MFTune: An Efficient Multi-fidelity Framework for Spark SQL Configuration Tuning
Summary: MFTune enables multi-fidelity Spark SQL tuning via representative SQL-subset fidelities that preserve bottleneck/performance correlations, unlike data reduction or early stopping. Density-based knob/range compression, transfer learning, and two-phase warm starts accelerate search and outperform five baselines. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Beicheng Xu (Peking University)
- 2. Lingching Tung (Peking University)
- 3. Yuchen Wang (Peking University)
- 4. Yupeng Lu (Peking University)
- 5. Bin Cui (Peking University)
BibTeX Citation
@article{xu_vldb26,
title = {{MFTune: An Efficient Multi-fidelity Framework for Spark SQL Configuration Tuning}},
author = {Xu, Beicheng and Tung, Lingching and Wang, Yuchen and Lu, Yupeng and Cui, Bin},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3649--3662},
doi = {10.14778/3836663.3836715},
url = {https://doi.org/10.14778/3836663.3836715},
year = {2026}
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