| 1 |
Access Path Selection in a Relational Database Management System |
1979 |
SIGMOD |
0.0040449103 |
| 60 |
Efficiently Compiling Efficient Query Plans for Modern Hardware |
2011 |
VLDB |
0.00064439773 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 156 |
Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases |
2017 |
SIGMOD |
0.00040504295 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 424 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023616398 |
| 488 |
TiDB: A Raft-based HTAP Database |
2020 |
VLDB |
0.000220409 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 735 |
Umbra: A Disk-Based System with In-Memory Performance |
2020 |
CIDR |
0.00017452467 |
| 853 |
Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask |
2018 |
VLDB |
0.00015940507 |
| 884 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015654004 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,547 |
Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions |
2011 |
VLDB |
0.00011442359 |
| 1,864 |
Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last |
2018 |
VLDB |
0.00010280966 |
| 2,121 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5017232e-05 |
| 2,762 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1585394e-05 |
| 2,783 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1293383e-05 |
| 2,985 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
7.7795847e-05 |
| 3,169 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
7.4498425e-05 |
| 3,348 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1904529e-05 |
| 3,625 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9055212e-05 |
| 3,628 |
OceanBase: A 707 Million tpmC Distributed Relational Database System |
2022 |
VLDB |
6.9031596e-05 |
| 3,812 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.7373184e-05 |
| 3,828 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
6.7208524e-05 |
| 3,869 |
MagicScaler: Uncertainty-aware, Predictive Autoscaling |
2023 |
VLDB |
6.6802432e-05 |
| 3,990 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5581983e-05 |
| 4,380 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
6.2396606e-05 |
| 4,512 |
Optimizer Plan Change Management: Improved Stability and Performance in Oracle 11g |
2008 |
VLDB |
6.1241619e-05 |
| 5,334 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5649836e-05 |
| 5,640 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3933314e-05 |
| 5,832 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3111109e-05 |
| 5,952 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2591691e-05 |
| 6,383 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0884322e-05 |
| 6,685 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
4.9627485e-05 |
| 6,862 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
4.9051979e-05 |
| 6,885 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.895386e-05 |
| 7,008 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
4.8643538e-05 |
| 7,011 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
4.8629458e-05 |
| 7,126 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
4.8232367e-05 |
| 7,753 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.660151e-05 |
| 8,220 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.5557328e-05 |
| 8,448 |
PARQO: Penalty-Aware Robust Plan Selection in Query Optimization |
2024 |
VLDB |
4.5100508e-05 |
| 8,659 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4722928e-05 |
| 8,834 |
ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation |
2024 |
SIGMOD |
4.4394021e-05 |
| 9,345 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
4.3536343e-05 |
| 9,587 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3215645e-05 |
| 9,693 |
ROME: Robust Query Optimization via Parallel Multi-Plan Execution |
2024 |
SIGMOD |
4.3027391e-05 |
| 9,956 |
SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression |
2025 |
VLDB |
4.2373024e-05 |