Have a table with the following schema design and the data residing inside it is like:
ID HITS MISS DDATE
1 10 3 20180101
1 33 21 20180122
1 84 11 20180901
1 11 2 20180405
1 54 23 20190203
1 33 43 20190102
4 54 22 20170305
4 56 88 20180115
5 87 22 20180809
5 66 48 20180617
5 91 53 20170606
DataTypes:
ID INT
HITS INT
MISS INT
DDATE STRING
The requirement is to calculate the total of the given (HITS and MISS) on yearly basis i.e 2017,2018,2019...
Written the following query:
SELECT ID,
SUM(HITS) AS HITS,SUM(MISS) AS MISS,
CASE
WHEN DDATE BETWEEN '201701' AND '201712' THEN '2017' ELSE
'NOTHING' END AS TTL_YR17_DATA
CASE
WHEN DDATE BETWEEN '201801' AND '201812' THEN '2018' ELSE
'NOTHING' END AS TTL_YR18_DATA
CASE
WHEN DDATE BETWEEN '201901' AND '201912' THEN '2019' ELSE
'NOTHING' END AS TTL_YR19_DATA
FROM
HST_TABLE
WHERE
DDATE BETWEEN '201801' AND '201812'
GROUP BY
ID,DDATE;
But, the query is not fetching the expected result.
Actual O/P:
1 10 3 2018
1 33 21 2018
1 84 11 2018
1 11 2 2018
1 54 23 2019
1 33 43 2019
4 54 22 2017
4 56 88 2018
5 87 22 2018
5 66 48 2018
5 91 53 2017
Expected O/P:
1 138 37 2018
4 56 88 2018
5 153 70 2018
1 87 66 2019
5 91 53 2017
Another related question:
Is there a way that I can avoid passing the DDATE range in the query? As this should be given by the user and shouldn't be hardcoded.
Any help/advice to achieve the above two requirements will be really helpful.
OK,it's easy to implement this with the substring function in HIVE, as below:
select
substring(dddate,0,4) as the_year,
id,
sum(hits) as hits_num,
sum(miss) as miss_num
from
hst_table
group by
substring(dddate,0,4),
id
order by
the_year,
id
The answer above by #Shawn.X is correct but has a logical flaw. Below is the corrected one:
select
substring(ddate,0,4) as the_year,
id,
sum(hits) as hits_num,
sum(miss) as miss_num
from
hst_table
group by
substring(ddate,0,4),
id
order by
the_year,
id;
2 Stores, each with its sales data per day. Both get equipped with promotion material but not at the same day. After the pr_day the promotion material will stay there. Meaning, there should be a sales boost from the day of the installation of the promotion material.
Installation Date:
Store A - 05/15/2019
Store B - 05/17/2019
To see if the promotion was a success we measure the sales before the pr-date and after by returning number of sales (not revenue but pieces sold) next to the int, indicating how far away it was from the pr-day: (sum of sales from both stores)
pr_date| sales
-28 | 35
-27 | 40
-26 | 21
-25 | 36
-24 | 29
-23 | 36
-22 | 43
-21 | 31
-20 | 32
-19 | 21
-18 | 17
-17 | 34
-16 | 34
-15 | 37
-14 | 32
-13 | 29
-12 | 25
-11 | 45
-10 | 43
-9 | 26
-8 | 27
-7 | 33
-6 | 36
-5 | 17
-4 | 34
-3 | 33
-2 | 21
-1 | 28
1 | 16
2 | 6
3 | 16
4 | 29
5 | 32
6 | 30
7 | 30
8 | 30
9 | 17
10 | 12
11 | 35
12 | 30
13 | 15
14 | 28
15 | 14
16 | 16
17 | 13
18 | 27
19 | 22
20 | 34
21 | 33
22 | 22
23 | 13
24 | 35
25 | 28
26 | 19
27 | 17
28 | 29
you may noticed, that i already removed the day from the installation of the promotion material.
