Abstract
Approximate query processing (AQP) is an effective way to provide approximate results for SQL queries, which relaxing accuracy in exchange for higher processing speed. In sampling-based AQP techniques, random sampling works well for uniformly distributed data but performs poorly on skewed data. To address this problem, we propose a distribution-aware approximation framework called AQapprox (aggregation queries approximation), to approximate queries more efficiently and accurately by extending Sapprox. We construct a probabilistic Map, which records the occurrences of sub-datasets on categorical columns and related statistics on numerical columns at each segment of the whole dataset. When a query arrives, AQapprox will combine Map and adaptively use different sampling methods based on the distribution. Experimental results on both real and synthetic datasets show that AQapprox can achieve a speedup by up to 5.9\(\times \) for skewed data, 64\(\times \) for uniform data over Sapprox, and has higher accuracy on multi-column queries.
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References
Amazon review data. http://jmcauley.ucsd.edu/data/amazon/
Tpc-h benchmark. http://www.tpc.org/tpch/
Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., Stoica, I.: BlinkDB: queries with bounded errors and bounded response times on very large data. In: Proceedings of the 8th ACM European Conference on Computer Systems, pp. 29–42 (2013)
Chaudhuri, S., Ding, B., Kandula, S.: Approximate query processing: no silver bullet. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 511–519 (2017)
Ding, B., Huang, S., Chaudhuri, S., Chakrabarti, K., Wang, C.: Sample+seek: approximating aggregates with distribution precision guarantee. In: Proceedings of the 2016 International Conference on Management of Data, pp. 679–694 (2016)
Gan, Y., Meng, X., Shi, Y.: Processing online aggregation on skewed data in mapreduce. In: Proceedings of the Fifth International Workshop on Cloud Data Management, pp. 3–10 (2013)
Gemulla, R., Lehner, W., Haas, P.J.: Maintaining bounded-size sample synopses of evolving datasets. VLDB J. 17(2), 173–201 (2008)
Goiri, I., Bianchini, R., Nagarakatte, S., Nguyen, T.D.: ApproxHadoop: bringing approximations to mapreduce frameworks. In: Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 383–397 (2015)
Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012)
Haas, P.J., König, C.: A bi-level bernoulli scheme for database sampling. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 275–286 (2004)
Kandula, S., et al.: Quickr: lazily approximating complex AdHoc queries in bigdata clusters. In: Proceedings of the 2016 International Conference on Management of Data, pp. 631–646 (2016)
Li, K., Zhang, Y., Li, G., Tao, W., Yan, Y.: Bounded approximate query processing. IEEE Trans. Knowl. Data Eng. 12, 2262–2276 (2019). https://doi.org/10.1109/TKDE.2018.2877362
Lohr, S.L.: Sampling: Design and Analysis. Nelson Education (2009)
Marwick, B., Krishnamoorthy, K.: cvequality: tests for the equality of coefficients of variation from multiple groups. R software package version, vol. 1, p. 3 (2018)
Pansare, N., Borkar, V.R., Jermaine, C., Condie, T.: Online aggregation for large mapreduce jobs. Proc. VLDB Endowment 4(11), 1135–1145 (2011)
Park, Y., Mozafari, B., Sorenson, J., Wang, J.: VerdictDB: universalizing approximate query processing. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1461–1476 (2018)
Wiegand, H.: Kish, l.: Survey Sampling. Wiley, New York (1965). ix + 643 s., 31 abb., 56 tab., preis 83 s. 10(1), 88–89 (2010)
Zhang, X., Wang, J., Yin, J.: Sapprox: enabling efficient and accurate approximations on sub-datasets with distribution-aware online sampling. Proc. VLDB Endowment 10(3), 109–120 (2016)
Acknowledgments
This work was supported by NSFC grants (No. 61532021 and 61972155), Shanghai Knowledge Service Platform Project (No. ZF1213).
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Wu, H., Wang, X., Lu, X. (2020). AQapprox: Aggregation Queries Approximation with Distribution-Aware Online Sampling. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12343. Springer, Cham. https://doi.org/10.1007/978-3-030-62008-0_28
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DOI: https://doi.org/10.1007/978-3-030-62008-0_28
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