What are the main types of database constraints?

The main types of database constraints are primary key, foreign key, unique, not null, and check constraints.

A primary key constraint is a field in a table which uniquely identifies each record in that table. It must contain a unique value for each row of data and cannot contain null values. This constraint is used to ensure that the table has no duplicate rows. For example, in a student database, the student ID could be the primary key as it is unique to each student.

A foreign key constraint is a field in a table that is a primary key in another table. It is used to prevent actions that would destroy links between tables. For example, in a university database, a student's course ID would be a foreign key linking the student table to the course table.

A unique constraint ensures that all values in a column are different. This is similar to a primary key constraint, but it can accept one null value and it does not automatically create an index. For example, in a user account table, the email address field could have a unique constraint to ensure that no two users can have the same email address.

A not null constraint is a rule that prevents null values from being entered into specific columns in a table. This means that if a column has a not null constraint, you cannot skip entering a value for that column when creating records. For example, in a customer table, the customer name field could have a not null constraint to ensure that a name is always provided for each customer.

A check constraint is a condition that the data in a table must meet. It allows you to specify that the value in a certain column must satisfy a Boolean (truth-value) expression. For example, in an employee table, a check constraint could be used on the age column to ensure that all employees are over 16 years old.

In summary, database constraints are rules that help maintain the integrity of data in a database. They ensure that the data is accurate, consistent and reliable, and they prevent errors and anomalies from creeping into the data.