I am going to assume that there isn't an index on the date columns, otherwise I think that the query would have been structured differently. If there is, you can probably find a better performing one than this.
The advantage of this query is that it can get all the data in one scan. The disadvantage is that it has to sort the data and join EventEmployee on the entire table. So as always, test with your own situation. This query also assumes that the MAX date is either unique or that equivalent rows would be acceptable.
USE AdventureWorks2012
GO
;
WITH Base AS (
SELECT
TransactionHistory.*
,ProductVendor.BusinessEntityID
,MAX(CASE WHEN TransactionDate < '2008-08-01' THEN TransactionDate END)
OVER (PARTITION BY ProductVendor.BusinessEntityID) AS PreviousVendorTransaction
,COUNT(CASE WHEN TransactionDate >= '2008-08-01' THEN 1 END )
OVER (PARTITION BY ProductVendor.BusinessEntityID) AS VendorAfterCutoff
FROM
Production.TransactionHistory
-- Doesn't make the most sense, but I need a repeating relation
INNER JOIN Purchasing.ProductVendor
ON TransactionHistory.ProductID = ProductVendor.ProductID
),
Filtered AS (
SELECT
*
FROM
Base
WHERE
Base.TransactionDate >= '2008-08-01'
OR (TransactionDate = PreviousVendorTransaction AND VendorAfterCutoff > 0)
)
SELECT DISTINCT
TransactionID
,ProductID
,ReferenceOrderID
,ReferenceOrderLineID
,TransactionDate
,TransactionType
,Quantity
,ActualCost
,ModifiedDate
FROM
Filtered
Edit:
Hmm, I think I may have to take back my comment on structuring it differently if there are indexes. The other suggestions that I have are probably fairly minor.
- Make sure the query is using the indexes you're expecting it to. Start and End date to build temp table, end date to drive the previous event loop.
- If the query to build the temp table is doing a lookup on the clustered index, it may be better to hold off and do that as part of the main query.
- Try using a cte instead of a temp table. I think that a cte might be more competitive with the way that the query is structured below.
- If you are returning a lot of events, it might be better to pull out the event table lookup to the main query to give the optimizer the option of doing a merge join.
- I don't see a way of optimizing the previous event lookup short of an indexed view.
Here's a query that combines a few of those ideas.
SELECT
e.[EventID]
INTO #EventTemp
FROM
[Events] AS e
WHERE
( e.[EventStart] >= @StartDate AND e.[EventStart] <= @EndDate )
OR ( e.[EventEnd] >= @StartDate AND e.[EventEnd] <= @EndDate )
;
WITH PrevEvent AS (
SELECT
EmpPrevEvent.[EventID]
FROM
(
SELECT DISTINCT
ee.[EmployeeID]
FROM
#EventTemp
INNER JOIN [EventEmployee] AS ee ON
#EventTemp.[EventID] = ee.[EventID]
) AS Emp
CROSS APPLY (
SELECT TOP 1
e.[EventID]
FROM
[Events] AS e
INNER JOIN [EventEmployee] AS ee ON
e.[EventID] = ee.[EventID]
WHERE
ee.[EmployeeID] = Emp.[EmployeeID]
AND e.[EventEnd] < @StartDate
ORDER BY
e.[EventEnd] DESC
) AS EmpPrevEvent
)
SELECT
e.[EventID],
e.[EventStart],
e.[EventEnd],
e.[EventTypeID]
FROM
[Events] AS e
WHERE
e.EventID IN (
SELECT EventID
FROM #EventTemp
UNION
SELECT EventID
FROM PrevEvent
)
Condition 1:
WITH ord as (
SELECT ID, CustomerID, CheckInDate, CheckOutDate
, n = ROW_NUMBER() over(partition by [CustomerID] order by [CheckInDate], [CheckOutDate])
FROM @data d1
), first as (
SELECT o1.ID, o1.CustomerID, o1.CheckInDate, o1.CheckOutDate, o1.n
, m = ROW_NUMBER() over(partition by o1.[CustomerID] order by o1.[CheckInDate], o1.[CheckOutDate])
FROM ord o1
INNER JOIN ord o2 ON o1.CustomerID = o2.CustomerID AND o2.n+1 = o1.n AND o1.CheckInDate > o2.CheckOutDate
), groups as (
SELECT o.ID, o.CustomerID, nx = MIN(coalesce(f.n, 1)), n = MAX(o.n)
, p = ROW_NUMBER() over(partition by o.CustomerID, MIN(coalesce(f.n, 1)) ORDER BY o.ID)
FROM ord o
LEFT JOIN first f ON o.CustomerID = f.CustomerID AND o.n < f.n
GROUP BY o.ID, o.CustomerID
), dates as (
