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Part V
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PART V
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DAY . . . level . . . ORDER_FACTS . . . order_dollars . . . PRODUCT
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Level indicator
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day_ key 700 755 762 766 775 level Month Day Day Day Day month_ hoilday_ name flag 12/31/9999 January N/A full_date 1/1/2009 1/8/2009 January January Y N N N year 2009 2009 2009 2009 2009
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day_ key 755 762 766 775 . . . 700 product_ key 98765 98765 98765 98765 . . . 98765 salesrep_ key 123 123 123 123 . . . 123 order_ dollars 2,000 3,000 1,000 1,000 . . . 7,000 order dollars is recorded on a daily basis... ... and also included in monthly totals
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1/12/2009 January 1/21/2009 January
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Figure 15-6 Single table design with level indicators
The drawback to this approach lies in the fact that the fact table records every transaction twice: once as the daily detail and once as part of the monthly summary. For example, the fact table in the illustration records order dollars for a particular product and salesrep for four different days in January of 2009. It also records these transactions in a summary row. If a user fails to constrain on the level column, these transactions may be double-counted. For example:
SELECT Sum(order_dollars) FROm day, order_facts WHERE day.year = 2009 AND day.month = "January" AND order_facts.day_key = day.day_key
This query will pick up all January 2009 orders twice: once for the daily totals and once from the monthly summary.
15 Aggregates 365
TiP Do not store different levels of aggregation in the same table. This can easily lead to doublecounting or worse. Avoiding this problem requires constraining the level column in every query. If it is necessary to store an aggregate that summarizes more than one dimension, the level column will be required in other dimensions as well. A month/brand/salesrep aggregate, for example, would require a level column in the day dimension to identify months, one in the product dimension to identify brand, and one in the customer dimension to distinguish between a specific customer and all customers. Each of these level columns must be constrained to avoid double-counting, even if the dimension in question is not part of the query.
Summary
Aggregates are the primary tool for boosting the performance of a dimensional model. As you have learned in this chapter, following some simple principles helps ensure they provide accurate results. Aggregates must provide the same query results as the original schema. Using the principle of conformance in their design helps guarantee this. Queries must be written to take advantage of an aggregate. The principle of conformance makes this as simple as possible. Aggregates may be hidden from end users so they do not have to choose which schema to use. Without an aggregate navigation function, however, they will not receive a performance benefit. Aggregates must be kept in sync with the base schema. While it is useful to process only changed data, a type 1 change in an aggregate dimension can make this very difficult. Cubes make excellent aggregates. They summarize data in a base schema in exactly the same way as an aggregate star.
An aggregate navigation function automatically formulates SQL to access aggregates. Tools offer this capability in a variety of ways. The best make it possible to leverage a new aggregate without having to rewrite a query or recompile a report. Some also offer the capability to take aggregates online and offline, in real time. Aggregates may be constructed using features of database products or specialized tools. To achieve the maximum benefit, these tools usually need to know a lot about the base schema. In return, they are able to generate performance structures automatically, and may also provide aggregate navigation services.
Part V
Aggregates can be implemented without adding any special tools to the data warehouse architecture. However, many software products have features that make them easier to create and use:
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