Database normalization is a process in database design that aims to organize relational database tables and their relationships to reduce data redundancy and improve data integrity. It involves systematically structuring tables and their attributes to minimize duplication of information and ensure data is stored efficiently and accurately.
There are several normal forms (stages) of database normalization, each with specific rules to achieve higher levels of organization and reduction of anomalies like data insertion, update, and deletion anomalies. Here’s a brief overview of the common normal forms:
- First Normal Form (1NF): Ensures that each column in a table contains only atomic (indivisible) values. It eliminates repeating groups or arrays of data.
- Second Normal Form (2NF): Builds on 1NF and eliminates partial dependencies. It separates data into multiple tables, ensuring that each table has a primary key and no non-key attributes are dependent on only a portion of the primary key.
- Third Normal Form (3NF): Builds on 2NF and eliminates transitive dependencies. It ensures that non-key attributes are not dependent on other non-key attributes.
- Boyce-Codd Normal Form (BCNF): A more advanced form that eliminates redundancy by addressing overlapping candidate keys.
- Fourth Normal Form (4NF): Addresses multi-valued dependencies, ensuring that no non-key attributes are dependent on other non-key attributes.
- Fifth Normal Form (5NF) or Project-Join Normal Form (PJNF): Deals with join dependencies, where tables can be combined based on a common relationship.
Normalization helps maintain data consistency and reduces the chances of data anomalies. However, over-normalization can lead to complex queries and potentially slower performance. Database designers need to strike a balance between normalization and performance optimization based on the specific requirements of the application and its use cases.
Let’s walk through an example of database normalization using a simple scenario. Consider a database for an online bookstore where you want to store information about books, authors, and the orders placed by customers.
Initial Unnormalized Table:
BookID | Author | Title | Genre | CustomerID | CustomerName | OrderDate |
---|---|---|---|---|---|---|
1 | Author1 | Book Title1 | Fiction | 101 | Customer A | 2023-01-15 |
2 | Author2 | Book Title2 | Non-Fiction | 102 | Customer B | 2023-02-10 |
1 | Author1 | Book Title1 | Fiction | 103 | Customer C | 2023-02-20 |
First Normal Form (1NF):
In 1NF, we ensure that each column contains atomic values. To achieve this, we separate the data into different tables to remove repeating groups:
Books Table:
BookID | Author | Title | Genre |
---|---|---|---|
1 | Author1 | Book Title1 | Fiction |
2 | Author2 | Book Title2 | Non-Fiction |
Customers Table:
CustomerID | CustomerName |
---|---|
101 | Customer A |
102 | Customer B |
103 | Customer C |
Orders Table:
BookID | CustomerID | OrderDate |
---|---|---|
1 | 101 | 2023-01-15 |
2 | 102 | 2023-02-10 |
1 | 103 | 2023-02-20 |
Second Normal Form (2NF):
In 2NF, we address partial dependencies. We notice that the BookID and Genre attributes in the Books table are functionally dependent on the Title. We split the Books table into two:
Books Table:
BookID | Title |
---|---|
1 | Book Title1 |
2 | Book Title2 |
BookDetails Table:
BookID | Author | Genre |
---|---|---|
1 | Author1 | Fiction |
2 | Author2 | Non-Fiction |
Third Normal Form (3NF):
In 3NF, we remove transitive dependencies. We see that the CustomerName in the Orders table depends on the CustomerID. We split the Customers table:
Customers Table:
CustomerID | CustomerName |
---|---|
101 | Customer A |
102 | Customer B |
103 | Customer C |
Orders Table:
BookID | CustomerID | OrderDate |
---|---|---|
1 | 101 | 2023-01-15 |
2 | 102 | 2023-02-10 |
1 | 103 | 2023-02-20 |
This is a simplified example of how normalization can be applied to a database to reduce redundancy, improve data integrity, and ensure efficient querying. The process ensures that data is organized logically and helps prevent anomalies during data manipulation.