SELECT g.CustomerID, g.nx, CheckInDate = MIN(o.CheckInDate)
, CheckOutDate = CASE WHEN SUM(CASE WHEN o.CheckOutDate IS NULL THEN 1 END) IS NULL THEN MAX(o.CheckOutDate) END
FROM groups g
INNER JOIN ord o ON g.ID = o.ID
GROUP BY g.nx, g.CustomerID
HAVING COUNT(g.nx) > 1
)
SELECT o.ID, o.CustomerID
, CheckInDate = CASE WHEN g.p = 1 THEN d.CheckInDate END
, CheckOutDate = CASE WHEN g.p = 1 THEN d.CheckOutDate END
FROM groups g
INNER JOIN ord o ON g.ID = o.ID
INNER JOIN dates d on g.CustomerID = d.CustomerID AND g.nx = d.nx
ORDER BY ID
This query output the rows that must be updated:
- ord = I first partition by CustomerID and order by CheckInDate
- first = I join consecutive rows from previous CTE where the next CheckInDate does not overlap previous CheckInDate and CheckDate and I partition and order them
- groups = I group by the previous partitioned number in order to know to which group a row belongs
- dates = I join with the original data in order to get the first and last date for each groups. Groups with only 1 row are removed
- main select output the dates for p=1 or NULL otherwise
Output:
ID CustomerID CheckInDate CheckOutDate
1 1 2015-03-04 NULL
3 1 NULL NULL
4 1 NULL NULL
Condition 2:
WITH ord as (
SELECT ID, CustomerID, CheckInDate, CheckOutDate
, n = ROW_NUMBER() over(partition by [CustomerID] order by [CheckInDate], [CheckOutDate])
FROM @data d1
), first as (
SELECT o1.ID, o1.CustomerID, o1.CheckInDate, o1.CheckOutDate, o1.n
, m = ROW_NUMBER() over(partition by o1.[CustomerID] order by o1.[CheckInDate], o1.[CheckOutDate])
FROM ord o1
INNER JOIN ord o2 ON o1.CustomerID = o2.CustomerID AND o2.n+1 = o1.n AND o1.CheckInDate > o2.CheckOutDate
), groups as (
SELECT o.ID, o.CustomerID, nx = MIN(coalesce(f.n, 1)), n = MAX(o.n)
, p = ROW_NUMBER() over(partition by o.CustomerID, MIN(coalesce(f.n, 1)) ORDER BY o.ID)
, last = ROW_NUMBER() over(partition by o.CustomerID, MIN(coalesce(f.n, 1)) ORDER BY o.ID DESC)
FROM ord o
LEFT JOIN first f ON o.CustomerID = f.CustomerID AND o.n < f.n
GROUP BY o.ID, o.CustomerID
), dates as (
SELECT g.CustomerID, g.nx, CheckInDate = MIN(o.CheckInDate)
, CheckOutDate = MAX(o2.CheckOutDate)
FROM groups g
INNER JOIN ord o ON g.ID = o.ID
INNER JOIN (SELECT ID, CustomerID, nx FROM groups WHERE last = 1) l ON g.CustomerID = l.CustomerID AND g.nx = l.nx
INNER JOIN ord o2 ON l.ID = o2.ID
GROUP BY g.nx, g.CustomerID
HAVING COUNT(g.nx) > 1
)
SELECT o.ID, o.CustomerID
, CheckInDate = CASE WHEN g.p = 1 THEN d.CheckInDate END
, CheckOutDate = CASE WHEN g.p = 1 THEN d.CheckOutDate END
FROM groups g
INNER JOIN ord o ON g.ID = o.ID
INNER JOIN dates d on g.CustomerID = d.CustomerID AND g.nx = d.nx
ORDER BY ID
Output:
ID CustomerID CheckInDate CheckOutDate
1 1 2015-03-04 2015-05-03
3 1 NULL NULL
4 1 NULL NULL
For Updates, replace SELECT by UPDATE:
UPDATE g SET
CheckInDate = CASE WHEN g.p = 1 THEN d.CheckInDate END
, CheckOutDate = CASE WHEN g.p = 1 THEN d.CheckOutDate END
FROM ...
Your data:
declare @data table([ID] int, [CustomerID] int, [CheckInDate] date, [CheckOutDate] date);
Insert into @data([ID], [CustomerID], [CheckInDate], [CheckOutDate])
VALUES
(1, 1, '2015-04-02', '2015-04-05'),
(2, 2, '2015-03-04', '2015-05-02'),
(3, 1, '2015-04-01', NULL),
(4, 1, '2015-03-04', '2015-05-03'),
(5, 1, '2015-01-03', '2015-02-03')
;
It works as well with this sample:
(1, 1, '2015-04-02', '2015-04-05'),
(2, 2, '2015-03-04', '2015-05-02'),
(3, 1, '2015-04-01', NULL),
(4, 1, '2015-03-04', '2015-05-03'),
(5, 1, '2015-01-03', '2015-02-03'),
(6, 1, '2015-01-02', '2015-02-03'),
(7, 1, '2015-03-04', '2015-03-06'),
(8, 1, '2015-03-04', '2015-05-06'),
(9, 1, '2014-04-02', '2014-04-05'),
(10, 1, '2014-03-04', '2014-05-02')
If it does not work with some of your data, update Input and Output table with more relevant values.
Best Answer
You can get the aggregates, MAX(Date) in this case, by using GROUP BY:
Quoted from MS Docs: