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: 0.0001657599
Overall Rank: 545 | 96.35%
DOI: 10.14778/3342263.3342646
PDF: Download (CC BY-NC-ND 4.0)

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 29 of 79 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,154	Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems	2024	VLDB	5.2050937e-05
9,394	Phoebe: A Learning-based Checkpoint Optimizer	2021	VLDB	5.1676892e-05
9,583	Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement	2025	SIGMOD	5.140977e-05
9,681	Are Joins over LSM-trees Ready? Take RocksDB as an Example	2025	VLDB	5.1258854e-05
9,731	Database Gyms	2023	CIDR	5.120066e-05
9,756	APQO: An Adaptive Framework for Parametric Query Optimization	2026	SIGMOD	5.115551e-05
9,819	Optimizing Dataflow Systems for Scalable Interactive Visualization	2024	SIGMOD	5.1106557e-05
9,822	Memory Efficient Scheduling of Query Pipeline Execution	2022	CIDR	5.1092136e-05
9,829	Wii: Dynamic Budget Reallocation In Index Tuning	2024	SIGMOD	5.1066866e-05
9,837	NeuSO: Neural Optimizer for Subgraph Queries	2026	SIGMOD	5.1064323e-05
9,995	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	5.0845476e-05
10,323	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	5.0241309e-05
10,392	E-LQO: A Comprehensive Energy Evaluation Framework for Learned Query Optimization	2027	SIGMOD	4.9605343e-05
10,393	EASE: Resource-aware Query Scheduling across Heterogeneous Cloud Compute Services	2027	SIGMOD	4.9605343e-05
10,490	Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis]	2026	SIGMOD	4.9605343e-05
10,676	Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis]	2026	SIGMOD	4.9605343e-05
10,764	Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking	2026	SIGMOD	4.9605343e-05
10,769	LIO: A lightweight and interpretable query optimizer based on an evolutionary forest	2026	VLDB	4.9605343e-05
10,812	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	4.9605343e-05
10,988	Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server	2026	VLDB	4.9605343e-05
10,994	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	4.9605343e-05
11,011	Ultron: History-Based Query Optimization at Databricks	2026	VLDB	4.9605343e-05
11,142	Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction	2026	VLDB	4.9605343e-05
11,207	Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym	2025	SIGMOD	4.9605343e-05
11,288	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	4.9605343e-05
11,302	PlanRGCN: Predicting SPARQL Query Performance	2025	VLDB	4.9605343e-05
11,447	Opening The Black-Box: Explaining Learned Cost Models For Databases	2025	VLDB	4.9605343e-05
11,484	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	4.9605343e-05
11,971	Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications	2022	VLDB	4.9605343e-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	0.00056737487
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046249672
53	Eddies: Continuously Adaptive Query Processing	2000	SIGMOD	0.00040739114
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035920657
98	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00034050614
158	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00028049555
224	Self-Driving Database Management Systems	2017	CIDR	0.00023973185
381	Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors	2009	VLDB	0.00019497061
457	Self-tuning Histograms: Building Histograms Without Looking at Data	1999	SIGMOD	0.00017915659
473	The Making of TPC-DS	2006	VLDB	0.00017656775
568	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD	0.00016221103
628	Performance Prediction for Concurrent Database Workloads	2011	SIGMOD	0.00015421836
676	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00014838681
1,159	Toward a Progress Indicator for Database Queries	2004	SIGMOD	0.00011739527
1,691	ParaTimer: A Progress Indicator for MapReduce DAGs	2010	SIGMOD	9.838545e-05
2,438	Cardinality Estimation Using Sample Views with Quality Assurance	2007	SIGMOD	8.4532693e-05
2,614	Statistical Learning Techniques for Costing XML Queries	2005	VLDB	8.2023561e-05
2,892	WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases	2016	VLDB	7.8872299e-05
3,062	Towards a Hands-Free Query Optimizer through Deep Learning	2019	CIDR	7.6809918e-05
3,384	Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads	2013	VLDB	7.3392425e-05
6,243	Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics	2016	SIGMOD	5.8220831e-05

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