My table looks like this:
Table 1:
Note: This table is very large in reality, with lots more columns (20ish) and rows (in the millions)
| Time | tmp | productionID |
| 10:00:00 | 2.2 | 5 |
| 10:00:05 | 5.2 | 5 |
| 10:00:11 | 7.4 | 5 |
| ...... | 3.2 | 5 |
| 10:10:02 | 4.5 | 5 |
Note: Timeis a varchar, so I assume I need to do something like this:
CONVERT(VARCHAR(8), DATEADD(mi, 10, time), 114)
What I need to do is:
select time, tmp
from mytable
where productionid = somevalue
and time = first_time_stamp associated to some productionID(ie. 10:00:00 table above)
time = 10 minutes after the first_time_stamp with some productionID
time = 20 minutes after the first_time_stamp with some productionID
...25, 30, 40, 60, 120, 180 minutes
I hope this makes sense. I'm not sure what the right way to do this is. I mean I thought of the following proccess:
-select first time stamp (with some productionID)
-add 10 minutes to that that time,
-add 20 minutes etc.. then use a pivot table and use joins to link to table 1
There must be an easier way.
Thank you in advance for the expertise.
Sample output expected:
| Time | tmp
| 10:00:00 | 2.2
| 10:10:02 | 4.5
| 10:20:54 | 2.3
| 10:30:22 | 5.3
If you create an interval table on-the-fly and cross join it with starting time for each ProductionID, you can extract records fromMyTable falling in the same category and choose to retrieve only the first one.
; with timeSlots (startSlot, endSlot) as (
select 0, 10
union all
select 10, 20
union all
select 25, 30
union all
select 30, 40
union all
select 40, 60
union all
select 60, 120
union all
select 120, 180
),
startTimes (ProductionID, minTime) as (
select ProductionID, min([Time])
from MyTable
group by ProductionID
),
groupedTime (ProductionID, [Time], [Tmp], groupOrder) as (
select myTable.ProductionID,
myTable.Time,
myTable.Tmp,
row_number () over (partition by myTable.productionid, timeSlots.startSlot
order by mytable.Time) groupOrder
from startTimes
cross join timeslots
inner join myTable
on startTimes.ProductionID = myTable.ProductionID
and convert(varchar(8), dateadd(minute, timeSlots.startSlot, convert(datetime, startTimes.MinTime, 114)), 114) <= mytable.Time
and convert(varchar(8), dateadd(minute, timeSlots.endSlot, convert(datetime, startTimes.MinTime, 114)), 114) > myTable.Time
)
select ProductionID, [Time], [Tmp]
from groupedTime
where groupOrder = 1
Sql Fiddle here.
This will work if there are no missing sequence in the time. I mean if there are time values for each increment of 10minutes then this will work.
create table times([Time] time,tmp float,productionID int)
INSERT INTO times
VALUES('10:10:00',2.2,5),
('10:00:05',5.2,5),
('10:00:11',7.4,5),
('10:00:18',3.2,5),
('10:10:02',4.5,5),
('10:20:22',5.3,5)
select * from times
Declare #min_time time
select #min_time = MIN(time) from times
;WITH times1 as (select row_number() over (order by (select 0)) as id,time, tmp from times where productionID = 5)
,times2 as(
select id,time,tmp from times1 where time=#min_time
union all
select t1.id,t1.time,t1.tmp from times2 t2 inner join times1 t1 on cast(t1.time as varchar(5))=cast(DATEADD(mi,10,t2.Time) as varchar(5)) --where t1.id-1=t2.id
)
,result as (select MAX(time) as time from times2
group by CAST(time as varchar(5)))
select distinct t2.Time,t2.tmp from times2 t2,Result r where t2.Time =r.time order by 1
Related
I have a problem with a SQL query. I have a list of dates in one column, I would like to create pairs of dates. The dates are sequenced, so I have to match the first date with the second and create a record, then the third date with the fourth date and create a record etc .. as in the following example:
ID DATA
50 10/04/2019
50 12/04/2019
50 13/04/2019
50 17/04/2019
50 18/04/2019
50 19/04/2019
ID DATA_START DATA_END
50 10/04/2019 12/04/2019
50 13/04/2019 17/04/2019
50 18/04/2019 19/04/2019
Thanks very much everyone for the help
You should mark your rows that should be grouped together (into single row) and which date will have which role (start or end).
