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Most data sets of multidimensional databases have two characteristics:. Data does not exist for the majority of member combinations. For example, all products may not be sold in all areas of the country. Essbase maximizes performance by dividing the standard dimensions of an application into two types: dense dimensions and sparse dimensions. This division allows Essbase to cope with data that is not smoothly distributed, without losing the advantages of matrix-style access to the data.
Essbase speeds data retrieval while minimizing memory and disk requirements. Most multidimensional databases are inherently sparse; they lack data values for the majority of member combinations. A sparse dimension is one with a low percentage of available data positions filled. For example, the outline of the Sample. Product represents the product units, Market represents the geographical regions in which the products are sold, and Measures represents the accounts data. Because not every product is sold in every market, Market and Product are chosen as sparse dimensions.
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Because not every product is sold in every market, Market and Product are chosen as sparse dimensions. Multidimensional databases also contain dense dimensions. A dense dimension has a high probability that one or more cells is occupied in every combination of dimensions. For example, in the Sample. Basic database, accounts data exists for almost all products in all markets, so Measures is chosen as a dense dimension. Year and Scenario are also chosen as dense dimensions.
Year represents time in months, and Scenario represents whether the accounts values are budget or actual values. See Working with Attributes. When an Essbase database is stored on disk, the cartesian product of dense member combinations form units of storage called blocks, and a block is written to disk for every sparse member combination in the database.
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