How do you solve Department Highest Salary on LeetCode?

Department Highest Salary (LeetCode #184) 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 a HashMap allows efficient storage and retrieval of maximum salaries.

What algorithm or data structure is used to solve Department Highest Salary?

Department Highest Salary uses the following concepts: Database. The recommended patterns to study are: Hash Map, Grouping, Aggregation.

What are the interview tips for Department Highest Salary?

Always clarify if you need to handle ties in salaries. Discuss your thought process and approach before jumping into coding. Practice writing SQL queries as they are common in technical interviews.

What are common mistakes when solving Department Highest Salary?

Not considering employees with the same highest salary. Overlooking the need to join with the Department table for final output.

Department Highest Salary — LeetCode #184 (Medium)

Tags: Database

Related patterns: Hash Map, Grouping, Aggregation

Brute Force approach

Time complexity: O(n²). Space complexity: O(1).

In the brute force approach, we will check each employee's salary against all other employees in the same department to find the highest salary. This method is straightforward but inefficient.

This complexity arises because for each employee (n), we are potentially comparing their salary with every other employee in the same department (n), leading to n * n comparisons.

Step 1: For each employee, retrieve their department ID.
Step 2: Compare the salary of the current employee with all other employees in the same department.
Step 3: If the current employee's salary is higher than all others in the department, store their information as the highest salary for that department.

1. For employee Joe (salary 70000, department 1), check Jim (90000) and Max (90000) — not the highest. 2. For employee Jim (salary 90000, department 1), no one has a higher salary — store Jim. 3. For employee Henry (salary 80000, department 2), check Sam (60000) — Henry is highest. 4. For employee Sam (salary 60000, department 2), not higher than Henry. 5. Result: Jim (90000, department 1) and Henry (80000, department 2).

Optimal Solution approach

Time complexity: O(n). Space complexity: O(n).

The optimal solution uses a single pass through the Employee table to find the highest salary for each department using a HashMap. This reduces the number of comparisons significantly.

This complexity is linear because we only pass through the Employee table once to build the HashMap, and then we perform a single query to get the results.

Step 1: Initialize a HashMap to store the highest salary and corresponding employee for each department.
Step 2: Iterate through the Employee table, updating the HashMap with the employee details if their salary is higher than the current stored salary for that department.
Step 3: Extract the results from the HashMap to get the final output.

1. Initialize HashMap: {}. 2. Process Joe (70000, department 1) -> HashMap: {1: (Joe, 70000)}. 3. Process Jim (90000, department 1) -> HashMap: {1: (Jim, 90000)}. 4. Process Henry (80000, department 2) -> HashMap: {1: (Jim, 90000), 2: (Henry, 80000)}. 5. Process Sam (60000, department 2) -> No change in HashMap. 6. Final result: Jim (90000, department 1) and Henry (80000, department 2).

Key Insights

Using a HashMap allows efficient storage and retrieval of maximum salaries.
Grouping by department reduces the need for nested loops.

Common Mistakes

Not considering employees with the same highest salary.
Overlooking the need to join with the Department table for final output.

Interview Tips

Always clarify if you need to handle ties in salaries.
Discuss your thought process and approach before jumping into coding.
Practice writing SQL queries as they are common in technical interviews.

#184

Department Highest Salary

Medium

Database↗Hash Map↗Grouping↗Aggregation↗

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)

The optimal solution uses a single pass through the Employee table to find the highest salary for each department using a HashMap. This reduces the number of comparisons significantly.

⚙️

Algorithm

3 steps

1Step 1: Initialize a HashMap to store the highest salary and corresponding employee for each department.
2Step 2: Iterate through the Employee table, updating the HashMap with the employee details if their salary is higher than the current stored salary for that department.
3Step 3: Extract the results from the HashMap to get the final output.

solution.py7 lines

1SELECT departmentId AS Department, name AS Employee, salary AS Salary
2FROM Employee
3WHERE (departmentId, salary) IN (
4    SELECT departmentId, MAX(salary)
5    FROM Employee
6    GROUP BY departmentId
7);

ℹ

Complexity note: This complexity is linear because we only pass through the Employee table once to build the HashMap, and then we perform a single query to get the results.

1Using a HashMap allows efficient storage and retrieval of maximum salaries.
2Grouping by department reduces the need for nested loops.

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