Denormalization in SQL is the process of restructuring a normalized database to improve performance, at the cost of introducing redundancy into the database.

Normalization vs. Denormalization:

• Normalization is the process of organizing a database to reduce redundancy and ensure data integrity. This often involves breaking down tables into smaller, more specific tables and linking them with relationships, such as foreign keys.

• Denormalization is the opposite. It involves adding redundant data or even redundant tables to reduce the number of joins required in queries. This can improve query performance at the expense of increasing the complexity of the data model and the potential for data anomalies.

Tradeoffs:

1. Query Efficiency: Denormalized databases can be faster for certain read-heavy operations since they often require fewer joins and can be optimized for specific queries.

2. Data Integrity: Denormalization introduces redundancy, which means that there's a risk of data anomalies. For instance, if a piece of data is stored in multiple places and gets updated in one location but not another, the database will have inconsistent data.

3. Storage: Denormalized data structures consume more storage since there's duplicated data.

4. Maintenance: As data requirements change, a denormalized database can be more challenging to refactor and maintain.

Use Cases for Denormalization:

1. Read-heavy Applications: If your application performs a lot of reads but few writes, denormalization can boost performance.

2. Reporting and Analytics: Analytical queries that aggregate a lot of data can benefit from denormalized structures.

3. Caching Layers: Denormalized data can be useful for caching strategies where you want to retrieve data quickly without computing joins.

4. Data Warehouses: These are often denormalized to speed up complex queries and aggregations.

Exercise in MySQL:

Create database:

CREATE DATABASE DenormalizationDB;
GRANT ALL PRIVILEGES ON DenormalizationDB.* TO 'user'@'localhost';
FLUSH PRIVILEGES;

Exercise 1: Consider a normalized database with two tables: authors and books.

CREATE TABLE authors (
    author_id INT PRIMARY KEY AUTO_INCREMENT,
    name VARCHAR(255)
);

CREATE TABLE books (
    book_id INT PRIMARY KEY AUTO_INCREMENT,
    author_id INT,
    title VARCHAR(255),
    FOREIGN KEY (author_id) REFERENCES authors(author_id)
);

Denormalize the structure by merging the two tables and fill it with combined data:

Solution:

CREATE TABLE denormalized_books (
    book_id INT PRIMARY KEY,
    author_name VARCHAR(255),
    title VARCHAR(255)
);

INSERT INTO denormalized_books (book_id, author_name, title)
SELECT b.book_id, a.name, b.title
FROM authors a
JOIN books b ON a.author_id = b.author_id;

Exercise 2: Given the denormalized_books table above, explain a scenario where data anomalies could occur.

Solution: If we have multiple books by the same author and we want to change the author's name, we have to do it for all the entries of that author in the denormalized_books table. If we forget to update even one entry, it leads to inconsistent data about the author's name in the table.

Remember, while denormalization can offer performance advantages, it comes with tradeoffs that need to be carefully considered. Always weigh the pros and cons based on the specific needs of your application and system.

References

1. Denormalization in databases

2. Denormalization