Here's the code:
with a as (
/*Source data*/
select 50 as id, convert(date, '2019-04-10', 23) as dt union all
select 50 as id, convert(date, '2019-04-12', 23) as dt union all
select 50 as id, convert(date, '2019-04-13', 23) as dt union all
select 50 as id, convert(date, '2019-04-17', 23) as dt union all
select 50 as id, convert(date, '2019-04-18', 23) as dt union all
select 50 as id, convert(date, '2019-04-19', 23) as dt
)
select
id,
[1] as dt_start,
[0] as dt_end
from (
select
id,
dt,
/*
the first row (with modulo = 1) is date from
and the second row (with modulo = 0) is date to
*/
(row_number() over(partition by id order by dt)) % 2 as dt_role,
/*Integer division by 2 will group rows together*/
(row_number() over(partition by id order by dt) + 1) / 2 as dt_group
from a
) as s
pivot (
max(dt) for dt_role in ([0], [1])
) as p
GO
id | dt_start | dt_end
-: | :--------- | :---------
50 | 2019-04-10 | 2019-04-12
50 | 2019-04-13 | 2019-04-17
50 | 2019-04-18 | 2019-04-19
db<>fiddle here
I have records of No. of calls coming to a call center. When a call comes into a call center a ticket is open.
So, let's say ticket 1 (T1) is open on 8/1/19 and it stays open till 8/5/19. So, if a person ran a query everyday then on 8/1 it will show 1 ticket open...same think on day 2 till day 5....I want to get records by day to see how many tickets were open for each day.....
In short, Frequency Distribution by Day.
Ticket Open_date Close_date
T1 8/1/2019 8/5/2019
T2 8/1/2019 8/6/2019
Result:
Result
Date # Tickets_Open
8/1/2019 2
8/2/2019 2
8/3/2019 2
8/4/2019 2
8/5/2019 2
8/6/2019 1
8/7/2019 0
8/8/2019 0
8/9/2019 0
8/10/2019 0
We can handle your requirement via the use of a calendar table, which stores all dates covering the full range in your data set.
WITH dates AS (
SELECT '2019-08-01' AS dt UNION ALL
SELECT '2019-08-02' UNION ALL
SELECT '2019-08-03' UNION ALL
SELECT '2019-08-04' UNION ALL
SELECT '2019-08-05' UNION ALL
SELECT '2019-08-06' UNION ALL
SELECT '2019-08-07' UNION ALL
SELECT '2019-08-08' UNION ALL
SELECT '2019-08-09' UNION ALL
SELECT '2019-08-10'
)
SELECT
d.dt,
COUNT(t.Open_date) AS num_tickets_open
FROM dates d
LEFT JOIN tickets t
ON d.dt BETWEEN t.Open_date AND t.Close_date
GROUP BY
d.dt;
Note that in practice if you expect to have this reporting requirement in the long term, you might want to replace the dates CTE above with a bona-fide table of dates.
This solution generates the list of dates from the tickets table using CTE recursion and calculates the count:
WITH Tickets(Ticket, Open_date, Close_date) AS
(
SELECT "T1", "8/1/2019", "8/5/2019"
UNION ALL
SELECT "T2", "8/1/2019", "8/6/2019"
),
Ticket_dates(Ticket, Dates) as
(
SELECT t1.Ticket, CONVERT(DATETIME, t1.Open_date)
FROM Tickets t1
UNION ALL
SELECT t1.Ticket, DATEADD(dd, 1, CONVERT(DATETIME, t1.Dates))
FROM Ticket_dates t1
inner join Tickets t2 on t1.Ticket = t2.Ticket
where DATEADD(dd, 1, CONVERT(DATETIME, t1.Dates)) <= CONVERT(DATETIME, t2.Close_date)
)
SELECT CONVERT(varchar, Dates, 1), count(*)
FROM Ticket_dates
GROUP by Dates
ORDER by Dates
A "general purpose" trick is to generate a series of numbers, which can be done using CTE's but there are many alternatives, and from that create the needed range of dates. Once that exists then you can left join your ticket data to this and then count by date.
