Challenging SQL-on-Hadoop Performance with Apache Druid

  • José CorreiaEmail author
  • Carlos Costa
  • Maribel Yasmina Santos
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)


In Big Data, SQL-on-Hadoop tools usually provide satisfactory performance for processing vast amounts of data, although new emerging tools may be an alternative. This paper evaluates if Apache Druid, an innovative column-oriented data store suited for online analytical processing workloads, is an alternative to some of the well-known SQL-on-Hadoop technologies and its potential in this role. In this evaluation, Druid, Hive and Presto are benchmarked with increasing data volumes. The results point Druid as a strong alternative, achieving better performance than Hive and Presto, and show the potential of integrating Hive and Druid, enhancing the potentialities of both tools.


Big Data Big Data Warehouse SQL-on-Hadoop Druid OLAP 



This work is supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within Project UID/CEC/00319/2013 and by European Structural and Investment Funds in the FEDER component, COMPETE 2020 (Funding Reference: POCI-01-0247-FEDER-002814).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ALGORITMI Research CentreUniversity of MinhoGuimarãesPortugal
  2. 2.NATIXIS, on Behalf of Altran PortugalPortoPortugal
  3. 3.Centre for Computer Graphics - CCGGuimarãesPortugal

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