Database Normalization

By now some of you are familiar with the basics of using in your cgi scripts. Many of your databases will be small, with one or two tables. But as you become braver, tackling bigger projects, you may start finding that the design of your tables is proving problematic. The SQL you write starts to become unwieldy, and data anomalies start to creep in. It is time to learn about database normalization, or the optimization of tables.

Let's begin by creating a sample set of data. Imagine we are working on a system to keep track of employees working on certain projects.

Click here for table 1.

A problem with the above data should immediately be obvious. Tables in relational databases, which would include most databases you'll work with, are in a simple grid, or table format. Here, each project has a set of employees. So we couldn't even enter the data into this kind of table. And if we tried to use null fields to cater for the fields that have no value, then we cannot use the project number, or any other field, as a primary key (a primary key is a field, or list of fields, that uniquely identify one record). There is not much use in having a table if we can't uniquely identify each record in it.

So, our solution is to make sure that each field has no sets, or repeating groups. Now we can place the data in a table.

Click here for table 2.

Notice that the project number cannot be a primary key on it's own. It does not uniquely identify a row of data. So, our primary key must be a combination of project number and employee number. Together these two fields uniquely identify one row of data. (Think about it. You would never add the same employee more than once to a project. If for some reason this could occur, you'd need to add something else to the key to make it unique).