Si va a tener una alta relación de lectura/escritura para estos datos, es posible que desee considerar una vista indizada. He utilizado este enfoque en todo el lugar para agregar por períodos de tiempo. Acabo de recibir en torno a blogging the example, aquí está el código:
create table timeSeries (
timeSeriesId int identity primary key clustered
,updateDate datetime not null
,payload float not null
)
insert timeSeries values ('2009-06-16 12:00:00', rand())
insert timeSeries values ('2009-06-16 12:00:59', rand())
insert timeSeries values ('2009-06-16 12:01:00', rand())
insert timeSeries values ('2009-06-16 12:59:00', rand())
insert timeSeries values ('2009-06-16 01:00:00', rand())
insert timeSeries values ('2009-06-16 1:30:00', rand())
insert timeSeries values ('2009-06-16 23:59:00', rand())
insert timeSeries values ('2009-06-17 00:01:00', rand())
insert timeSeries values ('2009-06-17 00:01:30', rand())
create view timeSeriesByMinute_IV with schemabinding as
select
dayBucket = datediff(day, 0, updateDate)
,minuteBucket = datediff(minute, 0, (updateDate - datediff(day, 0, updateDate)))
,payloadSum = sum(payLoad)
,numRows = count_big(*)
from dbo.timeSeries
group by
datediff(day, 0, updateDate)
,datediff(minute, 0, (updateDate - datediff(day, 0, updateDate)))
go
create unique clustered index CU_timeSeriesByMinute_IV on timeSeriesByMinute_IV (dayBucket, minuteBucket)
go
create view timeSeriesByMinute as
select
dayBucket
,minuteBucket
,payloadSum
,numRows
,payloadAvg = payloadSum/numRows
from dbo.timeSeriesByMinute_IV with (noexpand)
go
declare @timeLookup datetime, @dayBucket int, @minuteBucket int
select
@timeLookup = '2009-06-16 12:00:00'
,@dayBucket = datediff(day, 0, @timeLookup)
,@minuteBucket = datediff(minute, 0, (@timeLookup - datediff(day, 0, @timeLookup)))
select * from timeSeriesByMinute where dayBucket = @dayBucket and minuteBucket = @minuteBucket
se puede ver el ejemplo de búsqueda al final del bloque de código. Claramente puede definir los rangos para consultar a través de, en lugar de solo buscar un par particular dayBucket/minuteBucket.
+1 consulta agradable y fácil de leer – Andomar
Esto funciona un regalo, gracias! – Lloyd