The NULL value is one of the key features of the relational database. The NULL, in fact, doesn’t represent any value at all—it represents the lack of a value. When you create a column for a table that must have a value, you specify it as NOT NULL, meaning that it cannot contain a NULL value. If you try to write a row to a database table that doesn’t assign a value to a NOT NULL column, Oracle will return an error.
You can assign NULL as a value for any datatype. The NULL value introduces what is called three-state logic to your SQL operators. A normal comparison has only two states: TRUE or FALSE. If you’re making a comparison that involves a NULL value, there are three logical states: TRUE, FALSE, and neither.
None of the following conditions are true for Column A if the column contains a NULL value:
A > 0 A < 0 A = 0 A != 0
The existence of three-state logic can be confusing for end users, but your data may frequently require you to allow for NULL values for columns or variables.
You have to test for the presence of a NULL value with the relational operator IS NULL, since a NULL value is not equal to 0 or any other value. Even the expression:
NULL = NULL
will always evaluate to FALSE, since a NULL value doesn’t equal any other value.
Should You Use NULLs?
The idea of three-state logic may seem somewhat confusing, especially when you imagine your poor end users executing ad hoc queries and trying to account for a value that’s neither TRUE nor FALSE. This prospect may concern you, so you may decide not to use NULL values at all.
We believe that NULLs have an appropriate use. The NULL value covers a very specific situation: a time when a column has not had a value assigned. The alternative to using a NULL is using a value with another meaning—such as 0 for numbers—and then trying to somehow determine whether that value has actually been assigned or simply exists as a replacement for NULL.
If you choose not to use NULL values, you’re forcing a value to be assigned to a column for every row. You are, in effect, eliminating the possibility of having a column that doesn’t require a value, as well as potentially assigning misleading values for certain columns. This situation can be misleading for end users and can lead to inaccurate results for summary actions such as AVG (average).
Avoiding NULL values simply replaces one problem—educating users or providing them with an interface that implicitly understands NULL values—with another set of problems, which can lead to a loss of data integrity.
/ 6 