How do you solve Product Sales Analysis III on LeetCode?

Product Sales Analysis III (LeetCode #1070) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Using subqueries can help optimize performance by reducing the number of scans needed.

What is the time complexity of Product Sales Analysis III?

The optimal solution for Product Sales Analysis III runs in O(n) time and uses O(n) space. This is O(n) because we only need to scan through the sales table a couple of times, which is efficient.

What is the brute force solution for Product Sales Analysis III?

The brute force approach for Product Sales Analysis III is Brute Force, with O(n²) time complexity and O(1) space complexity. In this approach, we will check each product's sales and find the earliest year it was sold. Then, we will filter the sales for that product in the identified year. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Product Sales Analysis III?

Product Sales Analysis III uses the following concepts: Database. The recommended patterns to study are: Hash Map, Aggregate Functions.

What are the interview tips for Product Sales Analysis III?

Always think about how to reduce the number of iterations over your data. Explain your thought process clearly; interviewers appreciate understanding your reasoning. Practice writing SQL queries to become comfortable with syntax and logic.

What are common mistakes when solving Product Sales Analysis III?

Not using GROUP BY correctly, which can lead to incorrect results. Forgetting to join back to the original table after finding minimum values.

#1070

Product Sales Analysis III

Medium

DatabaseHash MapAggregate Functions

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

We can achieve better performance by using a single query to find the earliest year for each product and then join it back to the sales table to get the required details.

⚙️

Algorithm

2 steps

1Step 1: Use a subquery to find the minimum year for each product.
2Step 2: Join this result back to the Sales table to get the complete sales information for those years.

solution.py6 lines

1SELECT s.product_id, first_year, s.quantity, s.price
2FROM Sales s
3JOIN (SELECT product_id, MIN(year) AS first_year
4      FROM Sales
5      GROUP BY product_id) AS first_sales
6ON s.product_id = first_sales.product_id AND s.year = first_sales.first_year;

ℹ

Complexity note: This is O(n) because we only need to scan through the sales table a couple of times, which is efficient.

1Using subqueries can help optimize performance by reducing the number of scans needed.
2Joining tables effectively can simplify data retrieval and improve clarity.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.