Related
How it is possible to get - when was the last change (by date) - in
this table:
id
date
value
1
01.01.2021
0.0
1
02.01.2021
10.0
1
03.01.2021
15.0
1
04.01.2021
25.0
1
05.01.2021
25.0
1
06.01.2021
25.0
Of course I could use clause where and it will works, but i have a lot of rows and for some i don't now exactly day when this happend.
The resault should be:
id
date
value
1
04.01.2021
25.0
Try this one:
with mytable as (
select 1 as id, date '2021-01-01' as date, 0 as value union all
select 1, date '2021-01-02', 10 union all
select 1, date '2021-01-03', 15 union all
select 1, date '2021-01-04', 25 union all
select 1, date '2021-01-05', 25 union all
select 1, date '2021-01-06', 25
)
select id, array_agg(struct(date, value) order by last_change_date desc limit 1)[offset(0)].*
from (
select *, if(value != lag(value) over (partition by id order by date), date, null) as last_change_date
from mytable
)
group by id
in this scenario I would be using two field in my database "created_at and updated_at" with the type as "timestamp". You may simply fetch your records using OrderBY "updated_at" field.
see what this gives you:
SELECT MAX(date) OVER (PARTITION BY(value)) AS lastChange
FROM Table
WHERE id = 1
The following query and reproducible example on db-fiddle works. I've also included some additional test records.
CREATE TABLE my_data (
`id` INTEGER,
`date` date,
`value` INTEGER
);
INSERT INTO my_data
(`id`, `date`, `value`)
VALUES
('1', '01.01.2021', '0.0'),
('1', '02.01.2021', '10.0'),
('1', '03.01.2021', '15.0'),
('1', '04.01.2021', '25.0'),
('1', '05.01.2021', '25.0'),
('1', '06.01.2021', '25.0'),
('2', '05.01.2021', '25.0'),
('2', '06.01.2021', '23.0'),
('3', '03.01.2021', '15.0'),
('3', '04.01.2021', '25.0'),
('3', '05.01.2021', '17.0'),
('3', '06.01.2021', '17.0');
Query #1
SELECT
id,
date,
value
FROM (
SELECT
*,
row_number() over (partition by id order by date desc) as id_rank
FROM (
SELECT
id,
m1.date,
m1.value,
rank() over (partition by id,m1.value order by date asc) as id_value_rank,
CASE
WHEN (m1.date = (max(m1.date) over (partition by id,m1.value ))) THEN 1
ELSE 0
END AS is_max_date_for_group,
CASE
WHEN (m1.date = (max(m1.date) over (partition by id ))) THEN 1
ELSE 0
END AS is_max_date_for_id
from
my_data m1
) m2
WHERE (m2.is_max_date_for_group = m2.is_max_date_for_id and is_max_date_for_group <> 0 and id_value_rank=1) or (id_value_rank=1 and is_max_date_for_id=0)
) t
where t.id_rank=1
order by id, date, value;
id
date
value
1
04.01.2021
25
2
06.01.2021
23
3
05.01.2021
17
View on DB Fiddle
I actually find that the simplest method is to enumerate the rows by id/date and by id/date/value in descending order. These are the same for the last group . . . and the rest is aggregation:
select id, min(date), value
from (select t.*,
row_number() over (partition by id order by date desc) as seqnum,
row_number() over (partition by id, value order by date desc) as seqnum_2
from t
) t
where seqnum = seqnum_2
group by id;
If you use lag(), I would recommend using qualify for performance:
select t.*
from (select t.*
from t
qualify lag(value) over (partition by id order by date) <> value or
lag(value) over (partition by id order by date) is null
) t
qualify row_number() over (partition by id order by date desc) = 1;
Note: Both of these work if the value is the same for all rows. Other methods may not work in that situation.
I have a table like this in SQL Server:
id start_time end_time
1 10:00:00 10:34:00
2 10:38:00 10:52:00
3 10:53:00 11:23:00
4 11:24:00 11:56:00
5 14:20:00 14:40:00
6 14:41:00 14:59:00
7 15:30:00 15:40:00
What I would like to have is a query that outputs consolidated records based on the time difference between two consecutive records (end_time of row n and start_time row n+1) . All records where the time difference is less than 2 minutes should be combined into one time entry and the ID of the first record should be kept. This should also combine more than two records if multiple consecutive records have a time difference less than 2 minutes.
This would be the expected output:
id start_time end_time
1 10:00:00 10:34:00
2 10:38:00 11:56:00
5 14:20:00 14:59:00
7 15:30:00 15:40:00
Thanks in advance for any tips how to build the query.
Edit:
I started with following code to calculate the lead_time and the time difference but do not know how to group and consolidate.
