11 Top MySQL Window Functions with Use Cases

Here’s a list of MySQL window functions with a brief description of each and real-world use cases.

Top MySQL window functions

1. ROW_NUM

• Description: Assigns a unique sequential number to rows in a partition.
• Use Case: Get the first order per customer or remove duplicate rows by picking only the latest one.

ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC)

2. RANK()

• Description: Assigns a rank to rows in a partition, with gaps for ties.
• Use Case: Show top 3 scorers per department even if there’s a tie.

RANK() OVER (PARTITION BY department ORDER BY score DESC)

3. DENSE_RANK

• Description: Like RANK but without gaps.
• Use Case: Rank products by sales where ties should not cause skipped ranks.

DENSE_RANK() OVER (ORDER BY total_sales DESC)

4. NTILE(n)

• Description: Divides rows into n equal buckets.
• Use Case: Group customers into quartiles based on spending.

NTILE(4) OVER (ORDER BY total_spent DESC)

5. LAG()

• Description: Accesses a value from a previous row.
• Use Case: Track change in account balance or calculate difference from previous day.

LAG(balance) OVER (PARTITION BY account_id ORDER BY transaction_date)

6. LEAD()

• Description: Accesses a value from the next row.
• Use Case: Predict next appointment, calculate days until next order.

LEAD(appointment_date) OVER (PARTITION BY patient_id ORDER BY appointment_date)

7. FIRST_VALUE()

• Description: Returns the first value in the window frame.
• Use Case: Mark original price of a product before any discount.

FIRST_VALUE(price) OVER (PARTITION BY product_id ORDER BY change_date)

8. LAST_VALUE()

• Description: Returns the last value in the window frame.
• Use Case: Get most recent status or latest comment on a ticket.

LAST_VALUE(status) OVER (PARTITION BY ticket_id ORDER BY updated_at ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)

9. SUM(), AVG(), MIN(), MAX(), COUNT() (as window functions)

• Description: Aggregate functions used over a window.
• Use Case: Running totals, moving averages, cumulative counts, etc.

SUM(sales) OVER (PARTITION BY region ORDER BY month ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)

10. CUME_DIST()

• Description: Cumulative distribution; proportion of rows with values less than or equal to current row.
• Use Case: Percentile rankings like “what percentile does this salary fall into?”

CUME_DIST() OVER (ORDER BY salary)

11. PERCENT_RANK()

• Description: Percent rank of a row in the partition.
• Use Case: Compare a student’s performance relative to others.

PERCENT_RANK() OVER (PARTITION BY subject ORDER BY score DESC)

Conclusion

In summary, MySQL window functions simplify complex calculations over data rows, allowing developers and analysts to gain insights efficiently. Functions like ROW_NUMBER(), RANK(), and LAG() help users rank, aggregate, and compare values easily, without needing complicated joins or subqueries. Whether you’re analyzing sales or customer interactions, mastering these functions can streamline your data analysis and SQL queries. Embrace these tools to boost your data analysis skills!