Denormalization is a database optimization technique used to improve the read performance of a database by adding redundant data to a previously normalized database. This process involves selectively introducing redundancy into the database schema to reduce the need for complex join operations, which can be computationally expensive and slow down query performance.
Key Points about Denormalization:
Purpose: The primary goal of denormalization is to enhance the performance of read-heavy operations by minimizing the number of joins required to retrieve data. This is particularly useful in scenarios where the database needs to handle a large number of read operations efficiently[1][5].
Process: Denormalization involves adding redundant copies of data or grouping related data into fewer tables. This can include techniques such as storing precomputed values, adding attributes from one table to another, or creating materialized views[1][4][9].
Trade-offs: While denormalization can significantly speed up read operations, it comes with trade-offs. These include increased storage requirements due to data redundancy, potential data inconsistencies, and more complex data maintenance and update processes. Write operations (inserts, updates, deletes) can become slower because changes need to be propagated to multiple redundant copies of the data[2][3][6].
Use Cases: Denormalization is often used in data warehousing, business intelligence applications, and other read-intensive environments where the performance benefits outweigh the drawbacks of increased redundancy and potential inconsistencies[5][10][11].
Techniques: Common denormalization techniques include:
Materialized Views: Precomputed query results stored in a separate table to speed up query performance.
Partitioning: Dividing a table into smaller, more manageable pieces based on specific cr...
