Related
I need data for all 24 months of data even with missing months.
sample data
id custname reportdate sales
1 xx 31-JAN-17 1256
1 xx 31-MAR-17 3456
1 xx 30-JUN-17 5678
1 xx 31-DEC-17 6785
2 xx 31-JAN-17 1223
2 xx 31-APR-17 3435
2 xx 30-JUN-17 6777
2 xx 31-DEC-17 9643
what i need as a output
id custname reportdate sales
1 xx JAN-17 1256
1 xx FEB-17 <null>
1 xx MAR-17 3456
.....................................
.....................................
1 xx DEC-17 6785
And similarly for id 2 ....
Tried something like this without any luck
select CUSTNAME, reportdate, sales from
(
select TRIM( LEADING '0' FROM TO_CHAR( statementdate, 'YYYY-MM') ) AS REPORTDATE mm, CUSTNAME
froM MYTABLE) SALES,
(
select to_char(date '2017-01-01' + numtoyminterval(level,'month'), 'mm') MonthName
--i actually need format as MON-Last 2 digit of year eg:JAN-17
from dual
connect by level <= 24) ALLMONTHS
where mm = MonthName(+)
also tried with CTE and i cant use my_year.year_month CTE with outer join
my_year as (
select date '2017-01-31' start_date,date '2018-12-31' end_date from dual
)
select (to_char(add_months(trunc(start_date,'mm'),level - 1),'yyyy')||'-'||(to_char(add_months(trunc(start_date,'mm'),level - 1),'mm'))) year_month
from my_year
connect by trunc(end_date,'mm') >= add_months(trunc(start_date,'mm'),level - 1);
select id, customername, reportdate, sales,
TRIM( LEADING '0' FROM TO_CHAR( reportdate, 'YYYY-MM') ) AS stmntdate
from my_oracle_tbl a
where a.stmntdate = my_year.year_month (+)
also tried this as recommended by #Littlefoot, which isnt working
WITH mydates AS (
select LAST_DAY(add_months(date '2017-01-01', level - 1)) as mth, min_id,min_custname
from (
select min(id) as min_id, min(CUSTNAME) as min_custname
from my_oracle_tbl
)
connect by level <= 24)
select
nvl(t.id, a.min_id)id,
nvl(t.CUSTNAME,a.min_custname)CUSTNAME, a.mth, t.sales
from mydates a left join my_oracle_tbl t on a.mth= LAST_DAY(t.reporttdate)
where
t.id=2
;
You can use some old school tricks (UNION ALL IN COMBINATION WITH ADD_MONTHS FUNCTION AND SUM):
select id, custname,month,
decode(sum(sales),0,null,sum(sales)) sales from
(select id, custname, to_char(reportdate, 'mon-rrrr')
month,sales from my_oracle_tbl
UNION ALL
select a.*,b.*,0 sales from
(select distinct id, custname from my_oracle_tbl) a,
(
select to_char(sysdate,'mon')||'-2017' month from dual
UNION ALL
select ,to_char(add_months(sysdate,1),'mon')||'-2017' month from dual
UNION ALL
select ,to_char(add_months(sysdate,2),'mon')||'-2017' from dual
.......
UNION ALL
select ,to_char(add_months(sysdate,11),'mon')||'-2017' from dual) b
)
group by id, custname,month;
This is what i came up with do u see any concerns? is there a better way to write this? I need to get order by lowest to largest dates?? how can i achieve this. As of now it repeats order like this 12-2018,12-2017,11-2018,11-2017. I want 2017 dates first and then 2018
select CUSTNAME, reportdate, sum(sales), mth
from ( select to_char(add_months(date '2017-01-01', level - 1), 'mmyyyy') mth
from dual
connect by level <= 24)mo
left outer join oracle_tbl dc on mo.mth = to_char(reportdate, 'mmyyyy')
group by CUSTNAME, reportdate,mth
order by mth
Here's an example; see if it helps. It displays 12 months (you'd substitute it with 24 in line #21).
SQL> alter session set nls_date_format = 'dd.mm.yyyy';
Session altered.
