Solution

To transform the Products table into a pivoted format where each row represents a unique product_id with its corresponding prices across various stores, we need to dynamically generate SQL statements. This is essential because the number of stores can vary, and SQL doesn't inherently support dynamic column generation in static queries. The provided stored procedure PivotProducts accomplishes this using dynamic SQL, window functions, and conditional aggregation. Below is a comprehensive breakdown of the approach, step-by-step execution, and detailed explanations for each component of the SQL query.

Approach Overview

1. Understand the Data Structure:

   • Each row in the Products table represents the price of a specific product in a particular store.
   • The combination of (product_id, store) is unique, ensuring that each product-store pair appears only once.

2. Identify Unique Stores:

   • Since the number of stores can vary (up to 30 as per the problem statement), we need a dynamic way to handle each store as a separate column in the pivoted result.

3. Construct Dynamic SQL for Pivoting:

   • Use GROUP_CONCAT to dynamically generate the SQL segments required for each store.
   • Aggregate the prices using conditional logic to place them under their respective store columns.

4. Execute the Dynamic SQL:

   • Prepare, execute, and deallocate the dynamically constructed SQL statement to produce the final pivoted table.

SQL Query

CREATE PROCEDURE PivotProducts()
BEGIN
	# Write your MySQL query statement below.
    SET group_concat_max_len = 1000000; #This is tricky. There's a length limit on GROUP_CONCAT.
    SET @sql = NULL;
    SELECT
    GROUP_CONCAT(DISTINCT CONCAT(
      'SUM(IF(store = "', store, '", price, null)) AS ', store) ORDER BY store ASC)
    INTO @sql
    FROM Products;

    SET @sql = CONCAT('SELECT product_id, ', @sql, ' FROM Products GROUP BY product_id');
    

    PREPARE stmt FROM @sql;
    EXECUTE stmt;
    DEALLOCATE PREPARE stmt;
END

Step-by-Step Explanation

Step 1: Increase GROUP_CONCAT Maximum Length

SET group_concat_max_len = 1000000; #This is tricky. There's a length limit on GROUP_CONCAT.

• The GROUP_CONCAT function in MySQL has a default maximum length for the concatenated string it can produce. By setting group_concat_max_len to a large value (1,000,000 in this case), we ensure that the dynamic SQL string generated will not be truncated, especially when dealing with numerous stores.

Step 2: Initialize the Dynamic SQL Variable

SET @sql = NULL;

• Initializes the user-defined variable @sql to NULL. This variable will later store the dynamically constructed SQL statement.

Step 3: Dynamically Generate the SELECT Clause for Each Store

SELECT
GROUP_CONCAT(DISTINCT CONCAT(
  'SUM(IF(store = "', store, '", price, null)) AS ', store) ORDER BY store ASC)
INTO @sql
FROM Products;

• Purpose:

  • GROUP_CONCAT: Aggregates the generated SQL segments for each store into a single comma-separated string.
  • DISTINCT: Ensures that each store is processed only once, eliminating duplicates.
  • CONCAT: Constructs the SQL expression for each store.

• Breakdown of the CONCAT Function:

  • 'SUM(IF(store = "', store, '", price, null)) AS ', store:
    • IF(store = "StoreName", price, null): Checks if the current row's store matches the store being processed. If it does, it returns the price; otherwise, it returns null.
    • SUM(IF(...)): Aggregates the prices for each store. Since each (product_id, store) pair is unique, the SUM effectively retrieves the price if the store matches, or null otherwise.
    • AS StoreName: Aliases the resulting sum as the store's name, creating a column with the store's name in the final result.

• ORDER BY store ASC:
  Ensures that the store columns are ordered lexicographically (alphabetically) as required.

• Result of GROUP_CONCAT:
  Generates a string like:

  SUM(IF(store = "LC_Store", price, null)) AS LC_Store,
  SUM(IF(store = "Nozama", price, null)) AS Nozama,
  SUM(IF(store = "Shop", price, null)) AS Shop,
  SUM(IF(store = "Souq", price, null)) AS Souq
  

• INTO @sql:
  Stores the concatenated string into the @sql variable for later use.

Step 4: Construct the Final Dynamic SQL Statement

SET @sql = CONCAT('SELECT product_id, ', @sql, ' FROM Products GROUP BY product_id');

• Purpose:

  • CONCAT: Combines static and dynamic parts of the SQL query.
  • 'SELECT product_id, ': Begins the SELECT statement by selecting product_id.
  • @sql: Inserts the dynamically generated SUM statements for each store.
  • ' FROM Products GROUP BY product_id': Specifies the data source and groups the results by product_id.

• Resulting Dynamic SQL Statement (@sql):

  SELECT product_id, 
    SUM(IF(store = "LC_Store", price, null)) AS LC_Store,
    SUM(IF(store = "Nozama", price, null)) AS Nozama,
    SUM(IF(store = "Shop", price, null)) AS Shop,
    SUM(IF(store = "Souq", price, null)) AS Souq
  FROM Products
  GROUP BY product_id
  

• This dynamic SQL statement effectively pivots the Products table, turning unique store names into individual columns with their corresponding prices. If a product isn't sold in a particular store, the SUM(IF(...)) expression will return null for that store's column.

Step 5: Prepare and Execute the Dynamic SQL Statement

PREPARE stmt FROM @sql;
EXECUTE stmt;
DEALLOCATE PREPARE stmt;

• PREPARE stmt FROM @sql;

  • Prepares the dynamically constructed SQL statement stored in @sql for execution.

• EXECUTE stmt;

  • Executes the prepared statement stmt.

• DEALLOCATE PREPARE stmt;

  • Frees the resources associated with the prepared statement stmt.

Final Pivoted Output

Executing the stored procedure PivotProducts() will produce a result set similar to the following, depending on the data in the Products table:

+------------+-----------+-------+-------+-------+
| product_id | LC_Store  | Nozama| Shop  | Souq  |
+------------+-----------+-------+-------+-------+
| 1          | 100       | NULL  | 110   | NULL  |
| 2          | NULL      | 200   | NULL  | 190   |
| 3          | NULL      | NULL  | 1000  | 1900  |
+------------+-----------+-------+-------+-------+

• Interpretation:

  • product_id = 1:
    • LC_Store: $100
    • Shop: $110
    • Nozama & Souq: Not available (NULL)
  • product_id = 2:
    • Nozama: $200
    • Souq: $190
    • LC_Store & Shop: Not available (NULL)
  • product_id = 3:
    • Shop: $1000
    • Souq: $1900
    • LC_Store & Nozama: Not available (NULL)
  • product_id = 4:
    • Shop: $200
    • Souq: $300
    • LC_Store & Nozama: Not available (NULL)

• Note:
  If a product is not sold in a particular store, the corresponding column will display NULL as required.