Window Functions Complete Guide
Window Functions - Complete Guide with Examples
Window Functions are powerful analytical tools that allow you to perform calculations across rows that are related to the current row. Unlike regular aggregate functions that collapse rows into a single result, window functions maintain the individual rows while adding calculated values based on a "window" of data.
What Makes Window Functions Special?
- Perform calculations across related rows without grouping them together
- Access values from other rows (previous, next, first, last)
- Calculate running totals, rankings, and cumulative statistics
- Compare each row to others in its partition
Key Concepts
Partition By
Divides your data into groups (partitions). The window function resets for each partition. For example, partitioning by "Customer" would calculate separate running totals for each customer.
Order By
Determines the order in which rows are processed within each partition. This is crucial for functions like Running Total, Rank, and Row Number.
Order Direction
Specifies whether to order ascending (ASC) or descending (DESC).
Running Calculations
1. Running Total
Calculates a cumulative sum that grows with each row. Perfect for tracking balances, cumulative sales, or year-to-date totals.
- Bank account running balance
- Cumulative sales by month
- Year-to-date revenue
- Inventory accumulation
Scenario: Track cumulative sales throughout the year
| Month | Sales Amount | Running Total |
|---|---|---|
| January | $5,000 | $5,000 |
| February | $7,500 | $12,500 |
| March | $6,200 | $18,700 |
| April | $8,100 | $26,800 |
| May | $9,300 | $36,100 |
Explanation: Each month's Running Total = Previous Running Total + Current Month Sales. January starts at $5,000, February adds $7,500 to get $12,500, and so on.
Scenario: Track each sales representative's cumulative performance separately
| Sales Rep | Month | Sales | Running Total (by Rep) |
|---|---|---|---|
| John | January | $3,000 | $3,000 |
| John | February | $4,500 | $7,500 |
| John | March | $2,800 | $10,300 |
| Sarah | January | $5,200 | $5,200 |
| Sarah | February | $6,100 | $11,300 |
| Sarah | March | $4,900 | $16,200 |
Explanation: When partitioned by Sales Rep, the running total resets for each person. John's running total starts fresh, and Sarah's running total is calculated independently.
2. Running Average
Calculates the average of all values from the first row up to the current row. Useful for tracking moving averages and trends.
- Average customer satisfaction score over time
- Moving average of daily temperatures
- Cumulative GPA calculation
- Average response time trends
Scenario: Track a student's cumulative test average throughout the semester
| Test Number | Score | Running Average | Calculation |
|---|---|---|---|
| Test 1 | 85 | 85.0 | 85 ÷ 1 = 85.0 |
| Test 2 | 92 | 88.5 | (85 + 92) ÷ 2 = 88.5 |
| Test 3 | 78 | 85.0 | (85 + 92 + 78) ÷ 3 = 85.0 |
| Test 4 | 95 | 87.5 | (85 + 92 + 78 + 95) ÷ 4 = 87.5 |
| Test 5 | 88 | 87.6 | (85 + 92 + 78 + 95 + 88) ÷ 5 = 87.6 |
Explanation: The running average includes all tests from the first to the current one. After Test 3, even though the score dropped to 78, the running average reflects all three tests (85, 92, 78) for an average of 85.0.
Ranking Functions
3. Rank
Assigns a rank to each row within a partition, with gaps in ranking when there are ties. If two rows tie for rank 2, the next rank will be 4.
- Sales leaderboards with tie handling
- Competition standings (sports, contests)
- Top product rankings
- Employee performance rankings
Scenario: Rank sales representatives by total sales, with proper tie handling
| Sales Rep | Total Sales | Rank |
|---|---|---|
| Sarah | $95,000 | 1 |
| Mike | $87,000 | 2 |
| John | $87,000 | 2 |
| Lisa | $82,000 | 4 |
| Tom | $75,000 | 5 |
Explanation: Sarah is #1 with the highest sales. Mike and John both have $87,000, so they both receive rank #2 (a tie). The next person, Lisa, gets rank #4 (not #3) because ranks 2 and 3 were used for the tie. This creates gaps in the ranking sequence.