SQL> with test (id, custname, reportdate, sales) as
2 (select 1, 'xx', date '2017-01-31', 1256 from dual union all
3 select 1, 'xx', date '2017-03-31', 3456 from dual union all
4 select 1, 'xx', date '2017-06-30', 5678 from dual union all
5 --
6 select 2, 'xx', date '2017-03-31', 1223 from dual union all
7 select 2, 'xx', date '2017-07-31', 3435 from dual union all
8 select 2, 'xx', date '2017-09-30', 6777 from dual
9 ),
10 all_dates as
11 (select add_months(min_repdate, column_value - 1) c_mon,
12 min_id,
13 min_custname
14 from (select min(reportdate) min_repdate,
15 id min_id,
16 min(custname) min_custname
17 from test
18 group by id
19 ),
20 table(cast(multiset(select level from dual
21 connect by level <= 12
22 ) as sys.odcinumberlist))
23 )
24 select nvl(t.id, a.min_id) id,
25 nvl(t.custname, a.min_custname) custname,
26 a.c_mon,
27 t.sales
28 from all_dates a left join test t on a.min_id = t.id and a.c_mon = t.reportdate
29 order by id, a.c_mon;
ID CU C_MON SALES
---------- -- ---------- ----------
1 xx 31.01.2017 1256
1 xx 28.02.2017
1 xx 31.03.2017 3456
1 xx 30.04.2017
1 xx 31.05.2017
1 xx 30.06.2017 5678
1 xx 31.07.2017
1 xx 31.08.2017
1 xx 30.09.2017
1 xx 31.10.2017
1 xx 30.11.2017
1 xx 31.12.2017
2 xx 31.03.2017 1223
2 xx 30.04.2017
2 xx 31.05.2017
2 xx 30.06.2017
2 xx 31.07.2017 3435
2 xx 31.08.2017
2 xx 30.09.2017 6777
2 xx 31.10.2017
2 xx 30.11.2017
2 xx 31.12.2017
2 xx 31.01.2018
2 xx 28.02.2018
24 rows selected.
SQL>
I have the following data
PET_REF XDATE TYPE
123 01/01/2017 OBJ
123 01/01/2017 OBJ
123 01/01/2017 OBJ
123 02/01/2017 LVE
456 01/01/2017 OBJ
456 01/01/2017 LVE
456 02/01/2017 OBJ
Is it possible to only return rows for PET_REF where the latest (by XDATE) TYPE is not LVE
So, for the data above, the output should be
PET_REF XDATE TYPE
456 01/01/2017 OBJ
456 01/01/2017 LVE
456 02/01/2017 OBJ
Use FIRST_VALUE analytic function
Select * from
(
select PET_REF, XDATE, TYPE, First_Value(TYPE)over(Partition by PET_REF order by XDATE desc) as Latest_Type
from yourtable
)a
Where Latest_Type <> 'LVE'
SQLFIDDLE DEMO
One way of solving this is to try putting them in a subquery.
SELECT *
FROM t
WHERE c1 IN (
SELECT c1
FROM t
WHERE (c1,c2) IN (SELECT c1, MAX(c2)
FROM t
GROUP BY 1)
AND c3 <> 'LVE');
Here's one option:
SQL> with test (pet_ref, xdate, type) as
2 (select 123, date '2017-01-01', 'obj' from dual union all
3 select 123, date '2017-01-01', 'obj' from dual union all
4 select 123, date '2017-01-01', 'obj' from dual union all
5 select 123, date '2017-01-02', 'lve' from dual union all --
6 select 456, date '2017-01-01', 'obj' from dual union all
7 select 456, date '2017-01-01', 'lve' from dual union all --
8 select 456, date '2017-01-02', 'obj' from dual
9 ),
10 inter as
11 (select pet_ref, type,
12 rank() over (partition by pet_ref order by xdate desc) rnk
13 from test
14 )
15 select * from test t
16 where t.pet_ref not in (select i.pet_ref from inter i
17 where i.rnk = 1
18 and i.type = 'lve');
PET_REF XDATE TYP
---------- ---------- ---
456 02/01/2017 obj
456 01/01/2017 lve
456 01/01/2017 obj
SQL>
Easier way to do this just by using order by :
SELECT *
FROM datatable
WHERE PET_REF LIKE (SELECT MAX(PET_REF) FROM datatable)
ORDER BY XDATE ASC, TYPE DESC;
Try the SQLFiddle
I have a table EMPLOYEE as under:
Enroll Date STS EMP_ID EMP_Name DEPT Rank OST BLOCK
12-Jan-17 Q 123 ABC ABC123 12 Y 1000
14-Jan-17 Q 123 ABC DEF123 12 Y 1000
15-Jan-17 R 123 ABC DEF123 12 Y 100