The issue starts with the different installation date of the pr-material. If I group by weekday it will combine the sales from different days away from the installation. It will just start at whatever weekday i define:
Select DATEDIFF(wk, change_date, sales_date), sum(sales)
from tbl_sales
group by DATEDIFF(wk, change_date, sales_date)
result:
week | sales
-4 | 75
-3 | 228
-2 | 204
-1 | 235
0 | 149
1 | 173
2 | 151
3 | 167
4 | 141
the numbers are not from the right days and there is one week to many. Guess this is comming from sql grouping the sales starting from Sunday and because the pr_dates are different it generates more than just the 8 weeks (4 before, 4 after)
trying to find a sustainable solution i couldn't find the right fit and decided to post it here. Very thankfull for every thoughts of the community about this topics. Quite sure there is a smart solution for this problem cause it doesn't look like a rare request to me
I tried it with over as well but i don't see how to sum the 7 days together as they are not date days anymore but delta to the pr-date
Desired Result:
week | sales
-4 | 240
-3 | 206
-2 | 227
-1 | 202
1 | 159
2 | 167
3 | 159
4 | 163
Attachment from my analysis by hand what the Results should be:
Why do i need the weekly summary -> the Stores are performing differently depending on the weekday. With summing 7 days together I make sure we don't compare mondays to sundays and so on. Furthermore, the result will be represented in a Line- or Barchart where you could see the weekday variation in a ugly way. Meaning it will be hard for your eyes to see the trend/devolopment of the salesnumbers. Whereas the weekly comparison will absorb this variations.
If anything is unclear please feel free to let me know so i could provide you with futher details
Thank you very much
Additional the different Installation date overview:
Shop A:
store A
delta date sales
-28 17.04.2019 20
-27 18.04.2019 20
-26 19.04.2019 13
-25 20.04.2019 25
-24 21.04.2019 16
-23 22.04.2019 20
-22 23.04.2019 26
-21 24.04.2019 15
-20 25.04.2019 20
-19 26.04.2019 13
-18 27.04.2019 13
-17 28.04.2019 20
-16 29.04.2019 21
-15 30.04.2019 20
-14 01.05.2019 17
-13 02.05.2019 13
-12 03.05.2019 9
-11 04.05.2019 34
-10 05.05.2019 28
-9 06.05.2019 19
-8 07.05.2019 14
-7 08.05.2019 23
-6 09.05.2019 18
-5 10.05.2019 9
-4 11.05.2019 22
-3 12.05.2019 17
-2 13.05.2019 14
-1 14.05.2019 19
0 15.05.2019 11
1 16.05.2019 0
2 17.05.2019 0
3 18.05.2019 1
4 19.05.2019 19
5 20.05.2019 18
6 21.05.2019 14
7 22.05.2019 11
8 23.05.2019 12
9 24.05.2019 8
10 25.05.2019 7
11 26.05.2019 19
12 27.05.2019 15
13 28.05.2019 15
14 29.05.2019 11
15 30.05.2019 5
16 31.05.2019 8
17 01.06.2019 10
18 02.06.2019 19
19 03.06.2019 14
20 04.06.2019 21
21 05.06.2019 22
22 06.06.2019 7
23 07.06.2019 6
24 08.06.2019 23
25 09.06.2019 17
26 10.06.2019 9
27 11.06.2019 8
28 12.06.2019 23
Shop B:
store B
delta date sales
-28 19.04.2019 15
-27 20.04.2019 20
-26 21.04.2019 8
-25 22.04.2019 11
-24 23.04.2019 13
-23 24.04.2019 16
-22 25.04.2019 17
-21 26.04.2019 16
-20 27.04.2019 12
-19 28.04.2019 8
-18 29.04.2019 4
-17 30.04.2019 14
-16 01.05.2019 13
-15 02.05.2019 17
-14 03.05.2019 15
-13 04.05.2019 16
-12 05.05.2019 16
-11 06.05.2019 11
-10 07.05.2019 15
-9 08.05.2019 7
-8 09.05.2019 13
-7 10.05.2019 10
-6 11.05.2019 18
-5 12.05.2019 8
-4 13.05.2019 12
-3 14.05.2019 16
-2 15.05.2019 7
-1 16.05.2019 9
0 17.05.2019 9
1 18.05.2019 16
2 19.05.2019 6
3 20.05.2019 15
4 21.05.2019 10
5 22.05.2019 14
6 23.05.2019 16
7 24.05.2019 19
8 25.05.2019 18
9 26.05.2019 9
10 27.05.2019 5
11 28.05.2019 16
12 29.05.2019 15
13 30.05.2019 17
14 31.05.2019 9
15 01.06.2019 8
16 02.06.2019 3
17 03.06.2019 8
18 04.06.2019 8
19 05.06.2019 13
20 06.06.2019 11
21 07.06.2019 15
22 08.06.2019 7
23 09.06.2019 12
24 10.06.2019 11
25 11.06.2019 10
26 12.06.2019 9
27 13.06.2019 6
28 14.06.2019 9
Try
select wk, sum(sales)
from (
select
isnull(sa.sales,0) + isnull(sb.sales,0) sales
, isnull(sa.delta , sb.delta) delta
, case when isnull(sa.delta , sb.delta) = 0 then 0
else case when isnull(sa.delta , sb.delta) > 0 then (isnull(sa.delta , sb.delta) -1) /7 +1
else (isnull(sa.delta , sb.delta) +1) /7 -1
end
end wk
from shopA sa
full join shopB sb on sa.delta=sb.delta
) t
group by wk;
sql fiddle
A more readable version, it doesn't run faster, just using CROSS APLLY this way allows to indroduce sort of intermediate variables for cleaner code.
