Advertisement

Experimental Evaluation of Big Data Analytical Tools

  • Mário Rodrigues
  • Maribel Yasmina Santos
  • Jorge BernardinoEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 341)

Abstract

Due to the extensive use of SQL, the number of SQL-on-Hadoop systems has significantly increased, transforming Big Data Analytics in a more accessible practice and allowing users to perform ad-hoc querying and interactive analysis. Therefore, it is of upmost importance to understand these querying tools and the specific contexts in which each one of them can be used to accomplish specific analytical needs. Due to the high number of available tools, this work performs a performance evaluation, using the well-known TPC-DS benchmark, of some of the most popular Big Data Analytical tools, analyzing in more detail the behavior of Drill, Hive, HAWQ, Impala, Presto, and Spark.

Keywords

Big Data SQL-on-Hadoop Query processing Big Data Analytics 

Notes

Acknowledgements

This work is supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013 and by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project no 002814; Funding Reference: POCI-01-0247-FEDER-002814]. The hardware resources used were provided by INCD – In-fraestrutura Nacional de Computação Distribuída, an unit of FCT.

References

  1. 1.
    Transaction Processing Performance Council: TPC BenchmarkTM H Standard Specification Revision 2.17.2 (2017)Google Scholar
  2. 2.
    Floratou, A., Minhas, U.F., Ozcan, F.: SQL-on-Hadoop: full circle back to shared-nothing database architectures. Proc. VLDB Endow. 7(12), 1295–1306 (2014)CrossRefGoogle Scholar
  3. 3.
    Owl, C.: The SQL on Hadoop landscape: an overview (Part I) (2015). http://cleverowl.uk/2015/11/19/the-sql-on-hadoop-landscape-an-overview-part-i/
  4. 4.
    Owl, C.: The SQL on Hadoop landscape: an overview (Part II) (2015). http://cleverowl.uk/2015/12/25/the-sql-on-hadoop-landscape-an-overview-part-ii/
  5. 5.
    Devadutta Ghat, D.K., Rorke, D.: New SQL Benchmarks: Apache Impala (Incubating) Uniquely Delivers Analytic Database Performance (2016). https://blog.cloudera.com/blog/2016/02/new-sql-benchmarks-apache-impala-incubating-2-3-uniquely-delivers-analytic-database-performance/
  6. 6.
    Sakr, S.: A brief comparative perspective on SQL access for Hadoop. In: Recent Advances in Information Systems and Technologies, vol. 1, pp. 1–9 (2014)Google Scholar
  7. 7.
    Kornacker, M., et al.: Impala: A Modern, Open-Source SQL Engine for Hadoop. In: CIDR(Conference on Innovative Data Systems Research) (2015)Google Scholar
  8. 8.
    Grover, A., et al.: SQL-like big data environments: case study in clinical trial analytics. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2680–2689 (2015)Google Scholar
  9. 9.
  10. 10.
    Santos, M.Y., et al.: Evaluating SQL-on-Hadoop for big data warehousing on not-so-good hardware. In: Proceedings of the 21st International Database Engineering and Applications Symposium - IDEAS 2017, pp. 242–252 (2017)Google Scholar
  11. 11.
    Rodrigues, M., Santos, M.Y., Bernardino, J.: Describing and comparing big data querying tools. In: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST 2017. AISC, vol. 569, pp. 115–124. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-56535-4_12CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Polytechnic of Coimbra - ISEC (Coimbra Institute of Engineering)CoimbraPortugal
  2. 2.ALGORITMI Research CentreUniversity of MinhoGuimarãesPortugal
  3. 3.CISUC – Centre of Informatics of University of CoimbraCoimbraPortugal

Personalised recommendations