Cardinality estimation done right: Index-based join sampling
Leis V, Radke B, Gubichev A, Kemper A, Neumann T (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Conference on Innovative Data Systems Research (CIDR)
Conference Proceedings Title: CIDR 2017 - 8th Biennial Conference on Innovative Data Systems Research
Event location: Santa Cruz, CA
Abstract
After four decades of research, today’s database systems still suffer from poor query execution plans. Bad plans are usually caused by poor cardinality estimates, which have been called the “Achilles Heel” of modern query optimizers. In this work we propose index-based join sampling, a novel cardinality estimation technique for main-memory databases that relies on sampling and existing index structures to obtain accurate estimates. Results on a real-world data set show that this approach significantly improves estimation as well as overall plan quality. The additional sampling effort is quite low and can be configured to match the desired application profile. The technique can be easily integrated into most systems.
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Germany (DE)
Ireland (IE)How to cite
APA:
Leis, V., Radke, B., Gubichev, A., Kemper, A., & Neumann, T. (2017). Cardinality estimation done right: Index-based join sampling. In CIDR 2017 - 8th Biennial Conference on Innovative Data Systems Research. Santa Cruz, CA, US: Conference on Innovative Data Systems Research (CIDR).
MLA:
Leis, Viktor, et al. "Cardinality estimation done right: Index-based join sampling." Proceedings of the 8th Biennial Conference on Innovative Data Systems Research, CIDR 2017, Santa Cruz, CA Conference on Innovative Data Systems Research (CIDR), 2017.
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