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DowngradedOur downstream service providers are currently experiencing outages, and our engineering team is actively working on a resolution. Some services—including the Solver, Partner, and Tools—are temporarily degraded with higher latency and lower bandwidth. Rest assured, Intervipedia, Solutions, and the Question Bank features are not impacted and remain fully operational.DowngradedOur downstream service providers are currently experiencing outages, and our engineering team is actively working on a resolution. Some services—including the Solver, Partner, and Tools—are temporarily degraded with higher latency and lower bandwidth. Rest assured, Intervipedia, Solutions, and the Question Bank features are not impacted and remain fully operational.DowngradedOur downstream service providers are currently experiencing outages, and our engineering team is actively working on a resolution. Some services—including the Solver, Partner, and Tools—are temporarily degraded with higher latency and lower bandwidth. Rest assured, Intervipedia, Solutions, and the Question Bank features are not impacted and remain fully operational.DowngradedOur downstream service providers are currently experiencing outages, and our engineering team is actively working on a resolution. Some services—including the Solver, Partner, and Tools—are temporarily degraded with higher latency and lower bandwidth. Rest assured, Intervipedia, Solutions, and the Question Bank features are not impacted and remain fully operational.
The Question
SQL

Monthly Department vs Company Salary Benchmarking

You are given two tables: 'Salary' (containing 'id', 'employee_id', 'amount', and 'pay_date') and 'Employee' (containing 'employee_id' and 'department_id'). For every month present in the data, determine if the average salary within each department was higher, lower, or equal to the average salary of the entire company for that same month. Return a result table with the month (formatted as 'YYYY-MM'), the 'department_id', and a 'comparison' column indicating 'higher', 'lower', or 'same'.
PostgreSQL
Window Function
CTE
CASE
TO_CHAR
Inner Join
Questions & Insights

Clarifying Questions

How should the month be formatted in the output? Based on standard SQL challenges of this type, the expected format is typically YYYY-MM. I will use PostgreSQL's TO_CHAR function to achieve this.
What is the definition of "Company Average"? It is the average of all salary entries across the entire company for a specific month, regardless of department.
How to handle cases where an employee belongs to multiple departments? Based on the schema, employee_id is the Primary Key of the Employee table, implying a 1:N relationship (one department per employee).
Precision of Averages: Since salaries are integers, calculating averages might result in floating-point numbers. PostgreSQL's AVG on an INT column returns a NUMERIC type, which is ideal for precise comparisons.
Data Model Assumptions:
Salary (Fact Table): Contains transactional salary data. employee_id is a Foreign Key.
Employee (Dimension Table): Contains metadata about employees. employee_id is the Primary Key.
Relationships: Many-to-one between Salary and Employee.
Integrity: Every employee_id in Salary exists in Employee.

Thinking Process

Date Normalization: First, we need to extract the month and year from pay_date to group data by month.
Aggregation Strategy:
We need two levels of aggregation: Department-level and Company-level, both partitioned by month.
We can use Window Functions to calculate the Company Average for a month while simultaneously grouping by Department. This avoids multiple joins or complex subqueries.
Joining: Join Salary and Employee on employee_id to associate each salary record with a department_id.
Logical Steps:
Create a Common Table Expression (CTE) to calculate the average salary for each department per month.
Within that same CTE or a subsequent one, calculate the average salary for the whole company for that month.
Use a CASE statement to compare the department average to the company average.
Efficiency: Using AVG(...) OVER(PARTITION BY ...) allows us to look "outside" the current group (department) to the month-level data efficiently.
Implementation Breakdown

Problem Set

Goal: Compare department-specific monthly average salaries against the overall company monthly average.
Output:pay_month (string), department_id (int), comparison (string: 'higher', 'lower', 'same').
Constraints: Must handle month-by-month granularity.

Approach

Technologies: PostgreSQL.
Functions:
TO_CHAR(date, 'YYYY-MM') for period formatting.
AVG() as an aggregate function for department averages.
AVG() OVER(PARTITION BY ...) as a window function for company-wide averages.
CASE for conditional labeling.
Join Strategy: Inner Join between Salary and Employee.
Computational Cost:O(N \log N) due to the sorting required for window functions and grouping, where N is the number of rows in the Salary table.

Implementation

Wrap Up

Advanced Topics

Indexing: To optimize this query, a composite index on Salary(pay_date, employee_id) and a Primary Key index on Employee(employee_id) would drastically reduce the join and sort time.
Window Function Performance: In PostgreSQL, the OVER(PARTITION BY ...) clause triggers a window aggregate node in the execution plan. If the dataset is massive, ensuring the data is physically clustered or partitioned by pay_date (via declarative partitioning) would minimize disk I/O.
Numeric Precision: PostgreSQL's AVG on integers returns numeric. If the comparison requires extreme precision (e.g., fractional cents), numeric is safer than double precision to avoid floating-point rounding errors during the > or < comparison.