Slow Sort and Group By for Star Query

Dear List,

I have been attempting to get this simple "star" query to execute in less
than 12 secs :

select d0.d0f1,
count(f.f1)
from dim0 d0,
fact0 f
where d0.d0key = f.d0key
and d0.d0f1 between '1996-05-01' and '1996-05-31'
group by d0.d0f1 ;

this scans 100000 rows from fact0 ( 3000000 row table indexed on d0key)
and 31 rows from dim0 ( 900 rows indexed on d0key as well )
- see my posting on "4 databases" if you want more detail on the data
in these tables....

It gives the result :

d0f1 | count
-----------------------------+------
Thu May 30 00:00:00 1996 NZST|100000

( Note that only 1 of the 31 dim0 rows actually have corrosponding fact0 ones )

The query plan for this guy is :

Aggregate (cost=134804.38 rows=2289334 width=20)
-> Group (cost=134804.38 rows=2289334 width=20)
-> Sort (cost=134804.38 rows=2289334 width=20)
-> Nested Loop (cost=134804.38 rows=2289334 width=20)
-> Seq Scan on dim0 d0 (cost=36.70 rows=101 width=12)
-> Index Scan using fact0_q1 on fact0 f (cost=1334.33 rows=3000000 width=8)

After some fooling about I tried this query :

select max(d0.d0f1),
count(f.f1)
from dim0 d0,
fact0 f
where d0.d0key = f.d0key
and d0.d0f1 between '1996-05-01' and '1996-05-31' ;

which executes in 2 sec and gives the same result.

The query plan for this one is :

Aggregate (cost=134804.38 rows=2289334 width=20)
-> Nested Loop (cost=134804.38 rows=2289334 width=20)
-> Seq Scan on dim0 d0 (cost=36.70 rows=101 width=12)
-> Index Scan using fact0_q1 on fact0 f (cost=1334.33 rows=3000000 width=8)

therefore nested loop evaluation of the original query probably takes about
2 secs, and it is the sort / group by that takes the remaining 10 secs.

So can the sort / group by algorithm be improved ?

Cheers

Mark