WITH rows AS
(
SELECT *, ROW_NUMBER() OVER (ORDER BY Id) AS rn
FROM #temp
)
SELECT mc.id, mc.start_time, mc.end_time, mp.start_time lead_time, DATEDIFF(MINUTE, mc.[end_time], mp.[start_time]) as DiffToNewSession
FROM rows mc
LEFT JOIN rows mp
ON mc.rn = mp.rn - 1
The window function in t-sql can realize a lot of data statistics, such as
create table #temp(id int identity(1,1), start_time time, end_time time)
insert into #temp(start_time, end_time)
values ('10:00:00', '10:34:00')
, ('10:38:00', '10:52:00')
, ('10:53:00', '11:23:00')
, ('11:24:00', '11:56:00')
, ('14:20:00', '14:40:00')
, ('14:41:00', '14:59:00')
, ('15:30:00', '15:40:00')
;with c0 as(
select *, LAG(end_time,1,'00:00:00') over (order by id) as lag_time
from #temp
), c1 as(
select *, case when DATEDIFF(MI, lag_time, start_time) <= 2 then 1 else -0 end as gflag
from c0
), c2 as(
select *, SUM(case when gflag=0 then 1 else 0 end) over(order by id) as gid
from c1
)
select MIN(id) as id, MIN(start_time) as start_time, MAX(end_time) as end_time
from c2
group by gid
In order to better describe the process of data construction, I simply use c0, c1, c2... to represent levels, you can merge some levels and optimize.
If you can’t use id as a sorting condition, then you need to change the sorting part in the above statement.
You can use a recursive cte to get the result that you want. This method just simple compare current end_time with next start_time. If it is less than the 2 mintues threshold use the same start_time as grp_start. And the end, simple do a GROUP BY on the grp_start
with rcte as
(
-- anchor member
select *, grp_start = start_time
from tbl
where id = 1
union all
-- recursive member
select t.id, t.start_time, t.end_time,
grp_start = case when datediff(second, r.end_time, t.start_time) <= 120
then r.grp_start
else t.start_time
end
from tbl t
inner join rcte r on t.id = r.id + 1
)
select id = min(id), grp_start as start_time, max(end_time) as end_time
from rcte
group by grp_start
demo
I guess this should do the trick without recursion. Again I used several ctes in order to make the solution a bit easier to read. guess it can be reduced a little...
INSERT INTO T1 VALUES
(1,'10:00:00','10:34:00')
,(2,'10:38:00','10:52:00')
,(3,'10:53:00','11:23:00')
,(4,'11:24:00','11:56:00')
,(5,'14:20:00','14:40:00')
,(6,'14:41:00','14:59:00')
,(7,'15:30:00','15:40:00')
GO
WITH cte AS(
SELECT *
,ROW_NUMBER() OVER (ORDER BY id) AS rn
,DATEDIFF(MINUTE, ISNULL(LAG(endtime) OVER (ORDER BY id), starttime), starttime) AS diffMin
,COUNT(*) OVER (PARTITION BY (SELECT 1)) as maxRn
FROM T1
),
cteFirst AS(
SELECT *
FROM cte
WHERE rn = 1 OR diffMin > 2
),
cteGrp AS(
SELECT *
,ISNULL(LEAD(rn) OVER (ORDER BY id), maxRn+1) AS nextRn
FROM cteFirst
)
SELECT f.id, f.starttime, MAX(ISNULL(n.endtime, f.endtime)) AS endtime
FROM cteGrp f
LEFT JOIN cte n ON n.rn >= f.rn AND n.rn < f.nextRn
GROUP BY f.id, f.starttime
We have appointment table as shown below. Each appointment need to be categorized as "New" or "Followup". Any appointment (for a patient) within 30 days of first appointment (of that patient) is Followup. After 30 days, appointment is again "New". Any appointment within 30 days become "Followup".
I am currently doing this by typing while loop.
How to achieve this without WHILE loop?
Table
CREATE TABLE #Appt1 (ApptID INT, PatientID INT, ApptDate DATE)
INSERT INTO #Appt1
SELECT 1,101,'2020-01-05' UNION
SELECT 2,505,'2020-01-06' UNION
SELECT 3,505,'2020-01-10' UNION
SELECT 4,505,'2020-01-20' UNION
SELECT 5,101,'2020-01-25' UNION
SELECT 6,101,'2020-02-12' UNION
SELECT 7,101,'2020-02-20' UNION
SELECT 8,101,'2020-03-30' UNION
SELECT 9,303,'2020-01-28' UNION
SELECT 10,303,'2020-02-02'
You need to use recursive query.
