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Handling Estimation Inaccuracy in Query Optimization

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Book cover Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9932))

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Abstract

Cost-based Optimizers choose query execution plans using a cost model. The latter relies on the accuracy of estimated statistics. Unfortunately, compile-time estimates often differ significantly from run-time values, leading to a suboptimal plan choices. In this paper, we propose a compile-time strategy, wherein the optimization process is fully aware of the estimation inaccuracy. This is ensured by the use of intervals of estimates rather than single-point estimates of error-prone parameters. These intervals serve to identify plans that provide stable performance in several run-time conditions, so called robust. Our strategy relies on a probabilistic approach to decide which plan to choose to start the execution. Our experiments show that our proposal allows a considerable improvement of the ability of a query optimizer to produce a robust execution plan in case of large estimation errors.

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Notes

  1. 1.

    We only focus on the Cost-based Query Optimizers.

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Authors and Affiliations

  1. IRIT Laboratory, Paul Sabatier University, 118 Route de Narbonne, 31062, Toulouse Cedex 9, France

    Chiraz Moumen, Franck Morvan & Abdelkader Hameurlain

Authors
  1. Chiraz Moumen
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  2. Franck Morvan
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  3. Abdelkader Hameurlain
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Corresponding author

Correspondence to Chiraz Moumen .

Editor information

Editors and Affiliations

  1. School of Computing, University of Utah, Salt Lake City, Utah, USA

    Feifei Li

  2. School of Electrical Engineering, Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. Soochow University , Suzhou, China

    Kai Zheng

  4. Soochow University , Suzhou, China

    Guanfeng Liu

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© 2016 Springer International Publishing Switzerland

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Moumen, C., Morvan, F., Hameurlain, A. (2016). Handling Estimation Inaccuracy in Query Optimization. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_28

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  • DOI: https://doi.org/10.1007/978-3-319-45817-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45816-8

  • Online ISBN: 978-3-319-45817-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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