I have the below data. I want to subtract the first row from Total Qty (80) and then subtract the rest of the rows from QTY from the previous row of QTY1.
QTY QTY1 DATE TOTAL QTY
2 78 01-JAN-20 80
1 77 15-JAN-20
46 31 22-JAN-20
16 15 27-JAN-20
Is there a way to do this? Any help is greatly appreciated. Thanks
select
t.*
,first_value(TOTAL_QTY)over(order by DT) - sum(QTY)over(order by DT) as QTY1
from t;
Full example with your sample data:
with T(QTY, DT, TOTAL_QTY) as (
select 2 , to_date('01-JAN-20','dd-mon-yy'),80 from dual union all
select 1 , to_date('15-JAN-20','dd-mon-yy'),null from dual union all
select 46, to_date('22-JAN-20','dd-mon-yy'),null from dual union all
select 16, to_date('27-JAN-20','dd-mon-yy'),null from dual
)
select
t.*
,first_value(TOTAL_QTY)over(order by DT) - sum(QTY)over(order by DT) as QTY1
from t;
Result:
QTY DT TOTAL_QTY QTY1
2 2020-01-01 80 78
1 2020-01-15 77
46 2020-01-22 31
16 2020-01-27 15
SQL tables represent unordered sets. Your question seems to rely on the ordering of the rows. Let me assume you have a column that represents the ordering.
Use a cumulative sum:
select t.*,
sum(total_qty) over () - sum(qty) over (order by <ordering col>) as qty1
from t;
Here is a db<>fiddle.
Something like this (the CTE is just your data): if you add any more stuff later (in the total_qty column), then that would also get added to the total_qty calcuation (as would be typical for additions to, and subtractions from, inventory.
with d as
(select 2 qty, 78 qty1 , to_date('01-JAN-20','dd-mon-rr') datecol, 80 total_qty from dual union all
select 1, 77, to_date('15-JAN-20','dd-mon-rr'),null from dual union all
select 46 , 31, to_date('22-JAN-20','dd-mon-rr'),null from dual union all
select 16 , 15 , to_date('27-JAN-20','dd-mon-rr'),null from dual
)
select sum(total_qty) over (order by datecol) - sum(qty) over (order by datecol)
from d
You can do:
select
qty,
first_value(total_qty) over(order by date)
- sum(qty) over(order by date) as qty1,
date, total_qty
from t
order by date
Related
I have a table on which I want to compute the sum of revenue on last 7 days from the observed day. Here is my table -
with temp as
(
select DATE('2019-06-29') as transaction_date, "x"as id, 0 as revenue
union all
select DATE('2019-06-30') as transaction_date, "x"as id, 80 as revenue
union all
select DATE('2019-07-04') as transaction_date, "x"as id, 64 as revenue
union all
select DATE('2019-07-06') as transaction_date, "x"as id, 64 as revenue
union all
select DATE('2019-07-11') as transaction_date, "x"as id, 75 as revenue
union all
select DATE('2019-07-12') as transaction_date, "x"as id, 0 as revenue
)
select * from temp
I want to take a sum of last 7 days for each transaction_date. For instance for the last record which has transaction_date = 2019-07-12, I would like to add another column which adds up revenue for last 7 days from 2019-07-12 (which is until 2019-07-05), hence the value of new rollup_revenue column would be 0 + 75 + 64 = 139. Likewise, I need to compute the rollup for all the dates for every ID.
Note - the ID may or may not appear daily.
I have tried self join but I am unable to figure it out.
