They're used in different places. group by modifies the entire query, like:
select customerId, count(*) as orderCount
from Orders
group by customerIdBut partition by just works on a window function, like row_number:
select row_number() over (partition by customerId order by orderId)
as OrderNumberForThisCustomer
from Orders
A group by normally reduces the number of rows returned by rolling them up and calculating averages or sums for each row.
partition by does not affect the number of rows returned, but it changes how a window function's result is calculated.
回答2
PARTITION BY is analytic, while GROUP BY is aggregate. In order to use PARTITION BY, you have to contain it with an OVER clause.
回答3
As of my understanding Partition By is almost identical to Group By, but with the following differences:
That group by actually groups the result set returning one row per group, which results therefore in SQL Server only allowing in the SELECT list aggregate functions or columns that are part of the group by clause (in which case SQL Server can guarantee that there are unique results for each group).
Consider for example MySQL which allows to have in the SELECT list columns that are not defined in the Group By clause, in which case one row is still being returned per group, however if the column doesn't have unique results then there is no guarantee what will be the output!
But with Partition By, although the results of the function are identical to the results of an aggregate function with Group By, still you are getting the normal result set, which means that one is getting one row per underlying row, and not one row per group, and because of this one can have columns that are not unique per group in the SELECT list.
So as a summary, Group By would be best when needs an output of one row per group, and Partition By would be best when one needs all the rows but still wants the aggregate function based on a group.
Of course there might also be performance issues, see http://social.msdn.microsoft.com/Forums/ms-MY/transactsql/thread/0b20c2b5-1607-40bc-b7a7-0c60a2a55fba.