How do you solve Department Top Three Salaries on LeetCode?

Department Top Three Salaries (LeetCode #185) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(n) space complexity. Key insight: Using DENSE_RANK() allows us to handle ties in salaries effectively.

What algorithm or data structure is used to solve Department Top Three Salaries?

Department Top Three Salaries uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Department Top Three Salaries?

Understand the difference between DENSE_RANK() and ROW_NUMBER(). Practice writing SQL queries that involve window functions. Be prepared to explain your thought process and the reasoning behind your approach.

What are common mistakes when solving Department Top Three Salaries?

Not considering ties in salaries when ranking. Failing to use DISTINCT when necessary to avoid duplicate salaries.

Department Top Three Salaries — LeetCode #185 (Hard)

Tags: Database

Related patterns: Hash Map, Array

Brute Force approach

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

The brute-force approach involves checking each employee's salary against all other salaries in the same department to find the top three unique salaries. This method is straightforward but inefficient for larger datasets.

The time complexity is O(n²) because for each employee, we are potentially iterating through all employees in the same department to find the top salaries. The space complexity is O(1) since we are not using any additional data structures that scale with input size.

Step 1: For each employee, retrieve their department's employees and their salaries.
Step 2: Sort the salaries in descending order and select the top three unique salaries.
Step 3: Return the employees whose salaries match the top three unique salaries.

1. Employee: Joe (85000, Dept 1) -> Salaries in Dept 1: [85000, 90000, 85000] -> Top 3: [90000, 85000]. 2. Employee: Henry (80000, Dept 2) -> Salaries in Dept 2: [80000, 60000] -> Top 3: [80000]. 3. Employee: Sam (60000, Dept 2) -> Salaries in Dept 2: [80000, 60000] -> Top 3: [80000]. 4. Employee: Max (90000, Dept 1) -> Salaries in Dept 1: [85000, 90000, 85000] -> Top 3: [90000, 85000]. 5. Employee: Janet (69000, Dept 1) -> Salaries in Dept 1: [85000, 90000, 85000] -> Top 3: [90000, 85000]. 6. Employee: Randy (85000, Dept 1) -> Salaries in Dept 1: [85000, 90000, 85000] -> Top 3: [90000, 85000].

Optimal Solution approach

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

The optimal solution uses a single pass through the Employee table to group salaries by department and then selects the top three unique salaries for each department. This reduces the number of operations significantly compared to the brute-force approach.

The time complexity is O(n log n) due to the sorting operation required to rank the salaries. The space complexity is O(n) because we store the results of the ranking in a temporary table.

Step 1: Use a subquery to group salaries by department and find the unique salaries.
Step 2: Use the ROW_NUMBER() function to rank salaries within each department.
Step 3: Select employees with ranks 1 to 3 for each department.

1. Employee: Joe (85000, Dept 1) -> Rank 2 in Dept 1. 2. Employee: Max (90000, Dept 1) -> Rank 1 in Dept 1. 3. Employee: Randy (85000, Dept 1) -> Rank 2 in Dept 1. 4. Employee: Henry (80000, Dept 2) -> Rank 1 in Dept 2. 5. Employee: Sam (60000, Dept 2) -> Rank 2 in Dept 2. 6. Employee: Janet (69000, Dept 1) -> Rank 3 in Dept 1.

Key Insights

Using DENSE_RANK() allows us to handle ties in salaries effectively.
Partitioning by department helps in isolating the salary rankings.

Common Mistakes

Not considering ties in salaries when ranking.
Failing to use DISTINCT when necessary to avoid duplicate salaries.

Interview Tips

Understand the difference between DENSE_RANK() and ROW_NUMBER().
Practice writing SQL queries that involve window functions.
Be prepared to explain your thought process and the reasoning behind your approach.

#185

Department Top Three Salaries

Hard

Database↗Hash Map↗Array↗

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n log n)Space O(n)

⚙️

Algorithm

3 steps

1Step 1: Use a subquery to group salaries by department and find the unique salaries.
2Step 2: Use the ROW_NUMBER() function to rank salaries within each department.
3Step 3: Select employees with ranks 1 to 3 for each department.

solution.py9 lines

1# Full working Python code
2WITH RankedSalaries AS (
3    SELECT name, salary, departmentId,
4           DENSE_RANK() OVER (PARTITION BY departmentId ORDER BY salary DESC) AS rank
5    FROM Employee
6)
7SELECT name, salary, departmentId
8FROM RankedSalaries
9WHERE rank <= 3;

ℹ

Complexity note: The time complexity is O(n log n) due to the sorting operation required to rank the salaries. The space complexity is O(n) because we store the results of the ranking in a temporary table.

1Using DENSE_RANK() allows us to handle ties in salaries effectively.
2Partitioning by department helps in isolating the salary rankings.

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