I'm trying to return common date periods (per id) from below data, but I cannot find a way to handle case when date periods have a gap between common periods. Can anyone help?
|id|code_id|code|date_from|date_to|
|--|--|--|--|--|
|10|100| 1000 |02/02/2022 |03/02/2022 23:57:00|
|10|100| 1000 |07/02/2022 01:00:00 |08/02/2022 |
|10|100| 2000 |02/02/2022 |02/02/2022 23:00:00|
|10|100| 2000 |07/02/2022 03:00:00 |08/02/2022 |
|10|200| 2000 |02/02/2022 02:14:00 |04/02/2022 21:37:00|
|20|100| 1000 |01/02/2022 05:00:00 |03/02/2022 |
|30|100| 2000 |02/02/2022 |02/02/2022 23:00:00|
|30|200| 2000 |02/02/2022 02:14:00 |04/02/2022 |
|40|100| 2000 |07/02/2022 03:00:00 |08/02/2022 23:10:00|
|50|200| 2000 |04/02/2022 |04/02/2022 21:37:00|
|50|200| 3000 |04/02/2022 02:12:00 |05/02/2022 23:31:00|
Below simple query works fine, but only for ids which have one common period (with no gaps).
I would expect for id = 10 to return two rows (as there is a gap between dates) for periods which are:
I) 02/02/2022 00:00:00 <-> 04/02/2022 21:37:00
II) 07/02/2022 01:00:00 <-> 08/02/2022 00:00:00
SELECT id
,MIN(date_from) date_from
,MAX(date_to) date_to
FROM my_gtt
GROUP BY id
ORDER BY id
Current results (but id = 10 is incorrect):
|id|date_from|date_to|
|--|--|--|
|10| 02/02/2022 |08/02/2022 |
|20| 01/02/2022 05:00:00 |03/02/2022 |
|30| 02/02/2022 |04/02/2022 |
|40| 07/02/2022 03:00:00 |08/02/2022 23:10:00|
|50| 04/02/2022 |05/02/2022 23:31:00|
Data and table creation:
CREATE GLOBAL TEMPORARY TABLE my_gtt
(
id NUMBER(10),
code_id NUMBER(10),
code NUMBER(10),
date_from DATE,
date_to DATE
)
ON COMMIT PRESERVE ROWS;
INSERT INTO my_gtt VALUES (10, 100, 1000, TO_DATE('02-02-2022', 'dd-mm-yyyy'), TO_DATE('03-02-2022 23:57:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (10, 100, 1000, TO_DATE('07-02-2022 01:00:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('08-02-2022', 'dd-mm-yyyy'));
INSERT INTO my_gtt VALUES (10, 100, 2000, TO_DATE('02-02-2022', 'dd-mm-yyyy'), TO_DATE('02-02-2022 23:00:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (10, 100, 2000, TO_DATE('07-02-2022 03:00:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('08-02-2022', 'dd-mm-yyyy'));
INSERT INTO my_gtt VALUES (10, 200, 2000, TO_DATE('02-02-2022 02:14:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('04-02-2022 21:37:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (20, 100, 1000, TO_DATE('01-02-2022 05:00:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('03-02-2022', 'dd-mm-yyyy'));
INSERT INTO my_gtt VALUES (30, 100, 2000, TO_DATE('02-02-2022', 'dd-mm-yyyy'), TO_DATE('02-02-2022 23:00:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (30, 200, 2000, TO_DATE('02-02-2022 02:14:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('04-02-2022', 'dd-mm-yyyy'));
INSERT INTO my_gtt VALUES (40, 100, 2000, TO_DATE('07-02-2022 03:00:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('08-02-2022 23:10:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (50, 200, 2000, TO_DATE('04-02-2022', 'dd-mm-yyyy'), TO_DATE('04-02-2022 21:37:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (50, 200, 3000, TO_DATE('04-02-2022 02:12:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('05-02-2022 23:31:00', 'dd-mm-yyyy hh24:mi:ss'));
