I have the following table:
ID GroupID oDate oTime oValue
1 A 2014-06-01 00:00:00 100
2 A 2014-06-01 01:00:00 200
3 A 2014-06-01 02:00:00 300
4 A 2014-06-02 00:00:00 400
5 A 2014-06-02 01:00:00 425
6 A 2014-06-02 02:00:00 475
7 B 2014-06-01 00:00:00 1000
8 B 2014-06-01 01:00:00 1500
9 B 2014-06-01 02:00:00 2000
10 B 2014-06-02 00:00:00 3000
11 B 2014-06-02 01:00:00 3100
12 A 2014-06-03 00:00:00 525
13 A 2014-06-03 01:00:00 600
14 A 2014-06-03 02:00:00 625
I want to have the following result:
GroupID oDate oResult
A 2014-06-01 300
A 2014-06-02 125
B 2014-06-01 2000
oResult is coming from:
Value on next date at 00:00:00 subtract value on selected date at 00:00:00.
For example, I want to know the Result for 2014-06-01. Then,
2014-06-02 00:00:00 400 substract 2014-06-01 00:00:00 100
oResult = 400 - 100 = 300
How can I achieve this in SQL syntax?
Thank you.
You can write a query using Common Table Expression as :
;with CTE as
( select row_number() over ( partition by GroupID, oDate order by oTime Asc) as rownum,
GroupID, oDate, oValue,oTime
from Test
)
select CTE.GroupID,CTE1.oDate, (CTE.oValue - CTE1.oValue) as oResult
from CTE
inner join CTE as CTE1 on datediff (day,CTE1.oDate, CTE.oDate) = 1
and CTE1.rownum= CTE.rownum
and CTE1.GroupID= CTE.GroupID
where CTE.rownum = 1
Check Demo here ...
You can use cross apply operator here
Please check this,
select a.GroupID,a.oDate, (ab.oValue - a.oValue) oResult from T as a
cross apply
(
select top 1 * from T as b
where a.oDate < b.oDate
and oTime = '00:00:00.0000000'
and a.ID < b.ID
)as ab
where a.ID in(1,4,7)
Demo
Related
I have data frame with date like below :
id start_date end_date product supply_per_day
1 2020-03-01 2020-03-01 A 10
1 2020-03-01 2020-03-01 B 10
1 2020-03-01 2020-03-02 A 5
2 2020-02-28 2020-03-02 A 10
2 2020-03-01 2020-03-03 B 4
2 2020-03-02 2020-03-05 A 5
I want make this data wide to long like :
id date product supply_per_day
1 2020-03-01 A 10
1 2020-03-01 B 10
1 2020-03-01 A 5
1 2020-03-02 A 5
2 2020-02-28 A 10
2 2020-03-01 A 10
2 2020-03-02 A 10
2 2020-03-01 B 4
2 2020-03-02 B 4
2 2020-03-03 B 4
2 2020-03-02 B 5
2 2020-03-03 B 5
2 2020-03-04 B 5
2 2020-03-05 B 5
give me some idea please
For Oracle 12c and later, you can use:
SELECT t.id,
d.dt,
t.product,
t.supply_per_day
FROM table_name t
OUTER APPLY(
SELECT start_date + LEVEL - 1 AS dt
FROM DUAL
CONNECT BY start_date + LEVEL - 1 <= end_date
) d
Which, for the sample data:
CREATE TABLE table_name ( id, start_date, end_date, product, supply_per_day ) AS
SELECT 1, DATE '2020-03-01', DATE '2020-03-01', 'A', 10 FROM DUAL UNION ALL
SELECT 1, DATE '2020-03-01', DATE '2020-03-01', 'B', 10 FROM DUAL UNION ALL
SELECT 1, DATE '2020-03-01', DATE '2020-03-02', 'A', 5 FROM DUAL UNION ALL
SELECT 2, DATE '2020-02-28', DATE '2020-03-02', 'A', 10 FROM DUAL UNION ALL
SELECT 2, DATE '2020-03-01', DATE '2020-03-03', 'B', 4 FROM DUAL UNION ALL
SELECT 2, DATE '2020-03-02', DATE '2020-03-05', 'A', 5 FROM DUAL;
Outputs:
ID
DT
PRODUCT
SUPPLY_PER_DAY
1
2020-03-01 00:00:00
A
10
1
2020-03-01 00:00:00
B
10
1
2020-03-01 00:00:00
A
5
1
2020-03-02 00:00:00
A
5
2
2020-02-28 00:00:00
A
10
2
2020-02-29 00:00:00
A
10
2
2020-03-01 00:00:00
A
10
2
2020-03-02 00:00:00
A
10
2
2020-03-01 00:00:00
B
4
2
2020-03-02 00:00:00
B
4
2
2020-03-03 00:00:00
B
4
2
2020-03-02 00:00:00
A
5
2
2020-03-03 00:00:00
A
5
2
2020-03-04 00:00:00
A
5
2
2020-03-05 00:00:00
A
5
db<>fiddle here
In Postgres you can use generate_series() for this:
select t.id, g.day::date as date, t.product, t.supply_per_day
from the_table t
cross join generate_series(t.start_date, t.end_date, interval '1 day') as g(day)
order by t.id, g.day
Suppose I have one table (table_1) and one table stream (stream_1) that gets changes made to table_1, in my case only inserts of new rows. And once I have acted on these changes, the rowes will be removed from stream_1 but remain in table_1.
