Denormalization of tables

During physical design, analysts transform the entities into tables and the attributes into columns.

Denormalization is a key step in the task of building a physical relational database design. It is the intentional duplication of columns in multiple tables, and the consequence is increased data redundancy.

Consider an inventory entity that records quantities of specific parts that are stored at particular warehouses. The warehouse address column first appears as part of a table that contains information about parts and warehouses. To further normalize the design of the table, analysts remove the warehouse address column from that table. Analysts also define the column as part of a table that contains information only about warehouses.

Normalizing tables is generally the recommended approach. What if applications require information about both parts and warehouses, including the addresses of warehouses? The premise of the normalization rules is that SQL statements can retrieve the information by joining the two tables. The problem is that, in some cases, performance problems can occur as a result of normalization. For example, some user queries might view data that is in two or more related tables; the result is too many joins. As the number of tables increases, the access costs can increase, depending on the size of the tables, the available indexes, and so on. For example, if indexes are not available, the join of many large tables might take too much time. You might need to denormalize your tables. Denormalization is the intentional duplication of columns in multiple tables, and it increases data redundancy.

Example: Consider the design in which both tables have a column that contains the addresses of warehouses. If this design makes join operations unnecessary, it could be a worthwhile redundancy. Addresses of warehouses do not change often, and if one does change, you can use SQL to update all instances fairly easily.

Tip: Do not automatically assume that all joins take too much time. If you join normalized tables, you do not need to keep the same data values synchronized in multiple tables. In many cases, joins are the most efficient access method, despite the overhead they require. For example, some applications achieve 44-way joins in subsecond response time.

When you are building your physical design, you and your colleagues need to decide whether to denormalize the data. Specifically, you need to decide whether to combine tables or parts of tables that are frequently accessed by joins that have high performance requirements. This is a complex decision about which this book cannot give specific advice. To make the decision, you need to assess the performance requirements, different methods of accessing the data, and the costs of denormalizing the data. You need to consider the trade-off: is duplication, in several tables, of often-requested columns less expensive than the time for performing joins?

Recommendations:

Do not denormalize tables unless you have a good understanding of the data and the business transactions that access the data. Consult with application developers before denormalizing tables to improve the performance of users' queries.
When you decide whether to denormalize a table, consider all programs that regularly access the table, both for reading and for updating. If programs frequently update a table, denormalizing the table affects performance of update programs because updates apply to multiple tables rather than to one table.

In the following figure, information about parts, warehouses, and warehouse addresses appears in two tables, both in normal form.

Begin figure description. This figure shows two tables that satisfy the second normal form. End figure description. — Figure 1. Two tables that satisfy second normal form

The following figure illustrates the denormalized table.

Begin figure description. This figure shows a denormalized table. End figure description. — Figure 2. The denormalized table

Resolving many-to-many relationships is a particularly important activity because doing so helps maintain clarity and integrity in your physical database design. To resolve many-to-many relationships, you introduce associative tables, which are intermediate tables that you use to tie, or associate, two tables to each other.

Example: Employees work on many projects. Projects have many employees. In the logical database design, you show this relationship as a many-to-many relationship between project and employee. To resolve this relationship, you create a new associative table, EMPLOYEE_PROJECT. For each combination of employee and project, the EMPLOYEE_PROJECT table contains a corresponding row. The primary key for the table would consist of the employee number (EMPNO) and the project number (PROJNO).

Another decision that you must make relates to the use of repeating groups.

Example: Assume that a heavily used transaction requires the number of wires that are sold by month in a given year. Performance factors might justify changing a table so that it violates the rule of first normal form by storing repeating groups. In this case, the repeating group would be: MONTH, WIRE. The table would contain a row for the number of sold wires for each month (January wires, February wires, March wires, and so on).

Recommendation: If you decide to denormalize your data, document your denormalization thoroughly. Describe, in detail, the logic behind the denormalization and the steps that you took. Then, if your organization ever needs to normalize the data in the future, an accurate record is available for those who must do the work.