Product Sales Analysis II - Solution & Explanation

Problem Overview: You are given a Sales table where each row represents a sale of a product in a particular year with its quantity and price. The task is to compute the total quantity sold for each product_id. The output should list every product along with the sum of quantities across all its sales records.

Approach 1: SQL Aggregation with GROUP BY (O(n) time, O(k) space)

The most direct way to solve this problem is using SQL aggregation. Scan the Sales table and group rows by product_id. For each group, apply the SUM() aggregate function on the quantity column to compute the total number of units sold. Databases typically implement GROUP BY using a hash aggregation or sorting strategy internally. The query processes each row once, giving O(n) time complexity where n is the number of sales records. The extra memory required is proportional to the number of distinct products (k), resulting in O(k) space.

This approach relies on fundamental SQL operations and is the expected solution for most database interviews. The key idea is recognizing that the problem is purely an aggregation task rather than a join or filtering problem. Using GROUP BY product_id automatically groups all related rows so the database can compute the total quantity for each product efficiently.

Approach 2: Aggregation with Join (O(n) time, O(k) space)

In some variations of the product sales analysis problems, you may need additional attributes such as the product name from the Product table. In that case, you join Sales with Product on product_id and then perform the same aggregation. After the join, apply GROUP BY product_id and calculate SUM(quantity). The database optimizer usually pushes aggregation efficiently after the join, keeping the overall complexity close to O(n).

This variation is useful when the output requires descriptive product data instead of just IDs. The core logic still centers on SQL aggregation and grouping operations.

Both approaches rely on concepts from database querying, SQL, and aggregation. Mastering GROUP BY patterns is critical because many real-world analytics queries follow the same structure: scan records, group them by a key, and compute metrics like SUM, COUNT, or AVG.

Recommended for interviews: The simple GROUP BY aggregation is the expected solution. Interviewers want to see that you immediately recognize this as a grouping problem and apply SUM(quantity) efficiently. More complex joins are unnecessary unless the problem explicitly requires data from another table.