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The SQL2 standard provides complete support for outer joins using the same clauses described in the preceding section for inner joins and additional keywords. For example, the full outer join of the GIRLS and BOYS tables (without the AGE columns) is generated by this query: SELECT * FROM GIRLS FULL OUTER JOIN BOYS ON GIRLS.CITY = BOYS.CITY As explained earlier in this chapter, the query results will contain a row for each matched girl/boy pair, as well as one row for each unmatched boy, extended with NULL values in the columns from the other, unmatched table. SQL2 allows the same variations for outer joins as for inner joins; the query could also have been written: SELECT * FROM GIRLS NATURAL FULL OUTER JOIN BOYS or SELECT * FROM GIRLS FULL OUTER JOIN BOYS USING (CITY) Just as the keyword INNER is optional in the SQL2 language, the SQL2 standard also allows you to omit the keyword OUTER. The preceding query could also have been written: SELECT * FROM GIRLS FULL JOIN BOYS USING (CITY) The DBMS can infer from the word FULL that an outer join is required. By specifying LEFT or RIGHT instead of FULL, the SQL2 language extends quite naturally to left or right outer joins. Here is the left outer join version of the same query: SELECT * FROM GIRLS LEFT OUTER JOIN BOYS USING (CITY) As described earlier in the chapter, the query results will include matched girl/boy pairs and NULL-extended rows for each unmatched row in the GIRLS table (the "left" table in the join), but the results do not include unmatched rows from the BOYS table. Conversely, the right outer join version of the same query, specified like this: SELECT * FROM GIRLS RIGHT OUTER JOIN BOYS USING (CITY)

To create a Master server and define Target servers using Enterprise Manager: 1 Expand the server you want to define as a Master server 2 In the Management folder, right-click the SQL Server Agent item Select Multi-Server Administrator and then choose Make This a Master The Make MSX Wizard will appear (see Figure 6-22) Click Next to proceed through its pages..

In designing behavioral dimensions, you must carefully consider the implications on maintenance. Their slow change characteristics can cause undesirable growth in table size, and their frequency of updating can place undue stress on the ETL process. For example, if the date_of_last_order was to be designated as a type 2 attribute, its maintenance would require generating a new row in the customer each time an order is placed. This undesirable growth can be prevented by designating the column as a type 1 attribute. Each time it changes, customer rows are simply overwritten. In the rare case that a historic version of the attribute is needed, it can be computed by querying order_facts. For example, if people want to know what each customer s last order date was as of February of last year, they can query the fact table. The frequency of updating for behavioral attributes can overtax the ETL process. It might not be desirable, for example, to recompute the last year s worth of sales for every customer on a nightly basis. Rather than recomputing this information every day, the column can be defined as sales for the prior four quarters, allowing it to be updated once every three months. This aspect of the column s definition should be properly recorded in user documentation, so that the content is not misinterpreted. Once again, in the rare case that more up-to-date information is required, the report can be produced by querying the fact table.

- 130 -

Summary

6:

This chapter has covered several advanced topics surrounding dimension tables, ranging from the determination of when two attributes belong in the same dimension table to the transformation of facts into dimensions. A quick recap of the primary lessons: Two dimensions belong in separate tables if they participate in a relationship that is volatile or in multiple relationships with different contexts. They belong in a single dimension table if they have a less volatile affinity and are related in a single context.

Figure 6-22.

includes boy/girl pairs and unmatched rows in the BOYS table (the "right" table in the join) but does not include unmatched rows from the GIRLS table.

When dimensions have a very large number of attributes, the table can be broken into two, but some technical complications may arise Instead, you can look to see whether the table actually represents two dimensions, has free-form comment fields that can be removed to an outrigger, or is subject to the form of subtyping that will be discussed in 13 When a dimension table will grow too rapidly, or will necessitate a maintenance process that will become a processing bottleneck, offload volatile attributes into mini-dimensions This technique all but eliminates the new rows necessitated by source changes and streamlines the ETL process A fact table can have more than one relationship to a given dimension These relationships are called roles They do not require multiple copies of the dimension table; SQL aliasing allows each role to be accessed in a single query.