Database normalization and denormalization are two important concepts in database design that affect the performance, integrity, and maintainability of your data. In this article, you will learn what they mean, why they matter, and how to apply some best practices for both scenarios.
Normalization is the process of organizing your data into tables and columns that follow certain rules to avoid redundancy, inconsistency, and anomalies. The rules are called normal forms, and they range from the simplest (first normal form) to the most complex (sixth normal form). The higher the normal form, the more normalized your data is.
Let's say you have a list of friends and their phone numbers, and you want to store it efficiently. You could create a table with columns for the friend's name and phone number. However, what if one of your friends has multiple phone numbers? You don't want to repeat their name every time you list a phone number for them. This is where normalization comes in. In DB normalization, having the friend's name repeated for each phone number is an example of redundancy. By creating a separate table for phone numbers and linking it to the friend's table, you can avoid this redundancy and make the data more efficient. By following the rules of normalization, you can avoid inconsistencies and anomalies that can arise when dealing with complex data.
Denormalization is the opposite of normalization. It is the process of combining or duplicating data from multiple tables into one table to improve query performance, simplify data access, or support specific business requirements. Denormalization can reduce the number of joins, indexes, and foreign keys needed to retrieve data, but it can also introduce redundancy, inconsistency, and anomalies.
Normalization is usually the default approach for database design since it ensures data integrity, consistency, and quality, as well as making your data easier to understand, modify, and extend. Unless you have a valid reason to denormalize, you should normalize your data as much as possible. Some of the reasons to denormalize include optimizing queries for speed or complexity, supporting analytical or reporting functions that require aggregated or precomputed data, and accommodating legacy systems or applications that expect a certain data structure.
Denormalization is not inherently bad, but it should be done thoughtfully. It can improve query performance, but it can also have a negative impact on data integrity, consistency, and quality. You should only denormalize when the benefit outweighs the cost. Some common costs associated with denormalization are the need to update or delete data in multiple places, which can lead to errors or inconsistencies; storing more data, which can increase disk space and memory usage; and maintaining more complex logic or code to manage the denormalized data.
Normalizing your data requires following normal forms and applying them to your tables and columns. To begin, you need to identify the entities and attributes that represent your data and create a table for each entity, assigning a primary key. Additionally, any repeating groups or arrays should be removed and placed in separate tables, as should any partial dependencies, transitive dependencies, or non-key dependencies. By doing this, you can ensure your data is normalized.
To denormalize your data, you need to reverse some of the normalization steps and merge or copy data from multiple tables into one table. During this process, you should identify the queries or functions that require faster or simpler data access. Additionally, you should analyze the trade-offs between performance and integrity for each case, choose the appropriate level of denormalization, add or modify columns, indexes, and constraints to support the denormalized data, and implement or update the logic or code to handle the denormalized data.