我有以下表格：
video (id, name)
keyframe (id, name, video_id) /*video_id has fk on video.id*/
detector (id, concepts)
score (detector_id, keyframe_id, score) /*detector_id has fk on detector .id and keyframe_id has fk on keyframe.id*/
从本质上讲，视频有多个与之关联的关键帧，每个关键帧都经过了所有探测器的评分。每个探测器都有一系列概念，它们将对关键帧进行评分。
现在，我希望在单个查询中找到以下内容：
给定一系列探测器ID（例如，最大5），返回在这些探测器组合上得分最高的前10个视频。通过平均每个探测器的每个视频的关键帧得分，然后对探测器得分求和来对它们进行评分。
实施例： 对于具有3个关联关键帧的视频，其中包含以下两个检测器的分数：
detector_id | keyframe_id | score
1 1 0.0281
1 2 0.0012
1 3 0.0269
2 1 0.1341
2 2 0.9726
2 3 0.7125
这会得到视频的分数：
sum(avg(0.0281, 0.0012, 0.0269), avg(0.1341, 0.9726, 0.7125))
最终我想要以下结果：
video_id | score
1 0.417328
2 ...
我认为必须是这样的，但我还没有完成：
select
(select
(select sum(avg_score) summed_score
from
(select
avg(s.score) avg_score
from score s
where s.detector_id = ANY(array[1,2,3,4,5]) and s.keyframe_id = kf.id) x)
from keyframe kf
where kf.video_id = v.id) y
from video v
我的分数表非常大（100M行），所以我希望它尽可能快（我试过的所有其他选项需要几分钟才能完成）。我每个视频总共有大约3000个视频，500个探测器和大约15个关键帧。
如果在不到2秒的时间内无法做到这一点，那么我也对数据库模式的重组方式持开放态度。根本不会在数据库中插入/删除。
修改：
感谢GabrielsMessanger，我有一个答案，这是查询计划：
EXPLAIN (analyze, verbose)
SELECT
v_id, sum(fd_avg_score)
FROM (
SELECT
v.id as v_id, k.id as k_id, d.id as d_id,
avg(s.score) as fd_avg_score
FROM
video v
JOIN keyframe k ON k.video_id = v.id
JOIN score s ON s.keyframe_id = k.id
JOIN detector d ON d.id = s.detector_id
WHERE
d.id = ANY(ARRAY[1,2,3,4,5]) /*here goes detector's array*/
GROUP BY
v.id,
k.id,
d.id
) sub
GROUP BY
v_id
;
"GroupAggregate (cost=1865513.09..1910370.09 rows=200 width=12) (actual time=52141.684..52908.198 rows=2991 loops=1)"
" Output: v.id, sum((avg(s.score)))"
" Group Key: v.id"
" -> GroupAggregate (cost=1865513.09..1893547.46 rows=1121375 width=20) (actual time=52141.623..52793.184 rows=1121375 loops=1)"
" Output: v.id, k.id, d.id, avg(s.score)"
" Group Key: v.id, k.id, d.id"
" -> Sort (cost=1865513.09..1868316.53 rows=1121375 width=20) (actual time=52141.613..52468.062 rows=1121375 loops=1)"
" Output: v.id, k.id, d.id, s.score"
" Sort Key: v.id, k.id, d.id"
" Sort Method: external merge Disk: 37232kB"
" -> Hash Join (cost=11821.18..1729834.13 rows=1121375 width=20) (actual time=120.706..51375.777 rows=1121375 loops=1)"
" Output: v.id, k.id, d.id, s.score"
" Hash Cond: (k.video_id = v.id)"
" -> Hash Join (cost=11736.89..1711527.49 rows=1121375 width=20) (actual time=119.862..51141.066 rows=1121375 loops=1)"
" Output: k.id, k.video_id, s.score, d.id"
" Hash Cond: (s.keyframe_id = k.id)"
" -> Nested Loop (cost=4186.70..1673925.96 rows=1121375 width=16) (actual time=50.878..50034.247 rows=1121375 loops=1)"
" Output: s.score, s.keyframe_id, d.id"
" -> Seq Scan on public.detector d (cost=0.00..11.08 rows=5 width=4) (actual time=0.011..0.079 rows=5 loops=1)"
" Output: d.id, d.concepts"
" Filter: (d.id = ANY ('{1,2,3,4,5}'::integer[]))"
" Rows Removed by Filter: 492"
" -> Bitmap Heap Scan on public.score s (cost=4186.70..332540.23 rows=224275 width=16) (actual time=56.040..9961.040 rows=224275 loops=5)"
" Output: s.detector_id, s.keyframe_id, s.score"
" Recheck Cond: (s.detector_id = d.id)"
" Rows Removed by Index Recheck: 34169904"
" Heap Blocks: exact=192845 lossy=928530"
" -> Bitmap Index Scan on score_index (cost=0.00..4130.63 rows=224275 width=0) (actual time=49.748..49.748 rows=224275 loops=5)"
" Index Cond: (s.detector_id = d.id)"
" -> Hash (cost=3869.75..3869.75 rows=224275 width=8) (actual time=68.924..68.924 rows=224275 loops=1)"
" Output: k.id, k.video_id"
" Buckets: 16384 Batches: 4 Memory Usage: 2205kB"
" -> Seq Scan on public.keyframe k (cost=0.00..3869.75 rows=224275 width=8) (actual time=0.003..33.662 rows=224275 loops=1)"
" Output: k.id, k.video_id"
" -> Hash (cost=46.91..46.91 rows=2991 width=4) (actual time=0.834..0.834 rows=2991 loops=1)"
" Output: v.id"
" Buckets: 1024 Batches: 1 Memory Usage: 106kB"
" -> Seq Scan on public.video v (cost=0.00..46.91 rows=2991 width=4) (actual time=0.005..0.417 rows=2991 loops=1)"
" Output: v.id"
"Planning time: 2.136 ms"
"Execution time: 52914.840 ms"
答案 0 :(得分：1)
我的最终答案是基于coments并与作者扩展聊天讨论。有一点需要注意：每个keyframe_id只分配给一个视频
这跟下面的查询一样简单吗？：
SELECT
v_id, sum(fd_avg_score)
FROM (
SELECT
v.id as v_id, k.id as k_id, s.detector_id as d_id,
avg(s.score) as fd_avg_score
FROM
video v
JOIN keyframe k ON k.video_id = v.id
JOIN score s ON s.keyframe_id = k.id
WHERE
s.detector_id = ANY(ARRAY[1,2,3,4,5]) /*here goes detector's array*/
GROUP BY
v.id,
k.id,
detector_id
) sub
GROUP BY
v_id
LIMIT 10
;
首先，在子查询中，我们使用关键帧和关键帧加入视频。我们计算每个视频的平均得分，每个视频和每个探测器的每个关键帧（正如您所说）。最后，在主查询中，我们对每个视频的avg_score进行总结。
正如作者所说，他在每个表的PRIMARY KEYS列上都有id，并且在表score(detector_id, keyrame_id)上也有复合索引。这足以快速运行此查询。
但是，测试作者需要进一步优化。所以有两件事：
VACUUM ANALYZE，尤其是如果您插入100M行（如score表）。所以至少要执行VACUUM ANALYZE score。score(detector_id, keyrame_id)上的复合索引更改为score(detector_id, keyrame_id, score)上的复合索引。它可能允许PostgreSQL在计算平均值时使用Index Only Scan。