I am trying to insert records in a table in the below format
Name Amount Date Counter
A 100 Jan 1 1
A 100 Jan2 1
A 200 Jan 10 2
A 300 Mar 30 3
B 50 Jan 7 1
C 20 Jan 7 1
Could someone tell me the sql for generating the value for the Counter field .
The counter value should increment whenever the amount changes and reset when the name changes.
What you need is a DENSE_RANK function. Unfortunately it's not natively implemented before TD14.10, but it can be written using nested OLAP-functions:
SELECT
Name
,Amount
,date_col
,SUM(flag)
OVER (PARTITION BY Name
ORDER BY date_col
ROWS UNBOUNDED PRECEDING) AS "DENSE_RANK"
FROM
(
SELECT
Name
,Amount
,date_col
,CASE
WHEN Amount = MIN(Amount)
OVER (PARTITION BY Name
ORDER BY date_col
ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING)
THEN 0
ELSE 1
END AS flag
FROM dropme
) AS dt;
Related
I have below table which has customer's transaction details.
Tranactaction date
CustomerID
1/27/2022
1
1/29/2022
1
2/27/2022
1
3/27/2022
1
3/29/2022
1
3/31/2022
1
4/2/2022
1
4/4/2022
1
4/6/2022
1
In this table consecutive transactions occurred in every two days considered as a segment.
For example, Transactions between Jan 27th and Jan 29th considered as segment 1 & Transactions between Mar 29th and Apr 6th considered as Segment 2. I need to rank the transactions per segment with date order. If a transaction not fall under any segment by default the rank is 1. Expected output is below.
Segment Rank
Tranactaction date
CustomerID
1
1/27/2022
1
2
1/29/2022
1
1
2/27/2022
1
1
3/27/2022
1
2
3/29/2022
1
3
3/31/2022
1
4
4/2/2022
1
5
4/4/2022
1
6
4/6/2022
1
Can somebody guide how to achieve this in T-sql?
Using lag() to check for change in TransDate that is within 2 days and groups together (as a segment). After that use row_number() to generate the required sequence
with
cte as
(
select *,
g = case when datediff(day,
lag(t.TransDate) over (order by t.TransDate),
t.TransDate
) <= 2
then 0
else 1
end
from tbl t
),
cte2 as
(
select *, grp = sum(g) over (order by TransDate)
from cte
)
select *, row_number() over (partition by grp order by TransDate)
from cte2
db<>fiddle demo
I need to make a query that shows sales and stocks (incoming and outgoing) for each model in October 2021.
The point is that for obtaining incoming and outgoing stocks I need to get vt_stocks_cube_sz.qty respectively for the first day of month and for the last day of month .
Now I wrote just sum of stocks (SUM(vt_stocks_cube_sz.qty) as stocks) but it isn't correct.
Could you help me to split the stocks according to the rule above, I cannot understant how to write the query correctly.
%%time
SELECT vt_sales_cube_sz.modc_barc2 model,
SUM(vt_sales_cube_sz.qnt) sales,
SUM(vt_stocks_cube_sz.qty) as stocks
FROM vt_sales_cube_sz
LEFT JOIN vt_date_cube2
ON vt_sales_cube_sz.id_calendar_int = vt_date_cube2.id_calendar_int
LEFT JOIN vt_stocks_cube_sz ON
vt_stocks_cube_sz.parent_modc_barc = vt_sales_cube_sz.modc_barc AND
vt_stocks_cube_sz.id_stock = vt_sales_cube_sz.id_stock AND
vt_stocks_cube_sz.id_calendar_int = vt_sales_cube_sz.id_calendar_int AND
vt_stocks_cube_sz.vipusk_type = vt_sales_cube_sz.price_type
WHERE vt_date_cube2.wk_year_id = 2021
AND vt_date_cube2.wk_MoY_id = 10
AND vt_sales_cube_sz.id_stock IN
(SELECT id_stock
FROM vt_warehouse_cube
WHERE channel = \'OffLine\')
GROUP BY vt_sales_cube_sz.modc_barc2
If you're looking for a robust and generalizable approach I'd suggest using analytic functions such as FIRST_VALUE, LAST_VALUE or something slightly different with RANK or ROW_NUMBER.
A simple example follows, so you can rerun it on your side and adjust it to the specific tables/fields you're using.
N.B.: You might need some tiebreakers in case you had multiple entries for the same first/last day.
with dummy_table as (
SELECT 1 as month, 1 as day, 10 as value UNION ALL
SELECT 1 as month, 2 as day, 20 as value UNION ALL
SELECT 1 as month, 3 as day, 30 as value UNION ALL
SELECT 2 as month, 1 as day, 5 as value UNION ALL
SELECT 2 as month, 3 as day, 15 as value UNION ALL
SELECT 2 as month, 5 as day, 25 as value
)
SELECT
month,
day,
case when day = first_day then 'first' else 'last' end as type,
value,
FROM (
SELECT *
, FIRST_VALUE(day) over (partition by month order by day ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) as first_day
, LAST_VALUE(day) over (partition by month order by day ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) as last_day
FROM dummy_table
) tmp
WHERE day = first_day OR day=last_day
Dummy table:
Row
month
day
value
1
1
1
10
2
1
2
20
3
1
3
30
4
2
1
5
5
2
3
15
6
2
5
25
Result:
Row
month
day
type
value
1
1
1
first
10
2
1
3
last
30
3
2
1
first
5
4
2
5
last
25
I have a procedure sp_data_between_months (p_from_date DATE, p_to_date DATE) // example p_from_date = '01-jan-2021' and 'p_to_date' = '31-mar-2021'.
