为什么“IN”运算符在与子查询一起使用时会这么慢？
select *
from view1
where id in (1,2,3,4,5,6,7,8,9,10)
order by somedata;
在9ms内执行。
select *
from view1
where id in (select ext_id
from aggregate_table
order by somedata limit 10)
order by somedata;
在25000ms内执行，似乎在视图（view1）上使用顺序扫描，而不是像在第一次查询中那样对子查询返回的主键进行索引扫描。
子查询select ext_id from aggregate_table order by somedata limit 10在0.1毫秒内执行
因此第二个查询的缓慢是由作为视图的view1上的顺序扫描引起的
在每个UNION中包含三个UNIONS和大约三个JOINS。第一个UNION包含大约1M行，其他更少。连接有大约100K行的表。但这并不是那么相关，我只想了解IN运算符的行为。
我想要完成的是获取子查询（一组主键）的结果，并使用它们从复杂视图（view1）中选择数据。
我也不能用
select v1.*
from view1 v1,
aggregate_table at
where v1.id = at.ext_id
order by at.somedata
limit 10
因为我不想按somedata对大联接进行排序。我只想从主键视图中选择10个结果，然后只对这些结果进行排序。
问题是为什么当我明确列出这些键时IN运算符执行速度很快，而当我使用返回完全相同的键集的快速子查询时速度很慢？
按要求解析分析
第一次查询 - select * from view1 where id in (1,2,3,4,5,6,7,8,9,10) order by somedata;
Sort (cost=348.480..348.550 rows=30 width=943) (actual time=14.385..14.399 rows=10 loops=1)
Sort Key: "india".three
Sort Method: quicksort Memory: 30kB
-> Append (cost=47.650..347.440 rows=30 width=334) (actual time=11.528..14.275 rows=10 loops=1)
-> Subquery Scan "*SELECT* 1" (cost=47.650..172.110 rows=10 width=496) (actual time=11.526..12.301 rows=10 loops=1)
-> Nested Loop (cost=47.650..172.010 rows=10 width=496) (actual time=11.520..12.268 rows=10 loops=1)
-> Hash Join (cost=47.650..87.710 rows=10 width=371) (actual time=11.054..11.461 rows=10 loops=1)
Hash Cond: (hotel.alpha_five = juliet_xray.alpha_five)
-> Bitmap Heap Scan on sierra hotel (cost=42.890..82.800 rows=10 width=345) (actual time=10.835..11.203 rows=10 loops=1)
Recheck Cond: (four = ANY ('quebec'::integer[]))
-> Bitmap Index Scan on seven (cost=0.000..42.890 rows=10 width=0) (actual time=0.194..0.194 rows=10 loops=1)
Index Cond: (four = ANY ('quebec'::integer[]))
-> Hash (cost=4.340..4.340 rows=34 width=30) (actual time=0.184..0.184 rows=34 loops=1)
-> Seq Scan on six juliet_xray (cost=0.000..4.340 rows=34 width=30) (actual time=0.029..0.124 rows=34 loops=1)
-> Index Scan using charlie on juliet_two zulu (cost=0.000..8.390 rows=1 width=129) (actual time=0.065..0.067 rows=1 loops=10)
Index Cond: (zulu.four = hotel.victor_whiskey)
-> Subquery Scan "*SELECT* 2" (cost=4.760..97.420 rows=10 width=366) (actual time=0.168..0.168 rows=0 loops=1)
-> Hash Join (cost=4.760..97.320 rows=10 width=366) (actual time=0.165..0.165 rows=0 loops=1)
Hash Cond: (alpha_xray.alpha_five = juliet_xray2.alpha_five)
-> Nested Loop (cost=0.000..92.390 rows=10 width=340) (actual time=0.162..0.162 rows=0 loops=1)
-> Seq Scan on lima_echo alpha_xray (cost=0.000..8.340 rows=10 width=216) (actual time=0.159..0.159 rows=0 loops=1)
Filter: (four = ANY ('quebec'::integer[]))
-> Index Scan using charlie on juliet_two xray (cost=0.000..8.390 rows=1 width=128) (never executed)
Index Cond: (zulu2.four = alpha_xray.victor_whiskey)
-> Hash (cost=4.340..4.340 rows=34 width=30) (never executed)
-> Seq Scan on six uniform (cost=0.000..4.340 rows=34 width=30) (never executed)
-> Subquery Scan "*SELECT* 3" (cost=43.350..77.910 rows=10 width=141) (actual time=1.775..1.775 rows=0 loops=1)
-> Hash Join (cost=43.350..77.810 rows=10 width=141) (actual time=1.771..1.771 rows=0 loops=1)
Hash Cond: (golf.alpha_five = juliet_xray3.alpha_five)
-> Bitmap Heap Scan on lima_golf golf (cost=38.590..72.910 rows=10 width=115) (actual time=0.110..0.110 rows=0 loops=1)
Recheck Cond: (four = ANY ('quebec'::integer[]))
-> Bitmap Index Scan on victor_hotel (cost=0.000..38.590 rows=10 width=0) (actual time=0.105..0.105 rows=0 loops=1)
Index Cond: (four = ANY ('quebec'::integer[]))
-> Hash (cost=4.340..4.340 rows=34 width=30) (actual time=0.118..0.118 rows=34 loops=1)
-> Seq Scan on six victor_kilo (cost=0.000..4.340 rows=34 width=30) (actual time=0.007..0.063 rows=34 loops=1)
Total runtime: 14.728 ms
第二个查询 - select * from view1 where id in (select ext_id from aggregate_table order by somedata limit 10) order by somedata;
Sort (cost=254515.780..254654.090 rows=55325 width=943) (actual time=24687.475..24687.488 rows=10 loops=1)
