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Primary and Referential Horizontal Partitioning Selection Problems: Concepts, Algorithms and Advisor Tool
Abstract
Horizontal partitioning has evolved significantly in recent years and widely advocated by the academic and industrial communities. Horizontal Partitioning affects positively query performance, database manageability and availability. Two types of horizontal partitioning are supported: primary and referential. Horizontal fragmentation in the context of relational data warehouses is to partition dimension tables by primary fragmentation then fragmenting the fact table by referential fragmentation. This fragmentation can generate a very large number of fragments which may make the maintenance task very complicated. In this paper, we first focus on the evolution of horizontal partitioning in commercial DBMS motivated by decision support applications. Secondly, we give a formalization of the referential fragmentation schema selection problem in the data warehouse and we study its hardness to select an optimal solution. Due to its high complexity, we develop two algorithms: hill climbing and simulated annealing with several variants to select a near optimal partitioning schema. We present ParAdmin, an advisor tool assisting administrators to use primary and referential partitioning during the physical design of their data warehouses. Finally, extensive experimental studies are conducted using the data set of APB1 benchmark to compare the quality the proposed algorithms using a mathematical cost model. Based on these experiments, some recommendations are given to ensure the well use of horizontal partitioning.
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