Integrity Constraints in DBMS: Types, Benefits, and Challenges Explained
Updated on Mar 13, 2025 | 15 min read | 40.0k views
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Updated on Mar 13, 2025 | 15 min read | 40.0k views
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Integrity constraints in DBMS are sets of rules that keep your database accurate, consistent, and secure. They ensure each data entry, update, or deletion follows predefined standards, so you always work with information you can trust.
These constraints are especially useful in the following scenarios:
In this blog, you will explore different types of integrity constraints in DBMS, see how they work through real examples, and learn practical ways to apply them in your database projects.
Integrity constraints in DBMS define specific rules that keep your database free from invalid entries. They serve as structured safeguards that outline the kinds of values each table column can hold.
When these rules are active, they stop erroneous data from creeping into your system. They also maintain coherence among related records so you can see every piece of information clearly. Most importantly, these constraints help you avoid problems from duplicate or contradictory rows.
A good way to see how these constraints work is through concrete examples:
You can set DBMS (Database management system) constraints directly in your database or manage them through your application's code. Each approach has unique advantages and considerations.
Some constraints work directly inside your DBMS, while others are managed in your application code. Database-level rules act as a central defense by blocking invalid data before it even lands in your tables.
Application-level checks are added in your software’s logic, which can be more specific to a certain workflow or user request.
Here are the key differences between the two that you must know:
Aspect |
Database Level |
Application Level |
Performance | Faster as rules execute closer to the data. | Slower due to additional processing in the app. |
Maintenance | Centralized, easier to manage changes. | Distributed across applications, harder to update. |
Error Prevention | Blocks invalid data at the source. | Errors may slip through application loopholes. |
Use Case | Ideal for enforcing universal rules like key constraints. | Suitable for complex, app-specific validations. |
DBMS integrity constraints contribute to a dependable, predictable environment where your queries return the right details. They also remove the need for extra fixes caused by poor data practices.
Consider how these benefits shape your day-to-day data handling:
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When classifying your validation rules, you will encounter four main categories that cover unique aspects of data handling. Each one focuses on a different requirement, such as permissible values or required relationships between records.
These types — explained in the sections below — are particularly helpful when you want every row and column to match precise conditions.
Domain constraints dictate the type and permissible range of values each column in your database can hold. These rules serve as a filter that blocks incorrect data before it ever mixes with valid records.
You gain precision because every column must align with its declared data type, valid format, or a specific set of acceptable entries. This approach helps you avoid confusion that arises when a field ends up with values that do not belong there. By controlling data at the column level, you streamline your workflows and cut down on errors immediately.
A closer look at how domain constraints operate can be seen in the table below:
Employee_ID |
Name |
Birth_Year |
Department_ID |
1001 | Karan | 1998 | 101 |
1002 | Ronit | 2001 | 102 |
1003 | Jaya | ABC | 101 |
1004 | Varun | 1999 | XYZ |
In this table:
When domain constraints are in place, such entries are blocked immediately. This keeps your data valid and consistent right from the start.
Types of Domain Constraints
Domain constraints can be divided into two categories that control data at the most granular level. They either require certain columns to always contain a value or apply logical tests to ensure each value meets specific conditions.
Below is a comparison of these two types:
Aspect |
Not NULL Constraint |
Check Constraints |
Primary Purpose | Disallows empty values in the specified column. | Enforces a logical condition or range on data. |
Examples | Email column must not be blank. | Price must be > 0.” “Year must be <= current year. |
Flexibility | Straightforward rule, but no condition-based validation. | Can be combined with multiple conditions to form advanced validations. |
Violation Result | Any attempt to insert or update with a null value is rejected. | Any record failing the condition is blocked from being inserted or updated. |
What Makes Domain Constraints in DBMS Important?
Also Read: What is Normalization in DBMS? 1NF, 2NF, 3NF
Entity integrity constraints revolve around the idea that primary key columns cannot contain null values. This rule makes each row in a table distinguishable from every other entry.
