| 1 |
Access Path Selection in a Relational Database Management System |
1979 |
SIGMOD |
0.0024179717 |
| 20 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00058294381 |
| 26 |
Efficiently Compiling Efficient Query Plans for Modern Hardware |
2011 |
VLDB |
0.00054892115 |
| 77 |
Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases |
2017 |
SIGMOD |
0.00037304543 |
| 86 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.0003577267 |
| 89 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00035598024 |
| 157 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00028782395 |
| 301 |
TiDB: A Raft-based HTAP Database |
2020 |
VLDB |
0.00022033345 |
| 342 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00020771604 |
| 394 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019400224 |
| 435 |
Umbra: A Disk-Based System with In-Memory Performance |
2020 |
CIDR |
0.00018582199 |
| 524 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017177356 |
| 560 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00016570735 |
| 662 |
Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask |
2018 |
VLDB |
0.0001534723 |
| 1,081 |
Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions |
2011 |
VLDB |
0.00012387181 |
| 1,282 |
Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last |
2018 |
VLDB |
0.00011429165 |
| 1,371 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011100806 |
| 1,931 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.5483519e-05 |
| 1,976 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.4645971e-05 |
| 2,134 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
9.1799543e-05 |
| 2,329 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.8228164e-05 |
| 2,506 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.5603022e-05 |
| 2,780 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
8.1936279e-05 |
| 2,859 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.094221e-05 |
| 2,929 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
8.0220847e-05 |
| 2,960 |
OceanBase: A 707 Million tpmC Distributed Relational Database System |
2022 |
VLDB |
7.9703183e-05 |
| 3,108 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.8122622e-05 |
| 3,298 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
7.6094798e-05 |
| 3,882 |
MagicScaler: Uncertainty-aware, Predictive Autoscaling |
2023 |
VLDB |
7.106419e-05 |
| 4,507 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.723287e-05 |
| 4,570 |
Optimizer Plan Change Management: Improved Stability and Performance in Oracle 11g |
2008 |
VLDB |
6.6944481e-05 |
| 4,858 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.5410955e-05 |
| 5,206 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.3868465e-05 |
| 5,503 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
6.2627904e-05 |
| 5,640 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.2029974e-05 |
| 6,282 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
5.9906069e-05 |
| 6,373 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
5.9616981e-05 |
| 6,433 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.9423732e-05 |
| 6,499 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
5.917181e-05 |
| 6,617 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
5.8811727e-05 |
| 6,628 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
5.8775266e-05 |
| 7,269 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
5.7128028e-05 |
| 7,912 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
5.587029e-05 |
| 8,182 |
PARQO: Penalty-Aware Robust Plan Selection in Query Optimization |
2024 |
VLDB |
5.5385661e-05 |
| 8,463 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
5.4900503e-05 |
| 8,723 |
ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation |
2024 |
SIGMOD |
5.4407297e-05 |
| 9,290 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
5.3525489e-05 |
| 9,526 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
5.3206369e-05 |
| 9,652 |
ROME: Robust Query Optimization via Parallel Multi-Plan Execution |
2024 |
SIGMOD |
5.2993023e-05 |
| 9,955 |
SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression |
2025 |
VLDB |
5.2232357e-05 |