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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.

FIGURE 11-3

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.

FIGURE 11-4

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.

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application database design, but it is eventually reconciled in terms of function back to the overall design. This design of the trade system ORDERDB, shown in Figure 11-4, meets the constraints of the DBMS, and since it is derived directly from a composite data model, it also satisfies the system requirements. The rules ensure that the physical design is valid and that it follows good practice. During development, no attempt is made to achieve good performance. Rather, the design provides a sound starting point for physical tuning when the design of both database and Java classes is adjusted to achieve performance objectives. The database characteristics can vary depending on the DBMS being used. The key characteristics of many of the popular DBMS engines, such as Oracle, are built according to the following rules:

An aggregate schema is similar to a database index. Like an index, it takes up some extra space, and it makes queries go faster. Once it is defined, however, an index becomes invisible. Someone writing a query does not need to specify that it should be used, and no one needs to update the index as data changes. These things happen automatically. Aggregates do not necessarily share this invisibility. As you have seen, aggregates require attention on two fronts: It is necessary to write (or rewrite) queries to take advantage of the aggregate. It is necessary to load and maintain the aggregate, keeping it in sync with the original schema. Ideally, you shouldn t have to worry about these things. A truly invisible aggregate would automatically be exploited whenever appropriate and automatically updated as the original schema changes. Many software products strive to make this a reality. Some tools can rewrite queries automatically; some can generate and maintain aggregates; some can do parts of both. Software features that help achieve this invisibility may be found in reporting tools, ETL tools, and database management systems. Implementations vary widely, and none are perfect. Some basic principles will help you understand how to evaluate and leverage the capabilities of available tools. The two aspects of invisibility are aggregate navigation and aggregate generation. For some tools, these are separate concepts; for others, they are closely linked together.

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