1.9. Grouping and aggregation

Previous chapters have focused on mechanisms for storing and retrieving data. SQL also provides facilities for simple analyses of the data. In this chapter, we discuss methods of partitioning data and computing simple statistics on the partitions.

1.9.1. Tables used in this chapter

For this chapter, we will primarily work with the tables bookstore_inventory and bookstore_sales, which simulate a simple database that a seller of used books might reference. The bookstore_inventory table lists printed books that the bookstore either has in stock or has sold recently, along with the condition of the book and the asking price. When the bookstore sells a book, a record is added to the bookstore_sales table. This table lists the stock_number of the book sold, the date sold, and the type of payment. The column stock_number is a unique identifier for each book in the inventory, and can be used to join the tables.

We will also use the authors table to illustrate the interaction of NULL with aggregate functions.

A full description of these tables can be found in Appendix A.

1.9.2. Aggregate statistics

Aggregate statistics are values computed on entire sets of data. Counts, sums, and averages are examples of aggregate statistics. SQL provides a number of aggregate functions for computing such statistics. This section will cover some of the most commonly used functions; for documentation on all of the aggregate functions defined by SQL, see Appendix B - Aggregate functions.

We start by using the COUNT aggregate function to illustrate the basic rules for aggregate functions. At its simplest, COUNT can be used to return the number of rows in a table:

If you add a WHERE clause, COUNT will only consider rows matching the WHERE clause:

FROM bookstore_sales
WHERE payment = 'cash';

The use of * within the aggregate function is unique to COUNT; it simply means that we want to count rows, rather than any particular column. Other aggregate functions require application to a column or expression. When applied to a column or expression, COUNT and the other aggregate functions ignore NULL values. For example, observe the result when applying COUNT to different columns of the authors table:

SELECT COUNT(*), COUNT(author_id), COUNT(birth), COUNT(death)
FROM authors;

In each of the results above, we obtain a single number for each function in the SELECT clause. Only one row is returned, because we are (at the moment) applying the aggregate function to all rows matching the (optional) WHERE clause. This row represents a summary of the data. As a summary, details of the data cannot be included; it is an error to try to retrieve any expressions on the columns other than aggregates, when any aggregate function is used. The following query will result in an error in every database except SQLite:

SELECT title, COUNT(*) FROM bookstore_inventory;

While SQLite does not give us an error, the data returned demonstrates why this is an error in most databases; the returned title value represents just one row of the table, while the COUNT(*) value is a summary of the whole table. These two things do not match.

In addition to COUNT, the most common aggregate functions implemented by SQLite are SUM, AVG, MIN, and MAX. SQL defines a number of other aggregate functions as well, including statistical functions such as variance and standard deviation. As with COUNT, the argument of the function is a column or expression, which is evaluated for every row; NULL values are discarded. For all aggregates except COUNT, if the argument evaluates to NULL for every row, then the result is NULL (for COUNT the result is zero).

You can probably guess the meaning of these aggregate functions:

  • SUM computes the sum, and can only be applied to numbers

  • AVG computes the average, and can only be applied to numbers

  • MIN finds the minimum value according to the normal sort order for the value type

  • MAX finds the maximum value according to the normal sort order for the value type

We can apply all of these to the price column of bookstore_inventory:

SELECT COUNT(price), SUM(price), AVG(price), MIN(price), MAX(price)
FROM bookstore_inventory;

In addition to ignoring NULL values, it is possible to indicate that duplicate values should be ignored by putting the DISTINCT keyword before the argument of the aggregate function. This is mostly useful when combined with COUNT. For example, if we want to know the total number of books versus the number of unique titles in our inventory, we could write:

SELECT COUNT(title), COUNT(DISTINCT title) FROM bookstore_inventory;

1.9.3. Grouping

Aggregates can be very useful when applied to an entire table or to a set of rows matching a WHERE clause. Sometimes, though, we want more than one count, or sum, or average from a table; we may want these statistics over some subsets of rows, organized by some common attribute. For example, our bookstore_inventory table includes books in different conditions; we might be interested in the average price we are charging for books in “good” condition separately from books in “fair” condition, and so forth.

SQL provides the GROUP BY clause for this purpose. GROUP BY lets us specify an attribute, such as the condition of a book, by which to organize a table into groups. Membership in a specific group is based on the GROUP BY expression; all members of a group share the same value for the expression. The groups form a partition of the data; every row (matching the optional WHERE clause, if used) is assigned to a group, and no row is assigned to more than one group.

