Lag/Lead mit Frame-Fenstern

LAG/LEAD-Zusammenfassung

SELECT day, revenue,
       LAG(revenue) OVER (ORDER BY day) AS prev_day_revenue
FROM daily_revenue;

LAG mit Standardwert

LAG(revenue, 1, 0) OVER (ORDER BY day)
-- Returns 0 instead of NULL for the very first row.

Differenzen berechnen

SELECT day, revenue,
       revenue - LAG(revenue, 1, 0) OVER (ORDER BY day) AS delta
FROM daily_revenue;

Mehrstufiges LAG

LAG(revenue, 7) OVER (ORDER BY day)   -- 7 days ago
LAG(revenue, 365) OVER (ORDER BY day) -- 1 year ago

LEAD für Prognoseplätze

SELECT id, ts,
       LEAD(ts) OVER (PARTITION BY user_id ORDER BY ts) AS next_ts,
       LEAD(ts) OVER (PARTITION BY user_id ORDER BY ts) - ts AS gap_to_next
FROM events;

Muster pro Benutzer

SELECT user_id, ts, action,
       LAG(action) OVER (PARTITION BY user_id ORDER BY ts) AS prev_action
FROM events;
-- prev_action is the user's previous event, not globally.

Zustandsänderungen erkennen

SELECT id, status,
  CASE WHEN status <> LAG(status) OVER (ORDER BY ts) THEN 'changed' ELSE 'same' END
FROM orders;

FIRST_VALUE / LAST_VALUE

FIRST_VALUE(status) OVER (PARTITION BY order_id ORDER BY ts) AS first_status
-- The status when the order was first seen.

LAST_VALUE(status) OVER (
  PARTITION BY order_id ORDER BY ts
  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
) AS final_status

NTH_VALUE

NTH_VALUE(revenue, 3) OVER (
  ORDER BY revenue DESC
  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
)
-- Third-highest revenue value.

Frame-Funktionen pro Gruppe

SELECT user_id, ts, page,
       LAG(page) OVER (PARTITION BY user_id ORDER BY ts) AS prev_page,
       LEAD(page) OVER (PARTITION BY user_id ORDER BY ts) AS next_page
FROM page_views;

Leistung

Zusammenfassung

Schnelltest