4. Row Number
Assigns a unique sequential number to each row within a partition. Unlike RANK, there are no ties - every row gets a unique number.
- Invoice numbering
- Order of operations
- Pagination (page 1 items 1-10, page 2 items 11-20)
- Sequential record identification
Scenario: Number each customer's orders in sequence
| Customer | Order Date | Amount | Order Number |
|---|---|---|---|
| Company A | 2026-01-05 | $1,200 | 1 |
| Company A | 2026-01-12 | $850 | 2 |
| Company A | 2026-01-18 | $2,100 | 3 |
| Company B | 2026-01-03 | $500 | 1 |
| Company B | 2026-01-20 | $750 | 2 |
Explanation: When partitioned by Customer and ordered by Order Date, each customer gets their own sequence of order numbers starting from 1. Company A's orders are numbered 1, 2, 3, and Company B's orders start fresh at 1, 2.
5. NTILE
Divides rows into a specified number of roughly equal groups, assigning a group number (1, 2, 3, etc.) to each row.
- Quartile analysis (divide into 4 groups)
- Customer segmentation (divide into 3 tiers: high, medium, low)
- Performance buckets (top 25%, middle 50%, bottom 25%)
- Data sampling (split data into equal test groups)
Scenario: Divide customers into 4 groups based on annual spending (NTILE = 4)
| Customer | Annual Spending | Quartile | Segment |
|---|---|---|---|
| Customer A | $95,000 | 1 | Premium (Top 25%) |
| Customer B | $88,000 | 1 | Premium (Top 25%) |
| Customer C | $76,000 | 2 | Gold (26-50%) |
| Customer D | $65,000 | 2 | Gold (26-50%) |
| Customer E | $52,000 | 3 | Silver (51-75%) |
| Customer F | $45,000 | 3 | Silver (51-75%) |
| Customer G | $28,000 | 4 | Bronze (Bottom 25%) |
| Customer H | $15,000 | 4 | Bronze (Bottom 25%) |
Explanation: NTILE(4) divides 8 customers into 4 equal groups of 2 customers each. Group 1 contains the top spenders, Group 4 contains the lowest spenders. This is perfect for creating customer tiers or segments.
Comparative Functions (LAG & LEAD)
6. LAG - Access Previous Row
Retrieves the value from the previous row in the ordered set. Perfect for comparing current values to previous ones.
- Month-over-month comparisons
- Calculate growth rates
- Detect changes from previous period
- Compare to last transaction
Scenario: Compare each month's revenue to the previous month
| Month | Revenue | Previous Month | Change |
|---|---|---|---|
| January | $50,000 | - | - |
| February | $55,000 | $50,000 | +$5,000 (+10%) |
| March | $62,000 | $55,000 | +$7,000 (+12.7%) |
| April | $58,000 | $62,000 | -$4,000 (-6.5%) |
| May | $71,000 | $58,000 | +$13,000 (+22.4%) |
Explanation: LAG looks back one row to get the previous month's revenue. January has no previous month (returns NULL or -). February's LAG value is $50,000 (January's revenue), allowing you to calculate the $5,000 increase.
7. LEAD - Access Next Row
Retrieves the value from the next row in the ordered set. Useful for forward-looking comparisons.
- Forecast vs actual comparisons
- Upcoming deadline tracking
- Next appointment scheduling
- Future period planning
Scenario: Show the next milestone for each project phase
| Current Phase | End Date | Next Phase | Days Until Next |
|---|---|---|---|
| Planning | 2026-02-01 | Design | 7 days |
| Design | 2026-02-08 | Development | 14 days |
| Development | 2026-02-22 | Testing | 10 days |
| Testing | 2026-03-04 | Launch | 5 days |
| Launch | 2026-03-09 | - | Complete |
Explanation: LEAD looks forward one row to show what's coming next. The "Planning" phase can see that "Design" is next, helping teams prepare for upcoming phases. The final phase (Launch) has no next phase, returning NULL or -.
