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JG2Time: A Learned Time Estimator for Join Operators Based on Heterogeneous Join-Graphs

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Database Systems for Advanced Applications (DASFAA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13943))

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Abstract

The join operator is one of the key operators in RDBMS, and estimating its evaluation time is a fundamental task in query optimization, scheduling, etc. However, it is hard to make a precise estimation, which is not only related with the physical join implementations (hash, sort, loop) but also with the corresponding parameters, like the size of the data, the number of partitions, the number of threads in a modern hash join. Existing works rely on the time complexity analysis but yield rough results, or employ machine learning techniques to build a predictive model but require many training instances. In this paper, we propose a method, named JG2Time, to estimate the running time using the join-graphs constructed from the source codes. Specifically, we construct a heterogonous join-graph by annotating parameter nodes to a call-graph generated by running time analysis tools, and propose ReGAT, a heterogonous graph neural network, to fully capture the edge weights (the number of function calls) in the join-graph. The embeddings learned from ReGAT can be used to predict the running time. In addition, we optimize JG2Time with a multi-task model that also predicts the times of function calls, and an unsupervised code learning method to enhance its generalization. The experimental results illustrate the effectiveness of JG2Time and its optimization strategies.

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Notes

  1. 1.

    https://valgrind.org/.

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Acknowledgement

This work was partially supported by NSFC under Grant No. 62272008 and 61832001, and ZTE-PKU Joint Program.

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Authors and Affiliations

  1. School of Computer Science, Peking University, Beijing, China

    Hao Miao, Jiazun Chen & Jun Gao

  2. ZTE Corporation, Beijing, China

    Yang Lin, Mo Xu & Yinjun Han

Authors
  1. Hao Miao
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  2. Jiazun Chen
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  3. Yang Lin
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  4. Mo Xu
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  5. Yinjun Han
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  6. Jun Gao
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Corresponding author

Correspondence to Jun Gao .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

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Miao, H., Chen, J., Lin, Y., Xu, M., Han, Y., Gao, J. (2023). JG2Time: A Learned Time Estimator for Join Operators Based on Heterogeneous Join-Graphs. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13943. Springer, Cham. https://doi.org/10.1007/978-3-031-30637-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-30637-2_9

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  • Online ISBN: 978-3-031-30637-2

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