15-Jan-17 R 123 ABC DEF123 12 Y 200
15-Jan-17 R 123 ABC DEF123 12 Y 300
20-Jan-17 R 123 ABC DEF123 10 Y 300
26-Jan-17 R 456 RST DEF456 8 N 200
26-Jan-17 R 456 RST DEF456 8 N 300
2-Feb-17 Q 123 ABC ABC123 12 Y 300
Now i need to remove the duplicate rows for each emp_id (duplicate if EMP_Name, DEPT, OST and rank is same). If 2 rows have these 4 value same and enroll_date is different then i need not delete that row. And if 2 rows have same enroll date and the 4 fields (OST, EMP_Name, DEPT and rank) are same then i need to keep the row with highest block (1000 followed by 300 followed by 200 and so on)
So after deleting such data my table should have these rows:
Enroll Date STS EMP_ID EMP_Name DEPT Rank OST BLOCK
12-Jan-17 Q 123 ABC ABC123 12 Y 1000
14-Jan-17 Q 123 ABC DEF123 12 Y 1000
15-Jan-17 R 123 ABC DEF123 12 Y 100
2-Feb-17 Q 123 ABC ABC123 12 Y 300
20-Jan-17 R 123 ABC DEF123 10 Y 300
26-Jan-17 R 456 RST DEF456 8 N 200
26-Jan-17 R 456 RST DEF456 8 N 300
I tried using below query and will delete rows which have rn >1
SELECT enroll_date, STS, BLOCK, EMP_ID, EMP_NAME, DEPT,RANK, OST, row_number() over ( partition BY emp_id, enroll_date,emp_name, dept, ost, rank ORDER BY enroll_date ASC, block DESC)rn
FROM employee
But i am getting rn as 1 only everytime.
can someone check the issue here or suggest some other way to do so?
I am creating a temporary table which will have all non duplicate values:
create table employee_temp as
with duplicates as (
SELECT enroll_date, STS, BLOCK, EMP_ID, EMP_NAME, DEPT,RANK, OST, row_number() over ( partition BY emp_id, trunc(enroll_date),emp_name, dept, ost, rank ORDER BY enroll_date ASC, block DESC)rn FROM employee )
SELECT enroll_date, STS, BLOCK, EMP_ID, EMP_NAME, DEPT,RANK, OST from duplicates where rn =1;
It looks like your enroll_date values have non-midnight times, so partitioning by those also made those combinations unique (even though they don't look it when you only show the date part).
My initial thought was that your analytic row_number() was partitoned by too many columns, and that you shouldn't be including the date value you want to order by - it doesn't really make sense to partition by and order by the same thing, as it will be unique. Reducing the columns you actually want to check against, perhaps to:
row_number() over (partition BY emp_id, emp_name, dept, ost, rank
ORDER BY enroll_date ASC, block DESC)
would produce different ranks rather than all being 1. But I don't think that's right; that would probably make your secondary block ordering somewhat redundant, as you'll maybe be unlikely to have two rows with exactly the same time for one ID. Unlikely but not impossible, perhaps.
Re-reading your wording again I don't think you want to be ordering by the enroll_date at all, and you do want to be partitioning by the date instead; but, given that it contains non-midnight times that you apparently want to ignore for this exercise, the partitioning would have to be on the truncated date (which strips the time back to midnight, by default:
row_number() over (partition BY trunc(enroll_date), emp_id, emp_name, dept, ost, rank
ORDER BY block DESC)
With your sample data as a CTE, including slightly different times within each day, and one extra row to get everything the same but the date, this shows your original rn and my two calculated values:
with employee (enroll_date, sts, emp_id, emp_name, dept, rank, ost, block) as (
