An End-to-End Learning-based Cost Estimator

Summary: An end-to-end tree-structured estimator jointly predicts query cardinality and execution cost, encoding both query predicates and physical operators. Pattern-based string embeddings improve generalization to predicate values without enumerating them, while handling complex query structures. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: h65c46788cb871bbe
Venue: VLDB
Year: 2020
Pagerank: 0.0001801482
Overall Rank: 453 | 96.97%
DOI: 10.14778/3368289.3368296
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Ji Sun (Tsinghua University)
2. Guoliang Li (Tsinghua University)

BibTeX Citation

@article{sun_vldb20,
        title = {{An End-to-End Learning-based Cost Estimator}},
        author = {Sun, Ji and Li, Guoliang},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {3},
        pages = {307--319},
        doi = {10.14778/3368289.3368296},
        url = {https://doi.org/10.14778/3368289.3368296},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 109 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,769	LIO: A lightweight and interpretable query optimizer based on an evolutionary forest	2026	VLDB	4.9605343e-05
10,994	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	4.9605343e-05
11,198	OpenMLDB: A Real-Time Relational Data Feature Computation System for Online ML	2025	SIGMOD	4.9605343e-05
11,288	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	4.9605343e-05
11,484	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	4.9605343e-05
11,489	AXE: A Task Decomposition Approach to Learned LSM Tuning	2025	VLDB	4.9605343e-05
11,501	LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Comparison	2025	VLDB	4.9605343e-05
11,511	RankPQO: Learning-to-Rank for Parametric Query Optimization	2025	VLDB	4.9605343e-05
11,907	DeepO: A Learned Query Optimizer	2022	SIGMOD	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061463175
79	Practical Selectivity Estimation through Adaptive Sampling	1990	SIGMOD	0.00036394222
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035920657
142	Neo: A Learned Query Optimizer	2019	VLDB	0.00029114937
254	The History of Histograms (abridged)	2003	VLDB	0.00022999992
309	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.0002143249
411	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.0001874043
545	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.0001657599
676	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00014838681
689	Cardinality Estimation Done Right: Index-Based Join Sampling	2017	CIDR	0.00014725904
1,261	Sampling-Based Query Re-Optimization	2016	SIGMOD	0.00011289724
2,174	A Black-Box Approach to Query Cardinality Estimation	2007	CIDR	8.9067128e-05
2,614	Statistical Learning Techniques for Costing XML Queries	2005	VLDB	8.2023561e-05

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