查询：
SELECT "replays_game".* FROM "replays_game" INNER JOIN "replays_playeringame" ON "replays_game"."id" = "replays_playeringame"."game_id" WHERE "replays_playeringame"."player_id" = 50027
如果我设置SET enable_seqscan = off,那么它会做得很快,这是：
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
Nested Loop (cost=0.00..27349.80 rows=3395 width=72) (actual time=28.726..65.056 rows=3398 loops=1)
-> Index Scan using replays_playeringame_player_id on replays_playeringame (cost=0.00..8934.43 rows=3395 width=4) (actual time=0.019..2.412 rows=3398 loops=1)
Index Cond: (player_id = 50027)
-> Index Scan using replays_game_pkey on replays_game (cost=0.00..5.41 rows=1 width=72) (actual time=0.017..0.017 rows=1 loops=3398)
Index Cond: (id = replays_playeringame.game_id)
Total runtime: 65.437 ms
但是没有可怕的enable_seqscan,它选择做一个较慢的事情：
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
Hash Join (cost=7330.18..18145.24 rows=3395 width=72) (actual time=92.380..535.422 rows=3398 loops=1)
Hash Cond: (replays_playeringame.game_id = replays_game.id)
-> Index Scan using replays_playeringame_player_id on replays_playeringame (cost=0.00..8934.43 rows=3395 width=4) (actual time=0.020..2.899 rows=3398 loops=1)
Index Cond: (player_id = 50027)
-> Hash (cost=3668.08..3668.08 rows=151208 width=72) (actual time=90.842..90.842 rows=151208 loops=1)
Buckets: 1024 Batches: 32 (originally 16) Memory Usage: 1025kB
-> Seq Scan on replays_game (cost=0.00..3668.08 rows=151208 width=72) (actual time=0.020..29.061 rows=151208 loops=1)
Total runtime: 535.821 ms
以下是相关指标：
Index "public.replays_game_pkey" Column | Type | Definition --------+---------+------------ id | integer | id primary key,btree,for table "public.replays_game" Index "public.replays_playeringame_player_id" Column | Type | Definition -----------+---------+------------ player_id | integer | player_id btree,for table "public.replays_playeringame"
所以我的问题是,我在做错什么是Postgres错误估计两种加入方式的相对成本？我在成本估计中看到,它认为哈希加入会更快.而其对索引加入成本的估计则减少了500.
如何给Postgres更多的线索？在运行所有上述操作之前,我已经运行了一个VACUUM ANALYZE.
有趣的是,如果我为具有较小游戏次数的玩家运行此查询,则Postgres会选择进行索引扫描嵌套循环.所以关于大型游戏的一些事情会引发这种不必要的行为,其中相对估计的成本与实际的估计成本不一致.
最后,我应该使用Postgres吗？我不希望成为数据库调优的专家,所以我正在寻找一个数据库,它将以良好的开发人员的关注程度来表现得相当出色,而不是专门的DBA.恐怕如果我坚持使用Postgres,我将会有一个稳定的问题,这将使我成为一名Postgres专家,也许另一个DB将更加宽容一个更随意的方法.
Postgres专家(RhodiumToad)审查了我的完整数据库设置(http://pastebin.com/77QuiQSp),并建议设置cpu_tuple_cost = 0.1.这给了一个戏剧性的加速：http://pastebin.com/nTHvSHVd
或者,切换到MysqL也很好地解决了这个问题.我在我的OS X盒子上默认安装了MysqL和Postgres,MysqL的速度要快两倍,通过反复执行查询来比较“加热”的查询.在“冷”查询中,即第一次执行给定的查询时,MysqL的速度是5到150倍.冷查询的执行对于我的特定应用来说是非常重要的.
就我而言,最大的问题仍然很出色 – Postgres需要比MysqL更好的配置和运行方式吗？例如,考虑到这里的评论者没有提出任何建议.
我的猜测是你使用的是default_page_cost = 4,这太高了,使得索引扫描太贵了.