Below is for BigQuery Standard SQL
#standardSQL
SELECT *,
SUM(revenue) OVER(
PARTITION BY id ORDER BY UNIX_DATE(transaction_date)
RANGE BETWEEN 6 PRECEDING AND CURRENT ROW
) rollup_revenue
FROM `project.dataset.temp`
You can test, play with above using sample data from your question as in example below
#standardSQL
WITH `project.dataset.temp` AS (
SELECT DATE '2019-06-29' AS transaction_date, 'x' AS id, 0 AS revenue UNION ALL
SELECT '2019-06-30', 'x', 80 UNION ALL
SELECT '2019-07-04', 'x', 64 UNION ALL
SELECT '2019-07-06', 'x', 64 UNION ALL
SELECT '2019-07-11', 'x', 75 UNION ALL
SELECT '2019-07-12', 'x', 0
)
SELECT *,
SUM(revenue) OVER(
PARTITION BY id ORDER BY UNIX_DATE(transaction_date)
RANGE BETWEEN 6 PRECEDING AND CURRENT ROW
) rollup_revenue
FROM `project.dataset.temp`
-- ORDER BY transaction_date
with result
Row transaction_date id revenue rollup_revenue
1 2019-06-29 x 0 0
2 2019-06-30 x 80 80
3 2019-07-04 x 64 144
4 2019-07-06 x 64 208
5 2019-07-11 x 75 139
6 2019-07-12 x 0 139
One option uses a correlated subquery to find the rolling sum:
SELECT
transaction_date,
revenue,
(SELECT SUM(t2.revenue) FROM temp t2 WHERE t2.transaction_date
BETWEEN DATE_SUB(t1.transaction_date, INTERVAL 7 DAY) AND
t1.transaction_date) AS rev_7_days
FROM temp t1
ORDER BY
transaction_date;
I have data:
ID DUE AMT
4 2018-03-10 335.75
3 2018-04-10 334.75
1 2018-05-10 333.75
2 2018-06-10 332.75
I need to extract:
least due (03-10)
amt for least due (335.75)
sum of amt column.
Could it be done in single query?
Try keep dense rank:
with tt as (
select 4 id, date '2018-03-10' due, 335.75 amt from dual union all
select 3 id, date '2018-04-10' due, 334.75 amt from dual union all
select 1 id, date '2018-05-10' due, 333.75 amt from dual union all
select 2 id, date '2018-06-10' due, 332.75 amt from dual
)
select min(due) least_due,
min(amt) keep (dense_rank first order by due) amt_for_least_due,
sum(amt) sum_amt
from tt
We can try using analytic functions here:
WITH cte AS (
SELECT ID, DUE, AMOUNT,
SUM(AMOUNT) OVER () AS TOTALAMOUNT,
ROW_NUMBER() OVER (ORDER BY DUE) rn
FROM yourTable
)
SELECT ID, DUE, AMOUNT, TOTALAMOUNT
FROM cte
WHERE rn = 1;
I have a oracle table with the following format,
For eg:
JLID Dcode SID TDT QTY
8295783 3119255 9842 3/5/2018 14
8269771 3119255 9842 3/6/2018 11
8302211 3119255 1126 3/1/2018 19
Here I have different SID for the same Dcode, now I need to get the SID with the maximum Qty. (i.e) for SID 9842 - (14+11)=25, for SID 1126 it is 19, then the results should be on SID 9842. So, our query should returns the following results
JLID Dcode START_DT END_DT SID
111 3119255 3/1/2018 3/31/2018 12:00 9842
Startdate and enddate should be calculated from TDT (i.e) start date is the first date of the month and the end date is the last date of the month
Can anyone please suggest me some ideas to do it.
It might be as simple as this:
SELECT Dcode, start_date, end_date, SID FROM (
SELECT Dcode, SID, TRUNC(start_date, 'MONTH') AS start_date
, LAST_DAY(end_date) AS end_date
, ROW_NUMBER() OVER ( PARTITION BY Dcode ORDER BY total_qty DESC ) AS rn
FROM (
SELECT Dcode, SID, MIN(TDT) AS start_date, MAX(TDT) AS end_date
, SUM(QTY) AS total_qty
FROM mytable
GROUP BY Dcode, SID
)
) WHERE rn = 1
In the inner most subquery I aggregation to get the range of dates and total quantity for particular values of Dcode and SID. Then I use an anaylitic (window) function to get the row for which total quantity is the greatest. (You would want to use RANK() in place of ROW_NUMBER() in the event you want to return more than one value of SID with the same quantity.)
Here's one option which doesn't contain JLID = 111 in the final result as I have no idea where you took it from.
