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Adding a fact that always contains the value 1 makes SQL readable, allowing queries to use the SQL function SUM() rather than COUNT(). As your database administrator may point out, this technique may have an unwanted side effect: it can generate unnecessary database activity at query time. To understand this effect, it is necessary to think about how a relational database actually assembles query results. Every SQL query is evaluated by the database optimizer, which parses the request and determines the best plan for fetching the results. When responding to a query that calls for counting contacts that occurred in January 2009, for example, the optimizer will need to decide how to access the various tables, apply the query predicates, carry out joins, and compute the count. It may begin by identifying the day_keys that correspond to January 2009 and then use a fact table index to identify corresponding fact table rows. Here is the important part: since all that is needed is a count, it is not actually necessary for the RDBMS to read these rows; it simply counts the number of hits in the index. If, on the other hand, the database is asked to SUM() a fact table column called contact, it has no way of knowing that this column always contains the value 1. After determining which fact table rows apply to January 2009, it must read these rows from disk to get the contact values, and then add up the values. This means extra disk I/O will be required, and that may take some extra time. The SUM() will take longer than the COUNT(), particularly if the rows for January 2009 are not clustered together on the disk. This may seem like an isolated example, but remember that many RDBMS products have star-join optimizers that apply dimensional constraints first and access the fact table last. This query execution scheme can be applied even when the query selects one or more dimension values or when constraints are applied in more than one dimension table. Not all database optimizers work this way, and those that do may offer additional mechanisms to tune or change behavior based on expected usage. So, while the addition of a constant-valued fact is very useful, it pays to talk it over with your database administrator. The good news is you can have it both ways. Adding the additional fact does not stop anyone from writing a query that counts rows. COUNT() and SUM() are both available options.
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Conditions, Coverage, or Eligibility
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Factless fact tables can also be used in situations that do not clearly correspond to events or activities. Some common examples include: Tracking the salesperson assigned to each customer Logging the eligibility of individuals for programs or benefits Recording when severe weather alerts are in effect Capturing the marketing campaigns that are active at a given time
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These examples all describe conditions, coverage, or eligibility. They are usually not thought of in terms of transactions or activities. Despite this, they can be modeled in the same way as an activity: using a fact table. Fact tables that describe conditions are usually factless. Factless fact tables that describe conditions differ from those that describe activities in how they are used. In most cases, the information captured by these stars will rarely be studied on its own. Factless fact tables that describe conditions, coverage, or eligibility almost always serve as a basis for comparison with other business processes.
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Fact tables capture relationships between dimensions. They are massive intersect tables, each row associating instances of various dimension tables in a specific context. Normally, that context is a transaction or activity. Conditions at a point in time also link dimensions in a particular context. The environment at a point in time may link a salesperson with a customer, a product with a promotion, or an individual with an eligible benefit. These conditions can play an important part in understanding activities like orders, sales, or benefit participation. To understand how conditions might inform the analysis of a process, consider a star schema that tracks orders, once again. The star in Figure 12-3 is activity-based. The grain of the fact table order_facts is the order line. The order line is represented by a pair of degenerate dimensions: order_id and order_line. Each row in this table associates a customer, product, and salesperson on a particular day. The context of this association is the placement of an order. If Russell Wilkinson (a salesperson) books an order from Company XYZ (a customer) on January 31, 2009 (a day), for a single product, a row in the fact table will associate these dimension instances. This fact table does not contain rows for every possible combination of dimension rows. It only contains rows that represent the activity being tracked, as defined by its grain statement. Russell Wilkinson and Company XYZ are associated in order_facts because of an order placed.
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