sqlANYlag
sql
SELECT day,
revenue,
LAG(revenue) OVER (ORDER BY day) AS prev_revenue
FROM daily_revenue;`LAG` is the easiest way to compare the current row to the previous row.
SQL Window Functions: LAG, LEAD, Deltas, and Time-Series Comparisons
Compare rows to prior or next rows with LAG and LEAD for change detection, retention, and trend analysis.
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LAG and LEAD basics
Reach backward or forward without a self join.
sqlANYlead
sql
SELECT day,
revenue,
LEAD(revenue) OVER (ORDER BY day) AS next_revenue
FROM daily_revenue;Use `LEAD` for forward-looking comparisons or interval construction.
sqlANYlagdefault
sql
SELECT day,
revenue,
LAG(revenue, 1, 0) OVER (ORDER BY day) AS prev_revenue_or_zero
FROM daily_revenue;The third argument is the default when an offset row does not exist.
sqlANYoffset
sql
SELECT month,
signups,
LAG(signups, 2) OVER (ORDER BY month) AS signups_2_months_ago
FROM monthly_signups;Offsets larger than 1 are useful for month-over-month or quarter-over-quarter comparisons.
Time-series patterns
Practical delta and retention examples.
sqlANYchangedelta
sql
SELECT day,
revenue,
revenue - LAG(revenue) OVER (ORDER BY day) AS revenue_change
FROM daily_revenue;A simple difference often matters more than the raw value.
sqlANYpercent-change
sql
SELECT day,
revenue,
ROUND(
100.0 * (revenue - LAG(revenue) OVER (ORDER BY day))
/ NULLIF(LAG(revenue) OVER (ORDER BY day), 0),
2
) AS pct_change
FROM daily_revenue;Use `NULLIF` to avoid division by zero.
sqlANYgapsevents
sql
SELECT user_id,
event_time,
event_time - LAG(event_time) OVER (
PARTITION BY user_id
ORDER BY event_time
) AS time_since_prev
FROM user_events;The exact subtraction syntax varies by dialect, but the window pattern stays the same.
sqlANYsubscriptions
sql
SELECT customer_id,
billing_month,
status,
LEAD(status) OVER (
PARTITION BY customer_id
ORDER BY billing_month
) AS next_status
FROM subscription_history;Useful in churn modeling, handoff workflows, and lifecycle analysis.
More in SQL Window Functions
SQL Window Functions: Performance, EXPLAIN, and Dialect Notes
How to tune window queries, reduce sorts, structure indexes, and account for cross-dialect differences.
SQL Window Functions: Deduplication, Gaps and Islands, and Sessionization
Use ROW_NUMBER, LAG, and running groups to deduplicate rows, detect gaps, and form islands or sessions.
SQL Window Functions: Percentiles, Bucketing, and Distribution Analysis
Percent rank, cumulative distribution, percentile-style bucketing, and segmentation patterns.
SQL Window Functions: FIRST_VALUE, LAST_VALUE, NTH_VALUE, and Positional Analytics
Positional window functions for first, last, and nth values inside ordered partitions.
SQL Window Functions: Running Totals, Moving Averages, and Cumulative Metrics
Rolling metrics, cumulative sums, moving averages, and period-over-period analytics with frame clauses.
SQL Window Functions: Ranking, Top N per Group, and Percent Rank
Ranking functions such as ROW_NUMBER, RANK, DENSE_RANK, NTILE, PERCENT_RANK, and CUME_DIST.