Denormalization in RDBMS

Relational database management systems (RDBMS) have been the go-to choice for storing and managing data for decades. These systems are designed to ensure data integrity and maintain consistency, but their rigid structure can sometimes limit their performance. One way to overcome this limitation is through denormalization, which involves breaking some of the normal rules of database design to improve performance. In this blog post, we will dive deeper into what denormalization is, why it’s important, and provide examples of how it can be used effectively.

What is Denormalization?

Denormalization is a process of adding redundant data to a database to improve performance. In a typical relational database, data is organized into tables, each with a unique identifier or primary key. Normalization is the process of designing a database to minimize data redundancy and ensure data consistency. It involves dividing data into smaller, more manageable tables and establishing relationships between them. However, this normalization process can sometimes lead to performance issues, especially in large databases.

Denormalization, on the other hand, is the process of adding redundant data to a database to improve performance. It involves duplicating data from one table into another, usually a related table. This redundant data may be stored in the same table or in a separate table, and is often used to reduce the number of joins required to retrieve data. By denormalizing a database, you can improve its performance by reducing the number of joins required to retrieve data, which can be a significant performance bottleneck in large databases.

Related Articles: Indexing in RDBMS, Query planning in RDBMS, Partitioning in RDBMS, Query optimization in RDBMS, B-Tree Indexing in RDBMS, Query rewrite in RDBMS, Full-Text Indexing in RDBMS

Why is Denormalization Important?

There are several reasons why denormalization is important:

1. Improved Query Performance

Denormalization can significantly improve query performance by reducing the number of joins required to retrieve data. Joins can be a performance bottleneck in large databases, especially when dealing with complex queries that require joining multiple tables. By denormalizing the database, you can reduce the number of joins required to retrieve data, which can result in faster query performance.

2. Faster Data Retrieval

Denormalization can also improve data retrieval speed by reducing the number of disk accesses required to retrieve data. When data is normalized, it may be spread across multiple tables, which can result in more disk reads to retrieve the same amount of data. By denormalizing the database, you can consolidate related data into a single table, which can reduce the number of disk reads required to retrieve data.

3. Easier Data Maintenance

Denormalization can make data maintenance easier by reducing the complexity of database queries. When data is normalized, queries may require complex joins across multiple tables, which can make it difficult to maintain and update the database. By denormalizing the database, you can simplify queries and reduce the complexity of database maintenance.

Techniques of Denormalization

1. Aggregate Tables

One common way to denormalize a database is by creating aggregate tables. Aggregate tables store summarized data from multiple tables and are used to speed up queries that require complex calculations. For example, if you have a database that stores sales data, you could create an aggregate table that stores the total sales for each product by month. This aggregate table would eliminate the need to join multiple tables to retrieve this information and could significantly improve query performance.

2. Redundant Data

Another way to denormalize a database is by adding redundant data to a table. This redundant data may be duplicated from another table or calculated from existing data. For example, if you have a database that stores customer orders, you could add a customer name column to the orders table. This redundant data would eliminate the need to join the orders table with the customer table to retrieve the customer name and could improve query performance.

3. Materialized Views

Materialized views are another way to denormalize a database. A materialized view is a precomputed result set that is stored as a table. Materialized views can be used to speed up complex queries that require joining multiple tables. For example, if you have a database that stores sales data, you could create a materialized view that calculates the total sales for each product by month. This materialized view would eliminate the need to join multiple tables to retrieve this information and could significantly improve query performance.

4. Partitioning

Partitioning is a technique that can be used to denormalize a database by dividing large tables into smaller partitions. Each partition is stored on a separate disk, which can improve query performance by reducing the number of disk reads required to retrieve data. For example, if you have a database that stores sales data, you could partition the sales table by month. This would allow you to retrieve data for a specific month more quickly, as the data would be stored on a separate disk.

