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Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis]

Summary: Empirically evaluates workload-driven learned DBMS components under diverse drift scenarios using IMDb and STATS workloads. Shows drift can degrade both prediction accuracy and end-to-end execution, exposing robustness limits and motivating adaptive designs. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
he3b09edfda51e1df
Venue
SIGMOD
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,412 | 30.00%
DOI
10.1145/3802014

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Authors

BibTeX Citation

@inproceedings{meng_sigmod26,
        title = {{Are Learned DBMS Components Robust to Workload Drift?: [Experiments \& Analysis]}},
        author = {Meng, Zizhong and Cong, Gao and Luo, Siqiang},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802014},
        url = {https://dl.acm.org/doi/10.1145/3802014},
        year = {2026}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 34 of 34 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 0.00061066921
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021282642
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018315867
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
692 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014741011
868 Learning Multi-dimensional Indexes 2020 SIGMOD 0.00013354403
982 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012714044
1,156 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011777105
1,199 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011563985
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011226878
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
1,829 DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models 2019 SIGMOD 9.5510333e-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
2,690 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1258173e-05
2,885 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9094988e-05
2,915 F1 Query: Declarative Querying at Scale 2018 VLDB 7.8616593e-05
3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.4207879e-05
3,487 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.263041e-05
4,258 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.6994722e-05
4,311 ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads 2024 VLDB 6.6727978e-05
4,457 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.5913732e-05
4,563 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 6.5320994e-05
4,711 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.4573842e-05
5,209 SAM: Database Generation from Query Workloads with Supervised Autoregressive Models 2022 SIGMOD 6.2262056e-05
5,481 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.1125124e-05
5,683 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.0392183e-05
5,865 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9659203e-05
6,660 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.7178404e-05
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