CREATE TABLE mytable(
Ticket VARCHAR(8) NOT NULL PRIMARY KEY
,Open_date DATE NOT NULL
,Close_date DATE NOT NULL
);
INSERT INTO mytable(Ticket,Open_date,Close_date) VALUES ('T1','8/1/2019','8/5/2019');
INSERT INTO mytable(Ticket,Open_date,Close_date) VALUES ('T2','8/1/2019','8/6/2019');
Also note I am using a cross apply in this example to "attach" the min and max dates of your tickets to each numbered row. You would need to include your own logic on what data to select here.
;WITH
cteDigits AS (
SELECT 0 AS digit UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL
SELECT 5 UNION ALL SELECT 6 UNION ALL SELECT 7 UNION ALL SELECT 8 UNION ALL SELECT 9
)
, cteTally AS (
SELECT
[1s].digit
+ [10s].digit * 10
+ [100s].digit * 100 /* add more like this as needed */
AS num
FROM cteDigits [1s]
CROSS JOIN cteDigits [10s]
CROSS JOIN cteDigits [100s] /* add more like this as needed */
)
select
n.num + 1 rownum
, dateadd(day,n.num,ca.min_date) as on_date
, count(t.Ticket) as tickets_open
from cteTally n
cross apply (select min(Open_date), max(Close_date) from mytable) ca (min_date, max_date)
left join mytable t on dateadd(day,n.num,ca.min_date) between t.Open_date and t.Close_date
where dateadd(day,n.num,ca.min_date) <= ca.max_date
group by
n.num + 1
, dateadd(day,n.num,ca.min_date)
order by
rownum
;
result:
+--------+---------------------+--------------+
| rownum | on_date | tickets_open |
+--------+---------------------+--------------+
| 1 | 01.08.2019 00:00:00 | 2 |
| 2 | 02.08.2019 00:00:00 | 2 |
| 3 | 03.08.2019 00:00:00 | 2 |
| 4 | 04.08.2019 00:00:00 | 2 |
| 5 | 05.08.2019 00:00:00 | 2 |
| 6 | 06.08.2019 00:00:00 | 1 |
+--------+---------------------+--------------+
I'm having a issue with dates. I have a table with given from and to dates for an employee. For an evaluation, I'd like to display each date of the month with corresponding values from the second sql table.
SQL Table:
EmpNr | datefrom | dateto | hours
0815 | 01.01.2019 | 03.01.2019 | 15
0815 | 05.01.2019 | 15.01.2019 | 15
0815 | 20.01.2019 | 31.12.9999 | 40
The given employee (0815) worked during 01.01.-15.01. 15 hours, and during 20.01.-31.01. 40 hours
I'd like to have the following result:
0815 | 01.01.2019 | 15
0815 | 02.01.2019 | 15
0815 | 03.01.2019 | 15
0815 | 04.01.2019 | NULL
0815 | 05.01.2019 | 15
...
0815 | 15.01.2019 | 15
0815 | 16.01.2019 | NULL
0815 | 17.01.2019 | NULL
0815 | 18.01.2019 | NULL
0815 | 19.01.2019 | NULL
0815 | 20.01.2019 | 40
0815 | 21.01.2019 | 40
...
0815 | 31.01.2019 | 40
as for the dates, we have:
declare #year int = 2019, #month int = 1;
WITH numbers
as
(
Select 1 as value
UNion ALL
Select value + 1 from numbers
where value + 1 <= Day(EOMONTH(datefromparts(#year,#month,1)))
)
SELECT b.empnr, b.hours, datefromparts(#year,#month,numbers.value) Datum FROM numbers left outer join
emptbl b on b.empnr = '0815' and (datefromparts(#year,#month,numbers.value) >= b.datefrom and datefromparts(#year,#month,numbers.value) <= case b.dateto )
which is working quite well, yet I have the odd issue, that this code is only shoes the dates between 01.01.2019 and 03.01.2019
thank you very much in advance!
Did you check, if datefrom and dateto is in correct range?
Minimum value of DateTime field is 1753-01-01 and maximum value is 9999-12-31.
Look at your source table to check initial values.
