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QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning

Summary: QTune: a query-aware database tuning system using a Double-State DDPG (DS-DDPG) deep RL model to pick configurations from rich SQL-query features. Supports query-, workload-, and cluster-level tuning; validated across three real DB systems, it outperforms state-of-the-art tuners. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11933
Venue
VLDB
Year
2019
Pagerank
0.00016719473
Overall Rank
779 | 94.59%
DOI
10.14778/3352063.3352129

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 32 of 82 citing papers.

Rank Citing Paper Year Venue Pagerank
8,180 Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning 2021 SIGMOD 4.5627116e-05
8,340 Deep Learning: Systems and Responsibility 2021 SIGMOD 4.5381546e-05
8,452 Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual 2022 SIGMOD 4.5023516e-05
8,585 A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning 2024 VLDB 4.4856045e-05
8,612 The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that "Read the Manual" 2021 VLDB 4.4807455e-05
9,012 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 4.4059413e-05
9,196 Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale 2022 VLDB 4.3723457e-05
9,358 Db2une: Tuning Under Pressure via Deep Learning 2024 VLDB 4.3480651e-05
9,605 Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System 2022 VLDB 4.3136057e-05
9,621 ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation 2023 VLDB 4.3125802e-05
9,841 Machine Unlearning in Learned Databases: An Experimental Analysis 2024 SIGMOD 4.2685233e-05
9,844 DumpKV: Learning based lifetime aware garbage collection for key value separation in LSM-tree 2025 VLDB 4.2680295e-05
9,955 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 4.2332427e-05
10,031 PGTuner: An Efficient Framework for Automatic and Transferable Configuration Tuning of Proximity Graphs 2026 SIGMOD 4.1905499e-05
10,047 AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning 2026 SIGMOD 4.1905499e-05
10,093 MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration 2026 SIGMOD 4.1905499e-05
10,156 Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload 2026 SIGMOD 4.1905499e-05
10,164 ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning 2026 SIGMOD 4.1905499e-05
10,217 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 4.1905499e-05
10,247 Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents 2026 VLDB 4.1905499e-05
10,259 Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink 2026 VLDB 4.1905499e-05
10,347 Generic Version Control: Configurable Versioning for Application-Specific Requirements 2025 CIDR 4.1905499e-05
10,382 Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions 2025 SIGMOD 4.1905499e-05
10,527 High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration 2025 SIGMOD 4.1905499e-05
10,569 A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty 2025 VLDB 4.1905499e-05
10,641 AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines 2025 VLDB 4.1905499e-05
10,853 AXE: A Task Decomposition Approach to Learned LSM Tuning 2025 VLDB 4.1905499e-05
10,876 LASER: Buffer-Aware Learned Query Scheduling in Master-Standby Databases 2025 VLDB 4.1905499e-05
10,947 Predictive and Near-Optimal Sampling for View Materialization in Video Databases 2024 SIGMOD 4.1905499e-05
11,189 Regularized Pairwise Relationship based Analytics for Structured Data 2023 SIGMOD 4.1905499e-05
11,227 Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems 2023 VLDB 4.1905499e-05
11,281 Demonstrating Waffle: A Self-driving Grid Index 2023 VLDB 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

Rank Cited Paper Year Venue Pagerank
183 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036859633
423 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00023628474
510 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021420477
796 SageDB: A Learned Database System 2019 CIDR 0.00016541749
804 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001643674
841 Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering 2002 VLDB 0.00015987128
7,667 Human-in-the-loop Data Integration 2017 VLDB 4.6791871e-05
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510 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021420477
9,358 Db2une: Tuning Under Pressure via Deep Learning 2024 VLDB 4.3480651e-05