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Quantitative Contention Generation for Performance Evaluation on OLTP Databases

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Web and Big Data (APWeb-WAIM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12318))

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Abstract

Although we have achieved significant progress in improving the scalability of transactional database systems (OLTP), the presence of contention operations in workloads is still the fundamental limitation in improving throughput. The reason is that the overhead of managing conflict transactions with concurrency control mechanism is proportional to the amount of contentions. As a consequence, contention workload generation is urgent to evaluate performance of modern OLTP database systems. Though we have kinds of standard benchmarks which provide some ways in simulating resource contention, e.g. skew distribution control of transactions, they can not control the generation of contention quantitatively; even worse, the simulation effectiveness of these methods is affected by the scale of data. So in this paper we design a scalable quantitative contention generation method with fine contention granularity control, which is expected to generate resource contention specified by contention ratio and contention intensity.

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Acknowledgment

This work is partially supported by National Key Research and Development Plan Project (No. 2018YFB1003404). Ke Shu is supported by PingCAP.

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Authors and Affiliations

  1. East China Normal University, Shanghai, 200000, China

    Chunxi Zhang, Rong Zhang, Weining Qian & Aoying Zhou

  2. PingCAP Ltd., Beijing, 100000, China

    Ke Shu

Authors
  1. Chunxi Zhang
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  2. Rong Zhang
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  3. Weining Qian
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  4. Ke Shu
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  5. Aoying Zhou
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Corresponding author

Correspondence to Rong Zhang .

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Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Melbourne, Melbourn, NSW, Australia

    Rui Zhang

  3. Kyung Hee University, Yongin, Korea (Democratic People's Republic of)

    Young-Koo Lee

  4. Nanjing University of Information Science and Technology, Nanjing, China

    Le Sun

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

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Zhang, C., Zhang, R., Qian, W., Shu, K., Zhou, A. (2020). Quantitative Contention Generation for Performance Evaluation on OLTP Databases. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_34

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  • DOI: https://doi.org/10.1007/978-3-030-60290-1_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60289-5

  • Online ISBN: 978-3-030-60290-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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