select wk, sum(sales)
from (
select
isnull(sa.sales,0) + isnull(sb.sales,0) sales
, dlt delta
, case when dlt = 0 then 0
else case when dlt > 0 then (dlt - 1) / 7 + 1
else (dlt + 1) / 7 - 1
end
end wk
from shopA sa
full join shopB sb on sa.delta=sb.delta
cross apply (
select dlt = isnull(sa.delta, sb.delta)
) tmp
) t
group by wk;
Finally, if you already have a query which produces a dataset with the (pr_date, sales) columns
select wk, sum(sales)
from (
select sales
, case when pr_date = 0 then 0
else case when pr_date > 0 then (pr_date - 1) / 7 + 1
else (pr_date + 1) / 7 - 1
end
end wk
from (
-- ... you query here ...
)pr_date_sales
) t
group by wk;
I think you just need to take the day difference and use arithmetic. Using datediff() with week counts week-boundaries -- which is not what you want. That is, it normalizes the weeks to calendar weeks.
You want to leave out the day of the promotion, which makes this a wee bit more complicated.
I think this is the logic:
Select v.week_diff, sum(sales)
from tbl_sales s cross join
(values (case when change_date < sales_date
then (datediff(day, change_date, sales_date) + 1) / 7
else (datediff(day, change_date, sales_date) - 1) / 7
end)
) v(week_diff)
where change_date <> sales_date
group by v.week_diff;
There might be an off-by-one problem, depending on what you really want to do when the dates are the same.
I have a table where for some dates a certain number of entries are placed. Here is the table structure :
ID EntryName Entries DateOfEntry
1 A 20 2016-01-17
2 B 22 2016-01-29
3 C 23 2016-02-17
4 D 19 2016-02-17
5 E 29 2016-03-17
6 F 30 2016-03-17
7 G 43 2016-04-17
8 H 10 2016-04-17
9 I 5 2016-05-17
10 J 120 2016-05-17
11 K 220 2016-06-17
12 L 210 2016-06-17
13 M 10 2016-07-17
14 N 20 2016-07-17
15 O 15 2016-08-17
16 P 17 2016-08-17
17 Q 19 2016-09-17
18 R 23 2016-09-17
19 S 43 2016-10-17
20 T 56 2016-10-17
21 U 65 2016-11-17
22 V 78 2016-11-17
23 W 12 2016-12-17
24 X 23 2016-12-17
25 Y 43 2016-02-17
26 Z 67 2016-03-17
27 AA 35 2015-01-17
28 AB 23 2015-01-29
29 AC 43 2015-02-17
30 AD 35 2015-02-17
31 AE 45 2015-03-17
32 AF 23 2015-03-17
33 AG 43 2015-04-17
34 AH 19 2015-04-17
35 AI 21 2015-05-17
36 AJ 13 2015-05-17
37 AK 22 2015-06-17
38 AL 45 2015-06-17
39 AM 66 2015-07-17
40 AN 77 2015-07-17
41 AO 89 2015-08-17
42 AP 127 2015-08-17
43 AQ 19 2015-09-17
44 AR 223 2015-09-17
45 AS 143 2015-10-17
46 AT 36 2015-10-17
47 AU 45 2015-11-17
48 AV 28 2015-11-17
49 AW 72 2015-12-17
50 AX 24 2015-12-17
51 AY 46 2015-02-17
52 AZ 62 2015-03-17
The column EntryName is the entry identifier, the column Entries has the total number of entries for the date specified in the column DateOfEntry.
