LeetCode #3482 — HARD

Analyze Organization Hierarchy

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

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The Problem

Problem Statement

Table: Employees

+----------------+---------+
| Column Name    | Type    | 
+----------------+---------+
| employee_id    | int     |
| employee_name  | varchar |
| manager_id     | int     |
| salary         | int     |
| department     | varchar |
+----------------+----------+
employee_id is the unique key for this table.
Each row contains information about an employee, including their ID, name, their manager's ID, salary, and department.
manager_id is null for the top-level manager (CEO).

Write a solution to analyze the organizational hierarchy and answer the following:

  1. Hierarchy Levels: For each employee, determine their level in the organization (CEO is level 1, employees reporting directly to the CEO are level 2, and so on).
  2. Team Size: For each employee who is a manager, count the total number of employees under them (direct and indirect reports).
  3. Salary Budget: For each manager, calculate the total salary budget they control (sum of salaries of all employees under them, including indirect reports, plus their own salary).

Return the result table ordered by the result ordered by level in ascending order, then by budget in descending order, and finally by employee_name in ascending order.

The result format is in the following example.

Example:

Input:

Employees table:

+-------------+---------------+------------+--------+-------------+
| employee_id | employee_name | manager_id | salary | department  |
+-------------+---------------+------------+--------+-------------+
| 1           | Alice         | null       | 12000  | Executive   |
| 2           | Bob           | 1          | 10000  | Sales       |
| 3           | Charlie       | 1          | 10000  | Engineering |
| 4           | David         | 2          | 7500   | Sales       |
| 5           | Eva           | 2          | 7500   | Sales       |
| 6           | Frank         | 3          | 9000   | Engineering |
| 7           | Grace         | 3          | 8500   | Engineering |
| 8           | Hank          | 4          | 6000   | Sales       |
| 9           | Ivy           | 6          | 7000   | Engineering |
| 10          | Judy          | 6          | 7000   | Engineering |
+-------------+---------------+------------+--------+-------------+

Output:

+-------------+---------------+-------+-----------+--------+
| employee_id | employee_name | level | team_size | budget |
+-------------+---------------+-------+-----------+--------+
| 1           | Alice         | 1     | 9         | 84500  |
| 3           | Charlie       | 2     | 4         | 41500  |
| 2           | Bob           | 2     | 3         | 31000  |
| 6           | Frank         | 3     | 2         | 23000  |
| 4           | David         | 3     | 1         | 13500  |
| 7           | Grace         | 3     | 0         | 8500   |
| 5           | Eva           | 3     | 0         | 7500   |
| 9           | Ivy           | 4     | 0         | 7000   |
| 10          | Judy          | 4     | 0         | 7000   |
| 8           | Hank          | 4     | 0         | 6000   |
+-------------+---------------+-------+-----------+--------+

Explanation:

  • Organization Structure:
    • Alice (ID: 1) is the CEO (level 1) with no manager
    • Bob (ID: 2) and Charlie (ID: 3) report directly to Alice (level 2)
    • David (ID: 4), Eva (ID: 5) report to Bob, while Frank (ID: 6) and Grace (ID: 7) report to Charlie (level 3)
    • Hank (ID: 8) reports to David, and Ivy (ID: 9) and Judy (ID: 10) report to Frank (level 4)
  • Level Calculation:
    • The CEO (Alice) is at level 1
    • Each subsequent level of management adds 1 to the level
  • Team Size Calculation:
    • Alice has 9 employees under her (the entire company except herself)
    • Bob has 3 employees (David, Eva, and Hank)
    • Charlie has 4 employees (Frank, Grace, Ivy, and Judy)
    • David has 1 employee (Hank)
    • Frank has 2 employees (Ivy and Judy)
    • Eva, Grace, Hank, Ivy, and Judy have no direct reports (team_size = 0)
  • Budget Calculation:
    • Alice's budget: Her salary (12000) + all employees' salaries (72500) = 84500
    • Charlie's budget: His salary (10000) + Frank's budget (23000) + Grace's salary (8500) = 41500
    • Bob's budget: His salary (10000) + David's budget (13500) + Eva's salary (7500) = 31000
    • Frank's budget: His salary (9000) + Ivy's salary (7000) + Judy's salary (7000) = 23000
    • David's budget: His salary (7500) + Hank's salary (6000) = 13500
    • Employees with no direct reports have budgets equal to their own salary

