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The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions

Summary: Proto-X holistically tunes multiple DBMS configuration spaces, learning cross-space similarities and synthesizing proto-actions to coordinate search rather than sequentially tuning knobs, hints, and indexes. On PostgreSQL, it handles orders-of-magnitude larger spaces and improves performance up to 53% over prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h0d2ec23d398ffd06
Venue
VLDB
Year
2024
Pagerank
5.4142519e-05
Overall Rank
7,977 | 46.37%
DOI
10.14778/3681954.3682007

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhang_vldb24,
        title = {{The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions}},
        author = {Zhang, William and Lim, Wan Shen and Butrovich, Matthew and Pavlo, Andrew},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {3373--3387},
        doi = {10.14778/3681954.3682007},
        url = {https://doi.org/10.14778/3681954.3682007},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 17 of 67 cited papers.

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

Rank Cited Paper Year Venue Pagerank
5,286 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 6.1971399e-05
5,630 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 6.0582762e-05
5,674 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0430252e-05
5,938 Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud 2022 VLDB 5.9411704e-05
6,085 Dear User-Defined Functions, Inlining isn't working out so great for us. Let's try batching to make our relationship work. Sincerely, SQL 2024 CIDR 5.892166e-05
6,308 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8177833e-05
6,660 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.7178404e-05
6,726 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.6948731e-05
6,791 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6811782e-05
6,901 Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation 2021 VLDB 5.6520335e-05
7,363 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5418564e-05
7,386 Lachesis: Automatic Partitioning for UDF-Centric Analytics 2021 VLDB 5.538189e-05
7,900 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.4303143e-05
7,915 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.4276987e-05
8,202 Grep: A Graph Learning Based Database Partitioning System 2023 SIGMOD 5.378708e-05
8,403 The Case for Learned In-Memory Joins 2023 VLDB 5.3389852e-05
9,706 Database Gyms 2023 CIDR 5.1376763e-05
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