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Here, it is clear the number of contacts is a meaningful and fully additive fact. Aggregation does not occur solely at query time. As you will learn in 15, Aggregates, aggregate tables are sometimes used to improve query performance. An aggregate table partially summarizes data from the original fact table. In the case of contact_facts, an aggregate might summarize contacts by month. In this aggregate, the contact fact will take on a variety of values. In the same way, a monthly contacts cube constructed using data from contact_facts will also contain values other than 1 for the contact fact. It is often possible to find a fact if you look for it carefully. Designs that start out factless often become the home for measurements of duration or cost. A factless fact table that tracks phone calls, for example, might track the duration of each call. Cost-oriented facts may also emerge from other business areas. The finance department may have a standard average cost metric that can be assigned to each call. Don t try to make up a measurement like this on your own; make sure it comes from the business. Also, don t go out of your way to accommodate a fact if it doesn t quite fit. In the contacts example, e-mail messages do not have a duration. That fact is only relevant for telephone contacts.

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Count() vs. Sum()

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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.

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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..

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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