The First Normal Form, or 1NF, is the very lowest, basic arrangement of fields in a table. If your table is not in 1NF, then it isn't really a table. Sadly, many novice databases are not even in 1NF.
A table is said to be in First Normal Form if:
1) there is no row or column order
2) each row (record) is unique
3) each row by column value (field) contains exactly one value
4) there are no repeating columns
What does this mean?
First of all, the operation of the table will be unaffected by the order the rows are in or the order the fields are within the row. It means that each record must be complete unto itself without referencing another row positionally, for example, the row above. Likewise the position of the fields is irrelevant.
Since each record is unique, it means there are no duplicate records. This uniqueness is defined by a field or combination of fields whose value will never be duplicated. This is called the primary key. In order to assure uniqueness, no part of a primary key may be NULL.
Because a field must have a single value, it cannot contain a list or compound value. One over looked consequence of this rule is that each field MUST have at least one value. If the value of the field is not known, it is said to be NULL. (There is some debate over whether NULL is actually a value. I maintain it is, but the discussion is largely semantic.)
Lastly, there are no repeating columns. Repeating columns are columns that store essentially the same information. They may be columns like Product1, Product2, Product3; or multiple Yes/No columns that represent the same information like each product having its own column (Saw, Hammer, Nails).
Let's take a look at how these rules are implemented and what they mean for table design.
Suppose I want a simple Order table with OrderNum, CustomerNum, OrderDate, Quantity, Item, and ProductNum. Although the definition of 1NF is fairly simple, it precludes a wide range of data arrangements. Let's take a look at some of these arrangements.
Figure 1 shows one way such data can be arranged.
Figure 1: Records with Missing Values
To make each record unique, the primary key would have to be OrderNum and Item. However, since no part of the primary key may be Null, this arrangement won't work. All the values of the primary key must be filled in.
But even more than this, the record is not "complete" unto itself. That is, it refers to other records for information. It's not that the values of OrderNum, CustomerNum, or OrderDate are unknown and therefore NULL. I do know the value, but I'm attempting to represent that data positionally. This, of course, violates the first rule (order is irrelevant) and rule 3 (each field must have a value).
This arrangement is common in spreadsheets and reports, but it is not sufficient for storing data.
Figure 2 shows another way the data can be arranged.
Figure 2: Information Stored In Lists
This violates rule 3. Each field must hold one and only one piece of information and not a list. It would be a nightmare to do anything with the date in the Item field other than simply display it because the database management system is designed to treat fields as indivisible.
While Figure 2 is an extreme example that mixes multiple fields in addition to multiple field values, believe it or not, I have also seen database designed like Figure 3:
Figure 3: Data stored in multiple lists
While this is better than Figure 2 (at least it does not mix fields), it is still not atomic and you'd have difficultly associating a quantity with a particular product.
1NF also precludes compound values, things like full names in a single field or multi-part identification numbers.
Why not store a full name? Roger J. Carlson is certainly pertinent information about me. However, it is not indivisible. It is made up of a first name, middle initial, and last name. Because I may want to access just pieces of it (using the first name in the salutation of a letter or sorting by last name), the information should be stored in separate fields.
Often, a database requirement is to have an identification number that is composed of different, meaningful parts. A serial number may have a four-digit product code, followed by the manufacture date (8 digits), and ended with the facility ID. It might look like this COMP02222008BMH. While this may be a useful arrangement for humans, it is useless in a database. Each value should be stored in a separate field. When the serial number is needed, it can be concatenated easily enough in a query, form, or report.
Figure 4 shows data that is stored in repeated columns.
Figure 4: Data Stored in Repeated Columns
This arrangement is common for people who use spreadsheets a lot. In fact, this is so common it is called "committing spreadsheet". The problem, in addition to having multiple columns, is that in order to associate a quantity with a product, you would have to do it positionally, breaking rule 1.
Lastly, another version of the Repeated Columns error is multiple Yes/No columns. Figure 5 illustrates that.
Figure 5: Data Stored in Yes/No Columns
At first blush, this does not seem to have the same problem, but all I've done is replace generic field names (Product1, Product2, etc) with specific ones (wench, saw, etc). It would be extremely easy to check a second field in any row and they you would have no idea which was correct.
As we've seen, First Normal Form precludes a lot of possible data arrangements. So what's left? There's really only one possibility left. Figure 6 shows it.
Figure 6: 1NF Correct with Repeated Rows
Each row has a unique identifier and there are no duplicates. Each field contains a single value. The position of the row and field is irrelevant, and lastly there are no repeating columns.
It's perfect. Right? Well, no. While this table does conform to 1NF, it is still has some problems; problems that 1NF is not equipped to handle. For those, I need to look at the Second Normal Form (2NF), which is what I'll do next time.