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Rethinking Learned Cost Models: Why Start from Scratch?

Summary: Tuning the cost model by identifying key parameters and using a fast-learning adjuster per hardware/software config. Dynamic partitioning of the config space refines estimates from rough to fine, enabling transferable performance across DBMS instances. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
he7a08ed1423c2cd1
Venue
SIGMOD
Year
2023
Pagerank
5.6168049e-05
Overall Rank
7,021 | 52.82%
DOI
10.1145/3626769

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yang_sigmod23,
        title = {{Rethinking Learned Cost Models: Why Start from Scratch?}},
        author = {Yang, Jiani and Wu, Sai and Zhang, Dongxiang and Dai, Jian and Li, Feifei and Chen, Gang},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626769},
        url = {https://dl.acm.org/doi/10.1145/3626769},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank Citing Paper Year Venue Pagerank
5,316 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.1827415e-05
7,464 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 5.5189616e-05
10,343 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.0176429e-05
10,457 Eliminating Redundant Feature Tests in Decision Tree and Random Forest Inference on SQL Predicates 2026 SIGMOD 4.9769913e-05
10,713 Breaking the Isolation-Freshness Trade-off: Joint Adaptive Storage Optimization for HTAP Systems 2026 VLDB 4.9769913e-05
10,751 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 4.9769913e-05
11,431 Learned Cost Models for Query Optimization: From Batch to Streaming Systems 2025 VLDB 4.9769913e-05
11,436 AXE: A Task Decomposition Approach to Learned LSM Tuning 2025 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 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 0.0023943337
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036675568
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035876108
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021166957
322 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021034201
361 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020000855
462 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017836105
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016408613
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00014814858
1,195 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011574218
2,209 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8360101e-05
2,248 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7567205e-05
2,393 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5298464e-05
2,583 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.2589842e-05
2,833 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9539771e-05
2,837 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.9495917e-05
2,879 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9126862e-05
3,052 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.704739e-05
3,487 HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements 2022 SIGMOD 7.2603973e-05
3,979 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.8791817e-05
4,708 PreQR: Pre-training Representation for SQL Understanding 2022 SIGMOD 6.4559477e-05
5,188 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.2346845e-05
6,141 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 5.8708409e-05
6,149 Sia: Optimizing Queries using Learned Predicates 2021 SIGMOD 5.8683464e-05
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