Abstract
Most Data Warehouses (DW) are stored in Relational Database Management Systems (RDBMS) using a star-schema model. While this model yields a trade-off between performance and storage requirements, huge data warehouses experiment performance problems. Although parallel shared-nothing architectures improve on this matter by a divide-and-conquer approach, issues related to parallelizing join operations cause limitations on that amount of improvement, since they have implications concerning placement, the need to replicate data and/or on-the-fly repartitioning. In this paper, we show how these limitations can be overcome by replacing the star schema by a universal relation approach for more efficient and scalable parallelization. We evaluate the proposed approach using TPC-H benchmark, to both demonstrate that it provides highly predictable response times and almost optimal speedup.
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PostgreSQL, http://www.postgresql.org/
TPC-H Benchmark, http://www.tpc.org/tpch/
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Costa, J.P., Cecílio, J., Martins, P., Furtado, P. (2012). Overcoming the Scalability Limitations of Parallel Star Schema Data Warehouses. In: Xiang, Y., Stojmenovic, I., Apduhan, B.O., Wang, G., Nakano, K., Zomaya, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2012. Lecture Notes in Computer Science, vol 7439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33078-0_34
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DOI: https://doi.org/10.1007/978-3-642-33078-0_34
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