Solution Explanation for Dynamic Pivoting of a Table

This problem requires dynamically pivoting a table in SQL, meaning we need to create columns based on the distinct values in a column (in this case, store). A standard pivot operation requires knowing the columns beforehand, but here, the store names are dynamic.

Approach

The solution uses dynamic SQL to construct and execute a query. The process is as follows:

1. Identify Distinct Stores: We first query the Products table to get a list of distinct store names.

2. Construct the SQL Query: We use GROUP_CONCAT (or a similar function depending on the database system) to build a string representing the SQL query. This string dynamically generates the CASE statements needed for pivoting. Each CASE statement checks if the store column matches a particular store name and returns the corresponding price; otherwise, it returns NULL.

3. Prepare and Execute: The constructed SQL string is then prepared and executed using PREPARE and EXECUTE statements. This dynamic execution allows the database to handle the pivot operation based on the dynamically generated columns.

4. Deallocate: Finally, we deallocate the prepared statement to free resources.

Code Explanation (MySQL)

CREATE PROCEDURE PivotProducts()
BEGIN
	# Write your MySQL query statement below.
	SET group_concat_max_len = 5000; -- Increase the limit for GROUP_CONCAT to handle many stores
    SELECT GROUP_CONCAT(DISTINCT 'MAX(CASE WHEN store = \'',
               store,
               '\' THEN price ELSE NULL END) AS ',
               store
               ORDER BY store) 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

Line-by-line breakdown:

• SET group_concat_max_len = 5000;: This line is crucial. GROUP_CONCAT has a default length limit. If you have many stores, this limit might be exceeded, causing truncation. Increasing this limit prevents this issue.
• SELECT GROUP_CONCAT(...) INTO @sql ...: This selects distinct store names and constructs the core of the dynamic SQL query. GROUP_CONCAT joins the generated CASE statements into a single string. The ORDER BY store ensures lexicographical ordering of the columns. The result is stored in the variable @sql.
• SET @sql = CONCAT(...): This line concatenates the SELECT product_id, part, the dynamically generated CASE statements from @sql, and the FROM Products GROUP BY product_id part to form the complete SQL query string.
• PREPARE stmt FROM @sql;: This prepares the dynamic SQL query for execution.
• EXECUTE stmt;: This executes the prepared statement.
• DEALLOCATE PREPARE stmt;: This deallocates the prepared statement, releasing resources.

Time Complexity Analysis

The time complexity is dominated by the execution of the dynamically generated SQL query. The query performs a GROUP BY operation on the Products table, which generally has a time complexity of O(N log N) or O(N), depending on the database engine's implementation (N being the number of rows in the Products table). The construction of the dynamic SQL query itself takes O(M) time, where M is the number of distinct stores. Since M is usually much smaller than N (at most 30 stores are mentioned), the overall time complexity is primarily determined by the GROUP BY operation, making it approximately O(N log N) or O(N).

Space Complexity Analysis

The space complexity depends primarily on the size of the resulting pivoted table. In the worst case, the size of the pivoted table can be proportional to the number of rows in the original Products table (N) multiplied by the number of distinct stores (M). Thus, the space complexity is approximately O(N*M). The additional space used for storing intermediate variables like @sql is relatively small compared to the size of the result table and can be considered negligible in the overall analysis.