What are constraints in a relational database?

Constraints in a relational database are rules enforced on data columns or tables to ensure data integrity, accuracy, and consistency. They define limits or conditions that data must meet to be stored in the database, preventing invalid or nonsensical entries. Common types include primary keys, foreign keys, unique constraints, check constraints, and not-null constraints. For example, a primary key ensures each row in a table is uniquely identifiable, while a foreign key enforces relationships between tables by referencing valid data in another table. Constraints act as safeguards, automatically rejecting operations (like inserts or updates) that violate the rules.

Constraints are critical for maintaining reliable data relationships and preventing errors. Without them, databases could store duplicate records, orphaned data, or invalid values, leading to application bugs or incorrect query results. For instance, a foreign key constraint stops a user from deleting a customer record if that customer has associated orders in another table, avoiding orphaned orders. Similarly, a check constraint could enforce that a “salary” column only accepts positive numbers, preventing negative values from being entered. These rules reduce the need for manual validation in application code, shifting responsibility to the database layer, which is more efficient and consistent.

Examples illustrate how constraints work in practice. A primary key constraint on a “user_id” column ensures no two users share the same ID. A unique constraint on an “email” column prevents duplicate email addresses in a user table. A not-null constraint on a “birthdate” column forces every user to have a birthdate value, eliminating missing data. Check constraints can enforce business logic, like limiting a “discount” column to values between 0 and 100. Foreign keys might link an “orders” table to a “customers” table, ensuring every order references a valid customer. By defining these rules during table creation or modification, developers build a robust structure that actively enforces data quality, reducing errors and simplifying maintenance.