SQL technique: functions
Sometimes, the information that we need is not actually stored in the database, but has to be computed in some way from the stored data. In our order entry example, there are two derived attributes (/subtotal in OrderLines and /total in Orders) that are part of the class diagram but not part of the relation scheme. We can compute these by using SQL functions in the SELECT statement.
There are many, many functions in any implementation of SQL—far more than we can show here. Unfortunately, many of the functions are defined quite differently in different database packages, so you should always consult a reference manual for your specific software.
We can compute values from information that is in a table simply by showing the computation in the SELECT clause. Each computation creates a new column in the output table, just as if it were a named attribute.
Example: We want to find the subtotal for each line of the OrderLines table, just as shown in the UML class diagram. Obviously, the total of each line is simply the unit sale price times the quantity ordered, so we don’t even need a function yet—just the computation. We have included all three of the OrderLines PK attributes in the SELECT clause attribute list, to be sure that we show the subtotal for each distinct line.
SELECT custID, orderDate, UPC, unitSalePrice * quantity FROM orderlines;
|custid||orderdate||upc||unitsaleprice * quantity|
Computations are not limited just to column names; they may also include constants. For example, unitsaleprice * 1.06 might be used to find the sale price plus sales tax.
Notice that the computation itself is shown as the heading for the computed column. This is awkward to read, and doesn’t really tell us what the column means. We can create our own column heading or alias using the AS keyword as shown below. (In fact, we could simply write the new name of the column without saying AS. Please don’t do this—it hurts readability of your code.) If your want your column alias to have spaces in it, you will have to enclose it in double quote marks.
SELECT custID, orderDate, UPC, unitSalePrice * quantity AS subtotal FROM orderlines;
SQL aggregate functions let us compute values based on multiple rows in our tables. They are also used as part of the SELECT clause, and also create new columns in the output.
Example: First, let’s just find the total amount of all our sales. To compute this, all we need is to do is to add up all of the price-times-quantity computations from every line of the OrderLines. We will use the SUM function to do the calculation. The output table, as you should expect, will contain only one row.
SELECT SUM(unitSalePrice * quantity) AS totalsales FROM orderlines;
Next, we’ll compute the total for each order (the derived attribute shown in the UML Order class). We still need to add up order lines, but we need to group the totals for each order. We can do this with the GROUP BY clause. This time, the output will contain one row for every order, since the customerID and orderDate form the PK for Orders, not OrderLines. Notice that the SELECT clause and the GROUP BY clause contain exactly the same list of attributes, except for the calculation. In most cases, you will get an error message if you forget to do this.
SELECT custID, orderDate, SUM(unitSalePrice * quantity) AS total FROM orderlines GROUP BY custID, orderDate;
Other frequently-used functions that work the same way as SUM include MIN (minimum value of those in the grouping), MAX (maximum value of those in the grouping, and AVG (average value of those in the grouping).
The COUNT function is slightly different, since it returns the number of rows in a grouping. To count all rows, we can use the * (for example, to find out how many orders were placed).
SELECT COUNT(*) FROM orders;
We can also count groups of rows with identical values in a column. In this case, COUNT will ignore NULL values in the column. Here, we’ll find out how many times each product has been ordered.
SELECT prodname AS "product name", COUNT(prodname) AS "times ordered" FROM products NATURAL JOIN orderlines GROUP BY prodname;
|product name||times ordered|
|Hammer, framing, 20 oz.||3|
|Pliers, needle-nose, 4 inch||1|
|Saw, crosscut, 10 tpi||1|
|Screwdriver, Phillips #2, 6 inch||2|
A WHERE clause can be used as usual before the GROUP BY, to eliminate rows before the group function is executed. However, if we want to select output rows based on the results of the group function, the HAVING clause is used instead. For example, we could ask for only those products that have been sold more than once:
SELECT prodname AS "product name", COUNT(prodname) AS "times ordered" FROM products NATURAL JOIN orderlines GROUP BY prodname HAVING COUNT(prodname) > 1;
Most database systems offer a wide variety of functions that deal with formatting and other miscellaneous tasks. These functions tend to be proprietary, differing widely from system to system in both availability and syntax. Most are used in the SELECT clause, although some might appear in a WHERE clause expression or an INSERT or UPDATE statement. Typical functions include:
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