I need to get the latest record for the ID for each month, add these values, and populate against p_to_date for each ID from the below table using PLSQL.
Table Name: ID_Value
ID
Date
value
1
1-jan-2021
10
1
10-jan-2021
20
2
15-jan-2021
15
2
16-jan-2021
20
2
02-feb-2021
10
2
06-feb-2021
15
1
17-feb-2021
10
1
5-mar-2021
15
1
17-mar-2021
10
2
10-mar-2021
10
the expected output is to get the latest value for each ID for each month-end and the sum of its value between those months between the ranges.
Output: p_to_date ID Sum of latest record of value for each month
DATE
ID
VALUE
31-Mar-2021
1
40 //(20+10+10) sum of value oflatest record foreach month
31-Mar-2021
2
45 //(20+15+10)
Here you are. Read comments within code.
SQL> with
2 temp as
3 -- analytic function will return 1 for the latest row for that ID in that month
4 (select id, datum, value,
5 row_number() over (partition by id, trunc(datum, 'mm') order by datum desc) rn
6 from id_value
7 )
8 -- finally, select last day in MAX month and sum all values for RN = 1
9 select
10 id,
11 last_day(max(datum)) datum,
12 sum(value)
13 from temp
14 where rn = 1
15 group by id;
ID DATUM SUM(VALUE)
---------- ----------- ----------
1 31-mar-2021 40
2 31-mar-2021 45
SQL>
I have the following table,
id status price date
2 complete 10 2020-01-01 10:10:10
2 complete 20 2020-02-02 10:10:10
2 complete 10 2020-03-03 10:10:10
3 complete 10 2020-04-04 10:10:10
4 complete 10 2020-05-05 10:10:10
Required output,
id status_count price ratio
2 0 0 0
2 1 10 0
2 2 30 0.33
I am looking to add the price for previous row. Row 1 is 0 because it has no previous row value.
Find ratio ie 10/30=0.33
You can use analytical function ROW_NUMBER and SUM as follows:
SELECT
id,
ROW_NUMBER() OVER (PARTITION BY id ORDER BY date) - 1 AS status_count,
COALESCE(SUM(price) OVER (PARTITION BY id ORDER BY date), 0) - price as price
FROM yourTable;
DB<>Fiddle demo
I think you want something like this:
SELECT
id,
COUNT(*) OVER (PARTITION BY id ORDER BY date) - 1 AS status_count,
COALESCE(SUM(price) OVER (PARTITION BY id
ORDER BY date ROWS BETWEEN
UNBOUNDED PRECEDING AND 1 PRECEDING), 0) price
FROM yourTable;
Demo
Please also check another method:
with cte
as(*,ROW_NUMBER() OVER (PARTITION BY id ORDER BY date) - 1 AS status_count,
SUM(price) OVER (PARTITION BY id ORDER BY date) ss from yourTable)
select id,status_count,isnull(ss,0)-price price
from cte
I have this data and I want to sum the field USAGE_FLAG but reset when it drops to 0 or moves to a new ID keeping the dataset ordered by SU_ID and WEEK:
SU_ID WEEK USAGE_FLAG
100 1 0
100 2 7
100 3 7
100 4 0
101 1 0
101 2 7
101 3 0
101 4 7
102 1 7
102 2 7
102 3 7
102 4 0
So I want to create this table:
SU_ID WEEK USAGE_FLAG SUM
100 1 0 0
100 2 7 7
100 3 7 14
100 4 0 0
101 1 0 0
101 2 7 7
101 3 0 0
101 4 7 7
102 1 7 7
102 2 7 14
102 3 7 21
102 4 0 0
I have tried the MSUM() function using GROUP BY but it won't keep the order I want above. It groups the 7's and the week numbers together which I don't want.
Anyone know if this is possible to do? I'm using teradata
In standard SQL a running sum can be done using a windowing function:
select su_id,
week,
usage_flag,
sum(usage_flag) over (partition by su_id order by week) as running_sum
from the_table;
I know Teradata supports windowing functions, I just don't know whether it also supports an order by in the window definition.
Resetting the sum is a bit more complicated. You first need to create "group IDs" that change each time the usage_flag goes to 0. The following works in PostgreSQL, I don't know if this works in Teradata as well:
select su_id,
week,
usage_flag,
sum(usage_flag) over (partition by su_id, group_nr order by week) as running_sum
from (
select t1.*,
sum(group_flag) over (partition by su_id order by week) as group_nr
from (
select *,
case
when usage_flag = 0 then 1
else 0
end as group_flag
from the_table
) t1
) t2
order by su_id, week;
Try below code, with use of RESET function it is working fine.
select su_id,
week,
usage_flag,
SUM(usage_flag) OVER (
PARTITION BY su_id
ORDER BY week
RESET WHEN usage_flag < /* preceding row */ SUM(usage_flag) OVER (
PARTITION BY su_id ORDER BY week
ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING)
ROWS UNBOUNDED PRECEDING
)
from emp_su;
Please try below SQL:
select su_id,
week,
usage_flag,
SUM(usage_flag) OVER (PARTITION BY su_id ORDER BY week
RESET WHEN usage_flag = 0
ROWS UNBOUNDED PRECEDING
)
from emp_su;
Here RESET WHEN usage_flag = 0 will reset sum whenever sum usage_flag drops to 0