The Question

Monthly Departmental Salary Benchmarking

Given a salary ledger and employee department mappings, write a SQL query to benchmark each department's monthly average performance against the overall company average for that same month. The output should categorize each department as 'higher', 'lower', or 'same' compared to the company-wide mean, formatted by month (YYYY-MM).

PostgreSQL

Window Function

CTE

TO_CHAR

CASE Expression

JOIN

Questions & Insights

Clarifying Questions

How is the month defined? Since pay_date is a specific date, should we aggregate by the calendar month (YYYY-MM)?

Assumption: Yes, comparisons are performed on a per-month basis.

What is the definition of "Average Salary"? Is it the average of all individual salary records in that month, or the average of employee totals?

Assumption: Based on the prompt "salary of an employee in one month", we assume each row in the Salary table represents the total monthly pay for an individual. Thus, the average is the mean of the amount column for the specified grouping.

Are there potential NULLs or departments without employees?

Assumption: amount is non-null. Only months and departments present in the Salary table will be included in the output.

Data Model Assumptions:

Salary.id: Primary Key (Surrogate).

Employee.employee_id: Primary Key.

Salary.employee_id: Foreign Key to Employee.

Relationship: 1:N between Employee and Salary.

Thinking Process

Normalization & Formatting: First, extract the month from pay_date to create a pay_month string (e.g., '2024-03').

Joining: Join Salary with Employee to associate every salary record with a department_id.

Global Aggregation (Company Level): Calculate the average salary across the entire company for each specific pay_month.

Local Aggregation (Department Level): Calculate the average salary within each department_id for each specific pay_month.

Comparison Logic: Use a CASE statement to compare the Department Average to the Company Average.

Optimization with Window Functions: Instead of complex nested subqueries or multiple joins, use PostgreSQL window functions. AVG(...) OVER(PARTITION BY ...) allows us to compute both the company-wide and department-wide averages in a single pass over the joined dataset.

Implementation Breakdown

Problem Set

Goal: Compare the average salary of each department to the company's average salary for each month.

Output Columns: pay_month (string), department_id (int), comparison (string: 'higher', 'lower', 'same').

Constraints: Handle ties ('same'). The solution must be performant and readable.

Approach

CTE (Common Table Expression): To structure the query into logical steps (Base data -> Aggregations -> Comparison).

Window Functions:

AVG(amount) OVER (PARTITION BY pay_month) for the company average.

AVG(amount) OVER (PARTITION BY pay_month, department_id) for the department average.

Date Formatting: TO_CHAR(pay_date, 'YYYY-MM') for the required output format.

JOIN: Inner Join between Salary and Employee on employee_id.

Implementation

Wrap Up

Advanced Topics

Performance: The use of OVER(PARTITION BY ...) is generally efficient in PostgreSQL. However, on massive datasets, DISTINCT on windowed results can be slower than a GROUP BY. If performance bottlenecks arise, I would switch to a GROUP BY for the dept_avg and join it against a GROUP BY for the company_avg.

Indexing:

An index on Salary(pay_date) or a functional index on TO_CHAR(pay_date, 'YYYY-MM') would speed up the partitioning.

An index on Salary(employee_id) is crucial for the join with the Employee table.

Execution Plan: In a distributed environment (like Greenplum or Citus), partitioning by pay_month could cause data shuffling if the data is not distributed by a time-based key.

Scalability: If the Salary table is billions of rows, calculating the average every time is expensive. A Materialized View updated monthly or using Incremental Aggregation would be the preferred architectural choice for a production dashboard.