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OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning

Summary: OBELISK reframes offline plan management as optimizing cost-scaling knobs that steer CQO toward robust plans, instead of directly searching the plan space. Training-free closed loop: Bayesian optimization guides knob subspaces, LM reasoning proposes configs, and history-aware gating cuts redundant evaluations. (summarized by gpt-5.4-mini on May 27 2026)

Paper ID
h3fea651a66684c26
Venue
VLDB
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,741 | 27.79%
DOI
10.14778/3801059.3801077

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@article{pan_vldb26,
        title = {{OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning}},
        author = {Pan, Zhicheng and Sun, Wenwen and Zhang, Yuanjia and Purcell, Terence and Dong, Yu and Yang, Chengcheng and Zhang, Rong and Zhou, Xuan and Xu, Jianliang},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {7},
        pages = {1674--1687},
        doi = {10.14778/3801059.3801077},
        url = {https://doi.org/10.14778/3801059.3801077},
        year = {2026}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 50 of 51 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0023947656
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
21 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00056855599
63 Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases 2017 SIGMOD 0.00038531147
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036684414
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
234 TiDB: A Raft-based HTAP Database 2020 VLDB 0.00023765806
322 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021041865
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
373 Umbra: A Disk-Based System with In-Memory Performance 2020 CIDR 0.00019711632
512 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017050173
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
605 Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask 2018 VLDB 0.00015647561
1,060 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012224575
1,199 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011563985
1,341 Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last 2018 VLDB 0.00010968512
1,515 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010417728
1,734 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.7545773e-05
2,004 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.2065719e-05
2,210 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8257742e-05
2,250 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7533306e-05
2,772 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.035288e-05
2,812 OceanBase: A 707 Million tpmC Distributed Relational Database System 2022 VLDB 7.9811649e-05
2,834 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9560627e-05
2,846 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.9453616e-05
2,885 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9094988e-05
3,051 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.7055931e-05
4,025 MagicScaler: Uncertainty-aware, Predictive Autoscaling 2023 VLDB 6.8460581e-05
4,258 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.6994722e-05
4,683 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4716143e-05
4,709 Optimizer Plan Change Management: Improved Stability and Performance in Oracle 11g 2008 VLDB 6.4580009e-05
4,950 Debunking the Myth of Join Ordering: Toward Robust SQL Analytics 2025 SIGMOD 6.3421691e-05
5,214 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.2248104e-05
5,456 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1239873e-05
5,649 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.052326e-05
5,683 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.0392183e-05
5,871 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.9639223e-05
6,308 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8177833e-05
6,632 Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges 2023 VLDB 5.7270153e-05
6,710 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.7019157e-05
7,033 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.6168499e-05
7,363 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5418564e-05
8,332 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.3528188e-05
8,389 PARQO: Penalty-Aware Robust Plan Selection in Query Optimization 2024 VLDB 5.3413016e-05
8,524 ROME: Robust Query Optimization via Parallel Multi-Plan Execution 2024 SIGMOD 5.3231221e-05
9,012 ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation 2024 SIGMOD 5.2375369e-05
9,610 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.1526493e-05
9,670 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.1452097e-05
10,140 How to Optimize SQL Queries? A Comparison Between Split, Holistic, and Hybrid Approaches 2025 VLDB 5.0742707e-05
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