By preventing empty keys, you stop duplicate or ambiguous identifiers from causing confusion. You also ensure that each record in your system has a distinct place, which promotes better organization. When you rely on this principle, your data becomes simpler to query and maintain because every row is defined by its unique identifier.
Here’s why these constraints matter:
Let’s understand this with the help of an example.
Student_ID |
Name |
Semester |
Age |
101 | Alisha | 5th | 20 |
102 | Bobby | 6th | 21 |
null | Charu | 6th | 19 |
In this scenario, the third row contains a null Student_ID, which violates the entity integrity constraint. Because the primary key should never be empty, the database cannot uniquely identify Charu’s entry. This gap undermines accurate record tracking and creates uncertainty when you try to link other data to this row.
Also Read: A Comprehensive Guide to Entity in DBMS: Types, Entity Sets, and Practical Applications in 2025
Referential integrity constraints in DBMS help maintain valid connections across multiple tables. They require any foreign key in a child table to point to a valid primary key in a parent table or remain empty if no match exists.
Enforcing this setup prevents incomplete relationships or missing references in related records. This structure becomes especially important when you manage detailed data entries that rely on one another. It also keeps your system free from rows that reference entries that do not exist in the source table.
Let’s understand this with the help of an example:
Customer’s Table
Customer_ID |
Name |
City |
101 | Aarti | Bengaluru |
102 | Ravi | Delhi |
103 | Priya | Mumbai |
Orders Table
Order_ID |
Product |
Customer_ID |
5001 | Laptop | 101 |
5002 | Phone | 104 |
5003 | Printer | 102 |
In the Orders table, the entry with Customer_ID = 104 fails referential integrity because no corresponding Customer_ID of 104 exists in the Customers table. When this rule is in place, such a record would be blocked or flagged until corrected. This practice ensures consistent cross-referencing and protects your data from invalid links.
Also Read: Attributes in DBMS: Types of Attributes in DBMS
Key constraints in DBMS focus on columns or sets of columns that uniquely identify each record in a table. These rules prevent confusion by disallowing duplicate entries in specified fields. They also make retrieval and updates smoother since you have at least one reliable point of reference for every row. Defining key constraints keeps your data free from mix-ups and maintains a well-organized structure.
Let’s understand this through an example.
Below is a table tracking employee records, including a unique field for identification:
Employee_ID |
Full_Name |
Department |
2001 | Rohan Mehta | Sales |
2002 | Kritika Singh | HR |
2001 | Salim Ansari | Finance |
Here, the Employee_ID column has two rows with the same value, which breaks the fundamental rule of ensuring each employee is uniquely identified. If you allow the same ID twice, queries and updates can get tangled, especially when you try to figure out who belongs in Finance versus Sales.
Types of Key Constraints in DBMS
Key constraints can be classified according to the type of uniqueness or restrictions they enforce. Each type addresses a different level of precision in how you track your data.
To understand them better, consider the following groups:
1. Primary Key Constraints
Primary keys act as the central identifier for every row in a table. They must be unique and cannot accept null entries. This makes it simple to locate specific rows and ensure that no two rows ever share the same core identifier. When another table references this primary key, you establish a clear link that never points to a missing or duplicated record.
Below is an example table that uses a primary key to keep entries distinct:
Student_ID |
Name |
Age |
101 | Aarush | 20 |
102 | Priyanka | 21 |
101 | Manav | 22 |
In this layout, the Student_ID column is intended to be the primary key. Having 101 listed twice means your system does not know which record truly belongs to Aarush or Manav. If you set this column as a proper primary key, the database would reject any duplicate attempt.
This simple yet strict rule makes your queries more direct and removes guesswork when updating or deleting records.
2. Unique Key Constraints
Unique key constraints also demand distinct values for certain columns but can allow a single null entry if necessary. You might use this type of constraint when you have an attribute that needs to remain different for each row, such as an email address, while still allowing you to skip that field occasionally.
Here is a table that highlights how unique keys behave:
Employee_ID |
Name |
|
3001 | akash@example.com | Akash |
3002 | babita@example.com | Babita |
3003 | akash@example.com | Dinesh |
The Email column is marked as unique. Adding a second “akash@example.com” triggers a violation and halts the insertion. This constraint lets you maintain an exclusive set of emails without requiring every row to contain a non-null value.
