Is Product Sales Analysis II easy or hard?

Product Sales Analysis II is classified as an Easy problem. It focuses on basic SQL aggregation using GROUP BY and SUM, which are core concepts in database querying and analytics.

Product Sales Analysis II Python/Java solution

This problem is designed for SQL rather than general-purpose languages like Python or Java. The solution is typically written as a MySQL query using GROUP BY and SUM to aggregate the quantity column for each product_id.

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

Use a GROUP BY query on the Sales table. Aggregate rows by product_id and apply SUM(quantity) to compute the total sold units. SQL engines typically perform this using hash aggregation, allowing the query to process the dataset in linear time relative to the number of rows.

What is the best approach for Product Sales Analysis II?

The best approach is using SQL aggregation with GROUP BY. Group rows in the Sales table by product_id and compute SUM(quantity) for each group. This scans the table once and efficiently calculates the total quantity sold per product.

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

Database aggregation questions similar to Product Sales Analysis II appear in interviews at companies like Amazon and Meta. They test understanding of SQL fundamentals such as GROUP BY, aggregate functions, and efficient table scanning.

What data structure is used in Product Sales Analysis II?

Internally, most SQL engines use a hash table or sorting mechanism to implement GROUP BY aggregation. The hash table maps each product_id to its running total quantity while scanning the table.

What is the time complexity of Product Sales Analysis II?

The query runs in O(n) time where n is the number of rows in the Sales table. The database scans each record once and groups them by product_id. Space complexity is O(k) where k is the number of distinct products stored during aggregation.

1069. Product Sales Analysis II - Practice Online

Table: Sales

+-------------+-------+
| Column Name | Type  |
+-------------+-------+
| sale_id     | int   |
| product_id  | int   |
| year        | int   |
| quantity    | int   |
| price       | int   |
+-------------+-------+
(sale_id, year) is the primary key (combination of columns with unique values) of this table.
product_id is a foreign key (reference column) to Product table.
Each row of this table shows a sale on the product product_id in a certain year.
Note that the price is per unit.

Table: Product

+--------------+---------+
| Column Name  | Type    |
+--------------+---------+
| product_id   | int     |
| product_name | varchar |
+--------------+---------+
product_id is the primary key (column with unique values) of this table.
Each row of this table indicates the product name of each product.

Write a solution that reports the total quantity sold for every product id.

Return the resulting table in any order.

The result format is in the following example.

Example 1:

Input: 
Sales table:
+---------+------------+------+----------+-------+
| sale_id | product_id | year | quantity | price |
+---------+------------+------+----------+-------+ 
| 1       | 100        | 2008 | 10       | 5000  |
| 2       | 100        | 2009 | 12       | 5000  |
| 7       | 200        | 2011 | 15       | 9000  |
+---------+------------+------+----------+-------+
Product table:
+------------+--------------+
| product_id | product_name |
+------------+--------------+
| 100        | Nokia        |
| 200        | Apple        |
| 300        | Samsung      |
+------------+--------------+
Output: 
+--------------+----------------+
| product_id   | total_quantity |
+--------------+----------------+
| 100          | 22             |
| 200          | 15             |
+--------------+----------------+