Getting the same result set produced by the Query #3 but using the GROUPING SETS clause. You find that by using the GROUPING SETS operator you should get the same result set, but with less coding! This really motivates you, and you write the following query using GROUPING SETS: As a data analyst, you decided to search the internet to find a way to achieve the same results but with less work. In this example, the first grouping is by order year and the second grouping is by order year and month.Īlthough you obtained the desired result, Query 3 would be even larger if you added another grouping set, such as order day. This situation happens when one of the grouped queries doesn’t have the same number of columns grouped. When the row has a value in both the OrderYear and OrderMonth columns, it means the row belongs to the order year and month grouping. Looking at Figure 2 again, a row that has NULL in the OrderMonth column means the row belongs to the order year grouping. When there’s more than one group by expression list involved in the query, a NULL is used as a placeholder to identify one of the groupings in the results. Look carefully at Figure 2 which details the placeholders in the first grouped query. Remember you used the NULL as a dummy column to identify the results from the order year grouping. Maybe you’ve figured out how the NULL values appeared in the result set. What’s new to you is that each grouping result –year grouping result and year and month grouping result– is merged. In July 2005 the company’s income was around nine hundred sixty thousand dollars, and so on. This information doesn’t look new, because you already know that in 2005 the company’s income was around eleven million dollars. Company’s income per year and per year and month. Review the comments in the figure which identify the grouping sets.įigure 1. Company’s income per year and per year and month.įigure 1 shows the result set produced by Query 3. This dummy column will return NULL in the OrderMonth column, identifying the OrderYear total rows of this query. All queries merged by the UNION operator must have the same number of columns. First, modify Query 1 by adding a dummy column so it will have the same number of columns as Query 2. To accomplish this, you may merge the previous queries – Query 1 and Query 2 – by using the UNION ALL operator. Suppose the business manager at Adventure Works wants to visualize both results within a single result set. The former is grouped by order year, and the latter is grouped by order year and month. If you observe the structure of the two queries, you will see they’re grouped by a single set of grouping expressions. The more expressions or columns added to the GROUP BY clause, the more detailed the results will be. In August 2005, it was around two million dollars, and so on. In July 2005, their income was around nine hundred sixty thousand dollars. This result set is more detailed than the former. The following table contains the result set of Query 2: This means you need to group the company’s income per year and run the following query: Imagine you’re working as a data analyst at the bike manufacturer Adventure Works, and you’re interested in the company’s income over the last few years. Case Study: Data Analyst at Adventure Works
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If you wish to follow along with this article, download it from here. Prepare the data setĪll queries in this article will run in the AdventureWorks2012 database.
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In this article I’ll demonstrate how to achieve the same results using each method. The other approach is to use the GROUPING SETS operator along with the GROUP BY clause and define each grouping set within a single query. The first approach is to create one grouped query per combination of expressions and merge the results using the UNION ALL operator. However, if you want to group the data by multiple combinations of group by expressions, you may take one of two approaches.
One row per unique combination of the expressions in the GROUP BY clause is returned, and aggregate functions such as COUNT or SUM may be used on any columns in the query. This clause creates groupings which are defined by a set of expressions. In T-SQL, you summarize data by using the GROUP BY clause within an aggregate query. The best way to handle this situation is by summarizing the data to get a quick review. Maybe you have felt overwhelmed when you’re analyzing a dataset because of its size. Summarizing Data Using the GROUPING SETS Operator - Simple Talk Skip to content
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