Sub-skill of sql-queries: Window Functions (+4).
Scanned 9/9/2026
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---
name: sql-queries-window-functions
description: 'Sub-skill of sql-queries: Window Functions (+4).'
version: 1.0.0
category: data-analytics
type: reference
scripts_exempt: true
---
# Window Functions (+4)
## Window Functions
```sql
-- Ranking
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC)
RANK() OVER (PARTITION BY category ORDER BY revenue DESC)
DENSE_RANK() OVER (ORDER BY score DESC)
-- Running totals / moving averages
SUM(revenue) OVER (ORDER BY date_col ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as running_total
AVG(revenue) OVER (ORDER BY date_col ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as moving_avg_7d
-- Lag / Lead
LAG(value, 1) OVER (PARTITION BY entity ORDER BY date_col) as prev_value
LEAD(value, 1) OVER (PARTITION BY entity ORDER BY date_col) as next_value
-- First / Last value
FIRST_VALUE(status) OVER (PARTITION BY user_id ORDER BY created_at ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
LAST_VALUE(status) OVER (PARTITION BY user_id ORDER BY created_at ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
-- Percent of total
revenue / SUM(revenue) OVER () as pct_of_total
revenue / SUM(revenue) OVER (PARTITION BY category) as pct_of_category
```
## CTEs for Readability
```sql
WITH
-- Step 1: Define the base population
base_users AS (
SELECT user_id, created_at, plan_type
FROM users
WHERE created_at >= DATE '2024-01-01'
AND status = 'active'
),
-- Step 2: Calculate user-level metrics
user_metrics AS (
SELECT
u.user_id,
u.plan_type,
COUNT(DISTINCT e.session_id) as session_count,
SUM(e.revenue) as total_revenue
FROM base_users u
LEFT JOIN events e ON u.user_id = e.user_id
GROUP BY u.user_id, u.plan_type
),
-- Step 3: Aggregate to summary level
summary AS (
SELECT
plan_type,
COUNT(*) as user_count,
AVG(session_count) as avg_sessions,
SUM(total_revenue) as total_revenue
FROM user_metrics
GROUP BY plan_type
)
SELECT * FROM summary ORDER BY total_revenue DESC;
```
## Cohort Retention
```sql
WITH cohorts AS (
SELECT
user_id,
DATE_TRUNC('month', first_activity_date) as cohort_month
FROM users
),
activity AS (
SELECT
user_id,
DATE_TRUNC('month', activity_date) as activity_month
FROM user_activity
)
SELECT
c.cohort_month,
COUNT(DISTINCT c.user_id) as cohort_size,
COUNT(DISTINCT CASE
WHEN a.activity_month = c.cohort_month THEN a.user_id
END) as month_0,
COUNT(DISTINCT CASE
WHEN a.activity_month = c.cohort_month + INTERVAL '1 month' THEN a.user_id
END) as month_1,
COUNT(DISTINCT CASE
WHEN a.activity_month = c.cohort_month + INTERVAL '3 months' THEN a.user_id
END) as month_3
FROM cohorts c
LEFT JOIN activity a ON c.user_id = a.user_id
GROUP BY c.cohort_month
ORDER BY c.cohort_month;
```
## Funnel Analysis
```sql
WITH funnel AS (
SELECT
user_id,
MAX(CASE WHEN event = 'page_view' THEN 1 ELSE 0 END) as step_1_view,
MAX(CASE WHEN event = 'signup_start' THEN 1 ELSE 0 END) as step_2_start,
MAX(CASE WHEN event = 'signup_complete' THEN 1 ELSE 0 END) as step_3_complete,
MAX(CASE WHEN event = 'first_purchase' THEN 1 ELSE 0 END) as step_4_purchase
FROM events
WHERE event_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY user_id
)
SELECT
COUNT(*) as total_users,
SUM(step_1_view) as viewed,
SUM(step_2_start) as started_signup,
SUM(step_3_complete) as completed_signup,
SUM(step_4_purchase) as purchased,
ROUND(100.0 * SUM(step_2_start) / NULLIF(SUM(step_1_view), 0), 1) as view_to_start_pct,
ROUND(100.0 * SUM(step_3_complete) / NULLIF(SUM(step_2_start), 0), 1) as start_to_complete_pct,
ROUND(100.0 * SUM(step_4_purchase) / NULLIF(SUM(step_3_complete), 0), 1) as complete_to_purchase_pct
FROM funnel;
```
## Deduplication
```sql
-- Keep the most recent record per key
WITH ranked AS (
SELECT
*,
ROW_NUMBER() OVER (
PARTITION BY entity_id
ORDER BY updated_at DESC
) as rn
FROM source_table
)
SELECT * FROM ranked WHERE rn = 1;
```
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