You still gain strong protection against duplicates, which is especially useful if you rely on that column to send critical updates or track user information.
Also Read: What Are The Types of Keys in DBMS? Examples, Usage, and Benefits
While basic constraints manage most validation tasks, there are times when you need solutions that handle event-driven logic or cover multiple tables. These specialized techniques – assertion and triggers – go beyond blocking invalid data; they actively respond to changes in your database.
They can trigger automated processes when a record is updated, or they can check conditions across different parts of your system simultaneously. By using these methods, you stay a step ahead of potential issues and avoid messy corrections down the line.
An assertion imposes a condition across your entire database rather than just a single table. It can link multiple tables and fields to enforce rules that go beyond basic checks. You might rely on this approach when you need to confirm that a specific numerical limit is respected or that a logical relationship is never broken.
Assertions run in the background and reject any operation that does not comply, which keeps your system aligned with critical requirements.
Below is an example of how an assertion might look in SQL.
It ensures that no department’s total salary surpasses a defined cap:
CREATE ASSERTION total_salary_cap
CHECK (
NOT EXISTS (
SELECT department_id
FROM employees
GROUP BY department_id
HAVING SUM(salary) > 600000
)
);
This snippet checks the employees' table for any department whose salary exceeds INR 6,00,000. If such a department is found, the statement triggers a violation and blocks the action.
This rule can protect data from unrealistic values or errors in salary entries. Since it covers all entries within the selected table, you gain a broad layer of control without having to manually validate every new record or update.
Triggers are automated procedures that launch when specific actions occur in your tables, such as inserting, updating, or deleting rows. They act as a safety net for scenarios where conventional constraints may not suffice on their own.
You can use triggers to perform extra checks, record events for auditing, or even generate a response when values go outside predefined bounds. By running without manual input, they help you uphold advanced rules and keep your data consistent.
Below is a short example of how a trigger might track salary changes within an employees’ table.
Employees Table
Employee_id |
Name |
Position |
Salary |
1001 | Rohan | Developer | 40000 |
1002 | Zoya | Manager | 55000 |
Salary_Audit Table
Audit_id |
Employee_id |
Previous_salary |
New_salary |
change_date |
1 |
1002 |
55000 |
60000 |
2025-03-12 |
The Salary_Audit table keeps a history of each salary increase by recording who received the raise, what the old and new amounts were, and when the change happened. If you modify an entry in employees, the trigger below will insert a row into Salary_Audit whenever there’s an increase in salary.
For instance, in the code snippet below, the trigger fires right after you update a salary. Notice how it ties directly into the employees’ table whenever an update happens:
CREATE TRIGGER check_salary_increase
AFTER UPDATE ON employees
FOR EACH ROW
BEGIN
IF NEW.salary > OLD.salary THEN
INSERT INTO salary_audit (employee_id, previous_salary, new_salary, change_date)
VALUES (NEW.employee_id, OLD.salary, NEW.salary, CURRENT_DATE);
END IF;
END;
This code fires each time the salary is updated.
This automated approach saves you time and ensures you never miss a salary adjustment. It also gives you a solid audit trail for tracking financial activities without having to write extra application-level logic.
Integrity constraints in DBMS offer practical advantages that streamline operations.
Below are the key benefits:
Implementing integrity constraints in DBMS presents unique hurdles. Below are common issues:
Managing integrity constraints in DBMS efficiently requires strategic planning. Below are essential tips:
Mastering integrity constraints in DBMS is essential for anyone serious about database management. As data continues to dominate decision-making, a solid understanding of constraint DBMS mechanisms will set you apart in your career.
Below are some courses by upGrad that focus on critical skills to enhance your expertise.
Course Name | Key Focus Areas |
Data Structures and Algorithm Course | Understanding data structures for efficient databases |
Excel for Data Analysis Course | Analyzing and managing data using Microsoft Excel |
Advanced SQL: Functions and Formulas | Master SQL with advanced window functions, partitioning, query optimization, and more. |
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