SQL Interview Questions

Denormalization is the process of combining or adding redundant data to a normalized database schema in order to improve read performance. It involves merging tables and including redundant data within tables to minimize the number of joins needed to retrieve data. While normalization aims to reduce redundancy and improve data integrity, denormalization focuses on optimizing query performance, especially in read-heavy systems.

Objectives of Denormalization

1. Improve Read Performance: By reducing the number of joins required to fetch related data, denormalization can speed up query performance.
2. Simplify Queries: Queries can become simpler and more straightforward when related data is stored together, reducing the complexity of SQL statements.
3. Optimize for Specific Use Cases: Denormalization can be tailored to specific application needs, optimizing the database for the most common and performance-critical queries.

Common Denormalization Techniques

1. Adding Redundant Columns:

   • Storing frequently accessed related data in the same table to avoid joins.

   -- Instead of joining between orders and customers table:
   SELECT orders.order_id, customers.customer_name
   FROM orders
   JOIN customers ON orders.customer_id = customers.customer_id;
   
   -- Denormalized table:
   SELECT order_id, customer_name
   FROM orders;

2. Storing Aggregated Values:

   • Pre-computing and storing aggregated data, such as totals or averages, to speed up read operations.

   -- Normalized:
   SELECT customer_id, COUNT(*) AS order_count
   FROM orders
   GROUP BY customer_id;
   
   -- Denormalized:
   SELECT customer_id, order_count
   FROM customers;

3. Duplicating Tables:

   • Creating read-optimized copies of tables or parts of tables for faster access.

   -- Example: Storing a summary of orders for quick access.
   CREATE TABLE orders_summary AS
   SELECT customer_id, COUNT(*) AS total_orders, SUM(order_amount) AS total_amount
   FROM orders
   GROUP BY customer_id;

4. Using Materialized Views:

   • Materialized views are precomputed views that store the result of a query and can be refreshed periodically.

   CREATE MATERIALIZED VIEW orders_summary AS
   SELECT customer_id, COUNT(*) AS total_orders, SUM(order_amount) AS total_amount
   FROM orders
   GROUP BY customer_id;

Advantages of Denormalization

1. Performance Gains:

   • Significant improvement in read performance, particularly for complex queries that would otherwise require multiple joins.

2. Simpler Queries:

   • Reduced complexity in SQL queries, making them easier to write and maintain.

3. Faster Aggregation:

   • Precomputed aggregates allow for faster reporting and analytics.

Disadvantages of Denormalization

1. Increased Storage Requirements:

   • Storing redundant data increases the amount of disk space required.

2. Data Inconsistency:

   • Redundant data can lead to inconsistencies if not managed correctly, as updates need to be synchronized across multiple locations.

3. Complexity in Data Maintenance:

   • Increased complexity in maintaining data integrity, as updates, inserts, and deletes may need to be propagated to multiple places.

Use Cases for Denormalization

1. Data Warehousing:

   • Read-heavy environments where quick access to aggregated and summarized data is crucial.

2. High-Performance Applications:

   • Applications requiring high read performance and low latency, such as real-time analytics, dashboards, and reporting tools.

3. Complex Query Optimization:

   • Systems where complex queries involving multiple joins are common, and query performance is critical.

Conclusion

Denormalization is a technique used to optimize read performance in a database by introducing redundancy. While it can significantly improve the efficiency of read operations and simplify queries, it comes with trade-offs, including increased storage requirements and potential data inconsistency. Denormalization should be carefully considered and applied selectively, based on the specific performance requirements and use cases of the application.