CONDITIONAL_TRUE_EVENT
CONDITIONAL_TRUE_EVENT
CONDITIONAL_TRUE_EVENT는 boolean 인자 expr1의 결과를 기반으로 윈도우 파티션 안의 각 행에 대해 윈도우 이벤트 번호를 반환하는 윈도우 함수예요. 번호는 0에서 시작하며 expr1이 true로 평가되는 각 행마다 1씩 증가해요.
본문
카테고리: 윈도우 함수 (General)
이 함수의 용도 중 하나는 윈도우 파티션을 세션화(sessionize)하는 거예요. 예를 들어 클릭 스트림 데이터에서 마지막 이벤트가 임계값보다 오래 전이었는지 확인해 사용자가 새 세션을 시작했는지 판단하는 데 사용할 수 있어요.
구문 (Syntax)
CONDITIONAL_TRUE_EVENT( <expr1> ) OVER ( [ PARTITION BY <expr2> ] ORDER BY <expr3> [ { ASC | DESC } ] [ NULLS { FIRST | LAST } ] )
인자 (Arguments)
<expr1> — true로 평가될 때 윈도우 이벤트 번호 값을 변경하는 boolean 표현식이에요.
<expr2> — 파티션을 나누는 선택적 표현식이에요.
<expr3> — 각 파티션 안에서 정렬하는 표현식이에요.
사용 시 유의사항 (Usage notes)
- 조건부 표현식
expr1은 순위 관련 함수 LAG와 LEAD를 포함할 수 있으며, 이를 통해 더 표현력 있는 윈도우를 만들 수 있어요. 사용된다면 이 함수들은 CONDITIONAL_TRUE_EVENT와 같은 OVER 지정을 사용해야 해요.
예시 (Examples)
첫 번째 예시는 다음을 보여줘요:
- 파티션 안의 번호는 지정한 열이 TRUE(이 경우 0이 아닌 값)일 때마다 증가해요.
- NULL 값은 TRUE 값으로 간주되지 않아요.
- 번호는 각 파티션에서 0부터 다시 시작해요.
테이블을 만들고 로드해요:
CREATE TABLE table1 (province VARCHAR, o_col INTEGER, o2_col INTEGER);
INSERT INTO table1 (province, o_col, o2_col) VALUES
('Alberta', 0, 10),
('Alberta', 0, 10),
('Alberta', 13, 10),
('Alberta', 13, 11),
('Alberta', 14, 11),
('Alberta', 15, 12),
('Alberta', NULL, NULL),
('Manitoba', 30, 30);
테이블을 조회해요:
SELECT province, o_col,
CONDITIONAL_TRUE_EVENT(o_col)
OVER (PARTITION BY province ORDER BY o_col)
AS true_event
FROM table1
ORDER BY province, o_col;
+----------+-------+------------+
| PROVINCE | O_COL | TRUE_EVENT |
|----------+-------+------------|
| Alberta | 0 | 0 |
| Alberta | 0 | 0 |
| Alberta | 13 | 1 |
| Alberta | 13 | 2 |
| Alberta | 14 | 3 |
| Alberta | 15 | 4 |
| Alberta | NULL | 4 |
| Manitoba | 30 | 1 |
+----------+-------+------------+
다음 예시는 다음을 보여줘요:
expr1은 열이 아닌 다른 표현식일 수 있어요. 이 쿼리는o_col > 20표현식을 사용하며, 쿼리 출력은 o_col 값이 20 이하에서 20보다 큰 값으로 변하는 시점을 보여줘요.expr3은expr1과 일치할 필요가 없어요. 즉 OVER 절의 ORDER BY 하위 절의 표현식이 CONDITIONAL_TRUE_EVENT 함수의 표현식과 일치할 필요가 없다는 뜻이에요.
