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Evolution from Shark to Spark SQL: Preliminary Analysis and Qualitative Evaluation

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Big Data Benchmarks, Performance Optimization, and Emerging Hardware (BPOE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9495))

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Abstract

Spark is a general distributed framework with the abstraction called resilient distributed datasets (RDD). Database analysis is one of the main kinds of workloads supported on Spark. The SQL component on Spark has evolved from Shark to Spark SQL, while the core components of Spark also have evolved a lot comparing with the original version. We analyzed on which aspects Spark have made efforts to support many workloads efficiently and whether the changes make the support for SQL achieve better performance.

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Acknowledgements

This work was supported by the National High Technology Research and Development Program of China (Grant No. 2015AA015308), the Major Program of National Natural Science Foundation of China (Grant No. 61432006), and the Key Technology Research and Development Programs of Guangdong Province, China (Grant No. 2015B010108006).

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Authors and Affiliations

  1. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xinhui Tian, Gang Lu & Xiexuan Zhou

  2. University of Chinese Academy of Sciences, Beijing, China

    Xinhui Tian, Gang Lu & Xiexuan Zhou

  3. Beijing Academy of Frontier Science and Technology, Beijing, China

    Jingwei Li

Authors
  1. Xinhui Tian
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  2. Gang Lu
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  3. Xiexuan Zhou
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  4. Jingwei Li
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Corresponding author

Correspondence to Xinhui Tian .

Editor information

Editors and Affiliations

  1. Institute of Computing, Chinese Academy of Sciences, Beijing, China

    Jianfeng Zhan

  2. ICT, Chinese Academy of Sciences, Beijing, China

    Rui Han

  3. FB12 - DBIS (5. Stock), Goethe Universität Frankfurt, Frankfurt, Hessen, Germany

    Roberto V. Zicari

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Tian, X., Lu, G., Zhou, X., Li, J. (2016). Evolution from Shark to Spark SQL: Preliminary Analysis and Qualitative Evaluation. In: Zhan, J., Han, R., Zicari, R. (eds) Big Data Benchmarks, Performance Optimization, and Emerging Hardware. BPOE 2015. Lecture Notes in Computer Science(), vol 9495. Springer, Cham. https://doi.org/10.1007/978-3-319-29006-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-29006-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29005-8

  • Online ISBN: 978-3-319-29006-5

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