Plan-Structured Deep Neural Network Models for Query Performance Prediction

Summary: Plan-structured deep neural networks that mirror optimizer plans to predict query latency. No hand-crafted features; learns operator-input interactions, adapts to workloads, with training optimizations, achieving state-of-the-art performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: hee6e281a3bbcfd74
Venue: VLDB
Year: 2019
Pagerank: 0.00016403151
Overall Rank: 560 | 96.24%
DOI: 10.14778/3342263.3342646

Incoming Non-self Citations Over Time

Authors

1. Ryan Marcus (Brandeis University)
2. Olga Papaemmanouil (Brandeis University)

BibTeX Citation

@article{marcus_vldb19,
        title = {{Plan-Structured Deep Neural Network Models for Query Performance Prediction}},
        author = {Marcus, Ryan and Papaemmanouil, Olga},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {11},
        pages = {1733--1746},
        doi = {10.14778/3342263.3342646},
        url = {https://doi.org/10.14778/3342263.3342646},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 27 of 77 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,610	LIMAO: A Framework for Lifelong Modular Learned Query Optimization	2025	VLDB	5.1526493e-05
9,646	Are Joins over LSM-trees Ready? Take RocksDB as an Example	2025	VLDB	5.1453267e-05
9,706	Database Gyms	2023	CIDR	5.1376763e-05
9,720	APQO: An Adaptive Framework for Parametric Query Optimization	2026	SIGMOD	5.1349531e-05
9,781	Optimizing Dataflow Systems for Scalable Interactive Visualization	2024	SIGMOD	5.1300393e-05
9,784	Memory Efficient Scheduling of Query Pipeline Execution	2022	CIDR	5.1285917e-05
9,790	Wii: Dynamic Budget Reallocation In Index Tuning	2024	SIGMOD	5.1260323e-05
9,797	NeuSO: Neural Optimizer for Subgraph Queries	2026	SIGMOD	5.1257999e-05
9,954	Conformal Prediction for Verifiable Learned Query Optimization	2025	VLDB	5.1038322e-05
9,956	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	5.1038322e-05
10,148	Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries	2025	VLDB	5.0715586e-05
10,290	This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!	2026	SIGMOD	5.0431863e-05
10,412	Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis]	2026	SIGMOD	4.9793485e-05
10,604	Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis]	2026	SIGMOD	4.9793485e-05
10,693	Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking	2026	SIGMOD	4.9793485e-05
10,698	LIO: A lightweight and interpretable query optimizer based on an evolutionary forest	2026	VLDB	4.9793485e-05
10,741	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	4.9793485e-05
10,918	Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server	2026	VLDB	4.9793485e-05
10,924	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	4.9793485e-05
10,941	Ultron: History-Based Query Optimization at Databricks	2026	VLDB	4.9793485e-05
11,072	Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction	2026	VLDB	4.9793485e-05
11,139	Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym	2025	SIGMOD	4.9793485e-05
11,224	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	4.9793485e-05
11,238	PlanRGCN: Predicting SPARQL Query Performance	2025	VLDB	4.9793485e-05
11,388	Opening The Black-Box: Explaining Learned Cost Models For Databases	2025	VLDB	4.9793485e-05
11,425	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	4.9793485e-05
11,921	Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications	2022	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 21 of 21 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
21	Efficiently Compiling Efficient Query Plans for Modern Hardware	2011	VLDB	0.00056855599
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046284649
53	Eddies: Continuously Adaptive Query Processing	2000	SIGMOD	0.00040860054
85	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035864347
98	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00034106982
158	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00028046388
224	Self-Driving Database Management Systems	2017	CIDR	0.00024013745
386	Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors	2009	VLDB	0.00019444411
454	Self-tuning Histograms: Building Histograms Without Looking at Data	1999	SIGMOD	0.00017962189
479	The Making of TPC-DS	2006	VLDB	0.00017622471
569	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD	0.00016245271
629	Performance Prediction for Concurrent Database Workloads	2011	SIGMOD	0.00015429949
682	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.0001481781
1,159	Toward a Progress Indicator for Database Queries	2004	SIGMOD	0.00011771949
1,688	ParaTimer: A Progress Indicator for MapReduce DAGs	2010	SIGMOD	9.8645784e-05
2,433	Cardinality Estimation Using Sample Views with Quality Assurance	2007	SIGMOD	8.4766785e-05
2,625	Statistical Learning Techniques for Costing XML Queries	2005	VLDB	8.2089383e-05
2,981	WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases	2016	VLDB	7.7851845e-05
3,060	Towards a Hands-Free Query Optimizer through Deep Learning	2019	CIDR	7.6928239e-05
3,519	Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads	2013	VLDB	7.2389387e-05
6,241	Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics	2016	SIGMOD	5.8381762e-05

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