Using group by on multiple columns

Question

I understand the point of GROUP BY x.

But how does GROUP BY x, y work, and what does it mean?

Answer 1

Group By X means put all those with the same value for X in the one group.

Group By X, Y means put all those with the same values for both X and Y in the one group.

To illustrate using an example, let's say we have the following table, to do with who is attending what subject at a university:

Table: Subject_Selection

+---------+----------+----------+
| Subject | Semester | Attendee |
+---------+----------+----------+
| ITB001  |        1 | John     |
| ITB001  |        1 | Bob      |
| ITB001  |        1 | Mickey   |
| ITB001  |        2 | Jenny    |
| ITB001  |        2 | James    |
| MKB114  |        1 | John     |
| MKB114  |        1 | Erica    |
+---------+----------+----------+

When you use a group by on the subject column only; say:

select Subject, Count(*)
from Subject_Selection
group by Subject

You will get something like:

+---------+-------+
| Subject | Count |
+---------+-------+
| ITB001  |     5 |
| MKB114  |     2 |
+---------+-------+

...because there are 5 entries for ITB001, and 2 for MKB114

If we were to group by two columns:

select Subject, Semester, Count(*)
from Subject_Selection
group by Subject, Semester

we would get this:

+---------+----------+-------+
| Subject | Semester | Count |
+---------+----------+-------+
| ITB001  |        1 |     3 |
| ITB001  |        2 |     2 |
| MKB114  |        1 |     2 |
+---------+----------+-------+

This is because, when we group by two columns, it is saying "Group them so that all of those with the same Subject and Semester are in the same group, and then calculate all the aggregate functions (Count, Sum, Average, etc.) for each of those groups". In this example, this is demonstrated by the fact that, when we count them, there are three people doing ITB001 in semester 1, and two doing it in semester 2. Both of the people doing MKB114 are in semester 1, so there is no row for semester 2 (no data fits into the group "MKB114, Semester 2")

Hopefully that makes sense.

Answer 2

The GROUP BY clause is used in conjunction with the aggregate functions to group the result-set by one or more columns. e.g.:

SELECT column_name, aggregate_function(column_name)
FROM table_name
WHERE column_name operator value
GROUP BY column_name;

Remember this order:

1. SELECT (is used to select data from a database)

2. FROM (clause is used to list the tables)

3. WHERE (clause is used to filter records)

4. GROUP BY (clause can be used in a SELECT statement to collect data across multiple records and group the results by one or more columns)

5. HAVING (clause is used in combination with the GROUP BY clause to restrict the groups of returned rows to only those whose the condition is TRUE)

6. ORDER BY (keyword is used to sort the result-set)

You can use all of these if you are using aggregate functions, and this is the order that they must be set, otherwise you can get an error.

Aggregate Functions are:

MIN returns the smallest value in a given column

SUM returns the sum of the numeric values in a given column

AVG returns the average value of a given column

COUNT returns the total number of values in a given column

COUNT(*) returns the number of rows in a table

SQL script examples about using aggregate functions:

Let's say we need to find the sale orders whose total sale is greater than $950. We combine the HAVING clause and the GROUP BY clause to accomplish this:

SELECT 
    orderId, SUM(unitPrice * qty) Total
FROM
    OrderDetails
GROUP BY orderId
HAVING Total > 950;

Counting all orders and grouping them customerID and sorting the result ascendant. We combine the COUNT function and the GROUP BY, ORDER BY clauses and ASC:

SELECT 
    customerId, COUNT(*)
FROM
    Orders
GROUP BY customerId
ORDER BY COUNT(*) ASC;

Retrieve the category that has an average Unit Price greater than $10, using AVG function combine with GROUP BY and HAVING clauses:

SELECT 
    categoryName, AVG(unitPrice)
FROM
    Products p
INNER JOIN
    Categories c ON c.categoryId = p.categoryId
GROUP BY categoryName
HAVING AVG(unitPrice) > 10;

Getting the less expensive product by each category, using the MIN function in a subquery:

SELECT categoryId,
       productId,
       productName,
       unitPrice
FROM Products p1
WHERE unitPrice = (
                SELECT MIN(unitPrice)
                FROM Products p2
                WHERE p2.categoryId = p1.categoryId)