Slow SQL lookup due to every field being listed in SORT KEY

Hi all.  I Have the following query (tested in postgres 8.4 and 9.0rc1)

SELECT distinct event0_.*
FROM event event0_ inner join account account1_ on event0_.account_id_owner=account1_.account_id
LEFT OUTER JOIN friend friendcoll2_ ON account1_.account_id=friendcoll2_.friend_account_id
WHERE (event0_.account_id_owner=2 or friendcoll2_.account_id=2
    AND friendcoll2_.status=2 AND (event0_.is_recomended is null OR event0_.is_recomended=false))
ORDER BY event0_.event_id DESC LIMIT 25

None of the tables listed here have more than a couple of thousand rows, and are all indexed.  If I run that query as is, it will take up to 5 seconds, if I remove the ORDER BY and LIMIT, it will run into about 200 ms.

Bellow is the output from SET enable_seqscan = off;EXPLAIN ANALYZE VERBOSE.  On Postgresql 9.0 this takes 2.3 seconds, on 8.4 it takes 4-5 seconds.  What I am noticing is that the Sort Key contains every row in event, not just event_id.  This seems to be causing the External Disk Merge.  This will use a memory merge if I have work_mem set to less than 30MB. If I set the SELECT to be SELECT distinct event0_.event_id, it will take about 19ms, but I need all rows returned.

