R. K. Klassen, V. A. Raikhlin Improving the Efficiency of ClusterixLike DBMS for Big Data Analytical Processing
R. K. Klassen, V. A. Raikhlin Improving the Efficiency of ClusterixLike DBMS for Big Data Analytical Processing


Commercial OLAP-systems are economically unavailable for organizations with limited financial capabilities. Analytical processing large amounts of data in these organizations can be accomplished using open source software systems on a cost-effective cluster platform. Previously created Clusterix-like DBMS were not efficient enough according to the «performance/cost» criterion. With a view to the enhance the effectiveness of such systems in the article considers their further development with a focus on a full load of processor cores and the using GPU acceleration (systems Clusterix-N, N – from New) up to the development of a system comparable in efficiency to the open source system Spark, which is currently considered the most promising. The development methodology was based on the constructive system modeling methodology.


analytic processing of significant data volumes, open source software systems on a cluster platform, increasing the efficiency of Clusterix-like DBMS, full loading of processor cores, full load of processor cores, GPU acceleration, comparison with Spark, accepted methodology.

PP. 43-59.

DOI 10.14357/20718632190405


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