Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty
Summary: Endure robustly tunes LSM compaction policy, size ratio, and memory by maximizing worst-case throughput over an explicitly parameterized workload neighborhood. RocksDB evaluations show up to 5× higher throughput under uncertainty with negligible loss on the nominal workload. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Andy Huynh (Boston University)
- 2. Harshal A. Chaudhari (Boston University)
- 3. Evimaria Terzi (Boston University)
- 4. Manos Athanassoulis (Boston University)
BibTeX Citation
@article{huynh_vldb22,
title = {{Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty}},
author = {Huynh, Andy and Chaudhari, Harshal A. and Terzi, Evimaria and Athanassoulis, Manos},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {8},
pages = {1605--1618},
doi = {10.14778/3529337.3529345},
url = {https://doi.org/10.14778/3529337.3529345},
year = {2022}
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