Table: Candidates
+-------------+------+
| Column Name | Type |
+-------------+------+
| employee_id | int |
| experience | enum |
| salary | int |
+-------------+------+
employee_id is the column with unique values for this table.
experience is an ENUM (category) of types ('Senior', 'Junior').
Each row of this table indicates the id of a candidate, their monthly salary, and their experience.
The salary of each candidate is guaranteed to be unique.
A company wants to hire new employees. The budget of the company for the salaries is $70000. The company's criteria for hiring are:
Write a solution to find the ids of seniors and juniors hired under the mentioned criteria.
Return the result table in any order.
The result format is in the following example.
Example 1:
Input: Candidates table: +-------------+------------+--------+ | employee_id | experience | salary | +-------------+------------+--------+ | 1 | Junior | 10000 | | 9 | Junior | 15000 | | 2 | Senior | 20000 | | 11 | Senior | 16000 | | 13 | Senior | 50000 | | 4 | Junior | 40000 | +-------------+------------+--------+ Output: +-------------+ | employee_id | +-------------+ | 11 | | 2 | | 1 | | 9 | +-------------+ Explanation: We can hire 2 seniors with IDs (11, 2). Since the budget is $70000 and the sum of their salaries is $36000, we still have $34000 but they are not enough to hire the senior candidate with ID 13. We can hire 2 juniors with IDs (1, 9). Since the remaining budget is $34000 and the sum of their salaries is $25000, we still have $9000 but they are not enough to hire the junior candidate with ID 4.
Example 2:
Input: Candidates table: +-------------+------------+--------+ | employee_id | experience | salary | +-------------+------------+--------+ | 1 | Junior | 25000 | | 9 | Junior | 10000 | | 2 | Senior | 85000 | | 11 | Senior | 80000 | | 13 | Senior | 90000 | | 4 | Junior | 30000 | +-------------+------------+--------+ Output: +-------------+ | employee_id | +-------------+ | 9 | | 1 | | 4 | +-------------+ Explanation: We cannot hire any seniors with the current budget as we need at least $80000 to hire one senior. We can hire all three juniors with the remaining budget.
Problem Overview: A company has a fixed hiring budget and a list of candidates with their experience level (Senior or Junior) and salary. Seniors must be hired first, but you still want to maximize the total number of hires. The task is to compute how many seniors and juniors can be hired without exceeding the total budget.
Approach 1: Salary Ordering with Window Functions (O(n log n) time, O(n) space)
The key observation: within each experience group you should always hire the cheapest candidates first. Sorting by salary ensures the company can hire the maximum possible number of employees under the budget. First filter all senior candidates, order them by salary, and compute a cumulative cost using a SQL window function like SUM(salary) OVER (ORDER BY salary). Keep only those whose cumulative salary does not exceed the budget. This determines how many seniors can be hired.
After hiring seniors, compute the remaining budget by subtracting the total senior salary from the original budget. Then apply the same idea to juniors: order junior candidates by salary and compute their cumulative salary with another window function. Select the rows whose running total stays within the remaining budget. The count of these rows gives the number of juniors hired.
This strategy works because hiring cheaper employees first always maximizes the count under a fixed budget. SQL window functions make the solution clean: they let you compute running totals without procedural loops. The database performs sorting and cumulative aggregation efficiently.
The approach mainly relies on sorting and cumulative aggregation, common techniques in database query optimization. Window functions such as SUM() OVER and conditional filtering are frequently used patterns in advanced SQL interview problems involving ranking, prefix sums, or budget allocation scenarios.
Recommended for interviews: The window-function solution is what most interviewers expect. It clearly shows you understand sorting strategies, greedy selection (cheapest first), and SQL analytical functions. A naive approach that manually aggregates rows or simulates iteration would be harder to maintain and less performant, while the window function approach expresses the logic directly in SQL.
MySQL
| Approach | Time | Space | When to Use |
|---|---|---|---|
| Window Function + Salary Sorting | O(n log n) | O(n) | Best general solution in SQL. Uses running totals to determine how many candidates fit within the budget. |
| Manual Aggregation with Subqueries | O(n log n) | O(n) | Alternative when window functions are unavailable. More verbose and harder to read. |
Leetcode HARD 2010 - Number of Seniors & Juniors to Join 2 : Explained by Everyday Data Science • Everyday Data Science • 697 views views
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