Solution Explanation for Dynamic Unpivoting of a Table

This problem requires dynamically unpivoting a table, meaning we need to transform the data from a wide format (multiple columns representing different stores) to a long format (one column for store names and another for prices). The challenge lies in the fact that the number of stores (and thus the number of columns) is not fixed and can vary.

Approach

The solution uses a dynamic SQL approach to generate and execute a SQL query that effectively unpivots the table. Here's a breakdown:

1. Information Schema: We query the information_schema.columns to get the names of all columns in the Products table, excluding the product_id column. This gives us the list of store names.

2. Dynamic SQL Construction: A crucial step is using GROUP_CONCAT to build a single SQL query string. For each store column obtained in step 1, we construct a SELECT statement that selects product_id, the store name (as a string literal), and the price from that column. A WHERE clause filters out rows where the price is NULL (meaning the product isn't available in that store). These individual SELECT statements are concatenated using UNION to create one large query that combines results from all stores.

3. Prepared Statement: The dynamically generated SQL query is stored in a variable (@sql). A prepared statement is then used to execute this query safely and efficiently. This prevents SQL injection vulnerabilities.

4. Execution and Deallocation: The prepared statement is executed (EXECUTE stmt), and finally deallocated (DEALLOCATE PREPARE stmt) to release resources.

MySQL Code Explanation

CREATE PROCEDURE UnpivotProducts()
BEGIN
    # Write your MySQL query statement below.
    SET group_concat_max_len = 5000; -- Increase the limit for GROUP_CONCAT to handle many columns.
    WITH
        t AS (
            SELECT column_name
            FROM information_schema.columns
            WHERE
                table_schema = DATABASE()
                AND table_name = 'Products'
                AND column_name != 'product_id'
        )
    SELECT
        GROUP_CONCAT(
            'SELECT product_id, \'', -- Selecting product_id
            column_name,             -- Store name (as string)
            '\' store, ',            
            column_name,             -- Price from the column
            ' price FROM Products WHERE ',
            column_name,             -- Filtering out NULL prices
            ' IS NOT NULL' SEPARATOR ' UNION ' -- Combining using UNION
        ) INTO @sql from t;          -- Store the complete query in @sql
    PREPARE stmt FROM @sql;         -- Prepare the statement for execution
    EXECUTE stmt;                   -- Execute the statement
    DEALLOCATE PREPARE stmt;        -- Deallocate the prepared statement
END;

Time Complexity Analysis

The time complexity is dominated by the execution of the dynamically generated SQL query. The query itself performs a series of SELECT operations (one for each store column) and UNION operations. In the worst case, with 'n' stores, the time complexity would be O(n*m), where 'm' is the average number of rows in the Products table. The initial steps of querying the information_schema and constructing the dynamic query string have a much lower time complexity (O(n) for querying column names and O(n) for string concatenation).

Therefore, the overall time complexity is approximately O(n*m). The space complexity is also influenced by the size of the Products table and the generated SQL query string, scaling with O(n*m) in the worst case. The group_concat_max_len setting is crucial as it limits the size of the dynamically generated query, preventing potential out-of-memory errors.

Note: The actual performance can be affected by database optimizations and indexing. The use of a prepared statement helps improve efficiency over executing multiple separate queries.