SQL> with test (jlid, dcode, sid, tdt, qty) as
2 (select 8295783, 3119255, 9842, date '2018-03-05', 14 from dual union
3 select 8269771, 3119255, 9842, date '2018-08-22', 11 from dual union
4 select 8302211, 3119255, 1126, date '2018-03-01', 19 from dual union
5 --
6 select 1234567, 1112223, 1000, date '2018-06-16', 88 from dual
7 )
8 select dcode,
9 min (trunc (tdt, 'mm')) start_dt, --> MIN
10 max (last_day (tdt)) end_dt, --> MAX
11 sid
12 from (select dcode,
13 sid,
14 tdt,
15 sqty,
16 rank () over (partition by dcode order by sqty desc) rnk
17 from (select dcode,
18 sid,
19 tdt,
20 sum (qty) over (partition by dcode, sid) sqty
21 from test))
22 where rnk = 1
23 group by dcode, sid; --> GROUP BY
DCODE START_DT END_DT SID
---------- ---------------- ---------------- ----------
1112223 01.06.2018 00:00 30.06.2018 00:00 1000
3119255 01.03.2018 00:00 31.08.2018 00:00 9842
SQL>
I have the following data:
ID |MPERIOD|FRDATE |FR
===+=======+==========+==
100|2017M01|01.01.2017|60 \ \ \
101|2017M01|02.01.2017|75 > YtD 2017M01 | |
103|2017M01|08.01.2017|48 / > Ytd 2017M02 |
104|2017M02|06.02.2017|55 | > YtD 2017M03
105|2017M02|15.02.2017|63 / |
106|2017M03|18.03.2017|41 |
107|2017M03|22.03.2017|71 /
...|.......|..........|..
I need to calculate 80% percentile for each month and for YtD in (up to) that month (from start of year up to current calculation moment).
I use the following SQL query:
SELECT DISTINCT mperiod,
ROUND(PERCENTILE_CONT(0.8) WITHIN GROUP (ORDER BY fr OVER (PARTITION BY mperiod),2) "80%_FR",
ROUND(PERCENTILE_CONT(0.8) WITHIN GROUP (ORDER BY fr OVER (PARTITION BY SUBSTR(mperiod,1,4)),2) "80%_FR_YtD"
FROM mytable
ORDER BY 1
If I run this query in last day of month when I do not have data for the following month yet then this SQL will correctly calculate YtD value. For example, if I have data for first six months and do not have data for seventh month, and calculate this for sixth month then calculation with year partition OVER (PARTITION BY SUBSTR(mperiod,1,4) will calculate correct YtD value. But if I have data after this month it will be included in PARTITION BY and will not calculate up to that moment.
How to calculate YtD retroactively, for previous months!? For example, the calculation of YtD for third month should include calculation for only those first three months in year, not all months in year.
Since you can't use a windowing clause or add in additional order by columns in PERCENTILE_CONT (boo!), here's one way of achieving your aims. N.B. it's not pretty, and I'm sure it won't be terrifically performant, but it should work at least!
WITH mytable AS (SELECT 100 ID, '2017M01' mperiod, to_date('01/01/2017', 'dd/mm/yyyy') frdate, 60 fr FROM dual UNION ALL
SELECT 101 ID, '2017M01' mperiod, to_date('02/01/2017', 'dd/mm/yyyy') frdate, 75 fr FROM dual UNION ALL
SELECT 103 ID, '2017M01' mperiod, to_date('08/01/2017', 'dd/mm/yyyy') frdate, 48 fr FROM dual UNION ALL
SELECT 104 ID, '2017M02' mperiod, to_date('06/02/2017', 'dd/mm/yyyy') frdate, 55 fr FROM dual UNION ALL
SELECT 105 ID, '2017M02' mperiod, to_date('15/02/2017', 'dd/mm/yyyy') frdate, 63 fr FROM dual UNION ALL
SELECT 106 ID, '2017M03' mperiod, to_date('18/03/2017', 'dd/mm/yyyy') frdate, 41 fr FROM dual UNION ALL
SELECT 107 ID, '2017M03' mperiod, to_date('22/03/2017', 'dd/mm/yyyy') frdate, 71 fr FROM dual UNION ALL
SELECT 108 ID, '2016M12' mperiod, to_date('22/12/2016', 'dd/mm/yyyy') frdate, 42 fr FROM dual UNION ALL
