Abstract
Data fragmentation and allocation in distributed and parallel Database Management Systems (DBMS) have been extensively studied in the past. Previous work tackled these two problems separately even though they are dependent on each other. We recently developed a combined algorithm that handles the dependency issue between fragmentation and allocation. A novel genetic solution was developed for this problem. The main issue of this solution and previous solutions is the lack of real life verifications of these models. This paper addresses this gap by verifying the effectiveness of our previous genetic solution on the Teradata DBMS. Teradata is a shared nothing DBMS with proven scalability and robustness in real life user environments as big as 10’s of petabytes of relational data. Experiments are conducted for the genetic solution and previous work using the SSB benchmark (TPC-H like) on a Teradata appliance running TD 13.10. Results show that the genetic solution is faster than previous work by a 38%.
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Bellatreche, L., Benkrid, S., Ghazal, A., Crolotte, A., Cuzzocrea, A. (2011). Verification of Partitioning and Allocation Techniques on Teradata DBMS. In: Xiang, Y., Cuzzocrea, A., Hobbs, M., Zhou, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2011. Lecture Notes in Computer Science, vol 7016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24650-0_14
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