Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies
- An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality.
Author details: | Rainer SchlosserORCiDGND, Jan KossmannORCiDGND, Martin BoissierORCiD |
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DOI: | https://doi.org/10.1109/ICDE.2019.00113 |
ISBN: | 978-1-5386-7474-1 |
ISSN: | 1084-4627 |
Title of parent work (English): | 2019 IEEE 35th International Conference on Data Engineering (ICDE) |
Publisher: | IEEE |
Place of publishing: | New York |
Publication type: | Other |
Language: | English |
Year of first publication: | 2019 |
Publication year: | 2019 |
Release date: | 2021/05/05 |
Number of pages: | 12 |
First page: | 1238 |
Last Page: | 1249 |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |