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: 12049
Venue: VLDB
Year: 2019
Pagerank: 0.0001650812
Overall Rank: 563 | 96.14%
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 25 of 75 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,536	Database Gyms	2023	CIDR	5.2529727e-05
9,612	Memory Efficient Scheduling of Query Pipeline Execution	2022	CIDR	5.2449175e-05
9,615	Wii: Dynamic Budget Reallocation In Index Tuning	2024	SIGMOD	5.2436464e-05
9,617	NeuSO: Neural Optimizer for Subgraph Queries	2026	SIGMOD	5.2434488e-05
9,734	Optimizing Dataflow Systems for Scalable Interactive Visualization	2024	SIGMOD	5.227679e-05
9,971	Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement	2025	SIGMOD	5.1845938e-05
10,066	Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes	2023	VLDB	5.1643809e-05
10,196	Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis]	2026	SIGMOD	5.093636e-05
10,343	APQO: An Adaptive Framework for Parametric Query Optimization	2026	SIGMOD	5.093636e-05
10,413	Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis]	2026	SIGMOD	5.093636e-05
10,506	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	5.093636e-05
10,508	Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking	2026	SIGMOD	5.093636e-05
10,513	LIO: A lightweight and interpretable query optimizer based on an evolutionary forest	2026	VLDB	5.093636e-05
10,559	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	5.093636e-05
10,586	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	5.093636e-05
10,626	Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction	2026	VLDB	5.093636e-05
10,706	Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym	2025	SIGMOD	5.093636e-05
10,815	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	5.093636e-05
10,830	PlanRGCN: Predicting SPARQL Query Performance	2025	VLDB	5.093636e-05
10,884	Conformal Prediction for Verifiable Learned Query Optimization	2025	VLDB	5.093636e-05
10,969	Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries	2025	VLDB	5.093636e-05
11,023	Opening The Black-Box: Explaining Learned Cost Models For Databases	2025	VLDB	5.093636e-05
11,065	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	5.093636e-05
11,083	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	5.093636e-05
11,613	Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications	2022	VLDB	5.093636e-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
23	Efficiently Compiling Efficient Query Plans for Modern Hardware	2011	VLDB	0.00054886415
43	The Case for Learned Index Structures	2018	SIGMOD	0.00046060254
53	Eddies: Continuously Adaptive Query Processing	2000	SIGMOD	0.00041071971
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035838391
100	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00034385207
176	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00027191081
234	Self-Driving Database Management Systems	2017	CIDR	0.00023810722
388	Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors	2009	VLDB	0.00019410042
448	Self-tuning Histograms: Building Histograms Without Looking at Data	1999	SIGMOD	0.00018292618
476	The Making of TPC-DS	2006	VLDB	0.00017860667
566	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD	0.00016436005
624	Performance Prediction for Concurrent Database Workloads	2011	SIGMOD	0.00015683402
682	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00015014887
1,143	Toward a Progress Indicator for Database Queries	2004	SIGMOD	0.00011999403
1,665	ParaTimer: A Progress Indicator for MapReduce DAGs	2010	SIGMOD	0.00010069173
2,404	Cardinality Estimation Using Sample Views with Quality Assurance	2007	SIGMOD	8.6225576e-05
2,589	Statistical Learning Techniques for Costing XML Queries	2005	VLDB	8.371643e-05
2,958	WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases	2016	VLDB	7.9197796e-05
3,051	Towards a Hands-Free Query Optimizer through Deep Learning	2019	CIDR	7.8121919e-05
3,482	Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads	2013	VLDB	7.3751635e-05
6,138	Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics	2016	SIGMOD	5.9640712e-05

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