5. Vertical Denormalization

Vertical denormalization is a technique that involves adding columns to a table to reduce the number of joins required to retrieve data. For example, if you have a database that stores customer orders, you could add columns to the orders table that store customer information, such as the customer name and address. This would eliminate the need to join the orders table with the customer table to retrieve this information and could improve query performance.

Use Cases for Denormalization

Denormalization is not a one-size-fits-all solution, and it should be used with caution. Here are some use cases where denormalization can be effective:

1. Large Databases

Denormalization can be effective in large databases where performance can be a bottleneck. By denormalizing the database, you can improve query performance and reduce the number of disk reads required to retrieve data.

2. Complex Queries

Denormalization can be effective for complex queries that require joining multiple tables. By denormalizing the database, you can reduce the number of joins required to retrieve data, which can improve query performance.

3. Read-Heavy Applications

Denormalization can be effective for read-heavy applications where data is frequently retrieved from the database. By denormalizing the database, you can improve query performance and reduce the number of disk reads required to retrieve data.

Related Articles: Indexing in RDBMS, Query planning in RDBMS, Partitioning in RDBMS, Query optimization in RDBMS, B-Tree Indexing in RDBMS, Query rewrite in RDBMS, Full-Text Indexing in RDBMS

Examples

Here are some query examples that illustrate how denormalization can improve query performance:

Example 1: Querying a Normalized Database

Consider a normalized database with the following schema:

CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    Name VARCHAR(255),
    Address VARCHAR(255)
);

CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerID INT,
    OrderDate DATE,
    Total DECIMAL(10, 2),
    FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID)
);

CREATE TABLE OrderItems (
    OrderID INT,
    ProductID INT,
    Quantity INT,
    Price DECIMAL(10, 2),
    PRIMARY KEY (OrderID, ProductID),
    FOREIGN KEY (OrderID) REFERENCES Orders(OrderID),
    FOREIGN KEY (ProductID) REFERENCES Products(ProductID)
);

CREATE TABLE Products (
    ProductID INT PRIMARY KEY,
    Name VARCHAR(255),
    Description TEXT,
    Price DECIMAL(10, 2)
);

Suppose we want to retrieve the names of all customers who have purchased a product with the name “iPhone”. Here’s the query we might use:

SELECT DISTINCT Customers.Name
FROM Customers
JOIN Orders ON Customers.CustomerID = Orders.CustomerID
JOIN OrderItems ON Orders.OrderID = OrderItems.OrderID
JOIN Products ON OrderItems.ProductID = Products.ProductID
WHERE Products.Name = 'iPhone';

This query requires four joins, which can be expensive in terms of query performance. If the database is large, this query could take a long time to execute.

Example 2: Querying a Denormalized Database

Now let’s consider a denormalized version of the same database, where we’ve added a column to the Orders table to store the customer name:

CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerName VARCHAR(255),
    OrderDate DATE,
    Total DECIMAL(10, 2),
    FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID)
);

Suppose we want to retrieve the names of all customers who have purchased a product with the name “iPhone” from this denormalized database. Here’s the query we might use:

SELECT DISTINCT Orders.CustomerName
FROM Orders
JOIN OrderItems ON Orders.OrderID = OrderItems.OrderID
JOIN Products ON OrderItems.ProductID = Products.ProductID
WHERE Products.Name = 'iPhone';

This query only requires two joins, which can significantly improve query performance compared to the normalized version of the database. By denormalizing the database and adding a column to the Orders table to store the customer name, we’ve eliminated the need to join the Customers table, which can be expensive in terms of query performance.

Example 3: Using Materialized Views

In this example, let’s consider a database that stores sales data for a large retail chain. Suppose we want to retrieve the total sales for each product by month for the past year. Here’s the query we might use:

SELECT Products.Name, YEAR(OrderDate) AS Year, MONTH(OrderDate) AS Month, SUM(Price * Quantity) AS TotalSales
FROM OrderItems
JOIN Orders ON OrderItems.OrderID = Orders.OrderID
JOIN Products ON OrderItems.ProductID = Products.ProductID
WHERE OrderDate >= DATE_SUB(NOW(), INTERVAL 1 YEAR)
GROUP BY Products.Name, YEAR(OrderDate), MONTH(OrderDate);

This query requires three joins and calculates the total sales for each product by month for the past year. If the database is large, this query could take a long time to execute.

