프레임 윈도에서 LAG/LEAD 사용하기

LAG/LEAD 요약

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

기본값을 사용하는 LAG

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

차이 계산

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

여러 단계의 LAG

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

예측 구간을 위한 LEAD

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;

사용자별 패턴

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.

상태 변화 감지

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.

그룹별 프레임 함수

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;

성능

요약

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