SQL Sales Summary Query Explanation

SQL Code Explanation

The provided code snippet is a SQL query. Below is a detailed explanation of its components, purpose, and functionality.

Objective

This SQL query aims to:

1. Count the number of sales for each product.
2. Calculate the total sales amount for each product.
3. Compute the average sale amount per product.

Code Breakdown

SELECT product_id, 
       COUNT(*) AS sales_count, 
       SUM(sale_amount) AS total_sales_amount, 
       AVG(sale_amount) AS average_sale_amount
FROM sales
GROUP BY product_id;

Components

1. SELECT Clause: Specifies the columns to retrieve.

   • product_id: Selects the column product_id from the sales table.
   • COUNT(*) AS sales_count: Counts the number of entries (sales) for each product. The result will be aliased as sales_count.
   • SUM(sale_amount) AS total_sales_amount: Sums the sale_amount for each product. The result will be aliased as total_sales_amount.
   • AVG(sale_amount) AS average_sale_amount: Calculates the average sale_amount for each product. The result will be aliased as average_sale_amount.

2. FROM Clause: Specifies the table from which to retrieve data.

   • sales: The table containing the sales data.

3. GROUP BY Clause: Groups the results by a specified column.

   • product_id: Groups the results by product_id such that all rows with the same product_id are combined.

Functionality

• Grouping: The query groups all records in the sales table by the product_id. This means that all sales transactions for each distinct product_id are considered together.
• Aggregation:
  • COUNT(*): Returns the number of sales transactions for each product.
  • SUM(sale_amount): Returns the total sales amount for each product.
  • AVG(sale_amount): Returns the average sale amount per transaction for each product.

Example

Assume the sales table has the following data:

After executing the query, the result would be:

Key Concepts

1. Aggregation Functions: Functions like COUNT, SUM, and AVG are used to perform calculations on multiple rows of data to return a single value.
2. Aliasing: Using AS to rename columns in the result set for better readability.
3. Grouping: Utilizing GROUP BY to aggregate data based on a common column value, enabling efficient summarization.

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