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AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines
Summary: AQETuner: BO-based per-query tuner for analytical query engines using attention to jointly encode knobs + query plans, capturing knob→plan effects. Dual-task Neural Process predicts latency and failures while PSO supplies parallel cold-start samples, improving latency 23.7% and failures 51.2%.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13914
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,641 | 26.05%
- DOI
-
10.14778/3742728.3742759
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No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 167 |
The Snowflake Elastic Data Warehouse |
2016 |
SIGMOD |
0.00039408116 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 423 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023628474 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 627 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018959896 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 1,358 |
Managing Intra-operator Parallelism in Parallel Database Systems |
1995 |
VLDB |
0.00012402333 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011050093 |
| 1,892 |
Black or White? How to Develop an AutoTuner for Memory-based Analytics |
2020 |
SIGMOD |
0.00010176219 |
| 3,167 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
7.4561078e-05 |
| 3,655 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.8723042e-05 |
| 3,995 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
6.5475871e-05 |
| 4,235 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
6.3297728e-05 |
| 4,377 |
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
SIGMOD |
6.2331947e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.0953507e-05 |
| 4,799 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.9082876e-05 |
| 5,537 |
Presto: A Decade of SQL Analytics at Meta |
2023 |
SIGMOD |
5.453017e-05 |
| 7,326 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
4.7563708e-05 |
| 8,048 |
Bouncer: Admission Control with Response Time Objectives for Low-latency Online Data Systems |
2024 |
SIGMOD |
4.593997e-05 |
| 8,834 |
ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation |
2024 |
SIGMOD |
4.4351469e-05 |
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