The 30days period is counted starting from prev(and no it is not possible to do it without recursion/quirky update/loop). That is why all the existing answer using only ROW_NUMBER failed.
WITH f AS (
SELECT *, rn = ROW_NUMBER() OVER(PARTITION BY PatientId ORDER BY ApptDate)
FROM Appt1
), rec AS (
SELECT Category = CAST('New' AS NVARCHAR(20)), ApptId, PatientId, ApptDate, rn, startDate = ApptDate
FROM f
WHERE rn = 1
UNION ALL
SELECT CAST(CASE WHEN DATEDIFF(DAY, rec.startDate,f.ApptDate) <= 30 THEN N'FollowUp' ELSE N'New' END AS NVARCHAR(20)),
f.ApptId,f.PatientId,f.ApptDate, f.rn,
CASE WHEN DATEDIFF(DAY, rec.startDate, f.ApptDate) <= 30 THEN rec.startDate ELSE f.ApptDate END
FROM rec
JOIN f
ON rec.rn = f.rn - 1
AND rec.PatientId = f.PatientId
)
SELECT ApptId, PatientId, ApptDate, Category
FROM rec
ORDER BY PatientId, ApptDate;
db<>fiddle demo
Output:
+---------+------------+-------------+----------+
| ApptId | PatientId | ApptDate | Category |
+---------+------------+-------------+----------+
| 1 | 101 | 2020-01-05 | New |
| 5 | 101 | 2020-01-25 | FollowUp |
| 6 | 101 | 2020-02-12 | New |
| 7 | 101 | 2020-02-20 | FollowUp |
| 8 | 101 | 2020-03-30 | New |
| 9 | 303 | 2020-01-28 | New |
| 10 | 303 | 2020-02-02 | FollowUp |
| 2 | 505 | 2020-01-06 | New |
| 3 | 505 | 2020-01-10 | FollowUp |
| 4 | 505 | 2020-01-20 | FollowUp |
+---------+------------+-------------+----------+
How it works:
f - get starting point(anchor - per every PatientId)
rec - recursibe part, if the difference between current value and prev is > 30 change the category and starting point, in context of PatientId
Main - display sorted resultset
Similar class:
Conditional SUM on Oracle - Capping a windowed function
Session window (Azure Stream Analytics)
Running Total until specific condition is true - Quirky update
Addendum
Do not ever use this code on production!
But another option, that is worth mentioning besides using cte, is to use temp table and update in "rounds"
It could be done in "single" round(quirky update):
CREATE TABLE Appt_temp (ApptID INT , PatientID INT, ApptDate DATE, Category NVARCHAR(10))
INSERT INTO Appt_temp(ApptId, PatientId, ApptDate)
SELECT ApptId, PatientId, ApptDate
FROM Appt1;
CREATE CLUSTERED INDEX Idx_appt ON Appt_temp(PatientID, ApptDate);
Query:
DECLARE #PatientId INT = 0,
#PrevPatientId INT,
#FirstApptDate DATE = NULL;
UPDATE Appt_temp
SET #PrevPatientId = #PatientId
,#PatientId = PatientID
,#FirstApptDate = CASE WHEN #PrevPatientId <> #PatientId THEN ApptDate
WHEN DATEDIFF(DAY, #FirstApptDate, ApptDate)>30 THEN ApptDate
ELSE #FirstApptDate
END
,Category = CASE WHEN #PrevPatientId <> #PatientId THEN 'New'
WHEN #FirstApptDate = ApptDate THEN 'New'
ELSE 'FollowUp'
END
FROM Appt_temp WITH(INDEX(Idx_appt))
OPTION (MAXDOP 1);
SELECT * FROM Appt_temp ORDER BY PatientId, ApptDate;
db<>fiddle Quirky update
You could do this with a recursive cte. You should first order by apptDate within each patient. That can be accomplished by a run-of-the-mill cte.
Then, in the anchor portion of your recursive cte, select the first ordering for each patient, mark the status as 'new', and also mark the apptDate as the date of the most recent 'new' record.
In the recursive portion of your recursive cte, increment to the next appointment, calculate the difference in days between the present appointment and the most recent 'new' appointment date. If it's greater than 30 days, mark it 'new' and reset the most recent new appointment date. Otherwise mark it as 'follow up' and just pass along the existing days since new appointment date.
