DBScholar

Back to papers

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
4.9769913e-05
Overall Rank
10,424 | 29.94%
DOI
10.1145/3802014
PDF
Download (CC BY 4.0)

Incoming Non-self Citations Over Time

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

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}
}

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 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 0.00061067652
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035876108
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021276452
361 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020000855
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018310278
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
691 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014737455
869 Learning Multi-dimensional Indexes 2020 SIGMOD 0.00013363241
981 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012713454
1,155 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011777046
1,195 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011574218
1,280 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011224914
1,515 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010418766
1,735 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.7566604e-05
1,828 DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models 2019 SIGMOD 9.547768e-05
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
2,686 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1300913e-05
2,879 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9126862e-05
2,916 F1 Query: Declarative Querying at Scale 2018 VLDB 7.8581972e-05
3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.4233639e-05
3,479 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.2665349e-05
4,240 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7064546e-05
4,299 ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads 2024 VLDB 6.6766173e-05
4,459 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.5883555e-05
4,559 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 6.5324275e-05
4,713 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.4543291e-05
5,211 SAM: Database Generation from Query Workloads with Supervised Autoregressive Models 2022 SIGMOD 6.2232582e-05
5,316 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.1827415e-05
5,482 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.1123461e-05
5,850 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9697921e-05
6,664 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.715134e-05
Previous Page 1 / 1 Next

Semantically Similar Papers

# Overall Rank Paper Year Venue
1 2,476 Learning a Partitioning Advisor for Cloud Databases 2020 SIGMOD
2 11,157 Demo of LearnedWMP: Workload Memory Prediction Using Deep Query Template Representations 2025 SIGMOD
3 4,059 Database-Agnostic Workload Management 2019 CIDR
4 461 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD
5 5,188 Database Workload Characterization with Query Plan Encoders 2022 VLDB
6 318 DeepDB: Learn from Data, not from Queries! 2020 VLDB
7 10,761 Toward Drift-Aware Database Benchmarking 2026 VLDB
8 3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB
9 10,495 NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] 2026 SIGMOD
10 5,850 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD