将所有行从表中的列返回到sql中的视图

Question

Could someone please help me with this question: So I have a table called Orders which has ordernumber, requireddate, shippeddate columns. I need to find what the most number of days between shippeddate and requireddate is and I need to list the ordernumber.

I created a view which I want it to include ordernumber and datediff(requireddate, shippeddate). This is my code for it

create view Numberdays (ordernumber, number_of_days)
as select ordernumber, datediff(requireddate, shippeddate) 
from CM_orders
group by ordernumber

But my view showed only 30 rows of the table even though I have about 300 rows in the Orders table. Could someone please explain what I got wrong and how to do the view which will display all the rows from the table, please? Thank you in advance.

Answer 1

Hoping i understood your problem correctly.

I have tried to create the similar situation. Check sqlfiddle link below. Removed group by from your create view query

create view Numberdays (ordernumber, number_of_days)
as select ordernumber, datediff(requireddate, shippeddate) 
from Orders; -- i think no need to group by if there is not any aggregate function

Getting complete order numbers.

http://sqlfiddle.com/#!9/03d58a/1/0

Answer 2

What you need to do is first find the max requiredate and the minimum shippeddate and then do the datediff

CREATE VIEW Numberdays (ordernumber, number_of_days)
AS SELECT ordernumber, datediff(rdate,sdate)
FROM(
  SELECT ordernumber, max(requiredate) AS rdate,min(shippeddate) AS sdate
  FROM CM_orders
  GROUP by ordernumber
)

Answer 3

You grouped by the ordernumber. I am guessing that you only have 30 ordernumbers, therefore only 30 rows returned.

I would suggest taking apart your query and rebuilding it. If you simplify it, you will likely find that many more rows are returned, thus eliminating a limiter as a possibility. Then follow the path, slowly adding in pieces to your query that narrow the returned data, until you either get the result you want, or identify the limiting factor.

Its a matter of trial and error to diagnose the problem when unexpected data is returned, and without the dataset to test against, nearly impossible for us to identify the problem. So the best advice I can give you, is to take it back and go simple, then add complexity until you identify the problem.