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LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration

Summary: LakeHelm is a zero-shot advisor that jointly selects lakehouse execution engines, table formats, and configurations. Its dual-gate MoE models cross-subsystem interactions and, using synthetic workloads, generalizes to unseen queries without costly online tuning. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h5c1c01f555f11c23
Venue
VLDB
Year
2026
Pagerank
4.9769913e-05
Overall Rank
10,757 | 27.71%
DOI
10.14778/3811243.3811250
PDF
Download (CC BY-NC-ND 4.0)

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Authors

BibTeX Citation

@article{xu_vldb26,
        title = {{LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration}},
        author = {Xu, Zhongwei and Dong, Siyuan and Gong, Haotian and Pham, Donna and Ma, Lin},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {8},
        pages = {1768--1781},
        doi = {10.14778/3811243.3811250},
        url = {https://doi.org/10.14778/3811243.3811250},
        year = {2026}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061067652
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036675568
174 Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation 2024 VLDB 0.00026787121
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021276452
940 Starfish: A Self-tuning System for Big Data Analytics 2011 CIDR 0.00012959992
950 Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics 2021 CIDR 0.00012889569
2,770 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.0343719e-05
3,052 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.704739e-05
3,648 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.1366536e-05
4,332 Analyzing and Comparing Lakehouse Storage Systems 2023 CIDR 6.6556222e-05
4,686 LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications 2022 SIGMOD 6.4690725e-05
5,284 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 6.1963031e-05
5,354 Adaptive and Robust Query Execution for Lakehouses at Scale 2024 VLDB 6.1661231e-05
7,072 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.6053987e-05
8,215 LST-Bench: Benchmarking Log-Structured Tables in the Cloud 2024 SIGMOD 5.3750141e-05
8,608 Towards Resource Efficiency: Practical Insights into Large-Scale Spark Workloads at ByteDance 2024 VLDB 5.3026618e-05
8,643 PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking 2025 VLDB 5.2956539e-05
9,031 Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment 2025 SIGMOD 5.2323895e-05
10,340 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 5.0257853e-05
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