With GROUP BY in effect, we can now retrieve information about each group as a whole; each row of our output will represent information about one group. If we put an aggregate function expression in our SELECT clause, the aggregate is applied to each group’s rows separately. In addition to aggregates, we can SELECT the GROUP BY expression - this is allowed (and makes sense) because all of the rows in each group will have the same value for the expression. You usually want to include the grouping expression as a label for the group - otherwise you will not know what group each aggregate expression belongs to!

The GROUP BY clause comes immediately after the WHERE clause, or after FROM if there is no WHERE clause. Here is an example of grouping on our bookstore inventory by book condition:

If we want to exclude books that have already been sold, we could add a WHERE clause (here we use a subquery as discussed in Chapter 1.8):

SELECT condition, COUNT(*), AVG(price)
FROM bookstore_inventory
WHERE stock_number NOT IN
  (SELECT stock_number FROM bookstore_sales)
GROUP BY condition;

It is also possible to group by more than one expression, in which case each group is defined by a unique setting for all of the expressions. Our bookstore_sales table contains information about the date in which a book was sold, as well as the type of payment used in the purchase. We might be very interested in knowing sales totals by month, or by type of payment, or both. To get the price that was paid, we will have to join in the bookstore_inventory table.

To start with, let’s retrieve sales totals by month (here we will use SQLite’s substring() function to extract the 2-digit month number; in other databases it may be possible to extract a month by name):

  substring(s.date_sold, 6, 2) AS month,
  SUM(i.price) AS total_sales
  bookstore_sales AS s
  JOIN bookstore_inventory AS i ON s.stock_number = i.stock_number
GROUP BY month;

Note that we can use the alias “month” defined in our SELECT clause in our GROUP BY clause without having to rewrite the function expression.

Now, let’s break down our total sales by type of month and type of payment:

  substring(s.date_sold, 6, 2) AS month,
  SUM(i.price) AS total_sales
  bookstore_sales AS s
  JOIN bookstore_inventory AS i ON s.stock_number = i.stock_number
GROUP BY month, s.payment
ORDER BY month, s.payment;

Here we have sorted by our grouping expressions as well, just to ensure that our groups come out in a consistent fashion. Filtering grouped data

When we group, we are generating a new set of rows representing the groups present in our data. If we include a WHERE clause in our query, it is applied to the data before grouping. The WHERE clause, then, cannot be used to filter the set of rows produced by grouping. If we want to filter the grouped data, we must do so using a HAVING clause.

The HAVING clause works just like the WHERE clause, but applies to the set of rows generated by grouping. Using HAVING, we can filter by expressions available to us after grouping: any expressions that we grouped by (our group labels), or aggregate functions on the groups. The HAVING clause comes after the GROUP BY clause.

Here we use HAVING to list books for which we have more than one copy in our bookstore inventory, in order by the number of copies:

SELECT author, title, COUNT(*)
FROM bookstore_inventory
GROUP BY author, title

We can, of course, use both WHERE and HAVING in the same query - here we group books that have not been sold by author and title; then we report the titles (groups) with multiple copies:

SELECT author, title, COUNT(*)
FROM bookstore_inventory
WHERE stock_number NOT IN
  (SELECT stock_number FROM bookstore_sales)
GROUP BY author, title

1.9.4. Self-check exercises

This section contains exercises on grouping and aggregation, using the bookstore_inventory and bookstore_sales tables. If you get stuck, click on the “Show answer” button below the exercise to see a correct answer.

Write a query to count the number of books in our inventory by the author Toni Morrison.

Write a query to find the minimum, maximum, and average price of a book in ‘good’ condition.

Write a query to find out how many different authors have written books in our inventory.

Write a query to get the average price of a book, by author; sort by highest average price first.

Write a query to get the average price of a book, by author and condition; sort by author and condition.

Write a query to give the number of books sold and the total sales from those books, grouped by condition. Exclude books for the payment type ‘trade in’.

Write a query to find which authors’ books have an average price less than 3 units of currency.

Write a query to get the difference between the maximum and minimum price of a book for each possible book condition.

Write a query to find the maximum price of any book in our inventory and list the books with that price. Hint: you will need to use a subquery for this one.

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