CoPhy

Author:

Dash Debabrata1,Polyzotis Neoklis2,Ailamaki Anastasia3

Affiliation:

1. ArcSight, an HP Company

2. University of California at Santa Cruz

3. École Polytechnique Federale de Lausanne

Abstract

Index tuning, i.e., selecting the indexes appropriate for a workload, is a crucial problem in database system tuning. In this paper, we solve index tuning for large problem instances that are common in practice, e.g., thousands of queries in the workload, thousands of candidate indexes and several hard and soft constraints. Our work is the first to reveal that the index tuning problem has a well structured space of solutions, and this space can be explored efficiently with well known techniques from linear optimization. Experimental results demonstrate that our approach outperforms state-of-the-art commercial and research techniques by a significant margin (up to an order of magnitude).

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 52 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Index Advisors on Quantum Platforms;Proceedings of the VLDB Endowment;2024-07

2. Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and Tuning;ACM Computing Surveys;2024-06-29

3. Wii: Dynamic Budget Reallocation In Index Tuning;Proceedings of the ACM on Management of Data;2024-05-29

4. MFIX: An Efficient and Reliable Index Advisor via Multi-Fidelity Bayesian Optimization;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

5. Refactoring Index Tuning Process with Benefit Estimation;Proceedings of the VLDB Endowment;2024-03

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