SELECT 109 ID, '2016M11' mperiod, to_date('22/11/2016', 'dd/mm/yyyy') frdate, 32 fr FROM dual),
unpckd AS (SELECT mt.ID,
mt.mperiod,
mt.frdate,
mt.fr,
CASE WHEN substr(mt.mperiod, -2) <= d.id THEN SUBSTR(mt.mperiod, 1, 5) || to_char(d.id, 'fm09')
END new_mperiod,
d.id dummy_id
FROM mytable mt
INNER JOIN (SELECT LEVEL ID
FROM dual
CONNECT BY LEVEL <= 12) d ON substr(mt.mperiod, -2) <= d.id),
res AS (SELECT mperiod,
new_mperiod,
ROUND(PERCENTILE_CONT(0.8) WITHIN GROUP (ORDER BY fr) OVER (PARTITION BY CASE WHEN mperiod = new_mperiod THEN mperiod END),2) fr_80,
ROUND(PERCENTILE_CONT(0.8) WITHIN GROUP (ORDER BY fr) OVER (PARTITION BY new_mperiod),2) fr_80_ytd
FROM unpckd)
SELECT DISTINCT new_mperiod mperiod,
fr_80 "80%_FR",
fr_80_ytd "80%_FR_YtD"
FROM res
WHERE new_mperiod = mperiod
ORDER BY 1;
MPERIOD 80%_FR 80%_FR_YtD
-------- ---------- ----------
2016M11 32 32
2016M12 42 40
2017M01 69 69
2017M02 61.4 65.4
2017M03 65 69.4
This works by doing a partial cross join between the numbers 1 to 12 (12 months in the year) and the last two digits of the mperiod. Once we have that, we now know the overall ytd period that the rows belong to (ie. number 1 will match to the 2017M01, 2 will match to 2017M01 and 2017M02, etc), so you can now produce a label for this calculated value (which I've called new_mperiod) and use that to partition against.
It's obviously going to be inefficient (since the partial cross join will generate more rows than is necessary for a year that's not got data for all its months, which get filtered out later, but I can't think of a better way of doing it.
Say I've got a table with two columns (date and price). If I select over a range of dates, then is there a way to count the number of price changes over time?
For instance:
Date | Price
22-Oct-11 | 3.20
23-Oct-11 | 3.40
24-Oct-11 | 3.40
25-Oct-11 | 3.50
26-Oct-11 | 3.40
27-Oct-11 | 3.20
28-Oct-11 | 3.20
In this case, I would like it to return a count of 4 price changes.
Thanks in advance.
You can use the analytic functions LEAD and LAG to access to prior and next row of a result set and then use that to see if there are changes.
SQL> ed
Wrote file afiedt.buf
1 with t as (
2 select date '2011-10-22' dt, 3.2 price from dual union all
3 select date '2011-10-23', 3.4 from dual union all
4 select date '2011-10-24', 3.4 from dual union all
5 select date '2011-10-25', 3.5 from dual union all
6 select date '2011-10-26', 3.4 from dual union all
7 select date '2011-10-27', 3.2 from dual union all
8 select date '2011-10-28', 3.2 from dual
9 )
10 select sum(is_change)
11 from (
12 select dt,
13 price,
14 lag(price) over (order by dt) prior_price,
15 (case when lag(price) over (order by dt) != price
16 then 1
17 else 0
18 end) is_change
19* from t)
SQL> /
SUM(IS_CHANGE)
--------------
4
Try this
select count(*)
from
(select date,price from table where date between X and Y
group by date,price )
Depending on the Oracle version use either analytical functions (see answer from Justin Cave) or this
SELECT
SUM (CASE WHEN PREVPRICE != PRICE THEN 1 ELSE 0 END) CNTCHANGES
FROM
(
SELECT
C.DATE,
C.PRICE,
MAX ( D.PRICE ) PREVPRICE
FROM
(
SELECT
A.Date,
A.Price,
(SELECT MAX (B.DATE) FROM MyTable B WHERE B.DATE < A.DATE) PrevDate
FROM MyTable A
WHERE A.DATE BETWEEN YourStartDate AND YourEndDate
) C
INNER JOIN MyTable D ON D.DATE = C.PREVDATE
GROUP BY C.DATE, C.PRICE
)