Back to list Standard SQL How to Multiply Two Columns in SQL Database: SQL MySQL MS SQL Server PostgreSQL Oracle SQLite Operators:Multiply two columns, JOIN, alias Problem: want to multiply values from two columns of a table. Example: Our database has a table named purchase with data in the following columns: id, name, price, quantity, and discount_id. idnamepricequantitydiscount_id 1pen731 2notebook582 3rubber1131 4pencil case2423 Let’s multiply the price by the quantity of the products to find out how much you paid for each item in your order. Solution: SELECT name, price*quantity AS total_price FROM purchase; This query returns records with the name of the product and its total price: nametotal_price pen21 notebook40 rubber33 pencil case48 Discussion: Do you need to select the name of each record (in our case, name) and compute for it the result of multiplying one numeric column by another (price and quantity)? All you need to do is use the multiplication operator (*) between the two multiplicand columns (price * quantity) in a simple SELECT query. You can give this result an alias with the AS keyword; in our example, we gave the multiplication column an alias of total_price. Note that you can also use data from two columns coming from different tables. We have another table in our database named discount that has columns named id and value; the latter represents the percent discount on the item with the given ID. idvalue 110 220 330 Look at the example below. Solution: SELECT p.name, p.price*p.quantity*(100-d.value)/100 AS total_price FROM purchase p JOIN discount d ON d.id=p.discount_id; Here’s the result: nametotal_price pen18.90 notebook32.00 rubber29.70 pencil case33.60 As you can see, it’s quite easy to multiply values from different joined tables. In our example above, we multiplied the price of each product by its quantity from one table (purchase) and then multiplied this total price by the percent discount using the discount table. Recommended courses: SQL Basics SQL Basics in SQL Server SQL Practice Set Tags: SQL MySQL MS SQL Server PostgreSQL Oracle SQLite Subscribe to our newsletter Join our weekly newsletter to be notified about the latest posts.