我尝试用这个脚本重建2个表：
CREATE TABLE replays_game (
id integer NOT NULL,PRIMARY KEY (id)
);
CREATE TABLE replays_playeringame (
player_id integer NOT NULL,game_id integer NOT NULL,PRIMARY KEY (player_id,game_id),CONSTRAINT replays_playeringame_game_fkey
FOREIGN KEY (game_id) REFERENCES replays_game (id)
);
CREATE INDEX ix_replays_playeringame_game_id
ON replays_playeringame (game_id);
-- 150k games
INSERT INTO replays_game
SELECT generate_series(1,150000);
-- ~150k players,~2 games each
INSERT INTO replays_playeringame
select trunc(random() * 149999 + 1),generate_series(1,150000);
INSERT INTO replays_playeringame
SELECT *
FROM
(
SELECT
trunc(random() * 149999 + 1) as player_id,150000) as game_id
) AS t
WHERE
NOT EXISTS (
SELECT 1
FROM replays_playeringame
WHERE
t.player_id = replays_playeringame.player_id
AND t.game_id = replays_playeringame.game_id
)
;
-- the heavy player with 3000 games
INSERT INTO replays_playeringame
select 999999,3000);
默认值为4：
game=# set random_page_cost = 4;
SET
game=# explain analyse SELECT "replays_game".*
FROM "replays_game"
INNER JOIN "replays_playeringame" ON "replays_game"."id" = "replays_playeringame"."game_id"
WHERE "replays_playeringame"."player_id" = 999999;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------
Hash Join (cost=1483.54..4802.54 rows=3000 width=4) (actual time=3.640..110.212 rows=3000 loops=1)
Hash Cond: (replays_game.id = replays_playeringame.game_id)
-> Seq Scan on replays_game (cost=0.00..2164.00 rows=150000 width=4) (actual time=0.012..34.261 rows=150000 loops=1)
-> Hash (cost=1446.04..1446.04 rows=3000 width=4) (actual time=3.598..3.598 rows=3000 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 106kB
-> Bitmap Heap Scan on replays_playeringame (cost=67.54..1446.04 rows=3000 width=4) (actual time=0.586..2.041 rows=3000 loops=1)
Recheck Cond: (player_id = 999999)
-> Bitmap Index Scan on replays_playeringame_pkey (cost=0.00..66.79 rows=3000 width=0) (actual time=0.560..0.560 rows=3000 loops=1)
Index Cond: (player_id = 999999)
Total runtime: 110.621 ms
降低到2：
game=# set random_page_cost = 2;
SET
game=# explain analyse SELECT "replays_game".*
FROM "replays_game"
INNER JOIN "replays_playeringame" ON "replays_game"."id" = "replays_playeringame"."game_id"
WHERE "replays_playeringame"."player_id" = 999999;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------
Nested Loop (cost=45.52..4444.86 rows=3000 width=4) (actual time=0.418..27.741 rows=3000 loops=1)
-> Bitmap Heap Scan on replays_playeringame (cost=45.52..1424.02 rows=3000 width=4) (actual time=0.406..1.502 rows=3000 loops=1)
Recheck Cond: (player_id = 999999)
-> Bitmap Index Scan on replays_playeringame_pkey (cost=0.00..44.77 rows=3000 width=0) (actual time=0.388..0.388 rows=3000 loops=1)
Index Cond: (player_id = 999999)
-> Index Scan using replays_game_pkey on replays_game (cost=0.00..0.99 rows=1 width=4) (actual time=0.006..0.006 rows=1 loops=3000)
Index Cond: (id = replays_playeringame.game_id)
Total runtime: 28.542 ms
(8 rows)
如果使用SSD,我会进一步降低到1.1.
至于你最后一个问题,我真的认为你应该坚持postgresql.我有postgresql和mssql的经验,我需要把三分之一的努力放在后面,以便执行一半以及前者.
