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)


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.



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.


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© IFIP International Federation for Information Processing 2016

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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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