SQL Window Function Generator - Advanced Analytics Made Simple

Window functions are among SQL most powerful features for analytics, but their syntax can be intimidating. AI2sql Window Function Generator creates these advanced queries from plain English descriptions.

What Are Window Functions?

Window functions perform calculations across rows related to the current row, without collapsing the result set like GROUP BY does. They enable:

• Ranking and numbering rows

• Calculating running totals

• Comparing to previous/next rows

• Calculating moving averages

• Percentile calculations

Window Function Categories

Ranking Functions

• ROW_NUMBER() - Sequential numbers

• RANK() - Ranking with gaps

• DENSE_RANK() - Ranking without gaps

• NTILE(n) - Divide into n groups

Value Functions

• LAG() - Previous row value

• LEAD() - Next row value

• FIRST_VALUE() - First value in window

• LAST_VALUE() - Last value in window

• NTH_VALUE() - Specific position value

Aggregate Window Functions

• SUM() OVER - Running/cumulative sum

• AVG() OVER - Moving average

• COUNT() OVER - Running count

Window Function Examples

Row Numbering

Input: "Number rows within each department by salary"

SELECT 
  employee_id,
  department,
  salary,
  ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) as salary_rank
 FROM employees;

Running Total

Input: "Calculate cumulative sales by date"

SELECT 
  order_date,
  daily_sales,
  SUM(daily_sales) OVER (ORDER BY order_date) as cumulative_sales
 FROM daily_sales_summary;

Compare to Previous Period

Input: "Show each month sales compared to previous month"

SELECT 
  month,
  revenue,
  LAG(revenue) OVER (ORDER BY month) as prev_month_revenue,
  revenue - LAG(revenue) OVER (ORDER BY month) as month_over_month_change,
  ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month)) / 
  NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 2) as pct_change
 FROM monthly_revenue;

Moving Average

Input: "Calculate 7-day moving average of daily active users"

SELECT 
  date,
  daily_active_users,
  ROUND(AVG(daily_active_users) OVER (
  ORDER BY date
  ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
  ), 0) as seven_day_avg
 FROM daily_metrics;

Percentile/Quartile

Input: "Divide customers into quartiles by spending"

SELECT 
  customer_id,
  total_spending,
  NTILE(4) OVER (ORDER BY total_spending DESC) as spending_quartile,
  CASE NTILE(4) OVER (ORDER BY total_spending DESC)
  WHEN 1 THEN 'Top 25%'
  WHEN 2 THEN 'Second 25%'
  WHEN 3 THEN 'Third 25%'
  WHEN 4 THEN 'Bottom 25%'
  END as spending_tier
 FROM customer_summary;

Top N Per Group

Input: "Get top 3 products by sales in each category"

WITH ranked_products AS (
  SELECT 
  category,
  product_name,
  total_sales,
  ROW_NUMBER() OVER (PARTITION BY category ORDER BY total_sales DESC) as rank
  FROM product_sales
 )
 SELECT * FROM ranked_products
 WHERE rank <= 3;

Window Frame Specifications

Our generator understands frame clauses:

• ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW

• ROWS BETWEEN 6 PRECEDING AND CURRENT ROW (moving window)

• RANGE BETWEEN specifications

Start Building Window Functions

Unlock advanced analytics capabilities. Describe the calculation you need, and get properly structured window function queries.