What Is Denormalization in SQL? – Concept, Use Cases, and Examples

Introduction

In SQL, text data types are used to store alphanumeric values like names, addresses, emails, and descriptions. Choosing the correct text type — CHAR, VARCHAR, or TEXT — is important for optimizing storage space, query speed, and database performance.

In this section, you'll learn the definitions, differences, and best use cases for each text data type.

1. CHAR (Fixed-Length String)

CHAR is used to store fixed-length strings. If the stored string is shorter than the defined length, SQL automatically pads it with spaces to match the specified size.

Features:

• Fixed length
• Fast and predictable performance
• Uses extra storage if the data is often shorter than the specified length

Syntax:

column_name CHAR(length);

length = number of characters (1 to 255 depending on the database system)

Example:

CREATE TABLE countries (
country_code CHAR(2),
country_name CHAR(50)
    );

country_code like 'US', 'IN', 'UK' will always take 2 characters.

When to Use CHAR:

• Data with a constant size, such as country codes, gender ('M', 'F'), state abbreviations
• Fixed-format fields like credit card types ('VISA', 'MC')
• When exact storage size is known and consistent

2. VARCHAR (Variable-Length String)

VARCHAR stands for Variable Character. It stores variable-length strings, meaning only the actual characters are stored without unnecessary padding.

Features:

• Variable length
• More space-efficient than CHAR for varying-length text
• Slightly slower than CHAR when processing large volumes (because of extra calculations for string lengths)

Syntax:

column_name VARCHAR(length);

length = maximum number of characters allowed

Example:

CREATE TABLE employees (
first_name VARCHAR(50),
email VARCHAR(100)
    );

Names and emails can vary in length, making VARCHAR ideal.

When to Use VARCHAR:

• Data with unpredictable or variable length
• Names, emails, addresses, and descriptions under 255-65535 characters
• Most general-purpose text fields

3. TEXT (Large Text Field)

TEXT is used to store large amounts of text like long descriptions, blog posts, comments, or articles.

Features:

• Meant for large text storage (up to 65,535 characters for standard TEXT in MySQL)
• Cannot have a default value (in some databases like MySQL)
• TEXT fields are stored outside the main table with a pointer reference
• Different variants exist (TINYTEXT, MEDIUMTEXT, LONGTEXT) for various sizes

Syntax:

column_name TEXT;

Example:

CREATE TABLE articles (
id INT,
title VARCHAR(255),
body TEXT
   );

body will store the full article content, which can be very large.

When to Use TEXT:

• Long-form text fields (comments, articles, reviews, reports)
• Data that exceeds normal VARCHAR limits
• When exact storage requirements are unknown or potentially very large

Quick Comparison: CHAR vs VARCHAR vs TEXT

Important Tips

• Use CHAR only when all values will be exactly the same length
• VARCHAR is the best choice for most standard text fields
• Reserve TEXT for content that exceeds VARCHAR limits
• Consider VARCHAR(MAX) in SQL Server for large text that might need indexing
• Be aware that TEXT fields may have limitations on default values and full-text indexing

Definition: What Is Denormalization?

Denormalization is the process of intentionally introducing redundancy into a database by combining normalized tables. The purpose is to improve read performance and simplify complex queries, especially in high-traffic or analytical environments.

While normalization focuses on data integrity and reducing duplication, denormalization focuses on speed and efficiency in specific use cases.

Why Use Denormalization?

• Reduce complex joins across multiple tables
• Improve read-heavy application performance
• Simplify reporting and analytics queries
• Speed up data aggregation and summary retrieval
• Useful in data warehousing, caching, and real-time systems

Normalization vs Denormalization

Denormalization Techniques

1. Adding Redundant Columns

Duplicate a column from a related table to avoid a join.

Example: Store department_name in the employees table instead of joining with departments.

2. Pre-joining Tables

Merge two or more related tables into one.

Example: Combine orders and order_details into a single denormalized table.

3. Storing Aggregated Data

Store summary data such as total sales or average ratings.

Example: Add a total_sales column in customers instead of calculating it each time.

4. Using Lookup Tables Inline

Replace foreign keys with full lookup values directly.

Example: From Normalized to Denormalized

Normalized:

-- products
product_id | product_name
-----------|--------------
1          | Laptop

-- sales
sale_id | product_id | quantity
--------|------------|---------
101     | 1          | 2

Denormalized:

-- sales
sale_id | product_id | product_name | quantity
--------|------------|--------------|---------
101     | 1          | Laptop       | 2

Risks of Denormalization

• Data anomalies (update, insert, delete)
• Increased storage
• More complex write operations and data syncing
• Risk of inconsistencies if updates aren't handled properly

When to Denormalize

• When performance gains outweigh data consistency concerns
• In reporting, analytics, or BI tools
• For read-heavy applications like dashboards
• In data warehousing systems (e.g., star and snowflake schemas)