select to_date('12-Jan-17 00:00:00', 'DD-Mon-RR HH24:MI:SS'), 'Q', 123, 'ABC', 'ABC123', 12, 'Y', 1000 from dual
union all select to_date('14-Jan-17 00:00:00', 'DD-Mon-RR HH24:MI:SS'), 'Q', 123, 'ABC', 'DEF123', 12, 'Y', 1000 from dual
union all select to_date('15-Jan-17 00:00:01', 'DD-Mon-RR HH24:MI:SS'), 'R', 123, 'ABC', 'DEF123', 12, 'Y', 100 from dual
union all select to_date('15-Jan-17 00:00:02', 'DD-Mon-RR HH24:MI:SS'), 'R', 123, 'ABC', 'DEF123', 12, 'Y', 200 from dual
union all select to_date('15-Jan-17 00:00:03', 'DD-Mon-RR HH24:MI:SS'), 'R', 123, 'ABC', 'DEF123', 12, 'Y', 300 from dual
union all select to_date('20-Jan-17 00:00:00', 'DD-Mon-RR HH24:MI:SS'), 'R', 123, 'ABC', 'DEF123', 10, 'Y', 300 from dual
union all select to_date('26-Jan-17 00:00:00', 'DD-Mon-RR HH24:MI:SS'), 'R', 456, 'RST', 'DEF456', 8, 'N', 200 from dual
union all select to_date('26-Jan-17 00:00:01', 'DD-Mon-RR HH24:MI:SS'), 'R', 456, 'RST', 'DEF456', 8, 'N', 300 from dual
union all select to_date('2-Feb-17 00:00:00', 'DD-Mon-RR HH24:MI:SS'), 'Q', 123, 'ABC', 'ABC123', 12, 'Y', 300 from dual
union all select to_date('3-Feb-17 00:00:00', 'DD-Mon-RR HH24:MI:SS'), 'Q', 123, 'ABC', 'ABC123', 12, 'Y', 300 from dual
)
SELECT to_char(enroll_date, 'DD-Mon-RR') as date_only,
enroll_date, sts, block, emp_id, emp_name, dept, rank, ost,
row_number() over ( partition BY emp_id, enroll_date, emp_name, dept, ost, rank
ORDER BY enroll_date ASC, block DESC) your_rn,
row_number() over (partition BY emp_id, emp_name, dept, ost, rank
ORDER BY enroll_date ASC, block DESC) my_rn_1,
row_number() over (partition BY trunc(enroll_date), emp_id, emp_name, dept, ost, rank
ORDER BY block DESC) as my_rn_2
FROM employee
ORDER BY enroll_date;
DATE_ONLY ENROLL_DATE S BLOCK EMP_ID EMP DEPT RANK O YOUR_RN MY_RN_1 MY_RN_2
--------- ------------------- - ----- ------ --- ------ ---- - ------- ------- -------
12-Jan-17 2017-01-12 00:00:00 Q 1000 123 ABC ABC123 12 Y 1 1 1
14-Jan-17 2017-01-14 00:00:00 Q 1000 123 ABC DEF123 12 Y 1 1 1
15-Jan-17 2017-01-15 00:00:01 R 100 123 ABC DEF123 12 Y 1 2 3
15-Jan-17 2017-01-15 00:00:02 R 200 123 ABC DEF123 12 Y 1 3 2
15-Jan-17 2017-01-15 00:00:03 R 300 123 ABC DEF123 12 Y 1 4 1
20-Jan-17 2017-01-20 00:00:00 R 300 123 ABC DEF123 10 Y 1 1 1
26-Jan-17 2017-01-26 00:00:00 R 200 456 RST DEF456 8 N 1 1 2
26-Jan-17 2017-01-26 00:00:01 R 300 456 RST DEF456 8 N 1 2 1
02-Feb-17 2017-02-02 00:00:00 Q 300 123 ABC ABC123 12 Y 1 2 1
03-Feb-17 2017-02-03 00:00:00 Q 300 123 ABC ABC123 12 Y 1 3 1
To identify the rows to delete you can use a subquery:
SELECT enroll_date, sts, block, emp_id, emp_name, dept, rank, ost
FROM (
SELECT enroll_date, sts, block, emp_id, emp_name, dept, rank, ost,
row_number() over (partition BY trunc(enroll_date), emp_id, emp_name, dept, ost, rank
ORDER BY block DESC) as my_rn_2
FROM employee
)
WHERE my_rn_2 > 1
ORDER BY enroll_date;
ENROLL_DATE S BLOCK EMP_ID EMP DEPT RANK O
------------------- - ----- ------ --- ------ ---- -
2017-01-15 00:00:01 R 100 123 ABC DEF123 12 Y
2017-01-15 00:00:02 R 200 123 ABC DEF123 12 Y
2017-01-26 00:00:00 R 200 456 RST DEF456 8 N
You'll need to decide what actually makes sense for your data and requirements though.
The goal is to select the count of distinct customer_id's who have not made a purchase in the rolling 30 day period prior to every day in the calendar year 2016. I have created a calendar table in my database to join to.