The recursive CTE needs to begin with MIN(datefrom) and MAX(dateto):
DECLARE #t TABLE (empnr INT, datefrom DATE, dateto DATE, hours INT);
INSERT INTO #t VALUES
(815, '2019-01-01', '2019-01-03', 15),
(815, '2019-01-05', '2019-01-15', 15),
(815, '2019-01-20', '9999-01-01', 40),
-- another employee
(999, '2018-01-01', '2018-01-31', 15),
(999, '2018-03-01', '2018-03-31', 15),
(999, '2018-12-01', '9999-01-01', 40);
WITH rcte AS (
SELECT empnr
, MIN(datefrom) AS refdate
, ISNULL(NULLIF(MAX(dateto), '9999-01-01'), CURRENT_TIMESTAMP) AS maxdate -- clamp year 9999 to today
FROM #t
GROUP BY empnr
UNION ALL
SELECT empnr
, DATEADD(DAY, 1, refdate)
, maxdate
FROM rcte
WHERE refdate < maxdate
)
SELECT rcte.empnr
, rcte.refdate
, t.hours
FROM rcte
LEFT JOIN #t AS t ON rcte.empnr = t.empnr AND rcte.refdate BETWEEN t.datefrom AND t.dateto
ORDER BY rcte.empnr, rcte.refdate
OPTION (MAXRECURSION 1000) -- approx 3 years
Demo on db<>fiddle
It could be in your select, try:
SELECT b.empnr, b.hours, datefromparts(#year,#month,numbers.value) Datum
FROM numbers
LEFT OUTER JOIN emptbl b ON b.empnr = '0815' AND
datefromparts(#year,#month,numbers.value) BETWEEN b.datefrom AND b.dateto
Your CTE produces only 31 number and therefore it is showing only January dates.
declare #year int = 2019, #month int = 1;
WITH numbers
as
(
Select 1 as value
UNion ALL
Select value + 1 from numbers
where value + 1 <= Day(EOMONTH(datefromparts(#year,#month,1)))
)
SELECT *
FROM numbers
https://dbfiddle.uk/?rdbms=sqlserver_2017&fiddle=a24e58ef4ce522d3ec914f90907a0a9e
You can try below code,
with t0 (i) as (select 0 union all select 0 union all select 0),
t1 (i) as (select a.i from t0 a ,t0 b ),
t2 (i) as (select a.i from t1 a ,t1 b ),
t3 (srno) as (select row_number()over(order by a.i) from t2 a ,t2 b ),
tbldt(dt) as (select dateadd(day,t3.srno-1,'01/01/2019') from t3)
select tbldt.dt
from tbldt
where tbldt.dt <= b.dateto -- put your condition here
https://dbfiddle.uk/?rdbms=sqlserver_2017&fiddle=b16469908b323b8d1b98d77dd09bab3d
I have the following data set:
I want to create a new column that sums the last 7 days of sales. So the query result should look be the following:
Pls help
Thanks!
In standard SQL, you would use a window function -- assuming you have data for each day:
select t.*,
sum(sales) over (partition by itemid order by date rows between 6 preceding and current row) as sales_7
from t;
use sum() aggregate function and group by
select country,itemid,year,monthnumber,week sum(sales) as sales_last_7days from your_table
where date>=DATEADD(day, -7, getdate()) and date< getdate()
group by country,itemid,year,monthnumber,week
with window:
select (list other columns here), sum(sum(sales)) over
(partition by week
order by day
rows between 6 preceding and current row)
from table
group by date, week;
note that week doesen't change group by beacause a date is reffered to one week only, but it is needed in window.
Seems you are working with SQL Server if so, then you can use apply :
select t.*, t1.[last7day]
from table t outer apply
(select sum(t1.sales) as [last7day]
from table t1
where t.itemid = t1.itemid and
t1.date <= dateadd(day, -6, t.dt)
) t1;
If you don't have exactly one day for each row, for example if you have a list of transactions...
The below example completely confused me the first time I saw it, so I've tried to comment as much as I can to explain what's happening.
Suppose we have a table tbl with date column dt and amount column amt, and for each date in tbl we want to return a rolling sum of the amount from the current day and the past 6 days.
select distinct -- see note after code on what this distinct is doing.
dt
, ( -- Has to be in brackets to denote we're returning 1 value per row.
