2 machines, same database, same query, 10 times slower?

Hi,

I am running PostgreSQL 8.1, on CentOS 5. I have two machines, same hardware, with the same database layout,
they have different data, and the same query run 10 times as slow on one machine compared to the other.

I have run ANALYZE on both machines, here are the query plans for both machines. Can someone explain
this to me? I have trouble reading EXPLAIN output...

Query:

explain select company.name as cname, call_flags, bill_duration as call_duration,
       date_part('epoch',start_time) as start_time,
       src_name, src_type, src_num, src_id,
       dial_name, dial_type, dial_num, dial_id,
       ans_name, ans_type, ans_num, ans_id,
       sessionid
  from cdr, company, phoneline, contact
  where (src_id = contact.id or dial_id = contact.id or ans_id = contact.id)
        and contact.id = '2' and phoneline.function='contact' and
        phoneline.lookupid = contact.id and phoneline.status != 'deleted' and
        (src_company=company.id or dial_company=company.id) and company.id > 2
  order by start_time DESC
  limit 10;

This is the query plan on machine #1 (query takes 2 seconds) :

 Limit  (cost=106128.33..106128.36 rows=10 width=160)
   ->  Sort  (cost=106128.33..106166.98 rows=15458 width=160)
         Sort Key: date_part('epoch'::text, cdr.start_time)
         ->  Nested Loop  (cost=49.38..104275.65 rows=15458 width=160)
               ->  Nested Loop  (cost=2.10..103880.57 rows=7729 width=164)
                     Join Filter: (("outer".src_company = "inner".id) OR ("outer".dial_company = "inner".id))
                     ->  Nested Loop  (cost=0.00..103054.09 rows=6595 width=163)
                           Join Filter: (("inner".src_id = "outer".id) OR ("inner".dial_id = "outer".id) OR
("inner".ans_id= "outer".id)) 
                           ->  Index Scan using contact_pkey on contact  (cost=0.00..5.94 rows=1 width=4)
                                 Index Cond: (id = 2)
                           ->  Seq Scan on cdr  (cost=0.00..77039.87 rows=1486187 width=159)
                     ->  Materialize  (cost=2.10..2.16 rows=5 width=13)
                           ->  Seq Scan on company  (cost=0.00..2.10 rows=5 width=13)
                                 Filter: (id > 2)
               ->  Materialize  (cost=47.28..47.30 rows=2 width=4)
                     ->  Seq Scan on phoneline  (cost=0.00..47.28 rows=2 width=4)
                           Filter: ((("function")::text = 'contact'::text) AND ((status)::text <> 'deleted'::text) AND
(lookupid= 2)) 
(17 rows)

This is the query plan on machine two (query takes 38 seconds):

 Limit  (cost=424555.76..424555.79 rows=10 width=170)
   ->  Sort  (cost=424555.76..424574.34 rows=7432 width=170)
         Sort Key: date_part('epoch'::text, cdr.start_time)
         ->  Nested Loop  (cost=422353.60..424077.90 rows=7432 width=170)
               ->  Nested Loop  (cost=422064.10..423621.19 rows=3716 width=174)
                     Join Filter: (("inner".src_company = "outer".id) OR ("inner".dial_company = "outer".id))
                     ->  Bitmap Heap Scan on company  (cost=2.09..49.23 rows=26 width=21)
                           Recheck Cond: (id > 2)
                           ->  Bitmap Index Scan on company_pkey  (cost=0.00..2.09 rows=26 width=0)
                                 Index Cond: (id > 2)
                     ->  Materialize  (cost=422062.01..422085.24 rows=2323 width=165)
                           ->  Nested Loop  (cost=0.00..422059.69 rows=2323 width=165)
                                 Join Filter: (("inner".src_id = "outer".id) OR ("inner".dial_id = "outer".id) OR
("inner".ans_id= "outer".id)) 
                                 ->  Index Scan using contact_pkey on contact  (cost=0.00..6.01 rows=1 width=4)
                                       Index Cond: (id = 2)
                                 ->  Seq Scan on cdr  (cost=0.00..408379.70 rows=781370 width=161)
               ->  Materialize  (cost=289.50..289.52 rows=2 width=4)
                     ->  Seq Scan on phoneline  (cost=0.00..289.50 rows=2 width=4)
                           Filter: ((("function")::text = 'contact'::text) AND ((status)::text <> 'deleted'::text) AND
(lookupid= 2)) 
(19 rows)

Thanks,
Antonio