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The multidimensional cube is intended as a high-performance data structure. Generally, cubes achieve near-instantaneous performance by precomputing the value of facts across the various members of each dimension. This permits the interactive analysis style for which OLAP is famous. This also means there may be little performance benefit in creating one cube that summarizes another. TiP When a cube is used as primary storage for a dimensional design, the data set is already optimized for high performance. A summary cube is not necessary. Of course, there is an exception to every rule. Vendor innovations that are intended to improve the scalability of the cube are also bringing back the value of summarizing it. As noted in 3, the cube has traditionally been less scalable than a star schema. Because of the need to precompute facts across each member, cubes grow exponentially larger as more and more dimensions are added. To cope with this drawback, some products now allow an administrator to control the degree of pre-summarization within a cube. This allows the cube to contain more data but also reduces its performance. In this case, additional cubes may be useful as high-performance summaries of the base cube. The two stars in Figure 15-1 might easily be replaced with cubes. The base cube, which contains orders by order line, may be configured to maximize storage efficiency of the facts. The aggregate cube, which contains orders by month, product, and salerep, can be configured to maximize performance.

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Many architectures incorporate both the star schema and the cube. As noted in 3, the star scales well, while the cube performs well. The best of both worlds is achieved by relying on the star to store granular, detailed data, with cubes containing high-performance extracts. In this scenario, one or more cubes summarize data stored in a base star. Queries and reports are designed to make use of the cubes, to afford optimum performance. In Figure 15-3, for example, cubes might replace one or all of the aggregate stars.

Just as the delete rule tells the DBMS what to do when a user tries to delete a row of the parent table, the update rule tells the DBMS what to do when a user tries to update the value of one of the primary key columns in the parent table Again, there are four possibilities, paralleling those available for delete rules: The RESTRICT update rule prevents you from updating the primary key of a row in the parent table if that row has any children An UPDATE statement that attempts to modify the primary key of such a parent row is rejected with an error message Changes to primary keys in the parent table are thus restricted to rows without any children Applied to Figure 11-1, this rule can be summarized as "You can't change an office number if salespeople are assigned to the office.

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This arrangement is the foundation for many highly successful data warehouse implementations. Since they are both dimensional, cubes and stars have a natural affinity. Many multidimensional products have been specifically built to support the design and creation of cubes based on an underlying star. This will be further explored later in this chapter.

" The CASCADE update rule tells the DBMS that when a primary key value is changed in a parent row, the corresponding foreign key value in all of its child rows should also automatically be changed in the child table, to match the new primary key For Figure 11-1, this rule can be summarized as "Changing an office number automatically changes the office number for all the salespeople assigned to that office" The SET NULL update rule tells the DBMS that when a primary key value in a parent row is updated, the foreign key values in all of its child rows should automatically be set to NULL Primary key changes in the parent table thus cause a "set to NULL" update on selected columns of the child table.