Unlocking Database Performance: Normalization Vs Denormalization [ADB2]

If you missed our first database tutorial — https://shaonmajumder.medium.com/how-to-optimize-your-database-for-bigger-userbase-adb1-06a8592c5018)

Optimizing a database for a bigger user base involves several strategies aimed at improving performance, scalability, and efficiency. Here are some general steps you can take:

A. Database Design:

A1. Normalize your database schema to reduce redundancy and ensure data integrity.

A2. Denormalize certain parts of your schema for performance reasons, such as frequently queried tables.

A3. Choose appropriate data types to minimize storage requirements.

A4. Use indexes wisely to speed up query performance.

A1. Normalize:

In a normalized database schema, we break down data into logical units and store them in separate tables to avoid redundancy and maintain data integrity. Suppose you’re developing an e-commerce platform. You have a users table storing user information and an orders table storing order details. To optimize database design, you normalize the schema by separating user information into the users table and order details into the orders table. Here’s how we might normalize the schema for our e-commerce platform:

• Users Table:

CREATE TABLE users (
    user_id INT PRIMARY KEY,
    username VARCHAR(50),
    email VARCHAR(100),
    /* Other user details */
);

• Products Table:

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    name VARCHAR(100),
    description TEXT,
    price DECIMAL(10, 2),
    /* Other product details */
);

• Orders Table:

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    user_id INT,
    product_id INT,
    quantity INT,
    order_date TIMESTAMP,
    /* Other order details */
    FOREIGN KEY (user_id) REFERENCES users(user_id),
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

In this normalized schema:

• User information is stored separately in the users table.
• Product details are stored in the products table.
• Order information, including references to users and products, is stored in the orders table.

Advantages of Normalization:

1. Reduced Redundancy: By breaking down data into logical units stored in separate tables, normalization eliminates data redundancy. Each piece of information is stored only once, reducing storage requirements and ensuring data consistency.
2. Improved Data Integrity: Normalization helps maintain data integrity by reducing the risk of anomalies such as update anomalies, insertion anomalies, and deletion anomalies. For example, updating a user’s email address in the users table automatically updates it for all corresponding orders in the orders table.
3. Simplified Maintenance: With a normalized schema, making changes to the database structure is more straightforward. Updates and modifications typically only need to be applied to a single table, reducing the likelihood of errors and simplifying maintenance tasks.

How Normalization Improves Performance:

Normalization improves performance by:

• Reducing the amount of redundant data stored in the database, leading to lower storage requirements and faster data retrieval.
• Minimizing the need for complex joins and queries, resulting in more efficient data retrieval and processing.
• Ensuring data consistency and integrity, reducing the likelihood of errors and anomalies that could impact performance.

Overall, normalization promotes a more efficient and streamlined database design, which ultimately contributes to improved performance and scalability.

A2. Denormalizing:

Denormalization involves adding redundant data to optimize query performance, especially for frequently queried tables.

To avoid joins when displaying order information, you can denormalize your database schema by including relevant information directly in the orders table. This approach eliminates the need to perform joins with other tables to retrieve additional details.

Here’s an example of how you can denormalize the schema for the orders table in an e-commerce platform. Let’s denormalize the orders table by including the user's name directly within it:

• Denormalized Orders Table:

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    user_id INT,
    product_id INT,
    user_name VARCHAR(255),  -- Denormalized column
    order_date TIMESTAMP,
    quantity INT,
    total_amount DECIMAL(10, 2),
    /* Other order details */
);

In this denormalized schema:

• We have added the user_name column directly to the orders table to store the name of the user who placed the order.
• When inserting new orders, we populate the user_name column with the corresponding user's name.

With this denormalized schema, when you need to display order information, you no longer need to join the orders table with the users table to retrieve the user's name.