Finallly, in the base query, just select the columns you want.
with orderings as (
select *,
rn = row_number() over(
partition by patientId
order by apptDate
)
from #appt1 a
),
markings as (
select apptId,
patientId,
apptDate,
rn,
type = convert(varchar(10),'new'),
dateOfNew = apptDate
from orderings
where rn = 1
union all
select o.apptId, o.patientId, o.apptDate, o.rn,
type = convert(varchar(10),iif(ap.daysSinceNew > 30, 'new', 'follow up')),
dateOfNew = iif(ap.daysSinceNew > 30, o.apptDate, m.dateOfNew)
from markings m
join orderings o
on m.patientId = o.patientId
and m.rn + 1 = o.rn
cross apply (select daysSinceNew = datediff(day, m.dateOfNew, o.apptDate)) ap
)
select apptId, patientId, apptDate, type
from markings
order by patientId, rn;
I should mention that I initially deleted this answer because Abhijeet Khandagale's answer seemed to meet your needs with a simpler query (after reworking it a bit). But with your comment to him about your business requirement and your added sample data, I undeleted mine because believe this one meets your needs.
I'm not sure that it's exactly what you implemented. But another option, that is worth mentioning besides using cte, is to use temp table and update in "rounds". So we are going to update temp table while all statuses are not set correctly and build result in an iterative way. We can control number of iteration using simply local variable.
So we split each iteration into two stages.
Set all Followup values that are near to New records. That's pretty easy to do just using right filter.
For the rest of the records that dont have status set we can select first in group with same PatientID. And say that they are new since they not processed by the first stage.
So
CREATE TABLE #Appt2 (ApptID INT, PatientID INT, ApptDate DATE, AppStatus nvarchar(100))
select * from #Appt1
insert into #Appt2 (ApptID, PatientID, ApptDate, AppStatus)
select a1.ApptID, a1.PatientID, a1.ApptDate, null from #Appt1 a1
declare #limit int = 0;
while (exists(select * from #Appt2 where AppStatus IS NULL) and #limit < 1000)
begin
set #limit = #limit+1;
update a2
set
a2.AppStatus = IIF(exists(
select *
from #Appt2 a
where
0 > DATEDIFF(day, a2.ApptDate, a.ApptDate)
and DATEDIFF(day, a2.ApptDate, a.ApptDate) > -30
and a.ApptID != a2.ApptID
and a.PatientID = a2.PatientID
and a.AppStatus = 'New'
), 'Followup', a2.AppStatus)
from #Appt2 a2
--select * from #Appt2
update a2
set a2.AppStatus = 'New'
from #Appt2 a2 join (select a.*, ROW_NUMBER() over (Partition By PatientId order by ApptId) rn from (select * from #Appt2 where AppStatus IS NULL) a) ar
on a2.ApptID = ar.ApptID
and ar.rn = 1
--select * from #Appt2
end
select * from #Appt2 order by PatientID, ApptDate
drop table #Appt1
drop table #Appt2
Update. Read the comment provided by Lukasz. It's by far smarter way. I leave my answer just as an idea.
I believe the recursive common expression is great way to optimize queries avoiding loops, but in some cases it can lead to bad performance and should be avoided if possible.
I use the code below to solve the issue and test it will more values, but encourage you to test it with your real data, too.
WITH DataSource AS
(
SELECT *
,CEILING(DATEDIFF(DAY, MIN([ApptDate]) OVER (PARTITION BY [PatientID]), [ApptDate]) * 1.0 / 30 + 0.000001) AS [GroupID]
FROM #Appt1
)
SELECT *
,IIF(ROW_NUMBER() OVER (PARTITION BY [PatientID], [GroupID] ORDER BY [ApptDate]) = 1, 'New', 'Followup')
FROM DataSource
ORDER BY [PatientID]
,[ApptDate];
The idea is pretty simple - I want separate the records in group (30 days), in which group the smallest record is new, the others are follow ups. Check how the statement is built:
SELECT *
,DATEDIFF(DAY, MIN([ApptDate]) OVER (PARTITION BY [PatientID]), [ApptDate])
,DATEDIFF(DAY, MIN([ApptDate]) OVER (PARTITION BY [PatientID]), [ApptDate]) * 1.0 / 30
,CEILING(DATEDIFF(DAY, MIN([ApptDate]) OVER (PARTITION BY [PatientID]), [ApptDate]) * 1.0 / 30 + 0.000001)
FROM #Appt1
ORDER BY [PatientID]
,[ApptDate];
So:
first, we are getting the first date, for each group and calculating the differences in days with the current one
then, we are want to get groups - * 1.0 / 30 is added
as for 30, 60, 90, etc days we are getting whole number and we wanted to start a new period, I have added + 0.000001; also, we are using ceiling function to get the smallest integer greater than, or equal to, the specified numeric expression
That's it. Having such group we simply use ROW_NUMBER to find our start date and make it as new and leaving the rest as follow ups.
