GROUP BY vs. WINDOW FUNCTIONS

These two queries use different SQL techniques (GROUP BY vs. WINDOW FUNCTIONS) and have distinct purposes, even though they both work with the same dataset. Here’s a detailed comparison:

1. First Query (Using GROUP BY):

SELECT dept_id, 
       AVG(sales) AS avg_sales
FROM employee
WHERE year = 2023
GROUP BY dept_id;

What it does:

• Purpose: This query calculates the average sales for each department in 2023.
• Mechanism:
  • The WHERE year = 2023 clause filters the data for the year 2023.
  • The GROUP BY dept_id groups the results by department.
  • The AVG(sales) function computes the average sales for each department.

Output:

• One row per department showing the average sales.
• ept_id avg_sales
  1 5000
  2 4500

Use Case:

• This is used when you want a summary view of average sales by department in 2023, without looking at individual employees.

Use Case:

• This is used when you want a summary view of average sales by department in 2023, without looking at individual employees.

2. Second Query (Using Window Function):

SELECT sales, dept_id,  
       AVG(sales) OVER (PARTITION BY dept_id) AS sales_average
FROM employee
WHERE year = 2023;

What it does:

• Purpose: This query calculates the average sales per department and attaches it to every employee’s sales record in 2023.
• Mechanism:
  • The WHERE year = 2023 clause filters the data for 2023.
  • The AVG(sales) OVER (PARTITION BY dept_id) calculates the average sales for each department but keeps every employee’s sales record.
  • There is no grouping by department, meaning all employee rows remain intact, and the average sales for the department are displayed along with each employee’s individual sales.

Output:

• One row per employee, showing their sales, their department, and their department’s average sales. Example result:

Use Case:

• This is useful when you want to analyze individual employee performance in relation to their department’s average sales. You can see which employees are above or below their department’s average.

Key Differences:

When to Use Which:

• First Query (GROUP BY):
  • Use this when you want to summarize data by department and calculate the average sales without needing individual employee data.
  • For example: “What is the average sales figure for each department in 2023?”
• Second Query (Window Function):
  • Use this when you need to analyze individual employee data alongside department-level statistics.
  • For example: “How does each employee’s sales compare to the department average?”

Both queries are useful depending on whether you need department-level summaries or employee-level comparisons.