Welcome back! So far, we've explored the COUNT function to count rows and the DISTINCT keyword to ensure data uniqueness. Now, we will dive into another powerful aggregate function in SQL: SUM.

Aggregate functions help us summarize and analyze data. For instance, when analyzing an online shopping dataset, you might want to find the total revenue generated from the sales of products. The SUM function allows you to add up values in a column, providing valuable insights.

Let's get started by understanding the tools and environment you'll need for this lesson.

The SUM function is an aggregate operation in SQL, used to calculate the total sum of a numerical column in a database. Think of it as a mathematical operation that adds up all the numbers in a set—simple, yet profound!

The syntax is as follows: SUM(column) where column is the name of the column for which you want to calculate the sum.

You might be wondering, "When would I need to use SUM?" Consider a situation where you have a products database like ours and want to determine the total revenue generated by the sales of all products. That's a perfect opportunity to deploy the SUM function. Let's see how it works!

Here's the basic syntax:

SQL
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1SELECT SUM(expression) FROM table_name;

• SUM(expression): The SUM() function expects an argument to specify what to sum. The correct usage is SUM(expression), where the expression is typically a column name or a numerical value, such as SUM(column_name) to sum up values from a specific column.
• table_name: The table containing the column you want to sum.

For example, if you want to find the total revenue generated by the products sold, you would use the SUM function on the product prices.

SQL
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1SELECT SUM(Products.product_price) AS TotalRevenue
2FROM OrderItems
3JOIN Products ON OrderItems.product_id = Products.product_id;
4
5-- Output:
6--  TotalRevenue 
7-- --------------
8--      16782.60 

Let's see a practical example emphasizing our shared interest: online shopping data, and then break it down:

SQL
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1SELECT Products.category_id, SUM(1) AS TotalItemsSold
2FROM OrderItems
3JOIN Products ON OrderItems.product_id = Products.product_id
4GROUP BY Products.category_id;
5
6-- Output:
7-- | category_id | TotalItemsSold |
8-- |-------------|----------------|
9-- |           1 |            270 |
10-- |           2 |             60 |
11-- |           3 |            120 |
12-- |           4 |             60 |
13-- |           5 |             90 |

Let's break it down:

1. SELECT Products.category_id, SUM(1) AS TotalItemsSold: In this query, SUM(1) is used to count the number of items sold for each product category. This utilizes the SUM() function in a straightforward manner to aggregate the total count of items sold by category. This part of the query selects rows separately for each group according to the category_id from the Products table and calculates the total number of items sold by summing 1 for each sold item in the group.
2. FROM OrderItems: This line informs SQL that our main table in this operation is OrderItems.
3. JOIN Products ON OrderItems.product_id = Products.product_id: Here, we express our intention to join the OrderItems table with the Products table on the common field product_id, linking order items and their respective products.
4. GROUP BY Products.category_id: Finally, we use the GROUP BY clause to group the total items sold by product categories.

When working with the SUM function, there are a few common pitfalls to be aware of:

• Bear in mind that SUM works with numerical data. Using it on non-numerical columns will result in errors.
• The AS keyword, as seen in the code, can make your output more readable by renaming the result of our SUM operation. Don’t forget to use it as necessary.

Let's also see the difference between using SUM(1) and COUNT(*):

• SUM(1): Adds the value 1 for each row, effectively counting rows within each group when used with GROUP BY. It's a creative use of the SUM function.
• COUNT(*): Directly counts all rows in the result set, including those with NULL values, and is typically more straightforward for counting rows.

In this lesson's context, both SUM(1) and COUNT(*) achieve the same result.

Great job! You've made excellent progress in mastering SQL functions. In this lesson, we learned about the SUM function and how to use it to perform aggregate calculations in SQL. We applied it to our online shopping database and calculated the total revenue generated from the sales of products.

In the upcoming practice exercises, you'll get the opportunity to apply the SUM function, deepen your understanding, and increase your confidence in handling it. Stay determined as you continue to unleash the power of SQL!