-- for each row of T1:
select sum(b.amt) -- the sum of amounts in T2. The where clause will restrict which rows in T2 will be summed.
from tbl T2
where T2.dt between T1.dt - 6 and T1.dt -- for each row in T1, give me all rows in T2 where the date is between 6 days before this T1 row's date and T1 row's date, giving us our rolling sum
-- WARNING: CHECK YOUR VERSION OF SQL FOR HOW TO SUBTRACT DAYS FROM A DATE, I'VE MADE IT (T1.dt - 6) FOR SIMPLICITY
-- we don't need a group by, because we're returning one value for each row in T1
)
from tbl T1
We have a main version of tbl, aliased T1. We then have a secondary table, aliased T2. For each row in T1, we're going to ask for a set of rows in T2 that we're going to sum before giving it to our main query.
To understand what's happening, run the code without the distinct. You'll notice that we have the same number of rows as in tbl, because the T2 statement is happening for every row in T1.
Notes:
If you have any days for which no rows exist in your table you will not get a calculation for this day. To be certain this doesn't happen, join your table to a table containing a distinct list of consecutive dates, and use this as your date column.
If you have nulls in your amount column the calculation will still work, but if the rolling average contains only nulls you will have null instead of 0 as your result. If that troubles you convert all your nulls to zero's before (or after) you use the query.
The beginning of the period will have a 'ramp up'. But this would be the same whatever method you use to do a rolling sum. If it bothers you, don't return the first 6 days.
Finally a worked example if you're playing along at home using SQL Server:
with tbl as (
-- a list of transactions from 1.10.2019 to 14.10.2019
select cast('2019-10-01' as date) dt, 1 amt
union select cast('2019-10-02' as date), 4
union select cast('2019-10-01' as date), 10
union select cast('2019-10-03' as date), 3
union select cast('2019-10-04' as date), 20
union select cast('2019-10-04' as date), 2
union select cast('2019-10-04' as date), 12
union select cast('2019-10-04' as date), 17
union select cast('2019-10-05' as date), null -- a whole week of null values because we all had the week off... I hope this data wasn't important
union select cast('2019-10-06' as date), null
union select cast('2019-10-07' as date), null
union select cast('2019-10-08' as date), null
union select cast('2019-10-09' as date), null
union select cast('2019-10-10' as date), null
union select cast('2019-10-10' as date), null
union select cast('2019-10-10' as date), null
union select cast('2019-10-11' as date), null
union select cast('2019-10-12' as date), 1
union select cast('2019-10-12' as date), 1
union select cast('2019-10-12' as date), 1
union select cast('2019-10-12' as date), 1
union select cast('2019-10-12' as date), 1
union select cast('2019-10-12' as date), 1
union select cast('2019-10-13' as date), 2
union select cast('2019-10-14' as date), 1000
)
select distinct
a.dt
, (
select sum(b.amt)
from tbl b
where b.dt between dateadd(dd, -6, a.dt) and a.dt
) past_7_days_amt
from tbl a
Returns:
+------------+-----------------+
| dt | past_7_days_amt |
+------------+-----------------+
| 2019-10-01 | 11 |
| 2019-10-02 | 15 |
| 2019-10-03 | 18 |
| 2019-10-04 | 69 |
| 2019-10-05 | 69 |
| 2019-10-06 | 69 |
| 2019-10-07 | 69 |
| 2019-10-08 | 58 |
| 2019-10-09 | 54 |
| 2019-10-10 | 51 |
| 2019-10-11 | NULL |
| 2019-10-12 | 1 |
| 2019-10-13 | 3 |
| 2019-10-14 | 1003 |
+------------+-----------------+
I am currently using this query (in SQL Server) to count the number of unique item each day:
SELECT Date, COUNT(DISTINCT item)
FROM myTable
GROUP BY Date
ORDER BY Date
How can I transform this to get for each date the number of unique item over the past 3 days (including the current day)?
