DBScholar

Back to papers

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
4.9605343e-05
Overall Rank
10,818 | 27.49%
DOI
10.14778/3811243.3811250
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

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 0.00061463175
74 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00037164008
163 Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation 2024 VLDB 0.00027456647
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
944 Starfish: A Self-tuning System for Big Data Analytics 2011 CIDR 0.00012927479
955 Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics 2021 CIDR 0.00012856295
2,779 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.0085666e-05
3,057 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.6865211e-05
3,655 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.1137504e-05
4,346 Analyzing and Comparing Lakehouse Storage Systems 2023 CIDR 6.6386388e-05
4,698 LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications 2022 SIGMOD 6.4476818e-05
5,304 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 6.1758143e-05
5,367 Adaptive and Robust Query Execution for Lakehouses at Scale 2024 VLDB 6.1462017e-05
7,092 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.5868637e-05
8,232 LST-Bench: Benchmarking Log-Structured Tables in the Cloud 2024 SIGMOD 5.357241e-05
8,626 Towards Resource Efficiency: Practical Insights into Large-Scale Spark Workloads at ByteDance 2024 VLDB 5.2851279e-05
8,662 PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking 2025 VLDB 5.2781431e-05
9,052 Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment 2025 SIGMOD 5.215088e-05
10,366 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 5.0091669e-05
Previous Page 1 / 1 Next

Semantically Similar Papers