SELECT province, o_col,
CONDITIONAL_TRUE_EVENT(o_col)
OVER (PARTITION BY province ORDER BY o_col)
AS true_event,
CONDITIONAL_TRUE_EVENT(o_col > 20)
OVER (PARTITION BY province ORDER BY o_col)
AS true_event_gt_20
FROM table1
ORDER BY province, o_col;
+----------+-------+------------+------------------+
| PROVINCE | O_COL | TRUE_EVENT | TRUE_EVENT_GT_20 |
|----------+-------+------------+------------------|
| Alberta | 0 | 0 | 0 |
| Alberta | 0 | 0 | 0 |
| Alberta | 13 | 1 | 0 |
| Alberta | 13 | 2 | 0 |
| Alberta | 14 | 3 | 0 |
| Alberta | 15 | 4 | 0 |
| Alberta | NULL | 4 | 0 |
| Manitoba | 30 | 1 | 1 |
+----------+-------+------------+------------------+
다음 예시는 CONDITIONAL_CHANGE_EVENT와 CONDITIONAL_TRUE_EVENT를 비교해요:
SELECT province, o_col,
CONDITIONAL_CHANGE_EVENT(o_col)
OVER (PARTITION BY province ORDER BY o_col)
AS change_event,
CONDITIONAL_TRUE_EVENT(o_col)
OVER (PARTITION BY province ORDER BY o_col)
AS true_event
FROM table1
ORDER BY province, o_col;
+----------+-------+--------------+------------+
| PROVINCE | O_COL | CHANGE_EVENT | TRUE_EVENT |
|----------+-------+--------------+------------|
| Alberta | 0 | 0 | 0 |
| Alberta | 0 | 0 | 0 |
| Alberta | 13 | 1 | 1 |
| Alberta | 13 | 1 | 2 |
| Alberta | 14 | 2 | 3 |
| Alberta | 15 | 3 | 4 |
| Alberta | NULL | 3 | 4 |
| Manitoba | 30 | 0 | 1 |
+----------+-------+--------------+------------+
이 예시도 CONDITIONAL_CHANGE_EVENT와 CONDITIONAL_TRUE_EVENT를 비교해요:
CREATE TABLE borrowers (
name VARCHAR,
status_date DATE,
late_balance NUMERIC(11, 2),
thirty_day_late_balance NUMERIC(11, 2)
);
INSERT INTO borrowers (name, status_date, late_balance, thirty_day_late_balance) VALUES
-- Pays late frequently, but catches back up rather than falling further behind.
('Geoffrey Flake', '2018-01-01'::DATE, 0.0, 0.0),
('Geoffrey Flake', '2018-02-01'::DATE, 1000.0, 0.0),
('Geoffrey Flake', '2018-03-01'::DATE, 2000.0, 1000.0),
('Geoffrey Flake', '2018-04-01'::DATE, 0.0, 0.0),
('Geoffrey Flake', '2018-05-01'::DATE, 1000.0, 0.0),
('Geoffrey Flake', '2018-06-01'::DATE, 2000.0, 1000.0),
('Geoffrey Flake', '2018-07-01'::DATE, 0.0, 0.0),
('Geoffrey Flake', '2018-08-01'::DATE, 0.0, 0.0),
-- Keeps falling further behind.
('Cy Dismal', '2018-01-01'::DATE, 0.0, 0.0),
('Cy Dismal', '2018-02-01'::DATE, 0.0, 0.0),
('Cy Dismal', '2018-03-01'::DATE, 1000.0, 0.0),
('Cy Dismal', '2018-04-01'::DATE, 2000.0, 1000.0),
('Cy Dismal', '2018-05-01'::DATE, 3000.0, 2000.0),
('Cy Dismal', '2018-06-01'::DATE, 4000.0, 3000.0),
('Cy Dismal', '2018-07-01'::DATE, 5000.0, 4000.0),
('Cy Dismal', '2018-08-01'::DATE, 6000.0, 5000.0),
-- Fell behind and isn't catching up, but isn't falling further behind.
('Leslie Safer', '2018-01-01'::DATE, 0.0, 0.0),
('Leslie Safer', '2018-02-01'::DATE, 0.0, 0.0),
('Leslie Safer', '2018-03-01'::DATE, 1000.0, 1000.0),
('Leslie Safer', '2018-04-01'::DATE, 2000.0, 1000.0),
('Leslie Safer', '2018-05-01'::DATE, 2000.0, 1000.0),
('Leslie Safer', '2018-06-01'::DATE, 2000.0, 1000.0),
('Leslie Safer', '2018-07-01'::DATE, 2000.0, 1000.0),
('Leslie Safer', '2018-08-01'::DATE, 2000.0, 1000.0),
-- Always pays on time and in full.
