Understanding Denormalization
Definition
Denormalization
Denormalization is the process of intentionally introducing redundancy into a normalized database to improve performance.
It involves combining tables or duplicating data to reduce the number of joins required for certain queries.
Note- Denormalization is not the same as having an unnormalized database.
- It is a deliberate design choice made after normalization to address specific performance needs.
Advantages of Denormalization
- Faster Data Retrieval
- Reduced Joins: By storing related data together, denormalization minimizes the need for complex joins, speeding up query execution.
- Optimized Read Performance: This is particularly beneficial in read-intensive applications where quick access to data is critical.
- Simpler Queries
- Easier Query Structure: With fewer tables to join, queries become more straightforward and easier to write and maintain.
Consider denormalization when your application experiences performance bottlenecks due to excessive joins in read-heavy operations.
Disadvantages of Denormalization
- Data Redundancy
- Increased Storage Requirements: Storing duplicate data consumes more disk space.
- Inconsistencies: Multiple copies of the same data can lead to inconsistencies if not properly managed.
- Complex Updates
- Challenging Maintenance: Updating data becomes more complex, as changes must be propagated to all redundant copies.
- Higher Risk of Anomalies: Insert, update, and delete anomalies are more likely to occur in denormalized databases.
- Assuming denormalization always improves performance.
- While it can speed up read operations, it may slow down write operations and increase the risk of data inconsistencies.
Situations Where Denormalization is Beneficial
- Read-Intensive Applications
- Example: E-commerce websites where product details and pricing are frequently accessed together. Denormalizing by storing product and pricing information in the same table can speed up page loads.
