Constraints in SQL are rules applied to database tables to enforce data integrity and ensure consistency. They define specific conditions that data must meet when being inserted, updated, or deleted. Constraints are part of a table’s structure and are enforced by the database engine automatically, eliminating the need for manual checks in application code. Common types include PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, and CHECK constraints. Each serves a distinct purpose, such as preventing duplicate values, linking related tables, or ensuring values fall within a valid range. By using constraints, developers can maintain reliable data relationships and validity without relying solely on application logic.
For example, a PRIMARY KEY constraint ensures a column (or combination of columns) uniquely identifies each row in a table. If you create a users table with user_id INT PRIMARY KEY, the database will block any insert or update that duplicates an existing user_id. A FOREIGN KEY constraint, such as FOREIGN KEY (department_id) REFERENCES departments(id), enforces that values in department_id must exist in the departments table, preventing orphaned records. A CHECK constraint like CHECK (salary > 0) ensures the salary column only contains positive numbers. NOT NULL forces a column to always have a value, and UNIQUE guarantees no duplicates in a column (e.g., email addresses). These constraints are typically defined during table creation using CREATE TABLE or added later with ALTER TABLE.
When using constraints, developers must balance data integrity with performance and flexibility. For instance, FOREIGN KEY constraints can slow down bulk operations due to referential checks, and UNIQUE constraints require indexing to avoid full table scans. However, they reduce application complexity by centralizing validation logic in the database. Constraints also help catch errors early—for example, a CHECK constraint blocking negative prices avoids downstream bugs in financial calculations. Properly applied, constraints act as a safety net, ensuring data adheres to business rules even as applications evolve. They are a foundational tool for designing robust, maintainable databases.