('Ida Idyll', '2018-01-01'::DATE, 0.0, 0.0),
('Ida Idyll', '2018-02-01'::DATE, 0.0, 0.0),
('Ida Idyll', '2018-03-01'::DATE, 0.0, 0.0),
('Ida Idyll', '2018-04-01'::DATE, 0.0, 0.0),
('Ida Idyll', '2018-05-01'::DATE, 0.0, 0.0),
('Ida Idyll', '2018-06-01'::DATE, 0.0, 0.0),
('Ida Idyll', '2018-07-01'::DATE, 0.0, 0.0),
('Ida Idyll', '2018-08-01'::DATE, 0.0, 0.0)
;
SELECT name, status_date, late_balance AS "OVERDUE",
thirty_day_late_balance AS "30 DAYS OVERDUE",
CONDITIONAL_CHANGE_EVENT(thirty_day_late_balance)
OVER (PARTITION BY name ORDER BY status_date) AS change_event_cnt,
CONDITIONAL_TRUE_EVENT(thirty_day_late_balance)
OVER (PARTITION BY name ORDER BY status_date) AS true_cnt
FROM borrowers
ORDER BY name, status_date;
+----------------+-------------+---------+-----------------+------------------+----------+
| NAME | STATUS_DATE | OVERDUE | 30 DAYS OVERDUE | CHANGE_EVENT_CNT | TRUE_CNT |
|----------------+-------------+---------+-----------------+------------------+----------|
| Cy Dismal | 2018-01-01 | 0.00 | 0.00 | 0 | 0 |
| Cy Dismal | 2018-02-01 | 0.00 | 0.00 | 0 | 0 |
| Cy Dismal | 2018-03-01 | 1000.00 | 0.00 | 0 | 0 |
| Cy Dismal | 2018-04-01 | 2000.00 | 1000.00 | 1 | 1 |
| Cy Dismal | 2018-05-01 | 3000.00 | 2000.00 | 2 | 2 |
| Cy Dismal | 2018-06-01 | 4000.00 | 3000.00 | 3 | 3 |
| Cy Dismal | 2018-07-01 | 5000.00 | 4000.00 | 4 | 4 |
| Cy Dismal | 2018-08-01 | 6000.00 | 5000.00 | 5 | 5 |
| Geoffrey Flake | 2018-01-01 | 0.00 | 0.00 | 0 | 0 |
| Geoffrey Flake | 2018-02-01 | 1000.00 | 0.00 | 0 | 0 |
| Geoffrey Flake | 2018-03-01 | 2000.00 | 1000.00 | 1 | 1 |
| Geoffrey Flake | 2018-04-01 | 0.00 | 0.00 | 2 | 1 |
| Geoffrey Flake | 2018-05-01 | 1000.00 | 0.00 | 2 | 1 |
| Geoffrey Flake | 2018-06-01 | 2000.00 | 1000.00 | 3 | 2 |
| Geoffrey Flake | 2018-07-01 | 0.00 | 0.00 | 4 | 2 |
| Geoffrey Flake | 2018-08-01 | 0.00 | 0.00 | 4 | 2 |
| Ida Idyll | 2018-01-01 | 0.00 | 0.00 | 0 | 0 |
| Ida Idyll | 2018-02-01 | 0.00 | 0.00 | 0 | 0 |
| Ida Idyll | 2018-03-01 | 0.00 | 0.00 | 0 | 0 |
| Ida Idyll | 2018-04-01 | 0.00 | 0.00 | 0 | 0 |
| Ida Idyll | 2018-05-01 | 0.00 | 0.00 | 0 | 0 |
| Ida Idyll | 2018-06-01 | 0.00 | 0.00 | 0 | 0 |
| Ida Idyll | 2018-07-01 | 0.00 | 0.00 | 0 | 0 |
| Ida Idyll | 2018-08-01 | 0.00 | 0.00 | 0 | 0 |
| Leslie Safer | 2018-01-01 | 0.00 | 0.00 | 0 | 0 |
| Leslie Safer | 2018-02-01 | 0.00 | 0.00 | 0 | 0 |
| Leslie Safer | 2018-03-01 | 1000.00 | 1000.00 | 1 | 1 |
| Leslie Safer | 2018-04-01 | 2000.00 | 1000.00 | 1 | 2 |
| Leslie Safer | 2018-05-01 | 2000.00 | 1000.00 | 1 | 3 |
| Leslie Safer | 2018-06-01 | 2000.00 | 1000.00 | 1 | 4 |
| Leslie Safer | 2018-07-01 | 2000.00 | 1000.00 | 1 | 5 |
