Is Product Sales Analysis V easy or hard?

Product Sales Analysis V is classified as Easy. The query mainly requires understanding table joins and GROUP BY aggregation, which are fundamental SQL concepts used in analytics and backend data processing.

Product Sales Analysis V Python/Java solution

This problem is categorized under Database on LeetCode, so the expected solution is written in SQL (commonly MySQL). Python or Java are not typically required because the query directly operates on relational tables.

How to solve Product Sales Analysis V in O(n)?

Join the Product and Sales tables using product_id, then aggregate the sales records using GROUP BY. Functions like SUM() calculate totals for each product group. Because the database processes each row once during aggregation, the query runs in linear time relative to the number of sales rows.

What is the best approach for Product Sales Analysis V?

The best approach uses an SQL JOIN between the Sales and Product tables followed by GROUP BY aggregation. This allows you to compute metrics such as total quantity or total sales per product efficiently. The database processes the rows in a single pass, resulting in O(n) time complexity.

Is Product Sales Analysis V asked at Google/Amazon/Meta?

SQL aggregation and relational joins appear frequently in data engineering and analytics interviews at companies like Amazon, Google, and Meta. While this exact problem may not always appear, the same pattern of joining tables and computing grouped metrics is very common.

What data structure is used in Product Sales Analysis V?

The problem relies on relational database tables and SQL aggregation. Internally, the database engine often uses hash-based grouping or sorting to implement the GROUP BY operation efficiently.

What is the time complexity of Product Sales Analysis V?

The typical SQL solution runs in O(n) time where n is the number of rows in the Sales table. The database scans the table once and performs grouping using internal aggregation algorithms. Space complexity is effectively O(1) outside the database engine.

2329. Product Sales Analysis V - Practice Online

Table: Sales

+-------------+-------+
| Column Name | Type  |
+-------------+-------+
| sale_id     | int   |
| product_id  | int   |
| user_id     | int   |
| quantity    | int   |
+-------------+-------+
sale_id contains unique values.
product_id is a foreign key (column with unique values) to Product table.
Each row of this table shows the ID of the product and the quantity purchased by a user.

Table: Product

+-------------+------+
| Column Name | Type |
+-------------+------+
| product_id  | int  |
| price       | int  |
+-------------+------+
product_id contains unique values.
Each row of this table indicates the price of each product.

Write a solution to report the spending of each user.

Return the resulting table ordered by spending in descending order. In case of a tie, order them by user_id in ascending order.

The result format is in the following example.

Example 1:

Input: 
Sales table:
+---------+------------+---------+----------+
| sale_id | product_id | user_id | quantity |
+---------+------------+---------+----------+
| 1       | 1          | 101     | 10       |
| 2       | 2          | 101     | 1        |
| 3       | 3          | 102     | 3        |
| 4       | 3          | 102     | 2        |
| 5       | 2          | 103     | 3        |
+---------+------------+---------+----------+
Product table:
+------------+-------+
| product_id | price |
+------------+-------+
| 1          | 10    |
| 2          | 25    |
| 3          | 15    |
+------------+-------+
Output: 
+---------+----------+
| user_id | spending |
+---------+----------+
| 101     | 125      |
| 102     | 75       |
| 103     | 75       |
+---------+----------+
Explanation: 
User 101 spent 10 * 10 + 1 * 25 = 125.
User 102 spent 3 * 15 + 2 * 15 = 75.
User 103 spent 3 * 25 = 75.
Users 102 and 103 spent the same amount and we break the tie by their ID while user 101 is on the top.