How do you use denormalization to improve performance and avoid anomalies in database design?

1 What is normalization and why is it important?

Normalization is a process of organizing the data in a database into tables and columns that follow some rules or normal forms. The main purpose of normalization is to eliminate data redundancy, dependency, and anomalies. Data redundancy means having the same data stored in multiple places, which wastes space and makes maintenance difficult. Data dependency means having the data in one table depend on the data in another table, which creates complex relationships and affects performance. Anomalies are errors or inconsistencies that occur when inserting, updating, or deleting data, such as losing information or creating duplicates.

3 What are the drawbacks of denormalization?

Denormalization is not a silver bullet and also has some drawbacks, such as increased storage space, data inconsistency, and update anomalies. Increased storage space can consume more disk space and memory resources, as well as affect the backup and recovery time and cost. Data inconsistency can arise when different versions of the same data are stored in different tables, leading to inaccurate information. Update anomalies can occur when redundant data is inserted or deleted in one table but not the other, creating a performance and integrity issue.

5 How to use denormalization best practices?

Denormalization is a powerful technique that can improve your database design and performance, but it requires careful planning and execution. To use denormalization effectively and avoid common pitfalls, you should follow some best practices. Denormalization should be used sparingly and selectively, as overuse can cause more problems than it solves. Furthermore, it should be used as a complement to normalization rather than a substitute. Lastly, denormalization must be done with caution and control; document and justify your decisions and use appropriate tools and techniques to manage your redundant data.