In today’s lesson, you’re going to learn about grouping results returned from your queries using the GROUP BY clause.
The objectives of today’s lesson are to:
- Learn how to group results using GROUP BY
- Use aggregate functions to perform calculations
- Understand how to filter groups using the HAVING clause
GROUP BY Clause
The SQL GROUP BY Clause is used to output a row across specified column values. It is typically used in conjunction with aggregate functions such as SUM or Count to summarize values. In SQL groups are unique combinations of fields. Rather than returning every row in a table, when values are grouped, only the unique combinations are returned.
The GROUP BY Clause is added to the SQL Statement after the WHERE Clause. Here is an example where we are listing OrderID, excluding quantities greater than 100.
SELECT OrderID FROM OrderDetails WHERE Quantity <= 100 GROUP BY OrderID;
There are a couple of things to note. First, the columns we want to summarize are listed, separated by commas, in the GROUP BY clause. Second, this same list of columns must be listed in the select statement; otherwise the statement fails.
When this statement is run, not every filtered row is returned. Only unique combinations of OrderID are included in the result. This statement is very similar to
SELECT DISTINCT OrderID FROM OrderDetails WHERE Quantity <= 100;
But there is a key difference. The DISTINCT modifier stops at outputting a unique combination of rows, whereas, with the GROUP BY statement, we can calculate values based on the underlying filtered rows for each unique combination.
In other words, using our example, with the GROUP BY, we can calculate the number or OrderDetails per order as follows:
SELECT OrderID, COUNT(OrderID) as NumOrderDetails FROM OrderDetails GROUP BY OrderID;
COUNT is an example of an aggregate function, these are what really give the GROUP BY statement its special value.
Some functions, such as SUM, are used to perform calculations on a group of rows, these are called aggregate functions. In most cases, these functions operate on a group of values that are defined using the GROUP BY clause. When there isn’t a GROUP BY clause, it is generally understood the aggregate function applies to all filtered results.
Some of the most common aggregate functions include:
|AVG(expression)||Calculate the average of the expression.|
|COUNT(expression)||Count occurrences of non-null values returned by the expression.|
|COUNT(*)||Count all rows in the specified table.|
|MIN(expression)||Finds the minimum expression value.|
|MAX(expression)||Finds the maximum expression value.|
|SUM(expression)||Calculate the sum of the expression.|
These functions can be used on their own in conjunction with the GROUP BY clause. On their own, they operate across the entire table; however, when used with GROUP BY, their calculations are “reset” each time the grouping changes. In this manner, they act as subtotals.
General Syntax of an Aggregate Function
When using the aggregate function you can either compute the result on all values or distinct values. For instance, to count all OrderDetails records we could use the expression:
SELECT COUNT(OrderID) FROM OrderDetails;
To count the distinct of orders making up the details we would use the following:
SELECT COUNT(DISTINCT OrderID) FROM OrderDetails;
Using Aggregate Functions with GROUP BY
AVG and SUM
The SUM function totals up the values returned, in similar fashion AVG calculates the average.
Let’s see if we can calculate the total order amount from the OrderDetails. From previous lessons, we know how to calculate the total amount for each detail as:
SELECT OrderID, ProductID, UnitPrice * Quantity as TotalPrice FROM OrderDetails;
Since we can apply an aggregate function to expressions, we can set up a grouping on OrderID to calculate the total price per order as
SELECT OrderID, SUM(UnitPrice * Quantity) as TotalPrice FROM OrderDetails GROUP BY OrderID;
We can even sort by the total to get the top orders first
SELECT OrderID, SUM(UnitPrice * Quantity) as TotalPrice FROM OrderDetails GROUP BY OrderID ORDER BY TotalPrice DESC;
In a similar fashion, we can calculate the average order detail amount as
SELECT OrderID, AVG(UnitPrice * Quantity) as AverageOrderAmount FROM OrderDetails GROUP BY OrderID;
For the curious, since an average is calculated as the sum of the sample divided by the sample count, then using AVG in the above statement is the same as:
SELECT OrderID, SUM(UnitPrice * Quantity) / COUNT(OrderID) as AverageOrderAmount FROM OrderDetails GROUP BY OrderID;
We covered a lot in this section. Here are some key points to remember:
- An aggregate function can evaluate an expression such as SUM(A + B)
- You should alias aggregate functions, so the column names are meaningful
- When working with aggregate functions and GROUP BY, it is sometimes is easier to think about the details first, that is writing a simple SELECT statement, inspect the results, then add in the fancy stuff.
The COUNT function is used when you need to know how many records exist in a table or within a group. COUNT(*) will count every record in the grouping; whereas COUNT(expression) counts every record where expression’s result isn’t null. You can also use Distinct with COUNT to find the number of unique values within a group.
To find the number of OrderDetail Lines per order
SELECT OrderID, COUNT(OrderDetailID) FROM OrderDetails GROUP BY OrderID;
To find the number of unique orders per product
SELECT ProductID, COUNT(DISTINCT OrderID) FROM OrderDetails GROUP BY ProductID;
MIN and MAX
Use MIN and MAX to find the smallest and largest values, respectively, within a table or group.
For example, to find the smallest and largest product quantities ordered within an order try
SELECT OrderID, MIN(Quantity) as MinQuantity, MAX(Quantity) as MaxQuantity FROM OrderDetails GROUP BY OrderID;
SELECT OrderID, MAX(UnitPrice * Quantity) as MaxAmount FROM OrderDetails GROUP BY OrderID;
The HAVING clause is used to filter groups according to the results of the aggregate functions. This makes it possible to solve problems such as select all orders that have more than two order detail lines.
That example looks like
SELECT OrderID, COUNT(OrderDetailID) FROM OrderDetails GROUP BY OrderID HAVING COUNT(OrderDetailID) > 2;
If we wanted to find all orders greater than $1000 we would write
SELECT OrderID, SUM(UnitPrice * Quantity) as TotalPrice FROM OrderDetails GROUP BY OrderID HAVING TotalPrice > 1000 ORDER BY TotalPrice DESC;
This query is the same as one from the previous section with the addition of the HAVING clause. We could have written the HAVING clause as
HAVING SUM(UnitPrice * Quantity) > 1000
But, since the column was already aliased, we used it instead.
To hammer home HAVING, I want to show one last example. Here you’ll see the HAVING statement includes an aggregate function that isn’t in the SELECT list.
SELECT OrderID, SUM(UnitPrice * Quantity) as TotalPrice FROM OrderDetails GROUP BY OrderID HAVING AVG(UnitPrice * Quantity) > 500 ORDER BY TotalPrice DESC;
In the above query, we’re getting the total price for orders where the average OrderDetail amount is greater than $500.00.
Final Statement about HAVING
To keep it straight in my head I like to think of the WHERE clause doing its work before any groupings take place, and then the HAVING clause taking over after the groups are formed.
It’s important to practice! Use the sample database to answer these questions.
- What is the average quantity ordered in the OrderDetails table?
- Display the Min, Max, and Average Quantity ordered for each product in OrderDetails.
- Return total sales, by product for all orders, but only include products included on 7 or more OrderDetails.
Answers to Exercises
Congratulations! You just learned how to use the GROUP BY and HAVING clauses to summarize and filter on summarized information. More tutorials are to follow! Remember! I want to remind you all that if you have other questions you want to be answered, then post a comment or tweet me.
I’m here to help you. What other topics would you like to know more about?