Add up conditional counts on multiple columns of the same table

Question

I am looking for a \"better\" way to perform a query in which I want to show a single player who he has played previously and the associated win-loss record for each such oppone

Answer 1

Something more readable than my original. Thoughts?

with W as (
    select loser_id as opponent_id,
    count(*) as n
    from match
    where winner_id = 1
    group by loser_id
),
L as (
    select winner_id as opponent_id,
    count(*) as n
    from match
    where loser_id = 1
    group by winner_id
)
select player.username, coalesce(W.n, 0) as wins, coalesce(L.n, 0) as losses
from player
left join W on W.opponent_id = player.player_id
left join L on L.opponent_id = player.player_id
where player.player_id != 1;

                                 QUERY PLAN                                  
-----------------------------------------------------------------------------
 Hash Left Join  (cost=73.78..108.58 rows=1224 width=48)
   Hash Cond: (player.player_id = l.opponent_id)
   CTE w
     ->  HashAggregate  (cost=36.81..36.83 rows=2 width=4)
           Group Key: match.loser_id
           ->  Seq Scan on match  (cost=0.00..36.75 rows=11 width=4)
                 Filter: (winner_id = 1)
   CTE l
     ->  HashAggregate  (cost=36.81..36.83 rows=2 width=4)
           Group Key: match_1.winner_id
           ->  Seq Scan on match match_1  (cost=0.00..36.75 rows=11 width=4)
                 Filter: (loser_id = 1)
   ->  Hash Left Join  (cost=0.07..30.15 rows=1224 width=44)
         Hash Cond: (player.player_id = w.opponent_id)
         ->  Seq Scan on player  (cost=0.00..25.38 rows=1224 width=36)
               Filter: (player_id <> 1)
         ->  Hash  (cost=0.04..0.04 rows=2 width=12)
               ->  CTE Scan on w  (cost=0.00..0.04 rows=2 width=12)
   ->  Hash  (cost=0.04..0.04 rows=2 width=12)
         ->  CTE Scan on l  (cost=0.00..0.04 rows=2 width=12)

The above has a performance killer with the player_id != 1. I think I can avoid that by only scanning the results of the joins, no?

explain with W as (
        select loser_id as opponent_id,
        count(*) as n
        from match
        where winner_id = 1 
        group by loser_id
    ),  
    L as (
        select winner_id as opponent_id,
        count(*) as n
        from match
        where loser_id = 1 
        group by winner_id
    )   
    select t.* from (
        select player.player_id, player.username, coalesce(W.n, 0) as wins, coalesce(L.n, 0) as losses
        from player
        left join W on W.opponent_id = player.player_id
        left join L on L.opponent_id = player.player_id
    ) t 
    where t.player_id != 1;

                                 QUERY PLAN                                  
-----------------------------------------------------------------------------
 Hash Left Join  (cost=73.78..74.89 rows=3 width=52)
   Hash Cond: (player.player_id = l.opponent_id)
   CTE w
     ->  HashAggregate  (cost=36.81..36.83 rows=2 width=4)
           Group Key: match.loser_id
           ->  Seq Scan on match  (cost=0.00..36.75 rows=11 width=4)
                 Filter: (winner_id = 1)
   CTE l
     ->  HashAggregate  (cost=36.81..36.83 rows=2 width=4)
           Group Key: match_1.winner_id
           ->  Seq Scan on match match_1  (cost=0.00..36.75 rows=11 width=4)
                 Filter: (loser_id = 1)
   ->  Hash Left Join  (cost=0.07..1.15 rows=3 width=44)
         Hash Cond: (player.player_id = w.opponent_id)
         ->  Seq Scan on player  (cost=0.00..1.05 rows=3 width=36)
               Filter: (player_id <> 1)
         ->  Hash  (cost=0.04..0.04 rows=2 width=12)
               ->  CTE Scan on w  (cost=0.00..0.04 rows=2 width=12)
   ->  Hash  (cost=0.04..0.04 rows=2 width=12)
         ->  CTE Scan on l  (cost=0.00..0.04 rows=2 width=12)