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On the Performance of Three In-Memory Data Systems for On Line Analytical Processing

Listed author(s):
  • Ionut HRUBARU


  • Marin FOTACHE


Registered author(s):

    In-memory database systems are among the most recent and most promising Big Data technologies, being developed and released either as brand new distributed systems or as extensions of old monolith (centralized) database systems. As name suggests, in-memory systems cache all the data into special memory structures. Many are part of the NewSQL strand and target to bridge the gap between OLTP and OLAP into so-called Hybrid Transactional Analytical Systems (HTAP). This paper aims to test the performance of using such type of systems for TPCH analytical workloads. Performance is analyzed in terms of data loading, memory footprint and execution time of the TPCH query set for three in-memory data systems: Oracle, SQL Server and MemSQL. Tests are subsequently deployed on classical on-disk architectures and results compared to in-memory solutions. As in-memory is an enterprise edition feature, associated costs are also considered.

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    Article provided by Academy of Economic Studies - Bucharest, Romania in its journal Informatica Economica.

    Volume (Year): 21 (2017)
    Issue (Month): 1 ()
    Pages: 5-15

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    Handle: RePEc:aes:infoec:v:21:y:2017:i:1:p:5-15
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    1. Martin Kowalczyk & Peter Buxmann, 2014. "Big Data and Information Processing in Organizational Decision Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 267-278, October.
    2. Kowalczyk, Martin & Buxmann, Peter, 2014. "Big Data and Information Processing in Organizational Decision Processes: A Multiple Case Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65730, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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