Calculate Running Totals with Window Functions
This challenge focuses on calculating cumulative sums (running totals) over ordered partitions of data. Understanding running totals is crucial for analyzing trends, tracking progress over time, and performing various financial and statistical analyses within databases. You'll use the power of SQL window functions to achieve this efficiently.
Problem Description
You are given a dataset representing sales transactions, including a transaction date, a product category, and the sale amount for that transaction. Your task is to calculate the running total of sales for each product category, ordered by the transaction date. This means that for each row, you need to sum up the sales amount of all transactions within the same category up to and including the current transaction's date.
Key Requirements:
- Calculate a running total of
sale_amount. - The running total should be partitioned by
product_category. - Within each partition, the running total should be ordered by
transaction_date. - The output should include all original columns, plus the new running total column.
Expected Behavior:
For each row, the running_total_sales column should represent the sum of sale_amount for all rows with the same product_category and a transaction_date less than or equal to the current row's transaction_date.
Edge Cases to Consider:
- Multiple transactions on the same date for the same category.
- Categories with only one transaction.
- Empty input table (though we won't explicitly test this, consider its implication).
Examples
Example 1:
Input Table: SalesTransactions
| transaction_date | product_category | sale_amount |
|---|---|---|
| 2023-01-01 | Electronics | 100 |
| 2023-01-02 | Clothing | 50 |
| 2023-01-03 | Electronics | 150 |
| 2023-01-04 | Clothing | 75 |
| 2023-01-05 | Electronics | 200 |
Output Table: SalesTransactionsWithRunningTotal
| transaction_date | product_category | sale_amount | running_total_sales |
|---|---|---|---|
| 2023-01-01 | Electronics | 100 | 100 |
| 2023-01-02 | Clothing | 50 | 50 |
| 2023-01-03 | Electronics | 150 | 250 |
| 2023-01-04 | Clothing | 75 | 125 |
| 2023-01-05 | Electronics | 200 | 450 |
Explanation: For 'Electronics':
- 2023-01-01: 100
- 2023-01-03: 100 + 150 = 250
- 2023-01-05: 250 + 200 = 450
For 'Clothing':
- 2023-01-02: 50
- 2023-01-04: 50 + 75 = 125
Example 2:
Input Table: SalesTransactions
| transaction_date | product_category | sale_amount |
|---|---|---|
| 2023-02-10 | Books | 25 |
| 2023-02-10 | Books | 30 |
| 2023-02-11 | Books | 40 |
| 2023-02-12 | Home Goods | 60 |
Output Table: SalesTransactionsWithRunningTotal
| transaction_date | product_category | sale_amount | running_total_sales |
|---|---|---|---|
| 2023-02-10 | Books | 25 | 25 |
| 2023-02-10 | Books | 30 | 55 |
| 2023-02-11 | Books | 40 | 95 |
| 2023-02-12 | Home Goods | 60 | 60 |
Explanation: For 'Books' on 2023-02-10, the running total accumulates correctly. The second 'Books' transaction on the same date adds to the previous one within the partition.
Constraints
- The
SalesTransactionstable will contain between 1 and 10,000 rows. transaction_datewill be a valid date string in 'YYYY-MM-DD' format.product_categorywill be a non-empty string.sale_amountwill be a non-negative integer.- The solution should be efficient and execute within reasonable time limits for typical database systems.
Notes
Consider using a SQL window function. The SUM() aggregate function can be used as a window function. Pay close attention to the PARTITION BY and ORDER BY clauses within the window function definition.
You are expected to write a single SQL query that returns the desired output.