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AQUA+: Query Optimization for Hybrid Database-MapReduce System

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Abstract

MapReduce has been widely recognized as an efficient tool for large-scale data analysis. It achieves high performance by exploiting parallelism among processing nodes while providing a simple interface for upper-layer applications. However, there are many existing applications maintaining their data in a distributed database. It is costly to export those data into the storage system of MapReduce (normally a distributed file system). Moreover, compared to MapReduce, database is equipped with many state-of-the-art techniques, such as index and optimizer. Therefore, a hybrid Database-MapReduce system inheriting the advantages of both systems is preferred. In this paper, we propose AQUA+, a query optimizer tailored for the hybrid system. AQUA+ is an extension work of our previous system AQUA. It generates a plan that adaptively assigns the operators to the database engine and MapReduce engine to optimize the performance. The intuition is to exploit the index, co-partition and other features provided by the database as much as possible and reduce the data volume processed by the MapReduce. Due to the complexity of query optimization, in AQUA+, we introduce a novel tuning technique, learning to optimize. In particular, two neural networks are trained to predict cost and refine query plan, respectively. We train them based on our log of real query processing. Experiments carried out on our in-house cluster confirm the effectiveness of our query optimizer.

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Notes

  1. http://www.aster.com.

  2. http://www.greenplum.com.

  3. http://hadoop.apache.org/.

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Acknowledgements

This work was supported by the Key Research Program of Zhejiang Province [grant numbers 2021C01109]; the Zhejiang Provincial Natural Science Foundation [grant numbers LZ21F020007]; and the Grid State Foundation [grant number 5211XT190033].

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Authors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Zhifei Pang, Sai Wu & Zhouzhenyan Hong

  2. State Grid Zhejiang Electric Power Company Information & Telecommunication Branch, Hangzhou, China

    Haichao Huang & Yuqing Xie

  3. Institute of Computing Innovation, Zhejiang University, Hangzhou, China

    Zhifei Pang

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  1. Zhifei Pang
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Correspondence to Sai Wu.

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Pang, Z., Wu, S., Huang, H. et al. AQUA+: Query Optimization for Hybrid Database-MapReduce System. Knowl Inf Syst 63, 905–938 (2021). https://doi.org/10.1007/s10115-020-01542-4

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