Denormalization in SQL is the reversal of the normalization performed to make the data free from anomalies and redundancy that ensures proper data integrity. However, denormalization is performed to make readability better so that the data can be better interpreted instead of referring to tables. However, this comes with the tradeoff of redundant data.
Why do we Denormalize Tables in SQL
There are several reasons for denormalization to be performed in SQL with the prominent of them being to improve the query performance as referencing and referenced technique in the normalized table leads to slower performance whereas denormalized table has all the relevant data present in a single working table.
The reason for the fast queries is that denormalization reduces the number of joins on tables for a better query execution rate.
In the above illustration, there are two tables named Order and Category that are already normalized. Then they are denormalized further to have the result table that constitutes attributes of both the sub-tables merged into one. A similar process can be followed for denormalization in SQL. Given below is another example that can help you build a strong grip on the topic with a real-world IPL franchise example.
Suppose there is a table of players and a table consisting of their respective teams. We can refer to the team of the players by defining a foreign key to access them in a normalized state.
Table Name: Teams
|Team ID||Team Name|
|2||Royal Challengers Bangalore|
|3||Chennai Super Kings|
|4||Kolkata Knight Riders|
|6||Kings XI Punjab|
Table Name: Players
|Player ID||Player Name||Team ID|
Thus, if we want to achieve better access time then we can denormalize this row to create duplicates of the data from the other table into one. On the denormalized table, although access can be fast, updating can be tedious and time-consuming.
On the above two tables, we can implement the flattening technique to denormalize both tables.
|Team ID||Team Name||Player Name||Player ID|
|1||Mumbai Indians||Rohit Sharma||1|
|2||Royal Challengers Bangalore||Virat Kohli||2|
|3||Chennai Super Kings||MS Dhoni||3|
|4||Kolkata Knight Riders||Dinesh Karthik||4|
|5||Rajasthan Royals||Steve Smith||5|
|6||Kings XI Punjab||KL Rahul||6|
|7||Delhi Capitals||Rishabh Pant||7|
|8||Sunrisers Hyderabad||David Warner||8|
The above contains all the attributes that were part of multiples tables of the normalized data. Here they are merged into one to denormalize the data.
Advantages and Disadvantages of Denormalization
Now that some of the key concepts about denormalization in SQL are clear to us, let us look at the pros and cons of the same given below:-
- It can provide quick and efficient access.
- Retrieving data is simple and easy.
- Fewer tables need to be checked.
- Redundant data can lead to data inconsistencies.
- Updation and Insertion of data can be costly.
- Not optimized for the storage of data, unlike normalized tables.
In this article focused on the topic of denormalization in SQL, we started with what denormalization in SQL stands for and how is it different from normalization in SQL. As we progressed, we saw how denormalization can be performed and the pros and cons that it offers.
We hope you liked this article on Denormalization in SQL and expect to see you again at PrepBytes with another informative article.
Frequently Asked Questions
1. How can I denormalize a table in SQL?
You can denormalize a table in SQL by adding redundant columns or tables that duplicate information from other tables. This can help improve query performance and simplify data retrieval.
2. How can I determine if denormalization is necessary in my SQL database?
You should analyze your query performance and data access patterns to identify areas where denormalization might be beneficial. This can involve evaluating the size and complexity of your database, as well as identifying frequently accessed data.
3. What are some best practices for denormalization in SQL?
Some best practices for denormalization in SQL include using it sparingly, only where it is necessary to improve performance, documenting changes carefully, and keeping redundant data consistent across all tables.
4. How can I minimize the risks of data inconsistencies when denormalizing in SQL?
You can minimize the risks of data inconsistencies by carefully designing your denormalized tables and using triggers or other mechanisms to ensure that changes to one table are reflected in all related tables.
5. How can I revert a denormalization change in SQL if necessary?
You should always keep backups of your database to ensure that you can revert any changes if necessary. In addition, you should carefully document any denormalization changes and test them thoroughly before implementing them in a production environment.