A database field is a single category or attribute of information associated with a record, commonly corresponding to a column in a relational database table. For example, a Customers table might have fields named customer_id, name, email and date_joined. The field definition specifies what kind of information is expected in that position and may include properties such as data type, whether NULL is permitted, default values, uniqueness requirements and validation constraints. In everyday database terminology, field and column are often used interchangeably, although database theory and particular database systems can distinguish the terms depending on context. A field is therefore best understood as one defined attribute within a structured record or table.
A field has a data type that determines the kinds of values it can represent and influences how those values are stored, compared and processed. Common relational types include integers, decimal numbers, character strings, dates, timestamps and Boolean values, while database engines may also provide JSON, arrays, binary data, spatial types and specialised numeric types. Choosing an appropriate data type matters because it affects storage requirements, validation, comparison semantics and query performance. For example, storing a date using a native date type is generally more meaningful than storing it as arbitrary text because the database can then perform date-aware comparisons, ordering and calculations.
A field can have constraints that restrict its permitted values. NOT NULL requires a value to be supplied, while UNIQUE can prevent duplicate values across the relevant rows. CHECK constraints can enforce conditions, such as requiring a price to be non-negative. A field may have a DEFAULT value that is supplied automatically when an INSERT operation omits it. A field can also participate in a primary key or foreign key. These constraints help move data-integrity rules into the database rather than relying entirely on application code. They can therefore prevent classes of invalid data even when multiple applications access the same database.
NULL is an important concept when discussing database fields. NULL generally represents missing, unknown or inapplicable information rather than a conventional value. In SQL's three-valued logic, comparisons involving NULL can produce UNKNOWN rather than TRUE or FALSE. For example, a comparison using an ordinary equality operator does not normally establish that a nullable field equals NULL; SQL provides IS NULL and IS NOT NULL for testing nullness. Aggregation also treats NULL specially in many contexts, such as COUNT(column), which normally counts non-null values rather than all rows. Correct treatment of NULL is therefore part of accurate database design and query writing.
A field can also be indexed. An index on an email field, for example, can help the database locate records matching a particular email address without scanning every row. However, indexing every field is usually undesirable because indexes consume storage and impose maintenance work when records are inserted, updated or deleted. Fields frequently used for filtering, joins, sorting or uniqueness may benefit from indexes, while rarely queried fields may not. The usefulness of an index depends on data distribution, query patterns, table size and the database optimiser.
Fields can represent different levels of structure. A simple relational field normally contains one logical value, consistent with the relational model's treatment of attributes. Some database systems support structured types or JSON fields that can contain multiple nested properties within one stored value. Although this can be convenient for semi-structured data, putting many logically independent attributes into one unstructured field can make validation, indexing and querying more complicated. The appropriate design depends on whether the information has a stable relational structure, how it will be queried and whether the database's specialised data types are appropriate.
A database field is different from a field value. The field or column is the defined attribute, while a particular record contains one value for that field. For example, email is the field, while a particular customer's email address is that field's value in one record. Likewise, a table contains many values for the same field, one per applicable record. This distinction is important when discussing schema design because changing a field definition, such as changing its data type, is different from changing one stored field value.
Good field design contributes significantly to data quality. Names should clearly communicate meaning, data types should reflect the underlying information, constraints should enforce appropriate rules, and NULLability should be intentional rather than accidental. Fields should generally represent atomic or appropriately structured attributes for the chosen database model, especially when relational normalisation is important. Poorly designed fields can lead to inconsistent formats, duplicated information, difficult queries and ambiguous semantics. A database field is therefore both a storage location for an attribute value and a formal part of the schema that tells the database what information that attribute represents and what rules govern it.
