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Oracle Dimensions

Oracle Database Tips by Donald Burleson

Using Oracle Dimensions

It appears that what we're talking about when referring to "compression" in 10g OLAP cubes is actually something called "Compressed Composites". Composites should be fairly familiar to anyone working with Express or Oracle OLAP and are structures that you set up when you've got very sparse cubes. In cases where there are few actual used combinations of dimensions for your variable, compared to the potential set of valid combinations, setting up composites, which only store within them the actual combinations of dimensions used, together with indexes to the underlying dimensions, reduces the amount of NA values stored in the variable and results in more efficient data storage.

The reduction in size occurs for those sets of base dimensions values that identify non-NA data at higher levels of hierarchical dimensions.

For variables dimensioned by compressed composites, Oracle OLAP reduces redundancy in the variable, composite, and composite index by using the"intelligence" of the AGGREGATE command that populates the variable. For sets of base dimensions values that represent parent nodes, Oracle OLAP creates a physical position in the composite only for those tuples that represent a parent with more than one descendant. Oracle OLAP then creates an index between this composite structure and the base dimensions and uses this composite structure as the dimensions of the variable. Since the actual structure of a compressed composite is smaller than that of a b-tree or hash composite, a variable dimensioned by a compressed composite is also smaller than a variable dimensioned by a b-tree or hash composite. Also, since the index for a compressed composite only has nodes for parents with more than one descendant, the index of a compressed composite has fewer levels and is smaller than the index of a b-tree composite.

Although performance varies depending on the depth of the hierarchies and the order of the dimensions in the composite, aggregating variables defined with compressed composites is typically much faster than aggregating variables defined with b-tree or hash composites.

ROLLUP enables an SQL statement to calculate multiple levels of subtotals across a specified group of dimensions. It also calculates a grand total. ROLLUP is a simple extension to the GROUP BY clause, so its syntax is extremely easy to use.

In multidimensional jargon, a "cube" is a cross-tabulated summary of detail rows. CUBE enables a SELECT statement to calculate subtotals for all possible combinations of a group of dimensions. It also calculates a grand total. This is the set of information typically needed for all cross-tabular reports, so CUBE can calculate a cross-tabular report with a single select statement.



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