Describe Situations In Which Denormalization Of A Database S
Describe situations in which denormalization of a database schema would be used? Answer in your own words and give examples
Denormalization is a database optimization technique used to improve read performance by intentionally introducing redundancy into a normalized database schema. Normally, database normalization aims to reduce data redundancy and ensure data integrity through multiple related tables. However, in specific situations, denormalization becomes advantageous, especially when read operations are frequent and performance is critical.
One common scenario for denormalization occurs in data warehousing and decision-support systems, where read operations dominate over write operations. In such cases, consolidating related data into fewer tables minimizes the need for complex joins, thus speeding up query response times. For example, instead of maintaining separate tables for customers and their orders, a denormalized table might directly include customer details alongside order information, enabling faster retrieval of all relevant data in a single query.
Another situation arises in high-volume online transaction processing (OLTP) systems where the system experiences a heavy load of queries that require quick access to certain fields. For instance, a retail website might duplicate product details across various tables to quickly display product information without requiring join operations, even though this introduces some redundancy and potential update anomalies.
Denormalization is also useful when reporting and analytical queries are common, and the system needs to generate summaries or aggregated data efficiently. For example, storing pre-calculated totals or averages within a table can expedite report generation, eliminating the need for costly aggregations over normalized structures each time a report is requested.
Despite these benefits, denormalization introduces challenges such as increased data redundancy, which can lead to data inconsistency if updates are not carefully managed. Therefore, it should be applied selectively, typically in systems where read performance is prioritized over data update operations and when the application logic incorporates mechanisms to maintain data integrity.