With due respect to everybody and in IMHO,
There is not much difference between While LOOP and Recursive CTE in terms of RBAR
There is not much performance gain when using Recursive CTE and Window Partition function all in one.
Appid should be int identity(1,1) , or it should be ever increasing clustered index.
Apart from other benefit it also ensure that all successive row APPDate of that patient must be greater.
This way you can easily play with APPID in your query which will be more efficient than putting inequality operator like >,< in APPDate.
Putting inequality operator like >,< in APPID will aid Sql Optimizer.
Also there should be two date column in table like
APPDateTime datetime2(0) not null,
Appdate date not null
As these are most important columns in most important table,so not much cast ,convert.
So Non clustered index can be created on Appdate
Create NonClustered index ix_PID_AppDate_App on APP (patientid,APPDate) include(other column which is not i predicate except APPID)
Test my script with other sample data and lemme know for which sample data it not working.
Even if it do not work then I am sure it can be fix in my script logic itself.
CREATE TABLE #Appt1 (ApptID INT, PatientID INT, ApptDate DATE)
INSERT INTO #Appt1
SELECT 1,101,'2020-01-05' UNION ALL
SELECT 2,505,'2020-01-06' UNION ALL
SELECT 3,505,'2020-01-10' UNION ALL
SELECT 4,505,'2020-01-20' UNION ALL
SELECT 5,101,'2020-01-25' UNION ALL
SELECT 6,101,'2020-02-12' UNION ALL
SELECT 7,101,'2020-02-20' UNION ALL
SELECT 8,101,'2020-03-30' UNION ALL
SELECT 9,303,'2020-01-28' UNION ALL
SELECT 10,303,'2020-02-02'
;With CTE as
(
select a1.* ,a2.ApptDate as NewApptDate
from #Appt1 a1
outer apply(select top 1 a2.ApptID ,a2.ApptDate
from #Appt1 A2
where a1.PatientID=a2.PatientID and a1.ApptID>a2.ApptID
and DATEDIFF(day,a2.ApptDate, a1.ApptDate)>30
order by a2.ApptID desc )A2
)
,CTE1 as
(
select a1.*, a2.ApptDate as FollowApptDate
from CTE A1
outer apply(select top 1 a2.ApptID ,a2.ApptDate
from #Appt1 A2
where a1.PatientID=a2.PatientID and a1.ApptID>a2.ApptID
and DATEDIFF(day,a2.ApptDate, a1.ApptDate)<=30
order by a2.ApptID desc )A2
)
select *
,case when FollowApptDate is null then 'New'
when NewApptDate is not null and FollowApptDate is not null
and DATEDIFF(day,NewApptDate, FollowApptDate)<=30 then 'New'
else 'Followup' end
as Category
from cte1 a1
order by a1.PatientID
drop table #Appt1
Although it's not clearly addressed in the question, it's easy to figure out that the appointment dates cannot be simply categorized by 30-day groups. It makes no business sense. And you cannot use the appt id either. One can make a new appointment today for 2020-09-06.
Here is how I address this issue. First, get the first appointment, then calculate the date difference between each appointment and the first appt. If it's 0, set to 'New'. If <= 30 'Followup'. If > 30, set as 'Undecided' and do the next round check until there is no more 'Undecided'. And for that, you really need a while loop, but it does not loop through each appointment date, rather only a few datasets. I checked the execution plan. Even though there are only 10 rows, the query cost is significantly lower than that using recursive CTE, but not as low as Lukasz Szozda's addendum method.
IF OBJECT_ID('tempdb..#TEMPTABLE') IS NOT NULL DROP TABLE #TEMPTABLE
SELECT ApptID, PatientID, ApptDate
,CASE WHEN (DATEDIFF(DAY, MIN(ApptDate) OVER (PARTITION BY PatientID), ApptDate) = 0) THEN 'New'
WHEN (DATEDIFF(DAY, MIN(ApptDate) OVER (PARTITION BY PatientID), ApptDate) <= 30) THEN 'Followup'
ELSE 'Undecided' END AS Category
INTO #TEMPTABLE
FROM #Appt1
WHILE EXISTS(SELECT TOP 1 * FROM #TEMPTABLE WHERE Category = 'Undecided') BEGIN
;WITH CTE AS (
SELECT ApptID, PatientID, ApptDate
,CASE WHEN (DATEDIFF(DAY, MIN(ApptDate) OVER (PARTITION BY PatientID), ApptDate) = 0) THEN 'New'
WHEN (DATEDIFF(DAY, MIN(ApptDate) OVER (PARTITION BY PatientID), ApptDate) <= 30) THEN 'Followup'
ELSE 'Undecided' END AS Category
FROM #TEMPTABLE
WHERE Category = 'Undecided'
)
UPDATE #TEMPTABLE
SET Category = CTE.Category
FROM #TEMPTABLE t
LEFT JOIN CTE ON CTE.ApptID = t.ApptID
WHERE t.Category = 'Undecided'
END
SELECT ApptID, PatientID, ApptDate, Category
FROM #TEMPTABLE
I hope this will help you.