Thanks all,
Mason

 Limit  (cost=32124.36..32125.55 rows=25 width=164) (actual time=2233.473..2301.552 rows=25 loops=1)
   Output: event0_.event_id, event0_.account_id_owner, event0_.event_name, event0_.account_id_remote, event0_.path_id, event0_.actual_user_workout_routine, event0_.user_workout_goal_id, event0_.cdate, event0
_.ctime, event0_.calories_burnt, event0_.distance_meters, event0_.duration_seconds, event0_.path_name, event0_.routine_name, event0_.dtype, event0_.item, event0_.is_deleted, event0_.is_recomended
   ->  Unique  (cost=32124.36..32128.26 rows=82 width=164) (actual time=2233.471..2301.544 rows=25 loops=1)
         Output: event0_.event_id, event0_.account_id_owner, event0_.event_name, event0_.account_id_remote, event0_.path_id, event0_.actual_user_workout_routine, event0_.user_workout_goal_id, event0_.cdate,
event0_.ctime, event0_.calories_burnt, event0_.distance_meters, event0_.duration_seconds, event0_.path_name, event0_.routine_name, event0_.dtype, event0_.item, event0_.is_deleted, event0_.is_recomended
         ->  Sort  (cost=32124.36..32124.57 rows=82 width=164) (actual time=2233.470..2299.043 rows=4435 loops=1)
               Output: event0_.event_id, event0_.account_id_owner, event0_.event_name, event0_.account_id_remote, event0_.path_id, event0_.actual_user_workout_routine, event0_.user_workout_goal_id, event0_.c
date, event0_.ctime, event0_.calories_burnt, event0_.distance_meters, event0_.duration_seconds, event0_.path_name, event0_.routine_name, event0_.dtype, event0_.item, event0_.is_deleted, event0_.is_recomended
               Sort Key: event0_.event_id, event0_.account_id_owner, event0_.event_name, event0_.account_id_remote, event0_.path_id, event0_.actual_user_workout_routine, event0_.user_workout_goal_id, event0_
.cdate, event0_.ctime, event0_.calories_burnt, event0_.distance_meters, event0_.duration_seconds, event0_.path_name, event0_.routine_name, event0_.dtype, event0_.item, event0_.is_deleted, event0_.is_recomend
ed
               Sort Method:  external merge  Disk: 6968kB
               ->  Merge Join  (cost=0.00..32121.75 rows=82 width=164) (actual time=0.105..197.393 rows=50895 loops=1)
                     Output: event0_.event_id, event0_.account_id_owner, event0_.event_name, event0_.account_id_remote, event0_.path_id, event0_.actual_user_workout_routine, event0_.user_workout_goal_id, eve
nt0_.cdate, event0_.ctime, event0_.calories_burnt, event0_.distance_meters, event0_.duration_seconds, event0_.path_name, event0_.routine_name, event0_.dtype, event0_.item, event0_.is_deleted, event0_.is_reco
mended
                     Merge Cond: (account1_.account_id = event0_.account_id_owner)
                     Join Filter: ((event0_.account_id_owner = 2) OR ((friendcoll2_.account_id = 2) AND (friendcoll2_.status = 2) AND ((event0_.is_recomended IS NULL) OR (NOT event0_.is_recomended))))
                     ->  Nested Loop Left Join  (cost=0.00..31843.58 rows=2155 width=10) (actual time=0.070..87.681 rows=3859 loops=1)
                           Output: account1_.account_id, friendcoll2_.account_id, friendcoll2_.status
                           ->  Index Scan using "AccountIDPKIndex" on public.account account1_  (cost=0.00..209.05 rows=1890 width=4) (actual time=0.025..0.981 rows=1890 loops=1)
                                 Output: account1_.account_id, account1_.user_name, account1_.password, account1_.account_type, account1_.is_active, account1_.is_quick_reg, account1_.name_last, account1_.nam
e_first, account1_.primary_image_url, account1_.ctime, account1_.cdate, account1_.email_address, account1_.address_street_1, account1_.address_street_2, account1_.address_city, account1_.address_state, accou
nt1_.address_zip_code_1, account1_.address_zip_code_2, account1_.date_of_birth, account1_.phone_home, account1_.phone_mobile, account1_.phone_buisness, account1_.phone_buisness_ext, account1_.lon, account1_.
lat, account1_.dtype, account1_.last_login_date, account1_.about_user_blurb, account1_.middle_initial, account1_.gender, account1_.address_country, account1_.weight_lbs, account1_.network_size, account1_.pri
mary_image_url_thumb, account1_.primary_image_url_small_thumb, account1_.is_activity_partner_listed, account1_.relationship_status, account1_.sec_profile_view, account1_.stats_build, account1_.stats_height_i
nches, account1_.stats_activity_level, account1_.list_profile_age, account1_.opt_in_third_party, account1_.opt_in_exclusive_offers, account1_.opt_in_new_features, account1_.lat_lon_is_current, account1_.woc_
no_of_entries, account1_.woc_weight_in_formula, account1_.woc_value_cardio, account1_.woc_value_strength, account1_.woc_value_body_sculpting, account1_.woc_value_body_flexibility, account1_.woc_value_weight_
management, account1_.woc_value_mental_vitality, account1_.woc_value_heart_health, account1_.woc_value_general_fitness, account1_.is_group, account1_.is_fivi_pro, account1_.is_group_open_invite, account1_.mi
ssion_statement, account1_.twitter_account_name, account1_.primary_photo_media_id, account1_.is_pro_listed, account1_.registered_on, account1_.is_password_autogenerated, account1_.is_set_password_hidden, acc
ount1_.is_notified_on_email_receipt, account1_.is_notified_on_friend_request
                           ->  Index Scan using "friendIdx1" on public.friend friendcoll2_  (cost=0.00..16.59 rows=12 width=10) (actual time=0.042..0.045 rows=1 loops=1890)
                                 Output: friendcoll2_.account_id, friendcoll2_.friend_account_id, friendcoll2_.cdate, friendcoll2_.ctime, friendcoll2_.status, friendcoll2_.friend_id
                                 Index Cond: (account1_.account_id = friendcoll2_.friend_account_id)
                     ->  Materialize  (cost=0.00..207.88 rows=2803 width=164) (actual time=0.024..26.091 rows=241058 loops=1)
                           Output: event0_.event_id, event0_.account_id_owner, event0_.event_name, event0_.account_id_remote, event0_.path_id, event0_.actual_user_workout_routine, event0_.user_workout_goal_i
d, event0_.cdate, event0_.ctime, event0_.calories_burnt, event0_.distance_meters, event0_.duration_seconds, event0_.path_name, event0_.routine_name, event0_.dtype, event0_.item, event0_.is_deleted, event0_.i
s_recomended
                           ->  Index Scan using "eventIdxTstdAccountIdOwner" on public.event event0_  (cost=0.00..200.88 rows=2803 width=164) (actual time=0.020..1.239 rows=2803 loops=1)
                                 Output: event0_.event_id, event0_.account_id_owner, event0_.event_name, event0_.account_id_remote, event0_.path_id, event0_.actual_user_workout_routine, event0_.user_workout_
goal_id, event0_.cdate, event0_.ctime, event0_.calories_burnt, event0_.distance_meters, event0_.duration_seconds, event0_.path_name, event0_.routine_name, event0_.dtype, event0_.item, event0_.is_deleted, eve
nt0_.is_recomended
 Total runtime: 2303.210 ms