What are the disadvantages of database normalization?

Database normalization is a process designed to reduce data redundancy and improve data integrity by organizing fields and tables of a relational database. However, it comes with several disadvantages:

1. Increased Complexity: Normalization can lead to more complex database structures. As tables are split into smaller, more specific entities, the relationships between these entities become more intricate. This complexity can make the database harder to understand and manage.

   • Example: Instead of having a single table for customer orders that includes all necessary details, normalization might require splitting this into multiple tables like Customers, Orders, OrderDetails, etc., each with its own set of relationships.

2. Performance Issues: The process of normalizing databases often involves breaking down tables into smaller ones, which can lead to an increase in the number of joins required to retrieve data. This can slow down query performance, especially on large datasets.

   • Example: Retrieving detailed information about an order might require joining multiple tables (Customers, Orders, OrderDetails), which can be time-consuming compared to a simple query on a denormalized table.

3. Higher Maintenance Costs: With more tables and relationships, the maintenance of the database becomes more challenging and costly. Updates, inserts, and deletes might require modifications across multiple tables, increasing the risk of errors.

   • Example: Adding a new field to track customer preferences might necessitate changes in multiple tables, adding complexity to the database management tasks.

4. Reduced Read Performance: While normalization improves write performance by reducing redundancy, it can adversely affect read performance. Complex queries with multiple joins are generally slower than simple queries on denormalized data.

5. Potential for Over-Normalization: Over-normalization can occur when the database is divided into too many small tables, leading to unnecessary complexity and performance issues. This can make the system difficult to use and maintain.

In scenarios where performance is critical, such as high-transaction environments or real-time systems, denormalization might be considered to improve read performance. However, for applications requiring high data integrity and minimal redundancy, normalization remains a fundamental practice.

For managing these complexities and optimizing performance, cloud-based database services like Tencent Cloud's Database Management Center offer robust solutions. These platforms provide tools for database design, optimization, and monitoring, helping to mitigate some of the disadvantages associated with database normalization.