Denormalizing data in this manner, can improve query performance, especially for reporting or analytics purposes and read-heavy workloads, where performance is critical. As it reduces the complexity of database queries. However, it’s important to carefully consider trade-offs such as data redundancy and consistency when denormalizing the database schema.

You can simply query the orders table directly, like this:

SELECT order_id, user_name, order_date, total_amount
FROM orders
WHERE user_id = '12345';

Advantages of Denormalization:

1. Improved Query Performance: Denormalization can significantly enhance query performance, especially for read-heavy workloads. By eliminating the need for complex joins across multiple tables, denormalized schemas can result in faster query execution times. This is particularly beneficial for applications where responsiveness and efficiency are critical.
2. Reduced Complexity: Denormalization simplifies query logic by removing the necessity for joins, which can be complex and resource-intensive. Simplified queries are easier to write, understand, and maintain, leading to improved developer productivity and reduced maintenance overhead.
3. Optimized for Specific Use Cases: Denormalization allows for tailoring the database schema to specific use cases or application requirements. By duplicating and organizing data for optimal retrieval, denormalization can significantly enhance the efficiency of queries commonly performed in the application.
4. Minimized Network Traffic: In distributed systems or architectures where network latency is a concern, denormalization can reduce the amount of data transmitted between database servers. By storing related data together in denormalized structures, queries can be executed locally without the need for cross-server communication, thereby minimizing network overhead.
5. Enhanced Scalability: Denormalization contributes to improved scalability by reducing the computational overhead associated with complex joins. This allows databases to handle higher volumes of concurrent requests and scale more effectively as the application grows, thereby enhancing overall system scalability.
6. Improved User Experience: Faster query response times resulting from denormalization can lead to a better user experience, particularly in applications where responsiveness is crucial. Users experience reduced waiting times, resulting in a smoother and more enjoyable interaction with the application.
7. Better Support for Reporting and Analytics: Denormalized schemas are well-suited for reporting and analytics tasks, where complex queries are common and performance is essential. By pre-aggregating and organizing data for analytical purposes, denormalization can streamline the process of generating insights and extracting meaningful information from the database.
8. Flexibility in Schema Design: Denormalization provides flexibility in schema design, allowing developers to optimize the database structure based on performance considerations without sacrificing data integrity. This flexibility enables database administrators to strike the right balance between normalization and denormalization based on the specific needs of the application.

Denormalization improves performance by:

Denormalization improves performance by reducing the complexity of database queries and enhancing query execution speed. Here are several ways denormalization achieves this:

1. Eliminating Joins: By duplicating and storing related data in a single table, denormalization eliminates the need for complex joins across multiple tables. This simplifies query execution and reduces the computational overhead associated with joining large datasets.
2. Reducing Disk I/O: Denormalization often involves storing redundant data in closer proximity, resulting in fewer disk I/O operations during query execution. This can lead to faster data retrieval times, especially for read-heavy workloads.
3. Improving Index Utilization: Denormalized schemas may allow for more efficient index usage, as indexes can be tailored to the denormalized structure to optimize query performance further. This can result in faster query execution times and improved overall system performance.
4. Enhancing Data Locality: By colocating related data in denormalized structures, denormalization can improve data locality, reducing the need for data fetching from disparate locations. This can minimize network latency and improve overall system responsiveness.
5. Optimizing Aggregations and Analytics: Denormalized schemas are often better suited for analytical queries and aggregations, as they reduce the complexity of querying and processing large datasets. This can lead to faster generation of reports, insights, and analytics, improving decision-making capabilities.
6. Streamlining Data Retrieval: Denormalization can streamline data retrieval by precomputing and storing aggregated or derived values, such as totals or averages, directly within the denormalized structure. This eliminates the need for expensive computations during query execution, resulting in faster data retrieval times.

Overall, denormalization improves performance by simplifying query execution, reducing disk I/O, improving index utilization, enhancing data locality, optimizing aggregations and analytics, and streamlining data retrieval. However, it’s essential to carefully consider the trade-offs and potential impacts on data consistency and integrity before implementing denormalization strategies in a database schema.

