Скачать или смотреть How to Use Aggregate Functions with Group By in Multiple Tables in MySQL

Learn how to calculate income revenue using `aggregate functions` and `group by` across multiple tables in MySQL. This guide targets records for accepted orders only.
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How to Use Aggregate Functions with Group By in Multiple Tables in MySQL

Are you looking to calculate revenue from orders while ensuring that you only include accepted orders? If you're working with several tables in a MySQL database setup, understanding how to effectively use aggregate functions with a GROUP BY clause is essential. This guide will guide you through the process step by step.

Understanding Your Database Structure

You have three tables for managing your sales operation:

Order Table: This table records the orders' identifiers and the dates they were placed.

Columns: OrderId, Date

Approval Table: This table indicates the status of each order, whether it has been accepted or rejected.

Columns: ApprovalId, OrderId, Status, SellerId

Item Table: This table contains the items related to each order, including the price and the quantity sold.

Columns: ItemId, OrderId, Price, Qty, SellerId

Your goal is to display the income revenue only for orders that have been accepted. You need to sum up the sales amount, divide the revenue among sellers, and derive net sales.

The Challenge

You attempted to utilize aggregate functions with GROUP BY but ended up including rejected orders in the results. This issue arises typically because the SQL query is not filtering correctly before the aggregation, leading to inaccurate results.

Solution Steps

Here’s how to properly construct the SQL query to achieve the correct output:

SQL Query Breakdown

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Query

SELECT Statement: This part of the query defines the columns you want to display in your result set:

o.date: The date of the orders.

SUM(i.price * i.qty): The total sales for each day across accepted orders.

SUM(i.price * i.qty) * 0.90: This calculates the seller's share (90%).

SUM(i.price * i.qty) * 0.10: This calculates the net sales (10%).

FROM Clause: Here we specify that we are selecting from the Order table as the primary source of data.

JOIN Operations:

First, a JOIN with the Approval table to filter only those orders that have been accepted using a condition in the ON clause (a.status = 'Accepted').

Next, a JOIN with the Item table to relate the orders to their corresponding sellers.

GROUP BY Clause: Finally, the GROUP BY clause groups the results by the order date, ensuring that our aggregate functions compute total sales per date.

Result Interpretation

Once you execute the SQL query, you will get a summarized table showing:

The date of each accepted order.

The total sales made on that date.

The portion of the sales that goes to the sellers.

The resulting net sales amount.

This clear separation of data allows you to analyze income effectively while filtering out rejected orders entirely.

Conclusion

By following the structure of this SQL query, you can accurately compute revenues associated with accepted orders only. This approach ensures that your financial data remains reliable and insightful. Understanding how to use aggregate functions with group by across multiple tables can empower you to make better decisions based on accurate sales figures.

Try implementing this solution in your database and explore the power of SQL in generating vital business insights!