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PostgreSQL - INTEGER Data Type

Last Updated : 15 Jul, 2025

In PostgreSQL, the INTEGER data type is widely used for storing numerical data efficiently. It is a 4-byte data type that allows us to store whole numbers within a specified range, making it ideal for various use cases like population counts, active user statistics, and more.

In this article, we will provide a comprehensive explanation of the INTEGER data type in PostgreSQL, including syntax, practical use cases, examples with outputs, and some important considerations when using this data type in our PostgreSQL database.

The INTEGER data type, also known as INT, is one of the most commonly used data types in PostgreSQL. It stores whole numbers (i.e., numbers without decimal points) and requires 4 bytes of storage. The range of values it can store is between -2,147,483,648 to 2,147,483,647.

This makes it suitable for various applications, such as:

• Storing the population of a country.
  
• Tracking user counts on websites or social media platforms.
  
• Representing counts in inventory management.

Syntax

variable_name INTEGER

Now let's look into some examples of use cases of INTEGER Data Type, which can be applied in a variety of real-world scenarios where whole numbers are needed, such as tracking inventory, user counts, and more.

In this example, we will create a table that stores the population of various countries by using the below commands. This approach is ideal for storing large numerical values like national populations efficiently.

Query:

CREATE TABLE countries_population(
    country_id SERIAL PRIMARY KEY,
    name VARCHAR (255) NOT NULL,
    population INTEGER NOT NULL CHECK (population> 0)
);
INSERT INTO countries_population(name, population)

VALUES
    ('India', 1352600000),
    ('Russia', 14450000), 
    ('Canada', 37600000),  
    ('Japan', 126500000);
SELECT * FROM countries_population;

Output

Explanation:

In this case, the population column is of type INTEGER, ensuring that the stored data represents whole numbers only. We also use the CHECK constraint to enforce that the population value must always be greater than zero, ensuring valid data.

In this example, we will create a table that stores the number of active users on various social media apps by using the below commands. We also use the CHECK constraint to enforce that the population value must always be greater than zero, ensuring valid data.

Query:

CREATE TABLE social_media(
    id SERIAL PRIMARY KEY,
    name VARCHAR (255) NOT NULL,
    active_users INTEGER NOT NULL CHECK (active_users> 0)
);
INSERT INTO social_media(name, active_users)

VALUES
    ('Facebook', 249279585),
    ('Twitter', 330000000), 
    ('Instagram', 1070000000),  
    ('Linkedin', 210000000);
SELECT * FROM social_media;

Output

Explanation:

In this case, the population column is of type INTEGER, ensuring that the stored data represents whole numbers only. The SERIAL data type for the country_id field automatically generates unique identifiers for each row, making it easier to reference and maintain the records.

• When no value is specified, the INTEGER type defaults to NULL unless a NOT NULL constraint is applied.
  
• PostgreSQL offers the SERIAL pseudo-type which is an auto-incrementing INTEGER. This is handy for creating unique identifiers.
  
• INTEGER operations are generally faster than operations on larger numeric types (BIGINT, NUMERIC).
  
• While INTEGER can store up to 2,147,483,647, calculations or intermediate results exceeding this range can cause overflow errors. Consider using BIGINT for operations where values may exceed this range.

The INTEGER data type in PostgreSQL is a highly flexible and efficient choice for storing whole numbers in our database. It is widely used in applications that require the representation of counts, population statistics, and more. However, if our application requires values beyond the INTEGER range, consider using BIGINT. By following best practices for defining and using INTEGER in PostgreSQL, we can ensure both data accuracy and performance in our database applications.

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