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Survey on performance optimization for database systems

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Abstract

The performance optimization of database systems has been widely studied for years. From the perspective of the operation and maintenance personnel, it mainly includes three topics: prediction, diagnosis, and tuning. The prediction of future performance can guide the adjustment of configurations and resources. The diagnosis of anomalies can determine the root cause of performance regression. Tuning operations improve performance by adjusting influencing factors, e.g., knobs, indexes, views, resources, and structured query language (SQL) design. In this review, we focus on the performance optimization of database systems and review notable research work on the topics of prediction, diagnosis, and tuning. For prediction, we summarize the techniques, strengths, and limitations of several proposed systems for single and concurrent queries. For diagnosis, we categorize the techniques by the input data, i.e., monitoring metrics, logs, or time metrics, and analyze their abilities. For tuning, we focus on the approaches commonly adopted by the operation and maintenance personnel, i.e., knob tuning, index selection, view materialization, elastic resource, storage management, and SQL antipattern detection. Finally, we discuss some challenges and future work.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61832001) and ZTE-PKU Joint Program (Grant No. HV-CN-20210407026).

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Authors and Affiliations

  1. Key Laboratory of High Confidence Software Technologies (MOE), School of Computer Science, Peking University, Beijing, 100871, China

    Shiyue Huang, Yanzhao Qin, Xinyi Zhang & Bin Cui

  2. ZTE Corporation, Nanjing, 210012, China

    Yaofeng Tu & Zhongliang Li

  3. Institute of Computational Social Science, Peking University (Qingdao), Qingdao, 266555, China

    Bin Cui

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  1. Shiyue Huang
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Huang, S., Qin, Y., Zhang, X. et al. Survey on performance optimization for database systems. Sci. China Inf. Sci. 66, 121102 (2023). https://doi.org/10.1007/s11432-021-3578-6

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