Curriculum
Course: SQL
Login
Text lesson

Denormalization

What is denormalization?

Denormalization is the process of intentionally adding redundancy by merging tables that were previously normalized. By organizing data into related tables, normalization eliminates redundancy and ensures data integrity, whereas denormalization optimizes performance by removing redundancy.

Why and when Denormalization is Used?

  • Implementation Improvement: A denormalized system can speed up data retrieval by reducing the need for complex joins. Accessing data from multiple normalized tables may be slow in certain situations due to the need for complex joins.
  • Heavy read operations: A denormalized database can reduce the need to join multiple tables for applications with a high read load and a low write load.
  • simplify query: Data retrieval is more straightforward with denormalized structures, as they simplify query design and reduce the complexity of SQL queries.

Examples of denormalization:

  • Merging tables: When two or more related tables are combined into one, even if duplicate data is introduced, it is referred to as merging tables.
  • Marge duplicate columns: Removing duplicate columns from one table and adding them to another to avoid joining the two tables.
  • Data aggregation: The process of storing aggregate data, like totals or averages, in a table rather than having to recalculate them each time.

Balance:

  • Increased storage: As a result of the redundant data generated by denormalization, storage requirements increase.
  • Inconsistency Risk: When redundancy is involved, changes must be made to multiple places, which can lead to data inconsistency issues.
  • Maintenance Overhead: Maintaining repetitious data requires extra effort and careful logic to ensure consistency.

In-conclusion, Denormalization is a balance between performance and data integrity, and it should be used appropriately based on the specific conditions of the application.

Â