Abstract
Accurate cardinality estimation contributes significantly to query optimization whereas traditional approaches such as histogram-based or sketching-based approaches relying on assumption of uniform distribution of data and appropriate pre-set parameters, often leading to dilemma in practical applications. In this paper, an accurate lightweight, dimensionally adaptive, strongly generalizable learned cardinality estimator for multi-dimensional range queries, CELA is proposed reflecting on the characteristics of desirable cardinality estimators. For the purpose of capturing relationship between dimensions, CELA raises a query-oriented approach of constructing constraint matrices to apply convolution. Experiments illustrates that CELA performs superbly on each defined indicators far superior to PostgreSQL. Furthermore, the strong generalization ability of CELA is demonstrated by the excellent performance trained with continuously scaled-down training set.
Supported by the National Key R&D Program of China (grant No. 2019YFB1705601) and the Natural Science Foundation of China (grant No. 62072075).
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References
Ding, J., et al.: Alex: an updatable adaptive learned index. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 969–984 (2020)
Hasan, S., Thirumuruganathan, S., Augustine, J., Koudas, N., Das, G.: Deep learning models for selectivity estimation of multi-attribute queries. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 1035–1050 (2020)
Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., Cali, E.G.: Database performance tuning and query optimization. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_1
Kipf, A., Kipf, T., Radke, B., Leis, V., Boncz, P., Kemper, A.: Learned cardinalities: estimating correlated joins with deep learning. arXiv preprint arXiv:1809.00677 (2018)
Marcus, R., et al.: Neo: a learned query optimizer. arXiv preprint arXiv:1904.03711 (2019)
Marcus, R., Papaemmanouil, O.: Towards a hands-free query optimizer through deep learning. arXiv preprint arXiv:1809.10212 (2018)
Markl, V., Raman, V., Simmen, D., Lohman, G., Pirahesh, H., Cilimdzic, M.: Robust query processing through progressive optimization. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 659–670 (2004)
Momjian, B.: PostgreSQL: Introduction and Concepts, vol. 192. Addison-Wesley, New York (2001)
Ortiz, J., Balazinska, M., Gehrke, J., Keerthi, S.S.: Learning state representations for query optimization with deep reinforcement learning. In: Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, pp. 1–4 (2018)
Panahi, V., Navimipour, N.J.: Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators. Concurr. Comput. Pract. Exp. 31(17), e5218 (2019)
Poess, M., Floyd, C.: New TPC benchmarks for decision support and web commerce. ACM Sigmod Rec. 29(4), 64–71 (2000)
Woltmann, L., Hartmann, C., Thiele, M., Habich, D., Lehner, W.: Cardinality estimation with local deep learning models. In: Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, pp. 1–8 (2019)
Xu, J., Zhang, Z., Xiao, X., Yang, Y., Yu, G., Winslett, M.: Differentially private histogram publication. VLDB J. 22(6), 797–822 (2013). https://doi.org/10.1007/s00778-013-0309-y
Yu, X., Li, G., Chai, C., Tang, N.: Reinforcement learning with tree-LSTM for join order selection. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1297–1308. IEEE (2020)
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Zhou, W., Zhan, S., Guo, L., Dai, B. (2021). CELA: An Accurate Learned Cardinality Estimator with Strong Generalization Ability and Dimensional Adaptability. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13080. Springer, Cham. https://doi.org/10.1007/978-3-030-90888-1_9
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