WITH CTE AS
(
SELECT #Appt1.*, RowNum = ROW_NUMBER() OVER (PARTITION BY PatientID ORDER BY ApptDate, ApptID) FROM #Appt1
)
SELECT A.ApptID , A.PatientID , A.ApptDate ,
Expected_Category = CASE WHEN (DATEDIFF(MONTH, B.ApptDate, A.ApptDate) > 0) THEN 'New'
WHEN (DATEDIFF(DAY, B.ApptDate, A.ApptDate) <= 30) then 'Followup'
ELSE 'New' END
FROM CTE A
LEFT OUTER JOIN CTE B on A.PatientID = B.PatientID
AND A.rownum = B.rownum + 1
ORDER BY A.PatientID, A.ApptDate
You could use a Case statement.
select
*,
CASE
WHEN DATEDIFF(d,A1.ApptDate,A2.ApptDate)>30 THEN 'New'
ELSE 'FollowUp'
END 'Category'
from
(SELECT PatientId, MIN(ApptId) 'ApptId', MIN(ApptDate) 'ApptDate' FROM #Appt1 GROUP BY PatientID) A1,
#Appt1 A2
where
A1.PatientID=A2.PatientID AND A1.ApptID<A2.ApptID
The question is, should this category be assigned based off the initial appointment, or the one prior? That is, if a Patient has had three appointments, should we compare the third appointment to the first, or the second?
You problem states the first, which is how I've answered. If that's not the case, you'll want to use lag.
Also, keep in mind that DateDiff makes not exception for weekends. If this should be weekdays only, you'll need to create your own Scalar-Valued function.
using Lag function
select apptID, PatientID , Apptdate ,
case when date_diff IS NULL THEN 'NEW'
when date_diff < 30 and (date_diff_2 IS NULL or date_diff_2 < 30) THEN 'Follow Up'
ELSE 'NEW'
END AS STATUS FROM
(
select
apptID, PatientID , Apptdate ,
DATEDIFF (day,lag(Apptdate) over (PARTITION BY PatientID order by ApptID asc),Apptdate) date_diff ,
DATEDIFF(day,lag(Apptdate,2) over (PARTITION BY PatientID order by ApptID asc),Apptdate) date_diff_2
from #Appt1
) SRC
Demo --> https://rextester.com/TNW43808
with cte
as
(
select
tmp.*,
IsNull(Lag(ApptDate) Over (partition by PatientID Order by PatientID,ApptDate),ApptDate) PriorApptDate
from #Appt1 tmp
)
select
PatientID,
ApptDate,
PriorApptDate,
DateDiff(d,PriorApptDate,ApptDate) Elapsed,
Case when DateDiff(d,PriorApptDate,ApptDate)>30
or DateDiff(d,PriorApptDate,ApptDate)=0 then 'New' else 'Followup' end Category from cte
Mine is correct. The authors was incorrect, see elapsed
I have a checking account table that contains columns Cust_id (customer id), Open_Date (start date), and Closed_Date (end date). There is one row for each account. A customer can open multiple accounts at any given point. I would like to know how long the person has been a customer.
eg 1:
CREATE TABLE [Cust]
(
[Cust_id] [varchar](10) NULL,
[Open_Date] [date] NULL,
[Closed_Date] [date] NULL
)
insert into [Cust] values ('a123', '10/01/2019', '10/15/2019')
insert into [Cust] values ('a123', '10/12/2019', '11/01/2019')
Ideally I would like to insert this into a table with just one row, that says this person has been a customer from 10/01/2019 to 11/01/2019. (as he opened his second account before he closed his previous one.
Similarly eg 2:
insert into [Cust] values ('b245', '07/01/2019', '09/15/2019')
insert into [Cust] values ('b245', '10/12/2019', '12/01/2019')
I would like to see 2 rows in this case- one that shows he was a customer from 07/01 to 09/15 and then again from 10/12 to 12/01.
Can you point me to the best way to get this?
I would approach this as a gaps and islands problem. You want to group together groups of adjacents rows whose periods overlap.
