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Set operations on stars
Part V
1 Intersect 2
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PART V
Performance
relationships. The circle on the left corresponds to customer assignments; the circle on the right corresponds to orders. Each is represented by its own star. The table beneath the Venn diagram illustrates the different ways these sets can be compared. The first example represents a set in which sales management may be particularly useful: salespeople who did not sell to assigned customers. This set is defined by the SQL MINUS operation, as in the query from 12. Precomputed results can be stored in a factless fact table, with dimensions for the period, salesperson, and customer. The derived table can also be computed by adding a NOT EXISTS predicate to a query for customer assignments; these SQL keywords will introduce a correlated subquery that looks for orders. See 12 for the full story. Additional set operations are also possible. The intersection of the two sets represents sales to assigned customers. This set may be computed and stored in a fact table that includes the same facts as order_facts. If salespeople are only compensated for sales to their assigned customers, this fact table will be useful for commission reporting. The sets can also be compared to identify sales to unassigned customers. This set result may include facts from the original order_facts table. If all customers are assigned to specific salespeople, then this set of orders represents those in which salesreps have taken orders from someone else s customer. This behavior is the source of great controversy in sales organizations with named customer assignments!
Choosing to Precompute Set operations
The application of set operations does not always have a useful result. If it cannot be clearly defined using business terms, the set is not likely to support relevant analysis and, therefore, is not useful from a business perspective. For example, it is possible to create a union of all salesperson/customer relationships for a period those that represent a customer assignment and those that result from orders being taken. This set has no real semantic significance or use to the business. In other applications, however, a UNION operation may be useful. Precomputing the results of set operations is most often useful with intersect and minus operations and almost always involves a coverage table. Other examples include identifying products that did or did not sell while under promotion, the effect of weather on sales, and the participation of eligible parties in a benefits program. Each of these examples compares the content of a factless fact table that describes conditions or coverage with a transaction fact table that describes events. Examination of reports sometimes reveals the potential for precomputation of set operations. When these comparisons are not computed in advance, reports that do the work grow excessively complex. Intricate procedural logic or calculations are the result. Precomputation can dramatically simplify reports, making them significantly easier to develop and maintain. As with any other form of derived table, appropriate use of derived fact tables for the results of set operations requires balancing the cost savings in terms of report development or execution time with the increased burden on the ETL process. Set operations can be expensive. If they will be required infrequently, it may make more sense to compute them within an occasional report. On the other hand, if 20 percent of reports will focus on a subset, it may be highly advantageous to precompute it, either as a derived star or as a cube.
14 Derived Schemas 343
Summary
This chapter covered the use of derived schemas to enhance query performance or reduce the complexity of report creation. Key lessons included the following: A single schema design can rarely meet 100 percent of the reporting needs with maximum efficiency. For a minority of business questions, an alternative format may be optimal. A derived schema takes data from the original dimensional schema and restructures it for specific kinds of use. A derived schema may be implemented as a star or as a cube. Supplementing a star containing granular data with cubes derived for reporting purposes is a powerful combination. Snapshots may be derived from transactions, although they are not always. When derived, they greatly simplify the process of determining the cumulative effects of individual transactions on status measurements like levels or balances. Accumulating snapshots are usually derived from one or more stars. Each row correlates multiple activities, greatly simplifying the otherwise complex process of studying the time spent at various process stages. Core fact tables are often derived from type-specific custom fact tables. The core fact table makes it possible to conduct analysis across all types, without the need to perform unions. Merged fact tables precompute the results of drilling across two or more fact tables. This makes cross-process comparison easier and faster, and is especially helpful when existing tools cannot be used to drill across. Pivoted fact tables transpose facts organized row-wise into multiple facts in a single row, or vice versa. There may not be a large performance benefit, but there may be value in the reduced complexity of report development. Sliced fact tables take subsets of the original star, normally defined based on a single attribute value. These tables have a smaller size and scope but do not sacrifice detail. They may be useful in departmental, distributed, or mobile applications, or as the basis of a role-based security architecture. Set operation fact tables precompute the results of comparing two fact tables using subqueries or union, intersect, or minus operations. They can be significant timesavers, and dramatically simplify the necessary report SQL. Remember that derived tables do not achieve their advantages for free. The cost is an increased burden for the loading and management of warehouse data structures. Do the heavy lifting up front is an admirable mantra but must be balanced with the business benefit of derived structures. This balancing act will be discussed in 18, How to Design and Document a Dimensional Model.
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