Summary of Window Functions in MySQL | Intermediate MySQL

Summary of "Window Functions in MySQL | Intermediate MySQL"

This video lesson provides an overview of Window Functions in MySQL, demonstrating their power and flexibility compared to traditional grouping methods. The speaker explains how Window Functions allow for calculations over a set of rows while retaining individual row data, unlike the GROUP BY clause which condenses data into single rows.

Main Ideas and Concepts:

• Window Functions: These functions perform calculations across a set of rows related to the current row, allowing for more granular data analysis without collapsing rows.
• Comparison with GROUP BY: The speaker compares Window Functions with GROUP BY, illustrating how the former retains individual rows while performing calculations.
• Key Window Functions:
  • Average Salary Calculation: Demonstrated using both GROUP BY and a window function, showing how Window Functions can provide average values without aggregating rows.
  • Partitioning: Using the PARTITION BY clause to segment data (e.g., by gender) while still allowing for individual row visibility.
  • Rolling Total: Explained as a cumulative total that adds values from subsequent rows, showcasing its application in financial contexts.
  • Row Number, Rank, and Dense Rank: These functions assign unique identifiers or rankings to rows based on specified criteria, with differences in how they handle duplicates.

Methodology/Instructions:

1. Using GROUP BY:
   • Write a query to aggregate data (e.g., average salary by gender).
   • Example:

     SELECT gender, AVG(salary) AS average_salary
     FROM demographics
     JOIN salaries ON demographics.employee_id = salaries.employee_id
     GROUP BY gender;

2. Using Window Functions:
   • Write a query to calculate average salary without grouping.
   • Example:

     SELECT gender, AVG(salary) OVER () AS average_salary
     FROM demographics
     JOIN salaries ON demographics.employee_id = salaries.employee_id;

3. Partitioning with Window Functions:
   • Use PARTITION BY to segment calculations.
   • Example:

     SELECT gender, AVG(salary) OVER (PARTITION BY gender) AS average_salary
     FROM demographics
     JOIN salaries ON demographics.employee_id = salaries.employee_id;

4. Calculating Rolling Totals:
   • Add an ORDER BY clause to create a Rolling Total.
   • Example:

     SELECT gender, salary, SUM(salary) OVER (PARTITION BY gender ORDER BY employee_id) AS rolling_total
     FROM demographics
     JOIN salaries ON demographics.employee_id = salaries.employee_id;

5. Using Row Number, Rank, and Dense Rank:
   • Assign row numbers or ranks based on salary.
   • Example for Row Number:

     SELECT *, ROW_NUMBER() OVER (PARTITION BY gender ORDER BY salary DESC) AS row_num
     FROM demographics
     JOIN salaries ON demographics.employee_id = salaries.employee_id;

   • Example for Rank:

     SELECT *, RANK() OVER (PARTITION BY gender ORDER BY salary DESC) AS rank_num
     FROM demographics
     JOIN salaries ON demographics.employee_id = salaries.employee_id;

   • Example for Dense Rank:

     SELECT *, DENSE_RANK() OVER (PARTITION BY gender ORDER BY salary DESC) AS dense_rank_num
     FROM demographics
     JOIN salaries ON demographics.employee_id = salaries.employee_id;

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Video