Here is an example table for reference, let's say you have customers orders normalized as follows:
+-------------+------------+----------+
| customer_id | date | order_id |
+-------------+------------+----------+
| 123 | 01/25/2016 | 1000 |
+-------------+------------+----------+
| 123 | 04/27/2016 | 1025 |
+-------------+------------+----------+
| 444 | 02/02/2016 | 1010 |
+-------------+------------+----------+
| 521 | 01/23/2016 | 998 |
+-------------+------------+----------+
| 521 | 01/24/2016 | 999 |
+-------------+------------+----------+
The goal output is effectively a calendar with 1 row for every single day of 2016 with a count on each day of how many customers "lapsed" on that day, meaning their last purchase was 30 days or more prior from that day of the year. The final output will look like this:
+------------+--------------+
| date | lapsed_count |
+------------+--------------+
| 01/01/2016 | 0 |
+------------+--------------+
| 01/02/2016 | 0 |
+------------+--------------+
| ... | ... |
+------------+--------------+
| 03/01/2016 | 12 |
+------------+--------------+
| 03/02/2016 | 9 |
+------------+--------------+
| 03/03/2016 | 7 |
+------------+--------------+
This data does not exist in 2015, therefore it's not possible for Jan-01-2016 to have a count of lapsed customers because that is the first possible day to ever make a purchase.
So for customer_id #123, they purchased on 01/25/2016 and 04/27/2016. They should have 2 lapse counts because their purchases are more than 30 days apart. One lapse occurring on 2/24/2016 and another lapse on 05/27/2016.
Customer_id#444 only purchased once, so they should have one lapse count for 30 days after 02/02/2016 on 03/02/2016.
Customer_id#521 is tricky, since they purchased with a frequency of 1 day we will not count the first purchase on 03/02/2016, so there is only one lapse starting from their last purchase of 03/03/2016. The count for the lapse will occur on 04/02/2016 (+30 days).
If you have a table of dates, here is one expensive method:
select date,
sum(case when prev_date < date - 30 then 1 else 0 end) as lapsed
from (select c.date, o.customer_id, max(o.date) as prev_date
from calendar c cross join
(select distinct customer_id from orders) c left join
orders o
on o.date <= c.date and o.customer_id = c.customer_id
group by c.date, o.customer_id
) oc
group by date;
For each date/customer pair, it determines the latest purchase the customer made before the date. It then uses this information to count the lapsed.
To be honest, this will probably work well on a handful of dates, but not for a full year's worth.
Apologies, I didn't read your question properly the first time around. This query will give you all the lapses you have. It takes each order and uses an analytic function to work out the next order date - if the gap is greater than 30 days then a lapse is recorded
WITH
cust_orders (customer_id , order_date , order_id )
AS
(SELECT 1, TO_DATE('01/01/2016','DD/MM/YYYY'), 1001 FROM dual UNION ALL
SELECT 1, TO_DATE('29/01/2016','DD/MM/YYYY'), 1002 FROM dual UNION ALL
SELECT 1, TO_DATE('01/03/2016','DD/MM/YYYY'), 1003 FROM dual UNION ALL
SELECT 2, TO_DATE('01/01/2016','DD/MM/YYYY'), 1004 FROM dual UNION ALL
SELECT 2, TO_DATE('29/01/2016','DD/MM/YYYY'), 1005 FROM dual UNION ALL
SELECT 2, TO_DATE('01/04/2016','DD/MM/YYYY'), 1006 FROM dual UNION ALL