| Leslie Safer | 2018-08-01 | 2000.00 | 1000.00 | 1 | 6 |
+----------------+-------------+---------+-----------------+------------------+----------+
더 광범위한 예시가 있어요:
CREATE OR REPLACE TABLE tbl
(p INT, o INT, i INT, r INT, s VARCHAR(100));
INSERT INTO tbl VALUES
(100, 1, 1, 70, 'seventy'),
(100, 2, 2, 30, 'thirty'),
(100, 3, 3, 40, 'fourty'),
(100, 4, NULL, 90, 'ninety'),
(100, 5, 5, 50, 'fifty'),
(100, 6, 6, 30, 'thirty'),
(200, 7, 7, 40, 'fourty'),
(200, 8, NULL, NULL, 'n_u_l_l'),
(200, 9, NULL, NULL, 'n_u_l_l'),
(200, 10, 10, 20, 'twenty'),
(200, 11, NULL, 90, 'ninety'),
(300, 12, 12, 30, 'thirty'),
(400, 13, NULL, 20, 'twenty');
SELECT *
FROM tbl
ORDER BY p, o, i;
+-----+----+--------+--------+---------+
| P | O | I | R | S |
+-----+----+--------+--------+---------+
| 100 | 1 | 1 | 70 | seventy |
| 100 | 2 | 2 | 30 | thirty |
| 100 | 3 | 3 | 40 | fourty |
| 100 | 4 | [NULL] | 90 | ninety |
| 100 | 5 | 5 | 50 | fifty |
| 100 | 6 | 6 | 30 | thirty |
| 200 | 7 | 7 | 40 | fourty |
| 200 | 8 | [NULL] | [NULL] | n_u_l_l |
| 200 | 9 | [NULL] | [NULL] | n_u_l_l |
| 200 | 10 | 10 | 20 | twenty |
| 200 | 11 | [NULL] | 90 | ninety |
| 300 | 12 | 12 | 30 | thirty |
| 400 | 13 | [NULL] | 20 | twenty |
+-----+----+--------+--------+---------+
SELECT p, o,
CONDITIONAL_TRUE_EVENT(o > 2) OVER (PARTITION BY p ORDER BY o)
FROM tbl
ORDER BY p, o;
+-----+----+--------------------------------------------------------------+
| P | O | CONDITIONAL_TRUE_EVENT(O>2) OVER (PARTITION BY P ORDER BY O) |
|-----+----+--------------------------------------------------------------|
| 100 | 1 | 0 |
| 100 | 2 | 0 |
| 100 | 3 | 1 |
| 100 | 4 | 2 |
| 100 | 5 | 3 |
| 100 | 6 | 4 |
| 200 | 7 | 1 |
| 200 | 8 | 2 |
| 200 | 9 | 3 |
| 200 | 10 | 4 |
| 200 | 11 | 5 |
| 300 | 12 | 1 |
| 400 | 13 | 1 |
+-----+----+--------------------------------------------------------------+
SELECT p, o,
CONDITIONAL_TRUE_EVENT(LAG(o) OVER (PARTITION BY p ORDER BY o) > 1)
OVER (PARTITION BY p ORDER BY o)
FROM tbl
ORDER BY p, o;
+-----+----+-----------------------------------------------------------------------------------------------------+
| P | O | CONDITIONAL_TRUE_EVENT(LAG(O) OVER (PARTITION BY P ORDER BY O) >1) OVER (PARTITION BY P ORDER BY O) |
|-----+----+-----------------------------------------------------------------------------------------------------|
| 100 | 1 | 0 |
| 100 | 2 | 0 |
| 100 | 3 | 1 |
| 100 | 4 | 2 |
| 100 | 5 | 3 |
| 100 | 6 | 4 |
| 200 | 7 | 0 |
| 200 | 8 | 1 |
| 200 | 9 | 2 |
| 200 | 10 | 3 |
| 200 | 11 | 4 |
| 300 | 12 | 0 |
| 400 | 13 | 0 |
+-----+----+-----------------------------------------------------------------------------------------------------+