I am trying to formulate a query where the total number of entries are displayed on a month-wise basis. I currently have this query :
SELECT DateName(MONTH, e.DateOfEntry) AS MonthOfEntry,
MONTH(e.DateOfEntry) AS MonthNumber,
SUM(e.Entries) AS TotalEntries
FROM #entry e
GROUP BY MONTH(e.DateOfEntry), DateName(MONTH,e.DateOfEntry)
ORDER BY MONTH(e.DateOfEntry) ASC
which works fine as far as displaying the results are concerned. However, my issue here is that I need to sort the results on a month-wise basis where the starting month would be dynamic i.e. arising from a parameter (supplied by the user).
This means that if the user selects May of 2015 the results should be sorted from May 2015 to April 2016. Similarly, if the user selects October 2015, the results would be displayed from October 2015 to September 2016.
How would I go about getting this condition within the ORDER BY clause ?
You can put an offset into the ORDER BY using modulo arithmetic. For April:
ORDER BY (MONTH(e.DateOfEntry) + 12 - 4) % 12
--------------------------------------^ month number to start with
(The + 12 is simply so I don't have to remember if % returns negative numbers with negative operands.)
If you want the results chronologically, you can instead do:
ORDER BY MIN(e.DateOfEntry)
You could use the belosw in order by
ORDER BY YEAR(e.DATEOFENTRY),
DATEPART(MM,e.DAREOFENTRY)
This will sort the result first for Year and next for month.
Here you need to specify these same columns in Select.
If I understood you correctly
"This means that if the user selects May of 2015 the results should be sorted from May 2015 to April 2016. Similarly, if the user selects October 2015, the results would be displayed from October 2015 to September 2016."
this should work:
SAMPLE DATA:
IF OBJECT_ID('tempdb..#entry') IS NOT NULL
DROP TABLE #entry;
CREATE TABLE #entry(ID INT ,EntryName VARCHAR(10) , Entries INT , DateOfEntry DATE);
INSERT INTO #entry (ID ,EntryName ,Entries ,DateOfEntry)
VALUES
(1 ,'A', 20 ,'2016-01-17'),
(2 ,'B', 22 ,'2016-01-29'),
(3 ,'C', 23 ,'2016-02-17'),
(4 ,'D', 19 ,'2016-02-17'),
(5 ,'E', 29 ,'2016-03-17'),
(6 ,'F', 30 ,'2016-03-17'),
(7 ,'G', 43 ,'2016-04-17'),
(8 ,'H', 10 ,'2016-04-17'),
(9 ,'I', 5 ,'2016-05-17'),
(10,'J', 120 ,'2016-05-17'),
(11,'K', 220 ,'2016-06-17'),
(12,'L', 210 ,'2016-06-17'),
(13,'M', 10 ,'2016-07-17'),
(14,'N', 20 ,'2016-07-17'),
(15,'O', 15 ,'2016-08-17'),
(16,'P', 17 ,'2016-08-17'),
(17,'Q', 19 ,'2016-09-17'),
(18,'R', 23 ,'2016-09-17'),
(19,'S', 43 ,'2016-10-17'),
(20,'T', 56 ,'2016-10-17'),
(21,'U', 65 ,'2016-11-17'),
(22,'V', 78 ,'2016-11-17'),
(23,'W', 12 ,'2016-12-17'),
(24,'X', 23 ,'2016-12-17'),
(25,'Y', 43 ,'2016-02-17'),
(26,'Z', 67 ,'2016-03-17'),
(27,'AA',35 ,'2015-01-17'),
(28,'AB',23 ,'2015-01-29'),
(29,'AC',43 ,'2015-02-17'),
(30,'AD',35 ,'2015-02-17'),
(31,'AE',45 ,'2015-03-17'),
(32,'AF',23 ,'2015-03-17'),
(33,'AG',43 ,'2015-04-17'),
(34,'AH',19 ,'2015-04-17'),
(35,'AI',21 ,'2015-05-17'),
(36,'AJ',13 ,'2015-05-17'),
(37,'AK',22 ,'2015-06-17'),
(38,'AL',45 ,'2015-06-17'),
(39,'AM',66 ,'2015-07-17'),
(40,'AN',77 ,'2015-07-17'),
(41,'AO',89 ,'2015-08-17'),
(42,'AP',127 ,'2015-08-17'),
(43,'AQ',19 ,'2015-09-17'),
(44,'AR',223 ,'2015-09-17'),
(45,'AS',143 ,'2015-10-17'),