SELECT 2, TO_DATE('01/06/2016','DD/MM/YYYY'), 1007 FROM dual UNION ALL
SELECT 2, TO_DATE('01/08/2016','DD/MM/YYYY'), 1008 FROM dual UNION ALL
SELECT 3, TO_DATE('01/09/2016','DD/MM/YYYY'), 1009 FROM dual UNION ALL
SELECT 3, TO_DATE('01/12/2016','DD/MM/YYYY'), 1010 FROM dual UNION ALL
SELECT 3, TO_DATE('02/12/2016','DD/MM/YYYY'), 1011 FROM dual UNION ALL
SELECT 3, TO_DATE('03/12/2016','DD/MM/YYYY'), 1012 FROM dual UNION ALL
SELECT 3, TO_DATE('04/12/2016','DD/MM/YYYY'), 1013 FROM dual UNION ALL
SELECT 3, TO_DATE('05/12/2016','DD/MM/YYYY'), 1014 FROM dual UNION ALL
SELECT 3, TO_DATE('06/12/2016','DD/MM/YYYY'), 1015 FROM dual UNION ALL
SELECT 3, TO_DATE('07/12/2016','DD/MM/YYYY'), 1016 FROM dual
)
SELECT
customer_id
,order_date
,order_id
,next_order_date
,order_date + 30 lapse_date
FROM
(SELECT
customer_id
,order_date
,order_id
,LEAD(order_date) OVER (PARTITION BY customer_id ORDER BY order_date) next_order_date
FROM
cust_orders
)
WHERE NVL(next_order_date,sysdate) - order_date > 30
;
Now join that to a set of dates and run a COUNT function (enter the year parameter as YYYY) :
WITH
cust_orders (customer_id , order_date , order_id )
AS
(SELECT 1, TO_DATE('01/01/2016','DD/MM/YYYY'), 1001 FROM dual UNION ALL
SELECT 1, TO_DATE('29/01/2016','DD/MM/YYYY'), 1002 FROM dual UNION ALL
SELECT 1, TO_DATE('01/03/2016','DD/MM/YYYY'), 1003 FROM dual UNION ALL
SELECT 2, TO_DATE('01/01/2016','DD/MM/YYYY'), 1004 FROM dual UNION ALL
SELECT 2, TO_DATE('29/01/2016','DD/MM/YYYY'), 1005 FROM dual UNION ALL
SELECT 2, TO_DATE('01/04/2016','DD/MM/YYYY'), 1006 FROM dual UNION ALL
SELECT 2, TO_DATE('01/06/2016','DD/MM/YYYY'), 1007 FROM dual UNION ALL
SELECT 2, TO_DATE('01/08/2016','DD/MM/YYYY'), 1008 FROM dual UNION ALL
SELECT 3, TO_DATE('01/09/2016','DD/MM/YYYY'), 1009 FROM dual UNION ALL
SELECT 3, TO_DATE('01/12/2016','DD/MM/YYYY'), 1010 FROM dual UNION ALL
SELECT 3, TO_DATE('02/12/2016','DD/MM/YYYY'), 1011 FROM dual UNION ALL
SELECT 3, TO_DATE('03/12/2016','DD/MM/YYYY'), 1012 FROM dual UNION ALL
SELECT 3, TO_DATE('04/12/2016','DD/MM/YYYY'), 1013 FROM dual UNION ALL
SELECT 3, TO_DATE('05/12/2016','DD/MM/YYYY'), 1014 FROM dual UNION ALL
SELECT 3, TO_DATE('06/12/2016','DD/MM/YYYY'), 1015 FROM dual UNION ALL
SELECT 3, TO_DATE('07/12/2016','DD/MM/YYYY'), 1016 FROM dual
)
,calendar (date_value)
AS
(SELECT TO_DATE('01/01/'||:P_year,'DD/MM/YYYY') + (rownum -1)
FROM all_tables
WHERE rownum < (TO_DATE('31/12/'||:P_year,'DD/MM/YYYY') - TO_DATE('01/01/'||:P_year,'DD/MM/YYYY')) + 2
)
SELECT
calendar.date_value
,COUNT(*)
FROM
(
SELECT
customer_id
,order_date
,order_id
,next_order_date
,order_date + 30 lapse_date
FROM
(SELECT
customer_id
,order_date
,order_id
,LEAD(order_date) OVER (PARTITION BY customer_id ORDER BY order_date) next_order_date
FROM
cust_orders
)
WHERE NVL(next_order_date,sysdate) - order_date > 30
) lapses
,calendar
WHERE 1=1
AND calendar.date_value = TRUNC(lapses.lapse_date)
GROUP BY
calendar.date_value
;
Or if you really want every date printed out then use this :
WITH
cust_orders (customer_id , order_date , order_id )
AS
(SELECT 1, TO_DATE('01/01/2016','DD/MM/YYYY'), 1001 FROM dual UNION ALL
SELECT 1, TO_DATE('29/01/2016','DD/MM/YYYY'), 1002 FROM dual UNION ALL
SELECT 1, TO_DATE('01/03/2016','DD/MM/YYYY'), 1003 FROM dual UNION ALL
SELECT 2, TO_DATE('01/01/2016','DD/MM/YYYY'), 1004 FROM dual UNION ALL
SELECT 2, TO_DATE('29/01/2016','DD/MM/YYYY'), 1005 FROM dual UNION ALL
