카테고리:: 윈도우 함수 (일반)

CONDITIONAL_CHANGE_EVENT¶

현재 행의 expr1 인자 값이 이전 행의 expr1 값과 다른 경우, 윈도우 파티션 내의 각 행에 대한 윈도우 이벤트 번호를 반환합니다. 윈도우 이벤트 번호는 0에서 시작하고 해당 윈도우 내에서 지금까지의 변경 사항 수를 나타내기 위해 1씩 증가합니다.

구문¶

CONDITIONAL_CHANGE_EVENT( <expr1> ) OVER ( [ PARTITION BY <expr2> ] ORDER BY <expr3> )

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인자¶

expr1: 이전 행의 식과 비교되는 식입니다.
expr2: 분할하는 선택적 식입니다.
expr3: 각 파티션 내에서 순서를 지정하는 식입니다.

사용법 노트¶

CONDITIONAL_CHANGE_EVENT (expr1) OVER (window_frame) 식은 다음과 같이 계산됩니다.

CONDITIONAL_TRUE_EVENT( <식1> != LAG(<식1>) OVER(window_frame)) OVER(window_frame)

CONDITIONAL_TRUE_EVENT에 대한 자세한 내용은 CONDITIONAL_TRUE_EVENT 를 참조하십시오.

예¶

이는 정전되었다가 다시 돌아온 횟수(즉, 전압이 0으로 떨어지거나 복구된 횟수)를 감지하는 방법을 보여줍니다. (이 예에서는 15분마다 전압을 샘플링하는 것으로 충분하다고 가정합니다. 정전은 15분 미만으로 지속될 수 있으므로 일반적으로는 더 빈번한 샘플을 얻거나 쿼리 결과를 근사치로 처리하는 것이 좋습니다.)

테이블을 만들고 로딩합니다.

CREATE TABLE voltage_readings (
    site_ID INTEGER, -- which refrigerator the measurement was taken in.
    ts TIMESTAMP,  -- the time at which the temperature was measured.
    VOLTAGE FLOAT
    );
INSERT INTO voltage_readings (site_ID, ts, voltage) VALUES
    (1, '2019-10-30 13:00:00', 120),
    (1, '2019-10-30 13:15:00', 120),
    (1, '2019-10-30 13:30:00',   0),
    (1, '2019-10-30 13:45:00',   0),
    (1, '2019-10-30 14:00:00',   0),
    (1, '2019-10-30 14:15:00',   0),
    (1, '2019-10-30 14:30:00', 120)
    ;

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이는 전압이 0인 샘플을 보여줍니다. 이러한 0볼트 이벤트가 동일 정전 또는 다른 정전의 일부인지 여부와 관계없이 보여줍니다.

SELECT site_ID, ts, voltage
    FROM voltage_readings
    WHERE voltage = 0
    ORDER BY ts;
+---------+-------------------------+---------+
| SITE_ID | TS                      | VOLTAGE |
|---------+-------------------------+---------|
|       1 | 2019-10-30 13:30:00.000 |       0 |
|       1 | 2019-10-30 13:45:00.000 |       0 |
|       1 | 2019-10-30 14:00:00.000 |       0 |
|       1 | 2019-10-30 14:15:00.000 |       0 |
+---------+-------------------------+---------+

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이는 전압 변경 여부를 나타내는 열과 함께 샘플을 보여줍니다.

SELECT
      site_ID,
      ts,
      voltage,
      CONDITIONAL_CHANGE_EVENT(voltage = 0) OVER (ORDER BY ts) AS power_changes
    FROM voltage_readings;
+---------+-------------------------+---------+---------------+
| SITE_ID | TS                      | VOLTAGE | POWER_CHANGES |
|---------+-------------------------+---------+---------------|
|       1 | 2019-10-30 13:00:00.000 |     120 |             0 |
|       1 | 2019-10-30 13:15:00.000 |     120 |             0 |
|       1 | 2019-10-30 13:30:00.000 |       0 |             1 |
|       1 | 2019-10-30 13:45:00.000 |       0 |             1 |
|       1 | 2019-10-30 14:00:00.000 |       0 |             1 |
|       1 | 2019-10-30 14:15:00.000 |       0 |             1 |
|       1 | 2019-10-30 14:30:00.000 |     120 |             2 |
+---------+-------------------------+---------+---------------+

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이는 전원이 중지되고 다시 시작된 시간을 보여줍니다.

WITH power_change_events AS
    (
    SELECT
      site_ID,
      ts,
      voltage,
      CONDITIONAL_CHANGE_EVENT(voltage = 0) OVER (ORDER BY ts) AS power_changes
    FROM voltage_readings
    )
SELECT
      site_ID,
      MIN(ts),
      voltage,
      power_changes
    FROM power_change_events
    GROUP BY site_ID, power_changes, voltage
    ORDER BY 2
    ;
+---------+-------------------------+---------+---------------+
| SITE_ID | MIN(TS)                 | VOLTAGE | POWER_CHANGES |
|---------+-------------------------+---------+---------------|
|       1 | 2019-10-30 13:00:00.000 |     120 |             0 |
|       1 | 2019-10-30 13:30:00.000 |       0 |             1 |
|       1 | 2019-10-30 14:30:00.000 |     120 |             2 |
+---------+-------------------------+---------+---------------+

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이는 전원이 몇 번 중지되고 다시 시작되었는지 보여줍니다.

