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Part III
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PART III
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The ETL developers must treat the split dimension table as a single logical table in order to process new and changed source data correctly. The two halves of each row must remain synchronized as slow changes take place. This means that ETL developers must work with long rows, even if they are to be stored across two tables. As a side effect of the arbitrary division, one or the other table may contain rows that are identical, other than their surrogate key. In Figure 6-3, you will notice that customer ID A472 has two rows in customer_part1 and customer_part2. These multiple versions of the customer resulted when a type 2 slow change to the customer s headquarters location occurred. This change is visible in customer_part2; headquarters moved from the city of Springfield to the city of Lawson. As with any type 2 change, a new row was created for the same natural key. Surrogate key 281 identifies the original version of the customer, and surrogate key 966 identifies the new one. In customer_ part1, these two rows look identical; other than their surrogate keys, there are no differences. Since the ETL developer must consider these two parts of the split table when processing new data, the ETL process does not see any benefit from the division of the table into two parts. If there are a large number of type 2 attributes, an ETL bottleneck may remain. Each time a source row changes, the ETL process must scrutinize all the type 2 columns to determine whether a type 2 change has occurred, regardless of whether the attributes are all in one customer table or divided across two.
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Splitting rows across two tables presents practical problems and does not always resolve ETL issues. Faced with exceedingly large dimension tables, schema designers may wish to look for alternatives. These may include dividing the dimension into two dimensions, relocating some free-form text fields, or looking for opportunities to construct a mini-dimension.
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When a dimension has an overwhelmingly large number of attributes, this is often a sign that there are two distinct dimensions. If so, the dimension can be redesigned as two tables, each with its own surrogate key. These two dimensions will then participate in explicit relationships via a fact table. Like the salesperson/customer distinction discussed earlier in this chapter, this also allows the attributes to relate in other contexts. Use the tests provided earlier in this chapter to evaluate this possibility. If there is potential for two dimensions, are the relationships volatile Do they relate in other contexts If so, separate dimensions are the way to go.
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Relocating Free-Form Text Fields to an outrigger
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Excessive row length is often a result of the inclusion of several free-form text fields in the dimension table. Carried over from an operational system, these fields may contain unstructured data that is occasionally used to filter reports. When the number or size of these fields is large, they can be relocated to a separate table and replaced with a foreign key reference. This is an example of the outrigger technique, which is discussed in 7. Use of an outrigger may impede some DBMS optimizers, but when a free-form text field is used to constrain a query, performance will already be impacted. Relocating such fields to separate tables can help maintain a reasonable row size.
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6 More on Dimension Tables 123
Looking for Subtypes
In many cases, a dimension will contain large groups of attributes that each apply to only one subset of its rows. In an operational system, this is referred to as subtyping. For example, the attributes of a dimension representing a product may vary significantly, depending on what type of product it is. Books, magazines and compact discs share some common attributes, such as name and SKU, but each subtype also has its own set of attributes. In situations that involve subtype-specific attributes, dimension row size can be controlled by building a core dimension with only the shared attributes, and separate custom dimensions for each subtype. The core dimension is used when analyzing all subtypes, such as products, and the custom dimensions are used when studying only one particular subtype, such as books, subscriptions, or compact discs. Core and custom dimensions are further explored in 13, Type-Specific Stars.
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