The output should be a table with 2 columns:
one columns with all dates in the original table. On the second column, we have the number of unique item per date.
for instance if original table is:
Date Item
01/01/2018 A
01/01/2018 B
02/01/2018 C
03/01/2018 C
04/01/2018 C
With my query above I currently get the unique count for each day:
Date count
01/01/2018 2
02/01/2018 1
03/01/2018 1
04/01/2018 1
and I am looking to get as result the unique count over 3 days rolling window:
Date count
01/01/2018 2
02/01/2018 3 (because items ABC on 1st and 2nd Jan)
03/01/2018 3 (because items ABC on 1st,2nd,3rd Jan)
04/01/2018 1 (because only item C on 2nd,3rd,4th Jan)
Using an apply provides a convenient way to form sliding windows
CREATE TABLE myTable
([DateCol] datetime, [Item] varchar(1))
;
INSERT INTO myTable
([DateCol], [Item])
VALUES
('2018-01-01 00:00:00', 'A'),
('2018-01-01 00:00:00', 'B'),
('2018-01-02 00:00:00', 'C'),
('2018-01-03 00:00:00', 'C'),
('2018-01-04 00:00:00', 'C')
;
CREATE NONCLUSTERED INDEX IX_DateCol
ON MyTable([Date])
;
Query:
select distinct
t1.dateCol
, oa.ItemCount
from myTable t1
outer apply (
select count(distinct t2.item) as ItemCount
from myTable t2
where t2.DateCol between dateadd(day,-2,t1.DateCol) and t1.DateCol
) oa
order by t1.dateCol ASC
Results:
| dateCol | ItemCount |
|----------------------|-----------|
| 2018-01-01T00:00:00Z | 2 |
| 2018-01-02T00:00:00Z | 3 |
| 2018-01-03T00:00:00Z | 3 |
| 2018-01-04T00:00:00Z | 1 |
There may be some performance gains by reducing the date column prior to using the apply, like so:
select
d.date
, oa.ItemCount
from (
select distinct t1.date
from myTable t1
) d
outer apply (
select count(distinct t2.item) as ItemCount
from myTable t2
where t2.Date between dateadd(day,-2,d.Date) and d.Date
) oa
order by d.date ASC
;
Instead of using select distinct in that subquery you could use group by instead but the execution plan will remain the same.
Demo at SQL Fiddle
The most straight forward solution is to join the table with itself based on dates:
SELECT t1.DateCol, COUNT(DISTINCT t2.Item) AS C
FROM testdata AS t1
LEFT JOIN testdata AS t2 ON t2.DateCol BETWEEN DATEADD(dd, -2, t1.DateCol) AND t1.DateCol
GROUP BY t1.DateCol
ORDER BY t1.DateCol
Output:
| DateCol | C |
|-------------------------|---|
| 2018-01-01 00:00:00.000 | 2 |
| 2018-01-02 00:00:00.000 | 3 |
| 2018-01-03 00:00:00.000 | 3 |
| 2018-01-04 00:00:00.000 | 1 |
GROUP BY should be faster then DISTINCT (make sure to have an index on your Date column)
DECLARE #tbl TABLE([Date] DATE, [Item] VARCHAR(100))
;
INSERT INTO #tbl VALUES
('2018-01-01 00:00:00', 'A'),
('2018-01-01 00:00:00', 'B'),
('2018-01-02 00:00:00', 'C'),
('2018-01-03 00:00:00', 'C'),
('2018-01-04 00:00:00', 'C');
SELECT t.[Date]
--Just for control. You can take this part away
,(SELECT DISTINCT t2.[Item] AS [*]
FROM #tbl AS t2
WHERE t2.[Date]<=t.[Date]
AND t2.[Date]>=DATEADD(DAY,-2,t.[Date]) FOR XML PATH('')) AS CountedItems
--This sub-select comes back with your counts
,(SELECT COUNT(DISTINCT t2.[Item])
FROM #tbl AS t2
WHERE t2.[Date]<=t.[Date]
AND t2.[Date]>=DATEADD(DAY,-2,t.[Date])) AS ItemCount
FROM #tbl AS t
GROUP BY t.[Date];
The result
Date CountedItems ItemCount
2018-01-01 AB 2
2018-01-02 ABC 3
2018-01-03 ABC 3
2018-01-04 C 1
This solution is different from other solutions. Can you check performance of this query on real data with comparison to other answers?