WITH power_change_events AS
    (
    SELECT
          site_ID,
          CONDITIONAL_CHANGE_EVENT(voltage = 0) OVER (ORDER BY ts) AS power_changes
        FROM voltage_readings
    )
SELECT MAX(power_changes) 
    FROM power_change_events
    GROUP BY site_ID
    ;
+--------------------+
| MAX(POWER_CHANGES) |
|--------------------|
|                  2 |
+--------------------+

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이 예는 다음을 보여줍니다.

파티션 내의 변경 번호는 지정된 값이 변경될 때마다 변경됩니다.
NULL 값은 새 값이나 변경된 값으로 간주되지 않습니다.

변경 횟수는 각 파티션에 대해 0부터 시작됩니다.

테이블을 만들고 로딩합니다.

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);

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테이블을 쿼리합니다.

SELECT province, o_col,
      CONDITIONAL_CHANGE_EVENT(o_col) 
        OVER (PARTITION BY province ORDER BY o_col) 
          AS change_event
    FROM table1
    ORDER BY province, o_col
    ;
+----------+-------+--------------+
| PROVINCE | O_COL | CHANGE_EVENT |
|----------+-------+--------------|
| Alberta  |     0 |            0 |
| Alberta  |     0 |            0 |
| Alberta  |    13 |            1 |
| Alberta  |    13 |            1 |
| Alberta  |    14 |            2 |
| Alberta  |    15 |            3 |
| Alberta  |  NULL |            3 |
| Manitoba |    30 |            0 |
+----------+-------+--------------+

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다음 예는 다음을 보여줍니다.

expr1 은 열이 아닌 식일 수 있습니다. 이 쿼리는 o_col < 15 식을 사용하며, 쿼리의 출력은 o_col의 값이 15보다 작은 값에서 15 이상의 값으로 변경될 때를 보여줍니다.

expr3 은 expr1 과 일치할 필요가 없습니다. 즉, OVER 절의 ORDER BY 하위 절에 있는 식이 CONDITIONAL_CHANGE_EVENT 함수에 있는 식과 일치할 필요가 없습니다.

테이블을 쿼리합니다.

SELECT province, o_col,
      'o_col < 15' AS condition,
      CONDITIONAL_CHANGE_EVENT(o_col) 
        OVER (PARTITION BY province ORDER BY o_col) 
          AS change_event,
      CONDITIONAL_CHANGE_EVENT(o_col < 15) 
        OVER (PARTITION BY province ORDER BY o_col) 
          AS change_event_2
    FROM table1
    ORDER BY province, o_col
    ;
+----------+-------+------------+--------------+----------------+
| PROVINCE | O_COL | CONDITION  | CHANGE_EVENT | CHANGE_EVENT_2 |
|----------+-------+------------+--------------+----------------|
| Alberta  |     0 | o_col < 15 |            0 |              0 |
| Alberta  |     0 | o_col < 15 |            0 |              0 |
| Alberta  |    13 | o_col < 15 |            1 |              0 |
| Alberta  |    13 | o_col < 15 |            1 |              0 |
| Alberta  |    14 | o_col < 15 |            2 |              0 |
| Alberta  |    15 | o_col < 15 |            3 |              1 |
| Alberta  |  NULL | o_col < 15 |            3 |              1 |
| Manitoba |    30 | o_col < 15 |            0 |              0 |
+----------+-------+------------+--------------+----------------+

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다음 예는 CONDITIONAL_CHANGE_EVENT와 CONDITIONAL_TRUE_EVENT를 비교합니다.

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 |
+----------+-------+--------------+------------+

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이 예도 CONDITIONAL_CHANGE_EVENT와 CONDITIONAL_TRUE_EVENT를 비교합니다.

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 and 
    -- further behind. Essentially, this person just 'failed' once.
    ('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)

    ;

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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 |
+----------------+-------------+---------+-----------------+------------------+----------+

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다음은 보다 광범위한 예입니다.

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_CHANGE_EVENT(o) OVER (PARTITION BY p ORDER BY o) FROM tbl ORDER BY p, o;

+-----+----+--------------------------------------------------------------+
|   P |  O | CONDITIONAL_CHANGE_EVENT(O) OVER (PARTITION BY P ORDER BY O) |
|-----+----+--------------------------------------------------------------|
| 100 |  1 |                                                            0 |
| 100 |  2 |                                                            1 |
| 100 |  3 |                                                            2 |
| 100 |  4 |                                                            3 |
| 100 |  5 |                                                            4 |
| 100 |  6 |                                                            5 |
| 200 |  7 |                                                            0 |
| 200 |  8 |                                                            1 |
| 200 |  9 |                                                            2 |
| 200 | 10 |                                                            3 |
| 200 | 11 |                                                            4 |
| 300 | 12 |                                                            0 |
| 400 | 13 |                                                            0 |
+-----+----+--------------------------------------------------------------+

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