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Automatic single table storage structure selection for hybrid workload

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Abstract

In the use of database systems, the design of the storage engine and data model directly affects the performance of the database when performing queries. Therefore, the users of the database need to select the storage engine and design data model according to the workload encountered. However, in a hybrid workload, the query set of the database is dynamically changing, and the design of its optimised storage structure is also changing. Motivated by this, we propose an automatic storage structure selection system based on learning cost, which is used to dynamically select the optimised storage structure of the database under hybrid workloads. In the system, we introduce a machine learning method to build a cost model for the storage engine, and a column-oriented data layout generation algorithm. Experimental results show that the proposed system can choose the optimal combination of storage engine and data model according to the current workload, which greatly improves the performance of the default storage structure. And the system is designed to be compatible with different storage engines for easy use in practical applications.

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Notes

  1. https://en.wikipedia.org/wiki/Bell_number.

  2. http://www.tpc.org/tpch/.

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Acknowledgements

This paper was supported by NSFC Grant (62232005, 62202126, U1866602).

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Author notes

  1. Yan Wei and Hao Yan have contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Hongzhi Wang, Yan Wei & Hao Yan

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  1. Hongzhi Wang
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  2. Yan Wei
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  3. Hao Yan
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Correspondence to Hongzhi Wang.

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Wang, H., Wei, Y. & Yan, H. Automatic single table storage structure selection for hybrid workload. Knowl Inf Syst 65, 4713–4739 (2023). https://doi.org/10.1007/s10115-023-01913-7

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