The basic idea is that each row can participate in the window for its own date, the day after, or the day after that. So this first expands the row out into three rows with those different dates attached and then it can just use a regular COUNT(DISTINCT) aggregating on the computed date. The HAVING clause is just to avoid returning results for dates that were solely computed and not present in the base data.
with cte(Date, Item) as (
select cast(a as datetime), b
from (values
('01/01/2018','A')
,('01/01/2018','B')
,('02/01/2018','C')
,('03/01/2018','C')
,('04/01/2018','C')) t(a,b)
)
select
[Date] = dateadd(dd, n, Date), [Count] = count(distinct Item)
from
cte
cross join (values (0),(1),(2)) t(n)
group by dateadd(dd, n, Date)
having max(iif(n = 0, 1, 0)) = 1
option (force order)
Output:
| Date | Count |
|-------------------------|-------|
| 2018-01-01 00:00:00.000 | 2 |
| 2018-01-02 00:00:00.000 | 3 |
| 2018-01-03 00:00:00.000 | 3 |
| 2018-01-04 00:00:00.000 | 1 |
It might be faster if you have many duplicate rows:
select
[Date] = dateadd(dd, n, Date), [Count] = count(distinct Item)
from
(select distinct Date, Item from cte) c
cross join (values (0),(1),(2)) t(n)
group by dateadd(dd, n, Date)
having max(iif(n = 0, 1, 0)) = 1
option (force order)
Use GETDATE() function to get current date, and DATEADD() to get the last 3 days
SELECT Date, count(DISTINCT item)
FROM myTable
WHERE [Date] >= DATEADD(day,-3, GETDATE())
GROUP BY Date
ORDER BY Date
SQL
SELECT DISTINCT Date,
(SELECT COUNT(DISTINCT item)
FROM myTable t2
WHERE t2.Date BETWEEN DATEADD(day, -2, t1.Date) AND t1.Date) AS count
FROM myTable t1
ORDER BY Date;
Demo
Rextester demo: http://rextester.com/ZRDQ22190
Since COUNT(DISTINCT item) OVER (PARTITION BY [Date]) is not supported you can use dense_rank to emulate that:
SELECT Date, dense_rank() over (partition by [Date] order by [item])
+ dense_rank() over (partition by [Date] order by [item] desc)
- 1 as count_distinct_item
FROM myTable
One thing to note is that dense_rank will count null as whereas COUNT will not.
Refer this post for more details.
Here is a simple solution that uses myTable itself as the source of grouping dates (edited for SQLServer dateadd). Note that this query assumes there will be at least one record in myTable for every date; if any date is absent, it will not appear in the query results, even if there are records for the 2 days prior:
select
date,
(select
count(distinct item)
from (select distinct date, item from myTable) as d2
where
d2.date between dateadd(day,-2,d.date) and d.date
) as count
from (select distinct date from myTable) as d
I solve this question with Math.
z (any day) = 3x + y (y is mode 3 value)
I need from 3 * (x - 1) + y + 1 to 3 * (x - 1) + y + 3
3 * (x- 1) + y + 1 = 3* (z / 3 - 1) + z % 3 + 1
In that case; I can use group by (between 3* (z / 3 - 1) + z % 3 + 1 and z)
SELECT iif(OrderDate between 3 * (cast(OrderDate as int) / 3 - 1) + (cast(OrderDate as int) % 3) + 1
and orderdate, Orderdate, 0)
, count(sh.SalesOrderID) FROM Sales.SalesOrderDetail shd
JOIN Sales.SalesOrderHeader sh on sh.SalesOrderID = shd.SalesOrderID
group by iif(OrderDate between 3 * (cast(OrderDate as int) / 3 - 1) + (cast(OrderDate as int) % 3) + 1
and orderdate, Orderdate, 0)
order by iif(OrderDate between 3 * (cast(OrderDate as int) / 3 - 1) + (cast(OrderDate as int) % 3) + 1
and orderdate, Orderdate, 0)
If you need else day group, you can use;
declare #n int = 4 (another day count)
SELECT iif(OrderDate between #n * (cast(OrderDate as int) / #n - 1) + (cast(OrderDate as int) % #n) + 1
and orderdate, Orderdate, 0)
, count(sh.SalesOrderID) FROM Sales.SalesOrderDetail shd
JOIN Sales.SalesOrderHeader sh on sh.SalesOrderID = shd.SalesOrderID
group by iif(OrderDate between #n * (cast(OrderDate as int) / #n - 1) + (cast(OrderDate as int) % #n) + 1
and orderdate, Orderdate, 0)
order by iif(OrderDate between #n * (cast(OrderDate as int) / #n - 1) + (cast(OrderDate as int) % #n) + 1
and orderdate, Orderdate, 0)