Here is one way to solve it using lag() and a cumulative sum(). Everytime the open date is greater than the closed date of the previous record, a new group starts.
select
cust_id,
min(open_date) open_date,
max(closed_date) closed_date
from (
select
t.*,
sum(case when not open_date <= lag_closed_date then 1 else 0 end)
over(partition by cust_id order by open_date) grp
from (
select
t.*,
lag(closed_date) over (partition by cust_id order by open_date) lag_closed_date
from cust t
) t
) t
group by cust_id, grp
In this db fiddle with your sample data, the query produces:
cust_id | open_date | closed_date
:------ | :--------- | :----------
a123 | 2019-10-01 | 2019-11-01
b245 | 2019-07-01 | 2019-09-15
b245 | 2019-10-12 | 2019-12-01
I would solve this with recursion. While this is certainly very heavy, it should accommodate even the most complex account timings (assuming your data has such). However, if the sample data provided is as complex as you need to solve for, I highly recommend sticking with the solution provided above. It is much more concise and clear.
WITH x (cust_id, open_date, closed_date, lvl, grp) AS (
SELECT cust_id, open_date, closed_date, 1, 1
FROM (
SELECT cust_id
, open_date
, closed_date
, row_number()
OVER (PARTITION BY cust_id ORDER BY closed_date DESC, open_date) AS rn
FROM cust
) AS t
WHERE rn = 1
UNION ALL
SELECT cust_id, open_date, closed_date, lvl, grp
FROM (
SELECT c.cust_id
, c.open_date
, c.closed_date
, x.lvl + 1 AS lvl
, x.grp + CASE WHEN c.closed_date < x.open_date THEN 1 ELSE 0 END AS grp
, row_number() OVER (PARTITION BY c.cust_id ORDER BY c.closed_date DESC) AS rn
FROM cust c
JOIN x
ON x.cust_id = c.cust_id
AND c.open_date < x.open_date
) AS t
WHERE t.rn = 1
)
SELECT cust_id, min(open_date) AS first_open_date, max(closed_date) AS last_closed_date
FROM x
GROUP BY cust_id, grp
ORDER BY cust_id, grp
I would also add the caveat that I don't run on SQL Server, so there could be syntax differences that I didn't account for. Hopefully they are minor, if present.
you can try something like that:
select distinct
cust_id,
(select min(Open_Date)
from Cust as b
where b.cust_id = a.cust_id and
a.Open_Date <= b.Closed_Date and
a.Closed_Date >= b.Open_Date
),
(select max(Closed_Date)
from Cust as b
where b.cust_id = a.cust_id and
a.Open_Date <= b.Closed_Date and
a.Closed_Date >= b.Open_Date
)
from Cust as a
so, for every row - you're selecting minimal and maximal dates from all overlapping ranges, later distinct filters out duplicates
I am struggling with a TSQL query and I'm all out of googling, so naturally I figured I might as well ask on SO.
Please keep in mind that I just began trying to learn SQL a few weeks back and I'm not really sure what rules there are and how you can and can not write your queries / sub-queries.
This is what I have so far:
Edit: Updated with DDL that should help create an example, also commented out unnecessary "Client"-column.
CREATE TABLE NumberTable
(
Number varchar(20),
Date date
);
INSERT INTO NumberTable (Number, Date)
VALUES
('55512345', '2015-01-01'),
('55512345', '2015-01-01'),
('55512345', '2015-01-01'),
('55545678', '2015-01-01'),
('55512345', '2015-02-01'),
('55523456', '2015-02-01'),
('55523456', '2015-02-01'),
('55534567', '2015-03-01'),
('55534567', '2015-03-01'),
('55534567', '2015-03-01'),
('55534567', '2015-03-01'),
('55545678', '2015-03-01'),
('55545678', '2015-04-01')
DECLARE
--#ClientNr AS int,
#FromDate AS date,
#ToDate AS date
--SET #ClientNr = 11111
SET #FromDate = '2015-01-01'
SET #ToDate = DATEADD(yy, 1, #FromDate)
SELECT
YEAR(Date) AS [Year],
MONTH(Date) AS [Month],
COUNT(Number) AS [Total Count]
FROM
NumberTable
WHERE
--Client = #ClientNr
Date BETWEEN #FromDate AND #ToDate
AND Number IS NOT NULL
AND Number NOT IN ('888', '144')
GROUP BY MONTH(Date), YEAR(Date)
ORDER BY [Year], [Month]
With this I am getting the Year, Month and Total Count.
I'm happy with only getting the top 1 most called number and count each month, but showing top 5 is preferable.