From that I would like to calculate delta values for var1 (var1 - lag(var1) as delta_var1) partitioned on a customer and just leave var2 as it is. So the data in table_1 could look something like this:
timemessage
customerid
var1
var2
2021-04-01 06:00:00
1
10
5
2021-04-01 07:00:00
2
100
7
2021-04-01 08:00:00
1
20
10
2021-04-01 09:00:00
1
40
3
2021-04-01 15:00:00
2
150
5
2021-04-01 23:00:00
1
50
6
2021-04-02 06:00:00
2
180
2
2021-04-02 07:00:00
1
55
9
2021-04-02 08:00:00
2
200
4
And the data in stream_1 that I want to act on could looks like this:
timemessage
customerid
var1
var2
2021-04-01 23:00:00
1
50
6
2021-04-02 06:00:00
2
180
2
2021-04-02 07:00:00
1
55
9
2021-04-02 08:00:00
2
200
4
But to be able to calculate delta_var1 for all customers I would need the previous row in time for each customer before the ones in stream_1.
For example: To be able to calculate how much var1 has increased for customerid = 1 between 2021-04-01 09:00:00 and 2021-04-01 23:00:00 I want to include the 2021-04-01 09:00:00 row for customerid = 1 in my output.
So I would like to create a select containing all rows in stream_1 + the previous row in time for each customerid from table_1: The wanted output is the following in regard to the mentioned table_1 and stream_1.
timemessage
customerid
var1
var2
2021-04-01 09:00:00
1
40
3
2021-04-01 15:00:00
2
150
5
2021-04-01 23:00:00
1
50
6
2021-04-02 06:00:00
2
180
2
2021-04-02 07:00:00
1
55
9
2021-04-02 08:00:00
2
200
4
So given you have the "last value per day" in your wanted output, you are want a QUALIFY to keep only the wanted rows and using ROW_NUMBER partitioned by customerid and timemessage. Assuming the accumulator it positive only you can order by accumulatedvalue thus:
WITH data(timemessage, customerid, accumulatedvalue) AS (
SELECT * FROM VALUES
('2021-04-01', 1, 10)
,('2021-04-01', 2, 100)
,('2021-04-02', 1, 20)
,('2021-04-03', 1, 40)
,('2021-04-03', 2, 150)
,('2021-04-04', 1, 50)
,('2021-04-04', 2, 180)
,('2021-04-05', 1, 55)
,('2021-04-05', 2, 200)
)
SELECT * FROM data
QUALIFY ROW_NUMBER() OVER (PARTITION BY customerid,timemessage ORDER BY accumulatedvalue DESC) = 1
ORDER BY 1,2;
gives:
TIMEMESSAGE CUSTOMERID ACCUMULATEDVALUE
2021-04-01 1 10
2021-04-01 2 100
2021-04-02 1 20
2021-04-03 1 40
2021-04-03 2 150
2021-04-04 1 50
2021-04-04 2 180
2021-04-05 1 55
2021-04-05 2 200
if you can trust your data and data in table2 starts right after data in table1 then you can just get the last records for each customer from table1 and union with table2:
select * from table1
qualify row_number() over (partitioned by customerid order by timemessage desc) = 1
union all
select * from table2
if not
select a.* from table1 a
join table2 b
on a.customerid = b.customerid
and a.timemessage < b.timemessage
qualify row_number() over (partitioned by a.customerid order by a.timemessage desc) = 1
union all
select * from table2
also you can add a condition to not look to data for more than 1 day (or 1 hour or whatever safe interval is to look at) for better performance
This is follow-up of my previous question:
PostgreSQL group by with interval
There was a very good answer but unfortunately it is not working with PostgreSQL 8.0 - some clients still use this old version.
