Relational databases handle NULL values as a specific marker to indicate that a data value is unknown, missing, or not applicable. Unlike other values, NULL does not represent a zero, an empty string, or any standard default value; it explicitly signifies the absence of a value. In SQL, NULL is treated as a distinct entity. For example, if a database table contains a column for a person's middle name, a NULL value in that column would indicate that the middle name is not provided or is unknown, rather than being an empty string or space.
When querying data, NULL values require special attention because standard comparison operations do not behave as one might expect. For example, using an equality comparison like = with a NULL value will yield unknown results rather than true or false. To check if a value is NULL, one must use the IS NULL or IS NOT NULL expressions. For instance, a query like SELECT * FROM users WHERE middle_name IS NULL will correctly retrieve rows where the middle name is not provided, while a query using = would not return these rows.
Additionally, NULL values can impact database operations such as aggregations, joins, and conditions. When calculating averages, for instance, NULL values in the dataset are generally ignored to avoid skewed results. However, this can lead developers to make careful considerations when crafting queries to ensure they manage NULLs appropriately. Therefore, developers should be familiar with how NULLs interact with their queries and the overall design of their database schema to avoid unexpected behavior in their applications.