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The same degenerate dimensions may appear in more than one fact table. For example, when a portion of an order ships, a shipments fact table may record the original order number and order line. This occurred in Figure 4-8. The degenerate dimensions order_id and order_line_num were present in both order_facts and shipment_facts. It is possible to drill across on degenerate dimensions. Success depends on the same requirements that have been repeated throughout this chapter: the common dimension attributes must have the same structure and the same content. This allows them to specify the same scope of aggregation when querying each fact table in Phase 1, and allows them to be joined on during Phase 2. With identical tables and conformed rollups, care is taken to ensure that the same combination of values is present in each table. This enables browse queries to provide the same results. You should be able to select distinct month and year values from either table in Figure 5-5 and get the same answer. In the case of degenerate dimensions, this requirement is relaxed. Enforcement of the requirement would lead to sparsity violations and complicate reporting efforts. For example, if we required the same distinct set of order_id values to appear in the order_facts and shipment_facts tables from Figure 4-8, it would be necessary to place a row into shipment_facts every time a new order occurred. This would generate unwanted rows in shipment_facts, with fact values of zero. These zero-valued facts would clutter shipment reports in much the same way encountered in the previous chapter, with multi-process fact tables. (See Figures 4-1 and 4-2 to refresh your memory.)

Figure 6-8.

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In rare cases, non-identical dimension tables may conform through a set of overlapping attributes. Although valid, these situations often beg for the creation of a third dimension. If it is not crucial to cross-browse the attributes in question, a third dimension will better provide for consistent content. If their relationships at any point in time are important, additional fact tables can track this information. The additional fact tables may be avoided by making use of a limited form of snowflaking.

6:

Intersecting Attributes When two tables share a set of common attributes, but one is not a perfect subset of the other, neither table can be described as a conformed rollup. A common example involves geography, as depicted in Figure 5-6. At the top of this diagram, dimension tables are shown that describe customers and salespeople. Both dimension tables contain information about regions. In the case of salespeople, these attributes identify the regions to which they are assigned. In the case of customers, these attributes identify the regions in which they exist. Neither of these tables is a conformed rollup of the other; each has a set of dimension attributes not shared by the other. This is illustrated by the Venn diagram in the center of Figure 5-6. It may be useful to drill across the overlapping attributes. For example, profitability analysis may factor in customer support calls and salesperson compensation, each rolled up by region. For this to work, the common dimension attributes must share the same structure and content.

specify SELECT DISTINCT. For the UNION operation, UNION (duplicates eliminated) is the default. To retain duplicate rows, you must explicitly specify UNION ALL. Database experts have criticized the handling of duplicate rows in SQL and point to this inconsistency as an example of the problems. The reason for the inconsistency is that the SQL defaults were chosen to produce the correct behavior most of the time: In practice, most simple SELECT statements do not produce duplicate rows, so the default is no duplicate elimination. In practice, most UNION operations would produce unwanted duplicate rows, so the default is duplicate elimination. Eliminating duplicate rows from query results is a very time-consuming process, especially if the query results contain a large number of rows. If you know, based on the individual queries involved, that a UNION operation cannot produce duplicate rows, you should specifically use the UNION ALL operation because the query will execute much more quickly.

Figure 6-9.

The specter of double-counting is enough to send many organizations retreating to the simplified relationship solution. Where the need is strong enough to warrant the bridge, however, measures can be taken to reduce the risk. Before looking at them, let s look at one adjustment that does not solve the problem. One well-intentioned impulse is to eliminate the many-to-many relationship between fact table and bridge by asserting that each group will be used only once. If the same members collaborate again, a new group will be created. While this does reduce the many-to-many relationship to a one-to-many relationship, it is a one-to-many relationship going in the wrong direction. Even if we create a second group when Ann and Henry collaborate again, the order still refers to two salespeople, and it is still in danger of being double-counted. Worse, the bridge table is likely to grow very large, and it may even contain more rows than the fact table. Luckily, other techniques will provide limited success.

Unions and Sorting *

Figure 6-10.