So I need to find another solution without using window functions
Here is what I have as a table:
id quantity price1 price2 date
1 100 1 0 2018-01-01 10:00:00
2 200 1 0 2018-01-02 10:00:00
3 50 5 0 2018-01-02 11:00:00
4 100 1 1 2018-01-03 10:00:00
5 100 1 1 2018-01-03 11:00:00
6 300 1 0 2018-01-03 12:00:00
I need to sum "quantity" grouped by "price1" and "price2" but only when they change
So the end result should look like this:
quantity price1 price2 dateStart dateEnd
300 1 0 2018-01-01 10:00:00 2018-01-02 10:00:00
50 5 0 2018-01-02 11:00:00 2018-01-02 11:00:00
200 1 1 2018-01-03 10:00:00 2018-01-03 11:00:00
300 1 0 2018-01-03 12:00:00 2018-01-03 12:00:00
It is not efficient, but you can implement the same logic with subqueries:
select sum(quantity), price1, price2,
min(date) as dateStart, max(date) as dateend
from (select d.*,
(select count(*)
from data d2
where d2.date <= d.date
) as seqnum,
(select count(*)
from data d2
where d2.price1 = d.price1 and d2.price2 = d.price2 and d2.date <= d.date
) as seqnum_pp
from data d
) t
group by price1, price2, (seqnum - seqnum_pp)
order by dateStart
I have the following table:
ID GroupCode oDate oTime oValue
1 A 2014-06-01 00:00:00 200
2 A 2014-06-01 01:00:00 300
3 A 2014-06-01 02:00:00 400
FF. until oTime reach 23:00:00 then it will create a new date. which is 2014-06-02
25 B 2014-06-01 00:00:00 600
26 B 2014-06-01 01:00:00 700
27 B 2014-06-01 02:00:00 725
FF. until oTime reach 23:00:00 then it will create a new date. which is 2014-06-02
my question is, how can I make it horizontally on SQL View? I want to have the following result:
GroupCode1 oDate1 oTime1 oValue1 GroupCode2 oDate2 oTime2 oValue2
A 2014-06-01 00:00:00 200 B 2014-06-01 00:00:00 600
A 2014-06-01 01:00:00 300 B 2014-06-01 01:00:00 700
A 2014-06-01 02:00:00 400 B 2014-06-01 02:00:00 725
Does anyone know how to do this?
Thank you. Really appreciate.
If there are only 3 group codes A,B,C and that each group code has 23 rows for a given date then you can write as:
select GroupCode1, oDate1,oTime1,oValue1,GroupCode2, oDate2,oTime2,oValue2
,GroupCode3, oDate3,oTime3,oValue3
from
(select GroupCode as GroupCode1,
oDate as oDate1,
oTime as oTime1,
oValue as oValue1
from table1
where GroupCode = 'A')T1 full join
(select GroupCode as GroupCode2,
oDate as oDate2,
oTime as oTime2,
oValue as oValue2
from table1
where GroupCode = 'B')T2 on T1.oDate1 = T2.oDate2 and T1.oTime1 = T2.oTime2
full join
(select GroupCode as GroupCode3,
oDate as oDate3,
oTime as oTime3,
oValue as oValue3
from table1
where GroupCode = 'C')T3 on T1.oDate1 = T3.oDate3 and T1.oTime1 = T3.oTime3
Demo
I have a table in SQLite called param_vals_breaches that looks like the following:
id param queue date_time param_val breach_count
1 c a 2013-01-01 00:00:00 188 7
2 c b 2013-01-01 00:00:00 156 8
3 c c 2013-01-01 00:00:00 100 2
4 d a 2013-01-01 00:00:00 657 0
5 d b 2013-01-01 00:00:00 23 6
6 d c 2013-01-01 00:00:00 230 12
7 c a 2013-01-01 01:00:00 100 0
8 c b 2013-01-01 01:00:00 143 9
9 c c 2013-01-01 01:00:00 12 2
10 d a 2013-01-01 01:00:00 0 1
11 d b 2013-01-01 01:00:00 29 5
12 d c 2013-01-01 01:00:00 22 14
13 c a 2013-01-01 02:00:00 188 7
14 c b 2013-01-01 02:00:00 156 8
15 c c 2013-01-01 02:00:00 100 2
16 d a 2013-01-01 02:00:00 657 0
17 d b 2013-01-01 02:00:00 23 6
18 d c 2013-01-01 02:00:00 230 12
I want to write a query that will show me a particular queue (e.g. "a") with the average param_val and breach_count for each param on an hour by hour basis. So transposing the data to get something that looks like this:
Results for Queue A
Hour 0 Hour 0 Hour 1 Hour 1 Hour 2 Hour 2
param avg_param_val avg_breach_count avg_param_val avg_breach_count avg_param_val avg_breach_count
c xxx xxx xxx xxx xxx xxx
d xxx xxx xxx xxx xxx xxx
is this possible? I'm not sure how to go about it. Thanks!
SQLite does not have a PIVOT function but you can use an aggregate function with a CASE expression to turn the rows into columns:
select param,
avg(case when time = '00' then param_val end) AvgHour0Val,
avg(case when time = '00' then breach_count end) AvgHour0Count,
avg(case when time = '01' then param_val end) AvgHour1Val,
avg(case when time = '01' then breach_count end) AvgHour1Count,
avg(case when time = '02' then param_val end) AvgHour2Val,
avg(case when time = '02' then breach_count end) AvgHour2Count
from
(
select param,
strftime('%H', date_time) time,
param_val,
breach_count
from param_vals_breaches
where queue = 'a'
) src
group by param;
See SQL Fiddle with Demo