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