SELECT 2, TO_DATE('01/04/2016','DD/MM/YYYY'), 1006 FROM dual UNION ALL
SELECT 2, TO_DATE('01/06/2016','DD/MM/YYYY'), 1007 FROM dual UNION ALL
SELECT 2, TO_DATE('01/08/2016','DD/MM/YYYY'), 1008 FROM dual UNION ALL
SELECT 3, TO_DATE('01/09/2016','DD/MM/YYYY'), 1009 FROM dual UNION ALL
SELECT 3, TO_DATE('01/12/2016','DD/MM/YYYY'), 1010 FROM dual UNION ALL
SELECT 3, TO_DATE('02/12/2016','DD/MM/YYYY'), 1011 FROM dual UNION ALL
SELECT 3, TO_DATE('03/12/2016','DD/MM/YYYY'), 1012 FROM dual UNION ALL
SELECT 3, TO_DATE('04/12/2016','DD/MM/YYYY'), 1013 FROM dual UNION ALL
SELECT 3, TO_DATE('05/12/2016','DD/MM/YYYY'), 1014 FROM dual UNION ALL
SELECT 3, TO_DATE('06/12/2016','DD/MM/YYYY'), 1015 FROM dual UNION ALL
SELECT 3, TO_DATE('07/12/2016','DD/MM/YYYY'), 1016 FROM dual
)
,lapses
AS
(SELECT
customer_id
,order_date
,order_id
,next_order_date
,order_date + 30 lapse_date
FROM
(SELECT
customer_id
,order_date
,order_id
,LEAD(order_date) OVER (PARTITION BY customer_id ORDER BY order_date) next_order_date
FROM
cust_orders
)
WHERE NVL(next_order_date,sysdate) - order_date > 30
)
,calendar (date_value)
AS
(SELECT TO_DATE('01/01/'||:P_year,'DD/MM/YYYY') + (rownum -1)
FROM all_tables
WHERE rownum < (TO_DATE('31/12/'||:P_year,'DD/MM/YYYY') - TO_DATE('01/01/'||:P_year,'DD/MM/YYYY')) + 2
)
SELECT
calendar.date_value
,(SELECT COUNT(*)
FROM lapses
WHERE calendar.date_value = lapses.lapse_date
)
FROM
calendar
WHERE 1=1
ORDER BY
calendar.date_value
;
Here's how I'd do it:
WITH your_table AS (SELECT 123 customer_id, to_date('24/01/2016', 'dd/mm/yyyy') order_date, 12345 order_id FROM dual UNION ALL
SELECT 123 customer_id, to_date('24/01/2016', 'dd/mm/yyyy') order_date, 12346 order_id FROM dual UNION ALL
SELECT 123 customer_id, to_date('25/01/2016', 'dd/mm/yyyy') order_date, 12347 order_id FROM dual UNION ALL
SELECT 123 customer_id, to_date('24/02/2016', 'dd/mm/yyyy') order_date, 12347 order_id FROM dual UNION ALL
SELECT 123 customer_id, to_date('16/03/2016', 'dd/mm/yyyy') order_date, 12348 order_id FROM dual UNION ALL
SELECT 123 customer_id, to_date('18/04/2016', 'dd/mm/yyyy') order_date, 12349 order_id FROM dual UNION ALL
SELECT 456 customer_id, to_date('20/02/2016', 'dd/mm/yyyy') order_date, 12350 order_id FROM dual UNION ALL
SELECT 456 customer_id, to_date('01/03/2016', 'dd/mm/yyyy') order_date, 12351 order_id FROM dual UNION ALL
SELECT 456 customer_id, to_date('03/03/2016', 'dd/mm/yyyy') order_date, 12352 order_id FROM dual UNION ALL
SELECT 456 customer_id, to_date('18/04/2016', 'dd/mm/yyyy') order_date, 12353 order_id FROM dual UNION ALL
SELECT 456 customer_id, to_date('20/05/2016', 'dd/mm/yyyy') order_date, 12354 order_id FROM dual UNION ALL
SELECT 456 customer_id, to_date('23/06/2016', 'dd/mm/yyyy') order_date, 12355 order_id FROM dual UNION ALL
SELECT 456 customer_id, to_date('19/01/2017', 'dd/mm/yyyy') order_date, 12356 order_id FROM dual),
-- end of mimicking your_table with data in it
lapsed_info AS (SELECT customer_id,
order_date,
CASE WHEN TRUNC(SYSDATE) - order_date <= 30 THEN NULL
WHEN COUNT(*) OVER (PARTITION BY customer_id ORDER BY order_date RANGE BETWEEN 1 FOLLOWING AND 30 FOLLOWING) = 0 THEN order_date+30
ELSE NULL
END lapsed_date
FROM your_table),
dates AS (SELECT to_date('01/01/2016', 'dd/mm/yyyy') + LEVEL -1 dt
FROM dual
CONNECT BY to_date('01/01/2016', 'dd/mm/yyyy') + LEVEL -1 <= TRUNC(SYSDATE))
SELECT dates.dt,
COUNT(li.lapsed_date) lapsed_count
