CHAPTER 12 Managing System Resources

Denormalization has risks

Denormalization can be successfully performed only with thorough knowledge of the application and should be performed only if performance issues indicate that it is needed. One of the things to consider when you denormalize is the amount of effort it will then take to keep your data up-to-date with changes.

This is a good example of the differences between decision support applications, which frequently need summaries of large amounts of data, and transaction processing needs, which perform discrete data modifications. Denormalization usually favors some processing, at a cost to others.

Whatever form of denormalization you choose, it has the potential for data integrity problems which must be carefully documented and addressed in application design.

Disadvantages of denormalization

Denormalization has these disadvantages:

Denormalization usually speeds retrieval but can slow updates. This is not a real concern in a DSS environment.

Denormalization is always application-specific and needs to be re- evaluated if the application changes.

Denormalization can increase the size of tables, which is not a problem in Adaptive Server IQ.

In some instances, denormalization simplifies coding; in others, it makes it more complex.

Performance benefits of denormalization

Denormalization can improve performance by:

Minimizing the need for joins

Precomputing aggregate values, that is, computing them at data modification time, rather than at select time

Reducing the number of tables, in some cases

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