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AutoSteer: Learned Query Optimization for Any SQL Database
Summary: AutoSteer: a portable, learning-based system that steers any SQL optimizer exposing tunable knobs by extending Bao with automated hint-set discovery and low-integration APIs for monolithic and disaggregated engines. Evaluated on PostgreSQL, Presto, Spark, MySQL and DuckDB, it outperforms native optimizers (up to ~40% for Presto), matches Bao while reducing human supervision, and ships open-source with a visual tool.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13184
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 5.3882121e-05
- Overall Rank
- 5,654 | 60.71%
- DOI
-
10.14778/3611540.3611544
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 20 of 20 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 6,883 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.8918682e-05 |
| 8,003 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6049527e-05 |
| 8,660 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4680058e-05 |
| 8,961 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
4.4171776e-05 |
| 9,581 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3186744e-05 |
| 9,709 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
4.2951473e-05 |
| 9,900 |
AnyBlox: A Framework for Self-Decoding Datasets |
2025 |
VLDB |
4.2539423e-05 |
| 9,959 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
4.2254157e-05 |
| 9,980 |
Survivorship Bias in Industrial Database Workloads |
2026 |
CIDR |
4.1905499e-05 |
| 10,018 |
GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,203 |
Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,217 |
This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,271 |
OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning |
2026 |
VLDB |
4.1905499e-05 |
| 10,300 |
TATA: An Efficient Framework for Task Transfer in Query Plan Representation |
2026 |
VLDB |
4.1905499e-05 |
| 10,501 |
Intra-Query Runtime Elasticity for Cloud-Native Data Analysis |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,573 |
PlanRGCN: Predicting SPARQL Query Performance |
2025 |
VLDB |
4.1905499e-05 |
| 10,784 |
GRewriter: Practical Query Rewriting with Automatic Rule Set Expansion in GaussDB |
2025 |
VLDB |
4.1905499e-05 |
| 10,856 |
CloudGlide: Deconstructing the Landscape of Cloud-Based Analytics |
2025 |
VLDB |
4.1905499e-05 |
| 10,863 |
Graph Transformers for Query Plan Representation: Potentials and Challenges |
2025 |
VLDB |
4.1905499e-05 |
| 11,087 |
Presto’s History-based Query Optimizer |
2024 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 66 |
Spark SQL: Relational Data Processing in Spark |
2015 |
SIGMOD |
0.00061707583 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 542 |
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources |
2018 |
SIGMOD |
0.00020522627 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,356 |
Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics |
2021 |
CIDR |
0.00012409986 |
| 1,699 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010848882 |
| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,247 |
Orca: A Modular Query Optimizer Architecture for Big Data |
2014 |
SIGMOD |
9.201975e-05 |
| 2,781 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1282042e-05 |
| 3,241 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.32744e-05 |
| 3,269 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
7.3026051e-05 |
| 3,455 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
7.0760196e-05 |
| 3,655 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.8723042e-05 |
| 3,729 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8078013e-05 |
| 4,687 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
5.9915268e-05 |
| 5,250 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
5.6007779e-05 |
| 5,994 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2367998e-05 |
| 6,053 |
POP/FED: Progressive Query Optimization for Federated Queries in DB2 |
2006 |
VLDB |
5.2310929e-05 |
| 9,709 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
4.2951473e-05 |
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