group by rollup function

The GROUP BY ROLLUP function is used to group and summarize data sets by columns, creating summaries of different levels, from the most detailed to the most summarized, including grouping by columns and summaries of all rows, thereby improving query efficiency and flexibility in data exploration.

GROUP BY ROLLUP: Understand its uses and examples

GROUP BY ROLLUP function

The GROUP BY ROLLUP function is used to group and summarize data sets by specified columns and create different hierarchies of summary. It allows you to view grouping and summary of data, from the most detailed level (including only unique values ​​for a specific column) to the most summarized level (including all rows).

grammar

 <code>GROUP BY ROLLUP(column_list)</code>

in:

• column_list is the list of columns to be grouped.

Example

Suppose we have a table with the following data:

Order number	product	price	Sales date
1	cell phone	100	2023-03-01
2	cell phone	200	2023-03-05
3	computer	500	2023-03-10
4	computer	600	2023-03-12

We can group prices by products using the GROUP BY ROLLUP function:

 <code>SELECT product, SUM(price) AS total_price FROM orders GROUP BY ROLLUP(product);</code>

This will return the following result:

product	Total price
cell phone	300
computer	1100
NULL	1400

This result shows the total price of the product, and a summary of grouped by product and all rows.

advantage

The GROUP BY ROLLUP function provides the following advantages:

• Create a summary hierarchy: It allows you to create summaries at different levels to quickly view different summary views of your data.
• Improve query efficiency: In some cases, using GROUP BY ROLLUP can be more efficient than using multiple GROUP BY queries.
• Enhanced Data Exploration: It helps with data exploration as it allows you to easily view different aggregated representations of your data.

limitation

The GROUP BY ROLLUP function also has some limitations:

• Performance Issues: In cases where the dataset is very large, it can cause performance issues.
• Data duplication: When using multiple grouped columns, data duplication may be caused.

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