Employees Project Allocation - Solution & Explanation

HardPremiumFree on FleetCodeDatabase6 min read

Problem Statement

Table: Project

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| project_id  | int     |
| employee_id | int     |
| workload    | int     |
+-------------+---------+
employee_id is the primary key (column with unique values) of this table.
employee_id is a foreign key (reference column) to Employee table.
Each row of this table indicates that the employee with employee_id is working on the project with project_id and the workload of the project.

Table: Employees

+------------------+---------+
| Column Name      | Type    |
+------------------+---------+
| employee_id      | int     |
| name             | varchar |
| team             | varchar |
+------------------+---------+
employee_id is the primary key (column with unique values) of this table.
Each row of this table contains information about one employee.

Write a solution to find the employees who are allocated to projects with a workload that exceeds the average workload of all employees for their respective teams

Return the result table ordered by employee_id, project_id in ascending order.

The result format is in the following example.

Example 1:

Input: 
Project table:
+-------------+-------------+----------+
| project_id  | employee_id | workload |
+-------------+-------------+----------+
| 1           | 1           |  45      |
| 1           | 2           |  90      | 
| 2           | 3           |  12      |
| 2           | 4           |  68      |
+-------------+-------------+----------+
Employees table:
+-------------+--------+------+
| employee_id | name   | team |
+-------------+--------+------+
| 1           | Khaled | A    |
| 2           | Ali    | B    |
| 3           | John   | B    |
| 4           | Doe    | A    |
+-------------+--------+------+
Output: 
+-------------+------------+---------------+------------------+
| employee_id | project_id | employee_name | project_workload |
+-------------+------------+---------------+------------------+  
| 2           | 1          | Ali           | 90               | 
| 4           | 2          | Doe           | 68               | 
+-------------+------------+---------------+------------------+
Explanation: 
- Employee with ID 1 has a project workload of 45 and belongs to Team A, where the average workload is 56.50. Since his project workload does not exceed the team's average workload, he will be excluded.
- Employee with ID 2 has a project workload of 90 and belongs to Team B, where the average workload is 51.00. Since his project workload does exceed the team's average workload, he will be included.
- Employee with ID 3 has a project workload of 12 and belongs to Team B, where the average workload is 51.00. Since his project workload does not exceed the team's average workload, he will be excluded.
- Employee with ID 4 has a project workload of 68 and belongs to Team A, where the average workload is 56.50. Since his project workload does exceed the team's average workload, he will be included.
Result table orderd by employee_id, project_id in ascending order.

Approach Overview

Problem Overview: You are given employee and project records and must determine the correct project allocations based on aggregated constraints. The task typically requires computing statistics (counts or aggregates) for groups of records and then matching those results back to the base tables to identify valid employee–project assignments.

Approach 1: Grouping Statistics + Equi-Join (O(n) time, O(g) space)

The core idea is to compute per-group statistics using GROUP BY, then join the aggregated result back to the original table using an equi-join on the grouping key. First, aggregate the dataset to compute metrics such as the number of employees per project, allocation counts per employee, or other constraints defined in the problem. This aggregation produces a compact intermediate result with one row per group. Next, perform an equi-join between this aggregated table and the base employee/project table so you can filter rows that satisfy the required allocation condition. In SQL, this usually appears as a subquery or CTE joined on keys like project_id or employee_id. Time complexity is O(n) for scanning and grouping the dataset, and space complexity is O(g), where g is the number of groups.

This pattern shows up frequently in database interview problems. The aggregation step extracts global information (like counts or maximum values), while the join step maps those results back to individual rows.

Approach 2: Window Functions (O(n) time, O(1) extra space)

Another option uses window functions such as COUNT() OVER(PARTITION BY ...) or SUM() OVER(PARTITION BY ...). Instead of building a separate aggregated table, the database calculates the group statistic for each row dynamically. You then filter rows using that computed value. This removes the need for an explicit join while preserving the same logical flow. Window functions are often easier to read and can perform well on modern query engines. The overall complexity remains O(n) because the database still scans the dataset once and computes partitioned aggregates.

Window functions are common in advanced SQL interview questions and are useful when you want group-level statistics while still keeping row-level detail.

Recommended for interviews: The grouping + equi-join solution is the most widely accepted approach because it clearly separates aggregation from filtering. Interviewers expect you to recognize the pattern: compute statistics with GROUP BY, then join the result back to the original dataset. Demonstrating both the aggregation logic and the join shows strong understanding of relational operations and data aggregation patterns.

Solution

First, we join the Project table and the Employees table based on employee_id, then group by team to calculate the average workload of each team, and record it in the temporary table T.

Then, we join the Project table and the Employees table again, and also join the T table, to find employees whose workload is greater than the average workload of the team. Finally, we sort by employee_id and project_id.

Code

MySQL Python

MySQL

Python

Try this approach in the editor →

Detailed Complexity Analysis

Approach	Time	Space	When to Use
Grouping Statistics + Equi-Join	O(n)	O(g)	Standard SQL solution when you need aggregated metrics and must map them back to original rows
Window Functions	O(n)	O(1)	When the database supports analytic functions and you want a cleaner query without subqueries

Video Solution

Leetcode HARD 3057 - Employees Project Allocation | Data Science Roadmap • Everyday Data Science • 1,109 views views

Frequently Asked Questions

Employees Project Allocation Python solution

In Python, the same logic can be implemented using pandas. Group the dataset with groupby() to compute the required statistics, then merge the aggregated result back with the original DataFrame using a key like project_id or employee_id. The workflow mirrors the SQL grouping and equi-join approach.

Is Employees Project Allocation easy or hard?

Employees Project Allocation is categorized as a Hard database problem because it requires understanding aggregation logic, relational joins, and filtering based on group-level statistics. While the SQL query itself can be concise, identifying the correct grouping strategy is the main challenge.

How to solve Employees Project Allocation in O(n)?

Use a GROUP BY query to compute the required statistics for each project or employee. Store that aggregated result in a subquery or CTE. Then perform an equi-join between the aggregated result and the base table to filter valid employee–project allocations. This avoids nested queries and keeps the runtime linear with respect to the dataset size.

What is the best approach for Employees Project Allocation?

The most reliable solution uses grouping statistics with an equi-join. First compute aggregated metrics such as counts or totals using GROUP BY. Then join the aggregated result back to the original employee or project table and filter rows that satisfy the allocation rule. This approach runs in O(n) time and is widely expected in SQL interviews.

Is Employees Project Allocation asked at Google/Amazon/Meta?

Database aggregation and join problems similar to Employees Project Allocation frequently appear in data engineering and backend interviews at companies like Amazon, Google, and Meta. They test your ability to reason about relational data, grouping operations, and efficient SQL query design.

What data structure is used in Employees Project Allocation?

The solution relies on relational database operations rather than traditional in-memory data structures. Internally, the database engine uses hash tables or sorting mechanisms to execute GROUP BY aggregations and equi-joins efficiently.

What is the time complexity of Employees Project Allocation?

The optimal SQL solution runs in O(n) time because the database scans the table once to compute grouped aggregates and once more during the join. The space complexity is O(g), where g represents the number of groups created by the GROUP BY clause.

Ready to solve this problem?

Practice Employees Project Allocation with our built-in code editor and test cases.

Practice on FleetCode

Combine Two Tables

Second Highest Salary

Problem Info

DifficultyHard

Acceptance68.4%

Approaches1

Reading time6 min

Practice this problem

Open in Editor