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Holistic Shuffler for the Parallel Processing of SQL Window Functions

  • Fábio CoelhoEmail author
  • José Pereira
  • Ricardo Vilaça
  • Rui Oliveira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9687)

Abstract

Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. Currently, systems bypass parallelization opportunities which become especially relevant when considering Big Data as data is naturally partitioned. We present a shuffling technique to improve the parallel execution of window functions when data is naturally partitioned when the query holds a partitioning clause that does not match the natural partitioning of the relation. We evaluated this technique with a non-cumulative ranking function and we were able to reduce data transfer among parallel workers in 85 % when compared to a naive approach.

Notes

Acknowledgments

This work was part-funded by project LeanBigData: Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics (FP7-619606), and by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project \(\ll \)POCI-01-0145-FEDER-006961\(\gg \), and by National Funds through the FCT – Fundação para a Ciẽncia e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.

References

  1. 1.
    Reactive programming (2015). http://reactivex.io
  2. 2.
    Reactive programming for java (2015). https://github.com/ReactiveX/RxJava
  3. 3.
    Cao, Y., Chan, C.Y., Li, J., Tan, K.L.: Optimization of analytic window functions. Proc. VLDB Endowment 5(11), 1244–1255 (2012)CrossRefGoogle Scholar
  4. 4.
    Chen, G., Vo, H.T., Wu, S., Ooi, B.C., Özsu, M.T.: A framework for supporting DBMS-like indexes in the cloud. Proc. VLDB Endowment 4(11), 702–713 (2011)Google Scholar
  5. 5.
    Garcia-Molina, H.: Database Systems: The Complete Book. Pearson Education, India (2008)Google Scholar
  6. 6.
    Poosala, V., Ganti, V., Ioannidis, Y.E.: Approximate query answering using histograms. IEEE Data Eng. Bull. 22(4), 5–14 (1999)Google Scholar
  7. 7.
    Poosala, V., Haas, P.J., Ioannidis, Y.E., Shekita, E.J.: Improved histograms for selectivity estimation of range predicates. ACM SIGMOD Record 25, 294–305 (1996). ACMCrossRefGoogle Scholar
  8. 8.
    Zuzarte, C., Pirahesh, H., Ma, W., Cheng, Q., Liu, L., Wong, K.: Winmagic: subquery elimination using window aggregation. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 652–656. ACM (2003)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Fábio Coelho
    • 1
    Email author
  • José Pereira
    • 1
  • Ricardo Vilaça
    • 1
  • Rui Oliveira
    • 1
  1. 1.INESC TEC & Universidade do MinhoBragaPortugal

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