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