What is an Attribute in a Database?

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In database terminology, an attribute is a piece of information that describes an entity. If an entity is a thing you want to store data about, attributes are the specific details you’re storing. They’re the characteristics, properties, or facts that define what you know about each entity.

When you create a relational database, attributes become the columns in your tables. Each attribute represents one type of information you’re tracking.

Examples of Attributes

Suppose you have a customer entity. The attributes for this entity might include first name, last name, email address, phone number, date of birth, and account creation date. Each of these is a distinct piece of information that helps describe who the customer is and how to interact with them.

For a product entity, attributes could include product name, SKU, description, price, weight, color, and manufacturer. For an employee entity, you might have employee ID, name, job title, department, hire date, and salary.

Each row in a table represents one instance of an entity, and each column represents one attribute. When you have 10,000 customers in your customers table, each one has values for all the customer attributes you’ve defined.

Types of Attributes

Attributes can be categorized in several ways based on their characteristics and behavior:

  • Simple vs composite attributes. Simple attributes can’t be meaningfully subdivided. For example a customer’s age or a product’s price. Composite attributes on the other hand, can be broken down into smaller components. An address is composite (it contains street, city, state, zip code). A full name is composite (first name, last name, middle initial). In database implementation, you typically break composite attributes into their components rather than storing them as single values.
  • Single-valued vs multi-valued attributes. Most attributes are single-valued, meaning that they hold one value per entity instance. A person has one date of birth, a product has one SKU, etc. But multi-valued attributes can hold multiple values. A person might have multiple phone numbers or email addresses. Relational databases don’t handle multi-valued attributes well in a single column. You typically create a separate related table to store multiple values.
  • Stored vs derived attributes. Stored attributes are directly entered and saved in the database. Derived attributes are calculated from other attributes. A person’s age is derived from their date of birth and the current date. An order total is derived from summing the line items. You can choose to store derived attributes for performance (avoiding recalculation) either as regular columns that you manually maintain or as generated/computed columns that the database automatically updates, or you can compute them on demand in queries to ensure they’re always current without storing them at all.
  • Required vs optional attributes. Some attributes must have a value for every entity instance. These are required and typically defined with NOT NULL constraints. Others are optional and can be left blank. Every customer must have an ID, but middle name might be optional. Every product needs a name, but the discount price might only apply to some products.

Key Attributes

Some attributes serve special purposes in identifying and organizing entities:

  • Primary key attributes uniquely identify each instance of an entity. Attributes such as customer_id, employee_number, and order_id are typical examples of primary keys. Every entity needs at least one attribute (or combination of attributes) that can serve this purpose. Primary keys must be unique and non-null.
  • Foreign key attributes reference primary keys in other tables to establish relationships. An order_id in the order_items table is a foreign key that links each line item to its parent order. A customer_id in the orders table links each order to the customer who placed it.
  • Candidate key attributes are attributes (or combinations) that could serve as primary keys. A customer might be uniquely identifiable by email address, by customer ID, or by a combination of first name, last name, and zip code. These are all candidate keys. You choose one as the primary key and the others become alternate keys.

Attribute Domains

Every attribute has a domain. This is the set of valid values it can hold. This includes both the data type and any constraints on those values.

A price attribute might have a domain of positive decimal numbers with two decimal places. An email attribute would have a domain of text strings that match email format. A status attribute might have a domain limited to specific values like “pending”, “approved”, “shipped”, or “delivered”.

Defining domains properly ensures data quality. You prevent invalid data from entering the system by specifying what’s acceptable for each attribute. This happens through data type definitions, CHECK constraints, foreign key constraints, and application-level validation.

Null Values

NULL is a special value that indicates the absence of data. It’s different from zero (for numbers) or an empty string (for text). It simply means “no value” or “unknown”.

Whether an attribute allows null values is an important design decision. Some attributes should never be null. For example, a customer must have an ID, an order must have a date, etc. Others might reasonably be null. A customer’s phone number might be unknown, a product’s discontinuation date is null while it’s still active.

Null values can complicate queries since they don’t behave like regular values in comparisons. WHERE phone = NULL doesn’t work. Instead, you need WHERE phone IS NULL. Understanding when and how to use nulls is important for both design and querying.

Attribute Naming

Good attribute names are clear, consistent, and follow conventions. Here are a few considerations:

  • Use descriptive names that indicate what the attribute contains. customer_registration_date is better than date1 or reg_dt.
  • Be consistent with naming patterns. If you use customer_id in one table, use customer_id (not cust_id or customerID) in related tables.
  • Avoid reserved words from SQL or your DBMS. Don’t name an attribute order or user if those are keywords.
  • Many developers prefer singular nouns for attribute names since each attribute holds one value per row (e.g., email_address rather than email_addresses). Others prefer plural forms that match business language or feel more natural. The most important thing is consistency. Pick one convention and use it throughout your schema.
  • Consider whether to use underscores (snake_case) or camelCase. Pick one convention and stick with it throughout your schema.

Atomicity

In relational database design, attributes should be atomic. That is, they should contain one piece of information, not multiple values packed together.

Storing a phone number as “555-1234, 555-5678” in a single attribute violates atomicity. If you need multiple phone numbers, create a related table. Storing a full address as “123 Main St, Springfield, IL 62701” in one column makes it hard to filter by city or state. Split it into separate street, city, state, and zip attributes.

Atomic attributes make querying easier, maintain data integrity, and follow normalization principles. They give you flexibility in how you use the data.

Attributes and Normalization

How you organize attributes across tables is central to database normalization. Normalization rules guide you in determining which attributes belong together in the same table and which should be split into separate tables.

If an attribute depends on only part of a composite key, it probably belongs in a different table. If an attribute depends on another non-key attribute, that’s also a sign it might need to be separated. The goal is eliminating redundancy and ensuring each attribute is stored in the right place.

As an example, a product’s category name is an attribute, but if that category name is repeated for thousands of products, you’re storing redundant data. Better to have a category_id attribute in the products table that references a separate categories table containing the category name.

However with all that being said, denormalization is sometimes intentional. In data warehouses and analytical systems, you might deliberately store redundant attributes to improve query performance. A dimension table might include both category_id and category_name to avoid having to perform joins during analysis. The tradeoff between storage efficiency and query speed depends on your specific use case.

Attributes in Different Contexts

Attributes serve different purposes in different types of database tables:

  • In transactional tables, attributes capture the current state and detailed information needed for operations. They typically change frequently through inserts, updates, and deletes.
  • In dimension tables (data warehousing), attributes provide descriptive context for analysis. They typically include many attributes to support filtering and grouping in analytical queries, and they change slowly over time.
  • In fact tables (data warehousing), attributes are mostly numeric measurements and foreign keys. The descriptive attributes live in the related dimension tables.

Choosing Attributes

When designing a database, deciding which attributes to include requires understanding your requirements. Some things to consider include:

  • What information do you need to store to support your application’s functionality? What do users need to search, filter, or report on? What’s required for business rules and calculations?
  • Avoid the temptation to store everything imaginable. Extra attributes add storage overhead, complicate maintenance, and can slow down queries. Focus on attributes that serve clear purposes.
  • Also consider how attributes might change over time. If you need historical tracking, you might need additional attributes (like effective dates) or entirely separate tables to maintain history.

Attributes are the fundamental unit of information in your database. Part of good database design includes choosing appropriate attributes, organizing them properly, defining correct domains, and implementing them with suitable data types and constraints. Getting your attributes right is essential to building a database that serves your needs reliably and efficiently.