Abstract
In today’s big data era, the capability of analyze massive data efficient and return the results within an short time limit is critical to decision making, thus many big data system proposed and various distributed and parallel processing techniques are heavily investigated. Among previous research, most of them are working on precise query processing, while approximate query processing (AQP) techniques which make interactive data exploration more efficiently and allows users to tradeoff between query accuracy and response time have not been investigate comprehensively. In this paper, we study the characteristics of aggregate query, a typical type of analytical query, and proposed an approximate query processing approach to optimize the execution of massive data based aggregate query with a histogram data structure. We implemented this approach into big data system Hive and compare it with Hive and AQP-enabled big data system BlinkDB, the experimental results verified that our approach is significantly fast than these existing systems in most scenarios.
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References
Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Zhang, N., Anthony, S., Liu, H., Murthy, R.: Hive - a petabyte scale data warehouse using Hadoop. In: ICDE, pp. 996–1005 (2010)
Huai, Y., Chauhan, A., Gates, A., Hagleitner, G., Hanson, E.N., O’Malley, O., Pandey, J., Yuan, Y., Lee, R., Zhang, X.: Major technical advancements in apache hive. In: SIGMOD Conference 2014, pp. 1235–1246 (2014)
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: EuroSys 2013, pp. 29–42 (2013)
Agarwal, S., Panda, A., Mozafari, B., Iyer, A.P., Madden, S., Stoica, I.: Blink and it’s done: interactive queries on very large data. PVLDB 5(12), 1902–1905 (2012)
Melnik, S., Gubarev, A., Long, J.J., Romer, G., Shivakumar, S., Tolton, M., Vassilakis, T.: Dremel: interactive analysis of web-scale datasets. PVLDB 3(1), 330–339 (2010)
Afrati, F.N., Delorey, D., Pasumansky, M., Ullman, J.D.: Storing and querying tree-structured records in dremel. PVLDB 7(12), 1131–1142 (2014)
Kornacker, M., Behm, A., Bittorf, V., Bobrovytsky, T., Ching, C., et al.: Impala: a modern, open-source SQL engine for hadoop. In: CIDR 2015 (2015)
Wanderman-Milne, S., Li, N.: Runtime code generation in Cloudera Impala. IEEE Data Eng. Bull. 37(1), 31–37 (2014)
Agarwal, S., Milner, H., Kleiner, A., Talwalkar, A., Jordan, M.I., Madden, S., Mozafari, B., Stoica, I.: Knowing when you’re wrong: building fast and reliable approximate query processing systems. In: SIGMOD Conference 2014, pp. 481–492 (2014)
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: HotCloud 2010 (2010)
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauly, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: NSDI 2012, pp. 15–28 (2012)
Armbrust, M., Xin, R.S., Lian, C., Huai, Y., Liu, D., Bradley, J.K., Meng, X., Kaftan, T., Franklin, M.J., Ghodsi, A., Zaharia, M.: Spark SQL: relational data processing in spark. In: SIGMOD Conference 2015, pp. 1383–1394 (2015)
Mozafari, B., Ramnarayan, J., Menon, S., Mahajan, Y., Chakraborty, S., Bhanawat, H., Bachhav, K.: SnappyData: a unified cluster for streaming, transactions and interactice analytics. In: CIDR 2017 (2017)
Li, K., Li, G.: Approximate query processing: what is new and where to go? - a survey on approximate query processing. Data Sci. Eng. 3(4), 379–397 (2018)
Han, X., Wang, B., Li, J., Gao, H.: Efficiently processing deterministic approximate aggregation query on massive data. Knowl. Inf. Syst. 57(2), 437–473 (2018)
Park, Y., Mozafari, B., Sorenson, J., Wang, J.: VerdictDB: universalizing approximate query processing. In: SIGMOD Conference 2018, pp. 1461–1476 (2018)
Peng, J., Zhang, D., Wang, J., Pei, J.: AQP++: connecting approximate query processing with aggregate precomputation for interactive analytics. In: SIGMOD Conference 2018, pp. 1477–1492 (2018)
Galakatos, A., Crotty, A., Zgraggen, E., Binnig, C., Kraska, T.: Revisiting reuse for approximate query processing. PVLDB 10(10), 1142–1153 (2017)
Chaudhuri, S., Ding, B., Kandula, S.: Approximate query processing: no silver bullet. In: SIGMOD Conference 2017, pp. 511–519 (2017)
Kaiping, F., Hua, Z., Chaoying, F., Heng, C.: In: Application of Histogram Method on Cost Estimate in Query Optimization. Computer & Digital Engineering (2010)
Acharya, S., Gibbons, P.B., Poosala, V.: Congressional samples for approximate answering of group-by queries. In: ACM SIGMOD, May 2000
Cormode, G.: Sketch techniques for massive data. In: Synopses for Massive Data: Samples, Histograms, Wavelets and Sketches (2011)
Acknowledgements
This paper is supported by Guizhou University Science and Technology Talent Support Program (No.KY [2016] 086).
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Yong, L., Zhaonan, M. (2019). Optimizing Performance of Aggregate Query Processing with Histogram Data Structure. In: Silhavy, R. (eds) Software Engineering Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-19807-7_33
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DOI: https://doi.org/10.1007/978-3-030-19807-7_33
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