Heres an example of how I would like the table to look in the end (having the months formatted as JAN, FEB etc instead of numbers is not really important, but would be a nice bonus):
╔══════╦═══════╦═════════════╦═══════════╦══════════╦═══════════╦══════════╗
║ Year ║ Month ║ Total Count ║ #1 Called ║ #1 Count ║ #2 Called ║ #2 Count ║
╠══════╬═══════╬═════════════╬═══════════╬══════════╬═══════════╬══════════╣
║ 2016 ║ JAN ║ 80431 ║ 555-12345 ║ 45442 ║ 555-94564 ║ 17866 ║
╚══════╩═══════╩═════════════╩═══════════╩══════════╩═══════════╩══════════╝
I was told this was "easily" done with a sub-query, but I'm not so sure...
Interesting one this, I believe you can do it with a CTE and PIVOT but this is off the top of my head... This may not work verbatim
WITH Rollup_CTE
AS
(
SELECT Client,MONTH(Date) as Month, YEAR(Date) as Year, Number, Count(0) as Calls, ROW_NUMBER() OVER (PARTITION BY Client,MONTH(Date) as SqNo, YEAR(Date), Number ORDER BY COUNT(0) DESC)
from NumberTable
WHERE Number IS NOT NULL AND Number NOT IN ('888', '144')
GROUP BY Client,MONTH(Date), YEAR(Date), Number
)
SELECT * FROM Rollup_CTE Where SqNo <=5
You may then be able to pivot the data as you wish using PIVOT
artm's query corrected (PARTITION) and the last step (pivoting) simplified.
with data AS
(select '2016-01-01' as called, '111' as number
union all select '2016-01-01', '111'
union all select '2016-01-01', '111'
union all select '2016-01-01', '222'
union all select '2016-01-01', '222'
union all select '2016-01-05', '111'
union all select '2016-01-05', '222'
union all select '2016-01-05', '222')
, ordered AS (
select called
, number
, count(*) cnt
, ROW_NUMBER() OVER (PARTITION BY called ORDER BY COUNT(*) DESC) rnk
from data
group by called, number)
select called, total = sum(cnt)
, n1= max(case rnk when 1 then number end)
, cnt1=max(case rnk when 1 then cnt end)
, n2= max(case rnk when 2 then number end)
, cnt2=max(case rnk when 2 then cnt end)
from ordered
group by called
EDIT Using setup provided by OP
WITH ordered AS(
-- compute order
SELECT
[Year] = YEAR(Date)
, [Month] = MONTH(Date)
, number
, COUNT(*) cnt
, ROW_NUMBER() OVER (PARTITION BY YEAR(Date), MONTH(Date) ORDER BY COUNT(*) DESC) rnk
FROM NumberTable
WHERE Date BETWEEN #FromDate AND #ToDate
AND Number IS NOT NULL
AND Number NOT IN ('888', '144')
GROUP BY YEAR(Date), MONTH(Date), number
)
-- pivot by order
SELECT [Year], [Month]
, total = sum(cnt)
, n1 = MAX(case rnk when 1 then number end)
, cnt1 = MAX(case rnk when 1 then cnt end)
, n2 = MAX(case rnk when 2 then number end)
, cnt2 = MAX(case rnk when 2 then cnt end)
-- n3, cnt3, ....
FROM ordered
GROUP BY [Year], [Month];
This query help you:
IF OBJECT_ID('tempdb..#Test','U') IS NOT NULL DROP TABLE #Test;
CREATE TABLE #Test(Number INT NOT NULL)
INSERT INTO #Test(Number)
VALUES(1),(2),(3),(1)
SELECT TOP 1 WITH TIES
Number
FROM (
SELECT DISTINCT
Number
, COUNT(*) OVER(PARTITION BY Number) AS cnt
FROM #Test) AS T
ORDER BY cnt DESC
I have used TOP 1 WITH TIES for case when max count exists for several values.
Try this, doesn't have to be CTE but I used it to populate data, you can extend it to include 3rd, 4th etc.
;with data AS
(select '2016-01-01' as called, '111' as number
union all select '2016-01-01', '111'
union all select '2016-01-01', '111'
union all select '2016-01-01', '222'
union all select '2016-01-01', '222')
, ordered AS (
select called
, number
, count(*) cnt
, ROW_NUMBER() OVER (ORDER BY COUNT(*) DESC) rnk
from data
group by called, number)
SELECT distinct *
FROM (SELECT DATENAME(month, called) mnth FROM ordered) AS mnth,
(SELECT number MostCalledNumber FROM ordered WHERE rnk = 1) AS MostCalledNumber,
(SELECT cnt MostCalledTimes FROM ordered WHERE rnk = 1) AS MostCalledTimes,
(SELECT number SecondMostCalledNumber FROM ordered WHERE rnk = 2) AS SecondMostCalledNumber,
(SELECT cnt SecondMostCalledTimes FROM ordered WHERE rnk = 2) AS SecondMostCalledTimes