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T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees

Summary: T3 introduces a compiled decision-tree model for fast, accurate relational-DB performance prediction. It uses pipeline-based plan decomposition and tuple-centric targets to estimate per-tuple costs and generalize across instances without retraining. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hd5f60202bcd0735c
Venue
SIGMOD
Year
2025
Pagerank
5.5189616e-05
Overall Rank
7,464 | 49.84%
DOI
10.1145/3725364
PDF
Download (CC BY 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{rieger_sigmod25,
        title = {{T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees}},
        author = {Rieger, Maximilian and Neumann, Thomas},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725364},
        url = {https://dl.acm.org/doi/10.1145/3725364},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
10,459 EncoderForge: Generating Efficient SQL for Encoders in Machine Learning Inference Pipelines 2026 SIGMOD 4.9769913e-05
10,926 No Silver Bullet: Boosting GaussDB Performance on the 30TB TPC-H Workload 2026 VLDB 4.9769913e-05
10,928 ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB 2026 VLDB 4.9769913e-05
11,081 Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction 2026 VLDB 4.9769913e-05
11,431 Learned Cost Models for Query Optimization: From Batch to Streaming Systems 2025 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 38 of 38 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035876108
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034099838
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021166957
361 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020000855
373 Umbra: A Disk-Based System with In-Memory Performance 2020 CIDR 0.00019705706
386 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019446558
462 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017836105
510 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017059914
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016408613
1,041 Generic Database Cost Models for Hierarchical Memory Systems 2002 VLDB 0.00012354465
1,185 Adaptive Optimization of Very Large Join Queries 2018 SIGMOD 0.00011607329
1,195 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011574218
1,301 Analysis of Two Existing and One New Dynamic Programming Algorithm for the Generation of Optimal Bushy Join Trees without Cross Products 2006 VLDB 0.00011107788
1,588 Estimating Progress of Execution for SQL Queries 2004 SIGMOD 0.00010137374
2,139 Quickstep: A Data Platform Based on the Scaling-Up Approach 2018 VLDB 8.9735524e-05
2,209 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8360101e-05
2,248 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7567205e-05
2,320 When Can We Trust Progress Estimators for SQL Queries? 2005 SIGMOD 8.6429103e-05
2,833 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9539771e-05
2,837 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.9495917e-05
2,879 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9126862e-05
2,983 WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases 2016 VLDB 7.7831414e-05
3,272 Estimating Cardinalities with Deep Sketches 2019 SIGMOD 7.4711788e-05
3,519 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.2361015e-05
3,565 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.200937e-05
4,299 ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads 2024 VLDB 6.6766173e-05
4,536 One Model to Rule them All: Towards Zero-Shot Learning for Databases 2022 CIDR 6.552998e-05
4,677 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4721041e-05
5,062 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.2896995e-05
5,216 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.2218868e-05
5,658 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.0460211e-05
6,141 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 5.8708409e-05
6,298 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8177684e-05
7,021 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.6168049e-05
7,869 Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD 2024 VLDB 5.4342182e-05
8,570 Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems 2022 VLDB 5.3113492e-05
10,223 DBMS Fitting: Why should we learn what we already know? 2020 CIDR 5.0558336e-05
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