| 1 |
Access Path Selection in a Relational Database Management System |
1979 |
SIGMOD |
0.0040465394 |
| 59 |
Efficiently Compiling Efficient Query Plans for Modern Hardware |
2011 |
VLDB |
0.0006445664 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 156 |
Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases |
2017 |
SIGMOD |
0.00040580118 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 423 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023628474 |
| 486 |
TiDB: A Raft-based HTAP Database |
2020 |
VLDB |
0.00022039625 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 729 |
Umbra: A Disk-Based System with In-Memory Performance |
2020 |
CIDR |
0.00017448059 |
| 848 |
Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask |
2018 |
VLDB |
0.00015933538 |
| 876 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015660534 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,536 |
Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions |
2011 |
VLDB |
0.00011458359 |
| 1,865 |
Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last |
2018 |
VLDB |
0.00010271333 |
| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,769 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1512848e-05 |
| 2,781 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1282042e-05 |
| 2,937 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
7.8552033e-05 |
| 3,167 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
7.4561078e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9017341e-05 |
| 3,641 |
OceanBase: A 707 Million tpmC Distributed Relational Database System |
2022 |
VLDB |
6.8877808e-05 |
| 3,655 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.8723042e-05 |
| 3,819 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
6.7267885e-05 |
| 3,871 |
MagicScaler: Uncertainty-aware, Predictive Autoscaling |
2023 |
VLDB |
6.6738199e-05 |
| 3,992 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5519369e-05 |
| 4,180 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
6.3725334e-05 |
| 4,513 |
Optimizer Plan Change Management: Improved Stability and Performance in Oracle 11g |
2008 |
VLDB |
6.1183817e-05 |
| 5,339 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5596755e-05 |
| 5,654 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3882121e-05 |
| 5,844 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3060581e-05 |
| 5,941 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2594013e-05 |
| 6,382 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0835686e-05 |
| 6,687 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
4.957987e-05 |
| 6,862 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
4.9004921e-05 |
| 6,883 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.8918682e-05 |
| 7,009 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
4.8597992e-05 |
| 7,011 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
4.8583284e-05 |
| 7,122 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
4.8199209e-05 |
| 7,742 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.6585812e-05 |
| 8,219 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.551524e-05 |
| 8,440 |
PARQO: Penalty-Aware Robust Plan Selection in Query Optimization |
2024 |
VLDB |
4.505741e-05 |
| 8,660 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4680058e-05 |
| 8,834 |
ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation |
2024 |
SIGMOD |
4.4351469e-05 |
| 9,350 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
4.3494621e-05 |
| 9,581 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3186744e-05 |
| 9,692 |
ROME: Robust Query Optimization via Parallel Multi-Plan Execution |
2024 |
SIGMOD |
4.2986161e-05 |
| 9,955 |
SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression |
2025 |
VLDB |
4.2332427e-05 |