DBScholar

Back to papers

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
5.3938336e-05
Overall Rank
7,997 | 46.40%
DOI
10.14778/3681954.3682007
PDF
Download (CC BY-NC-ND 4.0)

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)

Showing 6 of 6 citing papers.

Previous Page 1 / 1 Next

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
5,304 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 6.1758143e-05
5,645 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 6.0354456e-05
5,687 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0201937e-05
5,950 Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud 2022 VLDB 5.9187223e-05
6,100 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 5.8699028e-05
6,109 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8676956e-05
6,672 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.6975871e-05
6,752 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.6733557e-05
6,812 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6597129e-05
6,921 Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation 2021 VLDB 5.63068e-05
7,377 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5238616e-05
7,405 Lachesis: Automatic Partitioning for UDF-Centric Analytics 2021 VLDB 5.5172664e-05
7,715 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.4556951e-05
7,921 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.4098206e-05
8,225 Grep: A Graph Learning Based Database Partitioning System 2023 SIGMOD 5.3583852e-05
8,419 The Case for Learned In-Memory Joins 2023 VLDB 5.3198816e-05
9,731 Database Gyms 2023 CIDR 5.120066e-05
Previous Page 2 / 2 Next

Semantically Similar Papers