FROM dates
LEFT OUTER JOIN lapsed_info li ON dates.dt = li.lapsed_date
GROUP BY dates.dt
ORDER BY dates.dt;
Results:
DT LAPSED_COUNT
---------- ------------
01/01/2016 0
<snip>
23/01/2016 0
24/01/2016 0
25/01/2016 0
26/01/2016 0
<snip>
19/02/2016 0
20/02/2016 0
21/02/2016 0
22/02/2016 0
23/02/2016 0
24/02/2016 1
25/02/2016 0
<snip>
29/02/2016 0
01/03/2016 0
02/03/2016 0
03/03/2016 0
04/03/2016 0
<snip>
15/03/2016 0
16/03/2016 0
17/03/2016 0
<snip>
20/03/2016 0
21/03/2016 0
22/03/2016 0
<snip>
30/03/2016 0
31/03/2016 0
01/04/2016 0
02/04/2016 1
03/04/2016 0
<snip>
14/04/2016 0
15/04/2016 1
16/04/2016 0
17/04/2016 0
18/04/2016 0
19/04/2016 0
<snip>
17/05/2016 0
18/05/2016 2
19/05/2016 0
20/05/2016 0
21/05/2016 0
<snip>
18/06/2016 0
19/06/2016 1
20/06/2016 0
21/06/2016 0
22/06/2016 0
23/06/2016 0
24/06/2016 0
<snip>
22/07/2016 0
23/07/2016 1
24/07/2016 0
<snip>
18/01/2017 0
19/01/2017 0
20/01/2017 0
<snip>
08/02/2017 0
This takes your data, and uses an the analytic count function to work out the number of rows that have a value within 30 days of (but excluding) the current row's date.
Then we apply a case expression to determine that if the row has a date within 30 days of today's date, we'll count those as not lapsed. If a count of 0 was returned, then the row is considered lapsed and we'll output the lapsed date as the order_date plus 30 days. Any other count result means the row has not lapsed.
The above is all worked out in the lapsed_info subquery.
Then all we need to do is list the dates (see the dates subquery) and outer join the lapsed_info subquery to it based on the lapsed_date and then do a count of the lapsed dates for each day.
I am attempting to write Oracle SQL.
I am looking for solution something similar. Please find below data I have
start_date end_date customer
01-01-2012 31-06-2012 a
01-01-2012 31-01-2012 b
01-02-2012 31-03-2012 c
I want the count of customer in that date period. My result should look like below
Month : Customer Count
JAN-12 : 2
FEB-12 : 2
MAR-12 : 2
APR-12 : 1
MAY-12 : 1
JUN-12 : 1
One option would be to generate the months separately in another query and join that to your data table (note that I'm assuming that you intended customer A to have an end-date of June 30, 2012 since there is no June 31).
SQL> ed
Wrote file afiedt.buf
1 with mnths as(
2 select add_months( date '2012-01-01', level - 1 ) mnth
3 from dual
4 connect by level <= 6 ),
5 data as (
6 select date '2012-01-01' start_date, date '2012-06-30' end_date, 'a' customer from dual union all
7 select date '2012-01-01', date '2012-01-31', 'b' from dual union all
8 select date '2012-02-01', date '2012-03-31', 'c' from dual
9 )
10 select mnths.mnth, count(*)
11 from data,
12 mnths
13 where mnths.mnth between data.start_date and data.end_date
14 group by mnths.mnth
15* order by mnths.mnth
SQL> /
MNTH COUNT(*)
--------- ----------
01-JAN-12 2
01-FEB-12 2
01-MAR-12 2
01-APR-12 1
01-MAY-12 1
01-JUN-12 1
6 rows selected.
WITH TMP(monthyear,start_date,end_date,customer) AS (
select LAST_DAY(start_date),
CAST(ADD_MONTHS(start_date, 1) AS DATE),
end_date,
customer
from data
union all
select LAST_DAY(start_date),
CAST(ADD_MONTHS(start_date, 1) AS DATE),
end_date,
customer
from TMP
where LAST_DAY(end_date) >= LAST_DAY(start_date)
)
SELECT TO_CHAR(MonthYear, 'MON-YY') TheMonth,
Count(Customer) Customers
FROM TMP
GROUP BY MonthYear
ORDER BY MonthYear;
SQLFiddle