To improve query performance, we can use a materialized view to precompute the results of this query and store them in a table. Here’s how we might create a materialized view for this query:

CREATE MATERIALIZED VIEW ProductSalesByMonth AS
SELECT Products.Name, YEAR(OrderDate) AS Year, MONTH(OrderDate) AS Month, SUM(Price * Quantity) AS TotalSales
FROM OrderItems
JOIN Orders ON OrderItems.OrderID = Orders.OrderID
JOIN Products ON OrderItems.ProductID = Products.ProductID
WHERE OrderDate >= DATE_SUB(NOW(), INTERVAL 1 YEAR)
GROUP BY Products.Name, YEAR(OrderDate), MONTH(OrderDate);

Now we can query the materialized view instead of the original tables to retrieve the results of this query:

SELECT * FROM ProductSalesByMonth;

This query is much faster than the original query because the results have already been precomputed and stored in the materialized view.

Example 4: Partitioning Large Tables

In this example, let’s consider a database that stores log data for a large website. Suppose we want to retrieve all log entries for a specific date range. Here’s the query we might use:

SELECT *
FROM LogEntries
WHERE LogDate BETWEEN '2022-01-01' AND '2022-01-31';

This query retrieves all log entries for the month of January 2022. If the LogEntries table is very large, this query could take a long time to execute.

To improve query performance, we can partition the LogEntries table by month. Here’s how we might partition the table:

CREATE TABLE LogEntries_2022_01 (
    LogID INT PRIMARY KEY,
    LogDate DATE,
    Message TEXT
)
PARTITION BY RANGE (LogDate) (
    PARTITION p1 VALUES LESS THAN ('2022-02-01')
);

Now we can query the appropriate partition instead of the original table to retrieve the log entries for a specific date range:

SELECT *
FROM LogEntries_2022_01
WHERE LogDate BETWEEN '2022-01-01' AND '2022-01-31';

This query is much faster than the original query because it only needs to read the data from the partition that contains the log entries for the specified date range.

Example 5: Adding Redundant Columns

In this example, let’s consider a database that stores customer data for an online retailer. Suppose we want to retrieve the total amount spent by each customer. Here’s the query we might use:

SELECT Customers.CustomerID, SUM(Orders.TotalAmount) AS TotalSpent
FROM Customers
JOIN Orders ON Customers.CustomerID = Orders.CustomerID
GROUP BY Customers.CustomerID;

This query retrieves the total amount spent by each customer by joining the Customers and Orders tables. If the database is large, this query could take a long time to execute.

To improve query performance, we can add a redundant column to the Customers table that stores the total amount spent by each customer. Here’s how we might add the column:

ALTER TABLE Customers ADD TotalSpent DECIMAL(10, 2) DEFAULT 0;

Now we can update the TotalSpent column whenever a new order is placed for a customer:

UPDATE Customers
SET TotalSpent = TotalSpent + Orders.TotalAmount
FROM Orders
WHERE Customers.CustomerID = Orders.CustomerID;

This updates the TotalSpent column for each customer with the total amount spent on all their orders.

Now we can retrieve the total amount spent by each customer directly from the Customers table, without having to join the Customers and Orders tables:

SELECT CustomerID, TotalSpent
FROM Customers;

This query is much faster than the original query because the total amount spent by each customer is already stored in the Customers table.

Conclusion

Denormalization is a powerful technique for improving the performance of relational databases. By introducing redundancy and breaking normalization rules, we can reduce the complexity of queries and improve query performance. However, denormalization should be used judiciously and only after careful consideration of the trade-offs involved. It’s important to strike a balance between normalization and denormalization, and to choose the right denormalization strategies based on the specific needs of the application.

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