How can you identify when a database is over-normalized?

1 Slow performance

One of the most obvious indicators of over-normalization is slow performance. If your queries take too long to execute, or if you need to join many tables to retrieve the data you need, you might have over-normalized your database. To improve performance, you can try denormalizing your database, which means combining some tables or adding redundant data to reduce the number of joins. However, denormalization comes with a trade-off: you might compromise data integrity and consistency, and increase storage space.

2 Too many one-to-one relationships

Another sign of over-normalization is having too many one-to-one relationships between tables. This means that each record in one table corresponds to exactly one record in another table, and vice versa. While this might seem logical in some cases, it often leads to unnecessary fragmentation and complexity. For example, if you have a table for customers and a table for customer addresses, and each customer has only one address, you might as well merge the two tables into one. This way, you can simplify your queries and avoid redundant foreign keys.

3 Too many single-attribute tables

A single-attribute table is a table that has only one column, besides the primary key. This is usually a result of applying the normalization rule that says every column should depend on the whole primary key, and not on a part of it. However, this rule can sometimes create tables that are too granular and isolated. For example, if you have a table for products and a table for product colors, and each product has only one color, you might end up with a single-attribute table for colors. This can make your queries more complicated and inefficient, as you need to join the tables to get the product and its color. A better solution might be to store the color as an attribute in the product table, or use a lookup table that maps color codes to color names.

4 Too many null values

Another symptom of over-normalization is having too many null values in your tables. This means that some columns are not applicable or relevant for some records, and therefore are left empty. This can indicate that your tables are not well-defined or that you have split your data too much. For example, if you have a table for employees and a table for employee salaries, and some employees are not paid by salary but by commission, you might have a lot of null values in the salary table. This can waste storage space and make your queries less accurate. A possible solution is to use a different table structure that can accommodate different types of payments, or use a default value instead of null.