Note:

  • The result is ordered first by level in ascending order
  • Within the same level, employees are ordered by budget in descending order then by name in ascending order

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: Table: Employees +----------------+---------+ | Column Name | Type | +----------------+---------+ | employee_id | int | | employee_name | varchar | | manager_id | int | | salary | int | | department | varchar | +----------------+----------+ employee_id is the unique key for this table. Each row contains information about an employee, including their ID, name, their manager's ID, salary, and department. manager_id is null for the top-level manager (CEO). Write a solution to analyze the organizational hierarchy and answer the following: Hierarchy Levels: For each employee, determine their level in the organization (CEO is level 1, employees reporting directly to the CEO are level 2, and so on). Team Size: For each employee who is a manager, count the total number of employees under them (direct and indirect reports). Salary Budget: For each manager, calculate the total salary budget they

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: General problem-solving

Example 1

{"headers":{"Employees":["employee_id","employee_name","manager_id","salary","department"]},"rows":{"Employees":[[1,"Alice",null,12000,"Executive"],[2,"Bob",1,10000,"Sales"],[3,"Charlie",1,10000,"Engineering"],[4,"David",2,7500,"Sales"],[5,"Eva",2,7500,"Sales"],[6,"Frank",3,9000,"Engineering"],[7,"Grace",3,8500,"Engineering"],[8,"Hank",4,6000,"Sales"],[9,"Ivy",6,7000,"Engineering"],[10,"Judy",6,7000,"Engineering"]]}}
Step 02

Core Insight

What unlocks the optimal approach

  • No official hints in dataset. Start from constraints and look for a monotonic or reusable state.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Largest constraint values
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #3482: Analyze Organization Hierarchy
// Auto-generated Java example from py.
class Solution {
    public void exampleSolution() {
    }
}
// Reference (py):
// # Accepted solution for LeetCode #3482: Analyze Organization Hierarchy
// import pandas as pd
// 
// 
// def analyze_organization_hierarchy(employees: pd.DataFrame) -> pd.DataFrame:
//     # Copy the input DataFrame to avoid modifying the original
//     employees = employees.copy()
//     employees["level"] = None
// 
//     # Identify the CEO (level 1)
//     ceo_id = employees.loc[employees["manager_id"].isna(), "employee_id"].values[0]
//     employees.loc[employees["employee_id"] == ceo_id, "level"] = 1
// 
//     # Recursively compute employee levels
//     def compute_levels(emp_df, level):
//         next_level_ids = emp_df[emp_df["level"] == level]["employee_id"].tolist()
//         if not next_level_ids:
//             return
//         emp_df.loc[emp_df["manager_id"].isin(next_level_ids), "level"] = level + 1
//         compute_levels(emp_df, level + 1)
// 
//     compute_levels(employees, 1)
// 
//     # Initialize team size and budget dictionaries
//     team_size = {eid: 0 for eid in employees["employee_id"]}
//     budget = {
//         eid: salary
//         for eid, salary in zip(employees["employee_id"], employees["salary"])
//     }
// 
//     # Compute team size and budget for each employee
//     for eid in sorted(employees["employee_id"], reverse=True):
//         manager_id = employees.loc[
//             employees["employee_id"] == eid, "manager_id"
//         ].values[0]
//         if pd.notna(manager_id):
//             team_size[manager_id] += team_size[eid] + 1
//             budget[manager_id] += budget[eid]
// 
//     # Map computed team size and budget to employees DataFrame
//     employees["team_size"] = employees["employee_id"].map(team_size)
//     employees["budget"] = employees["employee_id"].map(budget)
// 
//     # Sort the final result by level (ascending), budget (descending), and employee name (ascending)
//     employees = employees.sort_values(
//         by=["level", "budget", "employee_name"], ascending=[True, False, True]
//     )
// 
//     return employees[["employee_id", "employee_name", "level", "team_size", "budget"]]
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n)
Space
O(1)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED
O(n) time
O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.