(46,'AT',36 ,'2015-10-17'),
(47,'AU',45 ,'2015-11-17'),
(48,'AV',28 ,'2015-11-17'),
(49,'AW',72 ,'2015-12-17'),
(50,'AX',24 ,'2015-12-17'),
(51,'AY',46 ,'2015-02-17'),
(52,'AZ',62 ,'2015-03-17')
QUERY WITH PARAMS:
DECLARE #Month VARCHAR(2) = '05', #Year VARCHAR(4) = '2015'
SELECT DateName(MONTH, e.DateOfEntry) AS MonthOfEntry,
MONTH(e.DateOfEntry) AS MonthNumber,
SUM(e.Entries) AS TotalEntries
FROM #entry e
WHERE CAST(e.DateOfEntry AS DATE) >= CAST( #Year+#Month+'01' AS DATE)
GROUP BY MONTH(e.DateOfEntry), DateName(MONTH,e.DateOfEntry)
ORDER BY MONTH(e.DateOfEntry) ASC
RESULTS:
add a where clause for the query
WHERE MONTH(e.DateOfEntry) < User.Month AND YEAR(e.DateOfEntry) < User.Year AND MONTH(e.DateOfEntry) > (User.Month-1) AND YEAR(e.DateOfEntry) > (User.Year+1)
Assuming your parameter is an Integer called #FirstMonth, you could get the proper month order using:
Case
WHEN MONTH(e.DateOfEntry) < #FirstMonth then MONTH(e.DateOfEntry) + 12
ELSE MONTH(e.DateOfEntry)
END AS MonthNumber
Of all the answers and suggestions I have come across here, I find this way (suggested by xQbert in the question's comments) to be the simplest one :
SELECT DateName(MONTH, e.DateOfEntry) + ' ' + CONVERT(NVARCHAR(100), YEAR(e.DateOfEntry)) AS MonthOfEntry,
MONTH(e.DateOfEntry) AS MonthNumber,
SUM(e.Entries) AS TotalEntries
FROM Entry e
WHERE e.DateOfEntry BETWEEN #StartDate AND (DATEADD(YEAR, 1, #StartDate))
GROUP BY MONTH(e.DateOfEntry), DateName(MONTH,e.DateOfEntry), YEAR(e.DateOfEntry)
ORDER BY YEAR(e.DateOfEntry) ASC, MONTH(e.DateOfEntry) ASC
A fiddle to demonstrate this : http://rextester.com/CJFFP5640
Initially, I was using the following query :
SELECT sortingList.MonthOfEntry,
sortingList.TotalEntries,
sortingList.MonthNumber
FROM (
SELECT DateName(MONTH, DATEADD(MONTH, MONTH(e.DateOfEntry), 0) - 1) + ' ' + CONVERT(nvarchar(20),YEAR(e.DateOfEntry)) AS MonthOfEntry,
SUM(e.Entries) as TotalEntries,
CASE
WHEN ((MONTH(e.DateOfEntry) - MONTH(#StartDate)) > 0)
THEN (MONTH(e.DateOfEntry) - MONTH(#StartDate)) + 1
WHEN ((MONTH(e.DateOfEntry) - MONTH(#StartDate)) = 0)
THEN 1
ELSE
((12 - MONTH(#StartDate)) + (MONTH(e.DateOfEntry))) + 1
END
AS MonthNumber
FROM Entry e
WHERE e.DateOfEntry >= #StartDate AND e.DateOfEntry < DATEADD(YEAR, 1, #StartDate)
GROUP BY DateName(MONTH, DATEADD(MONTH, MONTH(e.DateOfEntry), 0) - 1), YEAR(e.DateOfEntry), MONTH(e.DateOfEntry) - MONTH(#StartDate), MONTH(e.DateOfEntry)
) sortingList
ORDER BY sortingList.MonthNumber ASC
Here's an Fiddle to demonstrate this : http://rextester.com/LEVD30653
Explanation (non TL;DR)
You can see that it's essentially the same WHERE clause. However, the query at the top uses much simpler logic for sorting and is more fluent and readable.
Do note that the second solution (using the CASE statement) sorts the month numbers as per the user-provided month number i.e. if the user provides December 2015, then the second solution will number Dec 2015 as 1, January 2016 as 2, February 2016 as 3 and so on and so forth. This might be more beneficial in cases where you want to work on top of this data.
As far as my use-case is concerned, this makes more sense. However, as far as the scope of the question is concerned, the query at the top is the best one.