Duplicating Rows Based on Multiple Conditions

In this article, we’ll explore the process of duplicating rows in a dataset based on multiple conditions using recursive Common Table Expressions (CTEs) and some clever SQL tricks. We’ll also delve into the concepts behind CTEs, conditional logic, and data manipulation.

Introduction to Recursive CTEs

A Recursive Common Table Expression is a query technique used to solve problems that involve hierarchical or tree-like structures. It allows us to define a set of rules and conditions that are applied recursively to a table, resulting in a self-referential query.

In the context of our problem, we want to split each row into multiple rows based on certain conditions. A Recursive CTE is perfect for this task, as it enables us to create a virtual hierarchical structure from our data.

Understanding the Problem

Let’s break down the requirements:

• We have a dataset with six columns: item, batch_stock, expiry_date, avg_weekly_sales, and an additional calculated column weeks_of_stock.
• For each item, we need to split its row into multiple rows based on two conditions:
  • The expiry_date (i.e., the date when the item is no longer valid).
  • The weeks_of_stock value (i.e., how many weeks of stock are available).

Designing the Solution

To tackle this problem, we’ll employ a few strategies:

1. Splitting rows into individual records: We’ll use a combination of the EXCEPT operator and conditional logic to create separate records for each week.
2. Calculating weekly stock: We’ll use an additional calculated column to determine the number of weeks of stock available for each item.

Step 1: Splitting Rows into Individual Records

To split each row into multiple rows, we can use the EXCEPT operator in combination with a subquery that identifies the first row for each item. This approach ensures that each week is represented as a separate record.

WITH ItemStock AS (
    SELECT 
        item,
        batch_stock,
        expiry_date,
        avg_weekly_sales,
        weeks_of_stock = floor(batch_stock / avg_weekly_sales)
    FROM @data
)
SELECT 
    i.item,
    i.batch_stock - i.avg_weekly_sales * week_num AS new_batch_stock,
    i.expiry_date + INTERVAL i.weeks_of_stock DAY AS expiry_new_date
FROM (
    SELECT 
        item,
        batch_stock,
        expiry_date,
        avg_weekly_sales,
        weeks_of_stock = floor(batch_stock / avg_weekly_sales),
        ROW_NUMBER() OVER (PARTITION BY item ORDER BY expiry_date) AS row_num
    FROM ItemStock
) i
JOIN ItemStock sub ON i.item = sub.item AND sub.row_num + 1 = i.row_num
CROSS JOIN (
    SELECT 
        weeks_of_stock,
        week_num = FLOOR(weeks_of_stock)
    FROM @data
) ws
WHERE i.expiry_date + INTERVAL i.weeks_of_stock DAY < (SELECT MIN(expiry_date) FROM ItemStock WHERE item = i.item);

This query first calculates the weeks_of_stock value for each item. It then uses a subquery to identify the first row for each item and assigns a unique row_num. The main query joins this result with another CTE that generates weekly stock records using a subquery.

Step 2: Calculating Weekly Stock

To calculate the number of weeks of stock available for each item, we can use a simple formula:

weeks_of_stock = floor(batch_stock / avg_weekly_sales)

This approach assumes that the avg_weekly_sales value represents the average number of units sold per week.

WITH ItemStock AS (
    SELECT 
        item,
        batch_stock,
        expiry_date,
        avg_weekly_sales,
        weeks_of_stock = floor(batch_stock / avg_weekly_sales)
    FROM @data
)
SELECT 
    i.item,
    i.batch_stock - i.avg_weekly_sales * week_num AS new_batch_stock,
    i.expiry_date + INTERVAL i.weeks_of_stock DAY AS expiry_new_date,
    ws.weeks_of_stock AS stock_available
FROM (
    SELECT 
        item,
        batch_stock,
        expiry_date,
        avg_weekly_sales,
        weeks_of_stock = floor(batch_stock / avg_weekly_sales),
        ROW_NUMBER() OVER (PARTITION BY item ORDER BY expiry_date) AS row_num
    FROM ItemStock
) i
JOIN ItemStock sub ON i.item = sub.item AND sub.row_num + 1 = i.row_num
CROSS JOIN (
    SELECT 
        weeks_of_stock,
        week_num = FLOOR(weeks_of_stock)
    FROM @data
) ws
WHERE i.expiry_date + INTERVAL i.weeks_of_stock DAY < (SELECT MIN(expiry_date) FROM ItemStock WHERE item = i.item);

This query adds a new column stock_available to the final result, which represents the number of weeks of stock available for each item.

Conclusion

By employing recursive CTEs and clever SQL techniques, we’ve successfully duplicated rows in our dataset based on multiple conditions. This approach not only provides a clean and efficient solution but also offers flexibility for future data manipulation and analysis.

Remember to experiment with different queries and approaches to improve performance and adaptability, as the optimal strategy may vary depending on your specific use case.

Last modified on 2025-02-18