Value Functions (FIRST, LAST, NTH)
8. FIRST_VALUE
Returns the value from the first row in the ordered window. Compare all rows to a baseline value.
Example: Stock trading - Each price shows change from day's opening
Opening: $150 | 10am: $152.50 (+$2.50) | 11am: $148.75 (-$1.25) | 12pm: $151.20 (+$1.20)
FIRST_VALUE always returns $150 for all rows, making it easy to track change from opening.
9. LAST_VALUE
Returns the value from the last row in the window. Compare historical values to the most recent or final value.
Example: Year-end sales goal tracking - Each quarter sees percentage of final goal
Q1: $180K (22.5% of $800K goal) | Q2: $195K (24.4%) | Q3: $210K (26.3%) | Q4: $215K (26.9%)
LAST_VALUE returns $800K (year-end goal) for all quarters.
10. NTH_VALUE
Returns the value from a specific row position (e.g., 2nd row, 5th row).
Example: Sales team - Everyone compares to 2nd place ($87K)
Sarah: $95K (+$8K above 2nd) | Mike: $87K (in 2nd) | John: $82K (-$5K below 2nd)
NTH_VALUE(2) returns $87K for all rows, creating a consistent benchmark.
Statistical Functions
11. STDDEV_POP - Standard Deviation (Population)
Measures how spread out values are from the mean. Low std dev = consistent, high std dev = variable.
Example: Manufacturing weights - Target 500g, actual: 500g, 498g, 502g, 501g, 499g
Std Dev: 2.5g = Excellent consistency (values cluster tightly around 500g average)
If Std Dev was 25g = High variability, quality issues detected
12. STDDEV_SAMP - Standard Deviation (Sample)
Sample standard deviation - use when data represents a sample of a larger population.
13. VAR_POP - Variance (Population)
Population variance (standard deviation squared). Measures average squared differences from mean.
Example: Investment returns - Variance of 4.2 indicates moderate volatility/medium risk
14. VAR_SAMP - Variance (Sample)
Sample variance - use when working with sample data rather than complete population.
Window Functions vs Regular Rollups
| Feature | Regular Rollup | Window Function |
|---|---|---|
| Result Rows | Collapses to one row per group | Maintains all individual rows |
| Calculations | SUM, AVG, COUNT, MIN, MAX | All above PLUS running totals, rankings, comparisons |
| Access Other Rows | No | Yes (LAG, LEAD, FIRST_VALUE, etc.) |
| Sequential Analysis | Not possible | Yes (row numbering, ranking, ordering) |
| Best For | Summarizing groups | Analyzing trends and sequences |
Tips and Best Practices
Choosing the Right Window Function
- Need cumulative totals? Use Running Total
- Need to rank with ties? Use Rank
- Need unique numbering? Use Row Number
- Need to compare to previous period? Use LAG
- Need to compare to first value? Use FIRST_VALUE
- Need to divide into equal groups? Use NTILE
- Need to measure consistency? Use STDDEV
Performance Considerations
- Use partitioning to improve performance on large datasets
- Order by indexed columns when possible
- Window functions work best when you need row-level detail alongside aggregated data
Common Mistakes to Avoid
- Forgetting to set Order By - most window functions require ordering
- Using RANK when you want unique numbers - use ROW_NUMBER instead
- Not using Partition By when you need separate calculations per group
- Confusing LAG (previous) with LEAD (next)
Additional Resources
For more information about window functions and MySQL syntax:
Need help? Contact Tadabase support or visit the community forums for assistance with implementing window functions in your application.
Last Updated: January 2026
We'd love to hear your feedback.