SQL grouping and summing impossible?

I want to create a sql query to summarize some data, but I'm starting to think that it is impossible with sql only. The data I have is of the following form:

TRAN_DT     TRAN_RS     DEBT     CRED
10-Jan     701     100     0
20-Jan     701     150     0
21-Jan     701     250     0
22-Jan     705     0     500
23-Jan     571     100     0
24-Jan     571     50     0
25-Jan     701     50     0
26-Jan     701     20     0
27-Jan     705     0     300

The data are classified by TRAN_DT and by TRAN_RS. THA grouping and summing data based on tran_rs, but only when it changes. If in the table above, I don't want to see all the first 3 records but only one value DEBT the sum of these 3 i.e. 100 + 150 + 250 = 500. If the table above after grouping would be similar to that below:

TRAN_DT     TRAN_RS     DEBT     CRED
21-Jan     701     500     0
22-Jan     705     0     500
24-Jan     571     150     0
26-Jan     701     70     0
27-Jan     705     0     300

The TRAN_DT is the last value of the record sum. I undestand that the tran_dt are not selectable. What I've tried so far is the following query:

select tran_dt,
         tran_rs,
         sum(debt)over(partition by tran_rs order by tran_dt rows unbounded preceding),
         sum(cred)over(partition by tran_rs order by tran_dt rows unbounded preceding) from that_table

Is it still possible with only sql, any thoughts?

The report that I am creating in BI Publisher.Maybe it is possible to group the data in the model and my question here?

The Re: tutorial method Tabibitosan by Aketi Jyuuzou me would be very useful here:

with sample_data as (select to_date('10/01/2012', 'dd/mm/yyyy') tran_dt, 701 tran_rs, 100 debt, 0 cred from dual union all
                     select to_date('20/01/2012', 'dd/mm/yyyy') tran_dt, 701 tran_rs, 150 debt, 0 cred from dual union all
                     select to_date('21/01/2012', 'dd/mm/yyyy') tran_dt, 701 tran_rs, 250 debt, 0 cred from dual union all
                     select to_date('22/01/2012', 'dd/mm/yyyy') tran_dt, 705 tran_rs, 0 debt, 500 cred from dual union all
                     select to_date('23/01/2012', 'dd/mm/yyyy') tran_dt, 571 tran_rs, 100 debt, 0 cred from dual union all
                     select to_date('24/01/2012', 'dd/mm/yyyy') tran_dt, 571 tran_rs, 50 debt, 0 cred from dual union all
                     select to_date('25/01/2012', 'dd/mm/yyyy') tran_dt, 701 tran_rs, 50 debt, 0 cred from dual union all
                     select to_date('26/01/2012', 'dd/mm/yyyy') tran_dt, 701 tran_rs, 20 debt, 0 cred from dual union all
                     select to_date('27/01/2012', 'dd/mm/yyyy') tran_dt, 705 tran_rs, 0 debt, 300 cred from dual),
     tabibitosan as (select tran_dt,
                            tran_rs,
                            debt,
                            cred,
                            dense_rank() over (order by tran_dt, tran_rs, rownum)
                              - dense_rank() over (partition by tran_rs order by tran_dt, rownum) grp
                     from   sample_data)
select max(tran_dt),
       tran_rs,
       sum(debt) debt,
       sum(cred) cred
from   tabibitosan
group by tran_rs,
         grp
order by 1, 2;

TRAN_DT       TRAN_RS       DEBT       CRED
---------- ---------- ---------- ----------
21/01/2012        701        500          0
22/01/2012        705          0        500
24/01/2012        571        150          0
26/01/2012        701         70          0
27/01/2012        705          0        300

ETA: I added the rownum in analytical functions to take account of the "encounters" where a tran_rs has multiple entries for the same tran_dt. If you have some other unique key to use (for example, the primary key of the table) then I would use instead.

Published by: Boneist on February 17, 2012 11:41

Tags: Database

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