Normalization Vs Denormalization:

Let’s compare the normalized schema to a denormalized alternative:

Normalized Schema:

• Users Table: user_id, username, email
• Products Table: product_id, name, description, price
• Orders Table: order_id, user_id, product_id, quantity, order_date

Denormalized Alternative:

• Orders Table: order_id, user_id, user_name, product_id, product_name, product_description, price, quantity, order_date

Advantages of Normalized Schema Over Denormalized Alternative:

1. Reduced Storage Requirements: In the normalized schema, user and product details are stored only once in their respective tables. In contrast, the denormalized alternative duplicates user and product information for each order, leading to increased storage requirements.
2. Improved Data Consistency: Normalization ensures that user and product information is consistent throughout the database. In the denormalized alternative, if a user’s information changes, it must be updated in every order record, increasing the risk of inconsistencies.
3. Enhanced Query Performance: Normalization allows for more efficient querying by minimizing the amount of redundant data stored in the database. In the denormalized alternative, querying order information requires joining the orders table with the users and products tables, potentially leading to slower performance, especially as the dataset grows.

When to choose Denormalization over Normalization:

Choosing between denormalization and normalization depends on various factors, including the specific requirements of your application, the anticipated workload patterns, and the trade-offs between performance, data integrity, and maintenance complexity. Here are some scenarios where denormalization may be preferred over normalization:

1. Read-Heavy Workloads: If your application primarily involves read operations and performance is critical, denormalization can significantly improve query performance by reducing the need for complex joins. Denormalized schemas can streamline data retrieval and enhance overall system responsiveness.
2. Analytical and Reporting Applications: In applications focused on reporting and analytics, denormalization can facilitate efficient data retrieval and aggregation. By pre-calculating and storing aggregated data, denormalized schemas can accelerate query execution times and provide faster access to insights.
3. Limited Joins Support: If your database system or query execution environment does not efficiently support joins, denormalization may be preferred to avoid the performance overhead associated with join operations. Denormalized schemas eliminate the need for joins, making query execution more straightforward and efficient.
4. Reduced Complexity: Denormalization can simplify query logic and reduce the complexity of database interactions, making the application easier to develop, understand, and maintain. In scenarios where the overhead of managing normalized schemas outweighs the benefits, denormalization can offer a more pragmatic approach.
5. Optimized for Specific Use Cases: Denormalization allows you to optimize the database schema for specific use cases or application requirements. By duplicating and organizing data to align with the most frequent query patterns, denormalized schemas can enhance query performance and improve overall system efficiency.
6. Data Duplication Tolerance: If your application can tolerate some degree of data duplication or redundancy, denormalization may be an acceptable trade-off to achieve performance gains. However, it’s essential to carefully manage data consistency and synchronization to prevent inconsistencies and ensure data integrity.
7. Limited Resources: In environments with limited computational resources or where scalability is a concern, denormalization can help reduce the computational overhead associated with complex joins and data retrieval operations. Denormalized schemas can improve system scalability and performance under resource constraints.
8. Real-Time or Interactive Applications: For applications requiring real-time responsiveness or interactive user experiences, denormalization can help minimize latency and improve overall system agility. Denormalized schemas enable faster data access and retrieval, leading to a more seamless user interaction.

It’s important to note that denormalization involves trade-offs, including increased data redundancy, potential data inconsistency, and added complexity in managing data synchronization. Before opting for denormalization, carefully evaluate the specific requirements and constraints of your application, and consider the long-term implications on performance, data integrity, and maintenance overhead. In some cases, a hybrid approach that combines elements of both normalization and denormalization may offer the best balance of performance and data management.

If you missed our first database tutorial — https://shaonmajumder.medium.com/how-to-optimize-your-database-for-bigger-userbase-adb1-06a8592c5018)