ahmadi (Huawei)
- 3. Wei Zhang (Huawei)
BibTeX Citation
@article{heidari_vldb25,
title = {{DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees}},
author = {Heidari, Alireza and Ahmadi, Amirhossein and Zhang, Wei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {3965--3978},
doi = {10.14778/3749646.3749667},
url = {https://doi.org/10.14778/3749646.3749667},
year = {2025}
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