From Oracle 12, MATCH_RECOGNIZE is the simplest solution:
SELECT *
FROM my_gtt
MATCH_RECOGNIZE (
PARTITION BY id
ORDER BY date_from, date_to
MEASURES
MIN(date_from) AS start_date,
MAX(date_to) AS end_date
PATTERN (overlap* last_row)
DEFINE
overlap AS MAX(date_to) >= NEXT(date_from)
);
However, if you are on an earlier version you can find the output using:
SELECT id,
MIN(dt) AS date_from,
MAX(dt) AS date_to
FROM (
SELECT id,
dt,
SUM(value) OVER (PARTITION BY id ORDER BY dt, ROWNUM) AS match_no
FROM (
SELECT id,
dt,
type * SUM(type) OVER (PARTITION BY id ORDER BY dt, ROWNUM) AS value
FROM my_gtt
UNPIVOT (dt FOR type IN (date_from AS 1, date_to AS -1))
)
WHERE value IN (1,0)
)
GROUP BY id, match_no
Which, for the sample data:
INSERT INTO my_gtt VALUES (10, 100, 1000, TO_DATE('02-02-2022', 'dd-mm-yyyy'), TO_DATE('03-02-2022 23:57:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (10, 100, 1000, TO_DATE('07-02-2022 01:00:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('08-02-2022', 'dd-mm-yyyy'));
INSERT INTO my_gtt VALUES (10, 100, 2000, TO_DATE('02-02-2022', 'dd-mm-yyyy'), TO_DATE('02-02-2022 23:00:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (10, 100, 2000, TO_DATE('07-02-2022 03:00:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('08-02-2022', 'dd-mm-yyyy'));
INSERT INTO my_gtt VALUES (10, 200, 2000, TO_DATE('02-02-2022 02:14:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('04-02-2022 21:37:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (20, 100, 1000, TO_DATE('01-02-2022 05:00:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('03-02-2022', 'dd-mm-yyyy'));
INSERT INTO my_gtt VALUES (30, 100, 2000, TO_DATE('02-02-2022', 'dd-mm-yyyy'), TO_DATE('02-02-2022 23:00:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (30, 200, 2000, TO_DATE('02-02-2022 02:14:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('04-02-2022', 'dd-mm-yyyy'));
INSERT INTO my_gtt VALUES (40, 100, 2000, TO_DATE('07-02-2022 03:00:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('08-02-2022 23:10:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (50, 200, 2000, TO_DATE('04-02-2022', 'dd-mm-yyyy'), TO_DATE('04-02-2022 21:37:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (50, 200, 3000, TO_DATE('04-02-2022 02:12:00', 'dd-mm-yyyy hh24:mi:ss'), TO_DATE('05-02-2022 23:31:00', 'dd-mm-yyyy hh24:mi:ss'));
INSERT INTO my_gtt VALUES (60, 200, 3000, DATE '2022-01-01', DATE '2022-01-10');
INSERT INTO my_gtt VALUES (60, 200, 3000, DATE '2022-01-02', DATE '2022-01-04');
INSERT INTO my_gtt VALUES (60, 200, 3000, DATE '2022-01-06', DATE '2022-01-11');
INSERT INTO my_gtt VALUES (60, 200, 3000, DATE '2022-01-13', DATE '2022-01-16');
INSERT INTO my_gtt VALUES (60, 200, 3000, DATE '2022-01-14', DATE '2022-01-15');
Both output:
ID START_DATE END_DATE 10 2022-02-02 00:00:00 2022-02-04 21:37:00 10 2022-02-07 01:00:00 2022-02-08 00:00:00 20 2022-02-01 05:00:00 2022-02-03 00:00:00 30 2022-02-02 00:00:00 2022-02-04 00:00:00 40 2022-02-07 03:00:00 2022-02-08 23:10:00 50 2022-02-04 00:00:00 2022-02-05 23:31:00 60 2022-01-01 00:00:00 2022-01-11 00:00:00 60 2022-01-13 00:00:00 2022-01-16 00:00:00
db<>fiddle here