It is often necessary to summarize data for analysis and reporting purposes. Be it determining the number of rows in a table, obtaining the sum of column’s values, or finding the column’s highest, lowest, or average value.

Aggregate functions are used for this type of retrieval. Although each DBMS has its own set of aggregate functions, the common aggregate functions implemented by all major DBMSs include:

These functions are so efficient that they usually return results much faster than you can compute them within your client application.

Sample Table

To help you better understand the examples, and enable you to follow along with the tutorial, we are going to use the following sample table.

This table is part of an ‘Employee Management System’ that contains basic information about employees.

The AVG() Function

AVG() function is used to calculate the average value of a specific column. AVG() requires that the column name must be specified as a function parameter.

This first example uses AVG() to return the average salary of all employees in the ‘Employees’ table:

SELECT AVG(Salary) AS avg_salary
FROM Employees;

The SELECT statement above returns a single value containing the average salary of all employees. avg_salary is an alias for the newly computed column.

With AVG() you can also calculate the average value of specific rows. The following example returns the average salary of all ‘Managers’:

SELECT AVG(Salary) AS avg_salary
FROM Employees
WHERE Job='Manager';

This example is the same as the previous SELECT statement, but this time it contains a WHERE clause. The WHERE clause filters only employees with a manager’s job title, and therefore, the value returned in avg_salary is the average of the salaries of those employees only.

The MAX() Function

MAX() returns the highest value in a specified column. Just like AVG(), MAX() also requires that the column name be specified as a function parameter.

Here MAX() returns the salary of the highest paid employee in the ‘Employees’ table.

SELECT MAX(Salary) AS max_salary
FROM Employees;

Although MAX() is commonly used to find the highest value in a numeric column, many DBMS allow it to be used to return the highest value in a text column. When used with text data, MAX() returns the highest value in alphabetical order.

SELECT MAX(Name)
FROM Employees;

The MIN() Function

MIN() does the exact opposite of MAX(); it returns the lowest value in a specified column.

Here MIN() returns the salary of the lowest paid employee in the ‘Employees’ table.

SELECT MIN(Salary) AS min_salary
FROM Employees;

Like MAX(), MIN() is commonly used to find the lowest value in a numeric column. When used with text data, however, MIN() returns the lowest value in alphabetical order.

SELECT MIN(Name)
FROM Employees;

The SUM() Function

SUM() is used to calculate the sum of the values in a specific column. Like other aggregate functions, SUM() also requires that the column name be specified as a function parameter.

The following example returns the total of the salary (the sum of all the Salary values) paid to the employees.

SELECT SUM(Salary) AS total_salary
FROM Employees;

The COUNT() Function

COUNT(), as its name suggests, is used to count the number of rows in a table or the number of rows that match a specific criterion.

Unlike others, you can pass an asterisk or a column name to COUNT() as a function parameter. Depending on what you pass, COUNT() works differently:

1. COUNT(*): If you pass an asterisk, it counts the number of rows in the table, whether the column contains NULL values or not.
2. COUNT(column): If you pass a column name instead of an asterisk, it counts the number of values in that column, ignoring the NULL values.

The first example returns the total number of employees in the ‘Employees’ table:

SELECT COUNT(*) AS num_emp
FROM Employees;

In this example, an asterisk is passed to the COUNT(), so it counts the number of rows in the table even though some columns have NULL values.

The next example counts just the employees with an e-mail address:

SELECT COUNT(Email) AS num_emp
FROM Employees;

In this example, the column name ‘Email’ is passed to the COUNT() function, so it only counts non-NULL values in that column. Since only 4 out of 6 employees have email addresses, num_emp is 4.

Using Expressions

In addition to using a column name, you can also use an expression as an argument to aggregate functions.

For example, you may want to find the total of salary when each employee gets a 10% increase. You can achieve this through the following query:

SELECT SUM(Salary * 1.1) AS total_salary
FROM Employees;

While this example uses a simple expression, expressions used as arguments to aggregate functions can be as complex as necessary as long as they return a number, string, or date.

Aggregates on Distinct Values

When using aggregate functions, you have a choice of performing calculations on all values in a column or only on unique values. To only include unique values, you need to specify the DISTINCT argument.

The following example uses the COUNT() function to return the count of unique jobs.

SELECT COUNT(DISTINCT Job) AS jobs
FROM Employees;

Here the DISTINCT keyword makes sure that the COUNT only takes into account unique jobs.

Combining Aggregate Functions

So far we have been using only one aggregate function in our SELECT statement, but in fact you can use as many aggregate functions as you need.

Here is a query that uses all of the common aggregate functions to return five values (the number of employees, and the highest, lowest, average and total of their salary)

SELECT COUNT(*) AS num_emp,
       MIN(Salary) AS min_salary,
       MAX(Salary) AS max_salary,
       AVG(Salary) AS avg_salary,
	   SUM(Salary) AS total_salary
FROM Employees;

Aggregates on Grouped Data

So far we have executed aggregate functions on all the data in a table. But, what if you want to execute them separately for each job? To answer such a query, you will need to divide the data into logical sets by grouping them and then perform aggregate calculations on each group.

With the GROUP BY clause you can specify explicitly how the data should be grouped and then work on each group separately.

To demonstrate, let’s extend the previous query to execute the same five aggregate functions separately for each job.

SELECT Job,
       COUNT(*) AS num_emp,
       MIN(Salary) AS min_salary,
       MAX(Salary) AS max_salary,
       AVG(Salary) AS avg_salary,
	   SUM(Salary) AS total_salary
FROM Employees
GROUP BY Job;

Now that the GROUP BY clause is included, SQL grouped the rows of the same values present in the ‘Job’ column together and then applied five aggregate functions to each of the three groups.

How Nulls Are Handled

Before performing aggregations, it is important to know how these five aggregate functions handle NULL values in a column.

To demonstrate let’s consider the following ‘Grocery’ table:

Now let’s perform five aggregate functions on the ‘Qty’ column:

SELECT COUNT(*) AS num_rows,
       COUNT(Qty) AS num_vals,
       MIN(Qty) AS min_val,
       MAX(Qty) AS max_val,
       AVG(Qty) AS avg_val,
	   SUM(Qty) AS total
FROM Grocery;

The results are as you would expect: both count(*) and count(Qty) return the value 6, min(Qty) returns the value 5, max(Qty) returns 12, avg(Qty) returns 7, and sum(Qty) returns 43.

Next, let’s add a NULL value to the ‘Qty’ column:

If you run the same query again, you will get the following result:

As you can see even with the addition of the NULL value for the table, the sum(), min(), max(), and avg() functions all return the same value, indicating that they ignore any NULL value.

The count(*), however, now returns the value 7, which is valid since there are seven rows in the ‘grocery’ table, while the count(Qty) still returns the value 6. The difference is that count(*) counts the number of rows, while count(Qty) counts the number of values ​​contained in the ‘Qty’ column and ignores null values.