Clusterix-Like BigData DBMS

Abstract

Abstract Commercial OLAP systems are economically unavailable for organizations with limited financial capabilities. Analytical processing of 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 using a regular query processing plan is not efficient enough. Therefore, research on such systems was developed with a focus on a full load of processor cores and using the 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.

Keywords

• Clusterix-like DBMS
• Databases of significant volumes
• BigData
• Regular query processing plan
• Full load of processor cores
• GPU acceleration