What are the most effective denormalization techniques?

1 Pre-joining tables

One of the most basic denormalization techniques is to pre-join tables that are frequently queried together. This reduces the number of joins and the processing time required to retrieve the data. For example, if you have a normalized database with separate tables for customers, orders, and products, you can create a denormalized table that contains all the relevant information for each order. However, pre-joining tables also increases the size of the database and the risk of data duplication and inconsistency. Therefore, you should only pre-join tables that have a stable relationship and a low update frequency.

2 Adding redundant columns

Another common denormalization technique is to add redundant columns to a table that store calculated or derived values from other columns. This can improve the performance of queries that need to perform complex calculations or aggregations. For example, if you have a table that stores the sales data for each product, you can add a redundant column that stores the profit margin for each product. This way, you can avoid calculating the profit margin every time you query the table. However, adding redundant columns also increases the storage space and the complexity of maintaining the data. Therefore, you should only add redundant columns that are frequently used and have a simple calculation logic.

3 Creating summary tables

A more advanced denormalization technique is to create summary tables that store aggregated or grouped data from other tables. This can improve the performance of queries that need to access large amounts of data or perform analytical functions. For example, if you have a table that stores the daily transactions for each customer, you can create a summary table that stores the monthly revenue and retention rate for each customer segment. This way, you can access the summary table instead of scanning the entire transaction table. However, creating summary tables also increases the storage space and the difficulty of synchronizing the data. Therefore, you should only create summary tables that are relevant and updated regularly.

4 Using materialized views

A related denormalization technique is to use materialized views, which are similar to summary tables but are created and maintained by the database system. A materialized view is a query result that is stored as a table and refreshed periodically or on demand. This can improve the performance of queries that need to access data from multiple tables or perform complex joins or calculations. For example, if you have a query that joins several tables and calculates the average rating and number of reviews for each product, you can create a materialized view that stores the query result. This way, you can query the materialized view instead of running the original query. However, using materialized views also increases the storage space and the dependency on the database system. Therefore, you should only use materialized views that are supported by your database system and have a clear refresh strategy.

5 Flattening hierarchical data

Another denormalization technique is to flatten hierarchical data, which means to store data that has a nested or tree-like structure in a single table. This can improve the readability and usability of the data and simplify the queries that need to access the data. For example, if you have a table that stores the categories and subcategories of products, you can flatten the data by adding a column that stores the full path of each category or subcategory. This way, you can easily filter or group the data by the full path. However, flattening hierarchical data also increases the redundancy and the possibility of data anomalies. Therefore, you should only flatten hierarchical data that has a fixed or stable structure and a low level of depth.

6 Splitting tables

A less common but sometimes useful denormalization technique is to split tables, which means to divide a table into two or more tables based on some criteria. This can improve the performance and scalability of the database and reduce the locking and contention issues. For example, if you have a table that stores the user data and has a high update frequency, you can split the table into two tables: one that stores the static data and one that stores the dynamic data. This way, you can reduce the number of updates and locks on the static table and improve the query efficiency. However, splitting tables also increases the complexity and the number of joins. Therefore, you should only split tables that have a large size or a high update frequency and have a clear partitioning logic.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?