Sort Key: "five".xray_alpha
Sort Method: quicksort Memory: 30kB
-> Hash Semi Join (cost=54300.820..250157.370 rows=55325 width=943) (actual time=11921.783..24687.308 rows=10 loops=1)
Hash Cond: ("five".lima = "delta_echo".lima)
-> Append (cost=54298.270..235569.720 rows=1106504 width=494) (actual time=3412.453..23091.938 rows=1106503 loops=1)
-> Subquery Scan "*SELECT* 1" (cost=54298.270..234227.250 rows=1100622 width=496) (actual time=3412.450..20234.122 rows=1100622 loops=1)
-> Hash Join (cost=54298.270..223221.030 rows=1100622 width=496) (actual time=3412.445..17078.021 rows=1100622 loops=1)
Hash Cond: (three_victor.xray_hotel = delta_yankee.xray_hotel)
-> Hash Join (cost=54293.500..180567.160 rows=1100622 width=470) (actual time=3412.251..12108.676 rows=1100622 loops=1)
Hash Cond: (three_victor.tango_three = quebec_seven.lima)
-> Seq Scan on india three_victor (cost=0.000..104261.220 rows=1100622 width=345) (actual time=0.015..3437.722 rows=1100622 loops=1)
-> Hash (cost=44613.780..44613.780 rows=774378 width=129) (actual time=3412.031..3412.031 rows=774603 loops=1)
-> Seq Scan on oscar quebec_seven (cost=0.000..44613.780 rows=774378 width=129) (actual time=4.142..1964.036 rows=774603 loops=1)
-> Hash (cost=4.340..4.340 rows=34 width=30) (actual time=0.149..0.149 rows=34 loops=1)
-> Seq Scan on alpha_kilo delta_yankee (cost=0.000..4.340 rows=34 width=30) (actual time=0.017..0.095 rows=34 loops=1)
-> Subquery Scan "*SELECT* 2" (cost=4.760..884.690 rows=104 width=366) (actual time=7.846..10.161 rows=104 loops=1)
-> Hash Join (cost=4.760..883.650 rows=104 width=366) (actual time=7.837..9.804 rows=104 loops=1)
Hash Cond: (foxtrot.xray_hotel = delta_yankee2.xray_hotel)
-> Nested Loop (cost=0.000..877.200 rows=104 width=340) (actual time=7.573..9.156 rows=104 loops=1)
-> Seq Scan on four_india foxtrot (cost=0.000..7.040 rows=104 width=216) (actual time=0.081..0.311 rows=104 loops=1)
-> Index Scan using three_delta on oscar alpha_victor (cost=0.000..8.350 rows=1 width=128) (actual time=0.077..0.078 rows=1 loops=104)
Index Cond: (quebec_seven2.lima = foxtrot.tango_three)
-> Hash (cost=4.340..4.340 rows=34 width=30) (actual time=0.216..0.216 rows=34 loops=1)
-> Seq Scan on alpha_kilo quebec_foxtrot (cost=0.000..4.340 rows=34 width=30) (actual time=0.035..0.153 rows=34 loops=1)
-> Subquery Scan "*SELECT* 3" (cost=4.760..457.770 rows=5778 width=141) (actual time=0.264..58.353 rows=5777 loops=1)
-> Hash Join (cost=4.760..399.990 rows=5778 width=141) (actual time=0.253..39.062 rows=5777 loops=1)
Hash Cond: (four_uniform.xray_hotel = delta_yankee3.xray_hotel)
-> Seq Scan on whiskey four_uniform (cost=0.000..315.780 rows=5778 width=115) (actual time=0.112..15.759 rows=5778 loops=1)
-> Hash (cost=4.340..4.340 rows=34 width=30) (actual time=0.117..0.117 rows=34 loops=1)
-> Seq Scan on alpha_kilo golf (cost=0.000..4.340 rows=34 width=30) (actual time=0.005..0.059 rows=34 loops=1)
-> Hash (cost=2.430..2.430 rows=10 width=4) (actual time=0.303..0.303 rows=10 loops=1)
-> Subquery Scan "ANY_subquery" (cost=0.000..2.430 rows=10 width=4) (actual time=0.092..0.284 rows=10 loops=1)
-> Limit (cost=0.000..2.330 rows=10 width=68) (actual time=0.089..0.252 rows=10 loops=1)
-> Index Scan using tango_seven on zulu romeo (cost=0.000..257535.070 rows=1106504 width=68) (actual time=0.087..0.227 rows=10 loops=1)
Total runtime: 24687.975 ms
答案 0 :(得分：35)
似乎我终于找到了解决方案：
select *
from view1
where view1.id = ANY(
(select array(select ext_id
from aggregate_table
order by somedata limit 10)
)::integer[]
)
order by view1.somedata;
详细阐述了@Dukeling的想法：
我怀疑（1,2,3,4,5,6,7,8,9,10）中的id可以被优化 其中id（select ...）不能，原因就在于此 （1,2,3,4,5,6,7,8,9,10）是一个常量表达式，而select是 不
并将其定位在更快的查询计划中
Recheck Cond: (id = ANY ('{1,2,3,4,5,6,7,8,9,10}'::integer[]))
Index Cond: (id = ANY ('{1,2,3,4,5,6,7,8,9,10}'::integer[]))
这比问题中的第一个查询更快，大约1.2毫秒，现在它使用
Recheck Cond: (id = ANY ($1))
Index Cond: (id = ANY ($1))
和计划中的位图扫描。
答案 1 :(得分：3)
我怀疑where id in (1,2,3,4,5,6,7,8,9,10)可以优化而where id in (select ...)不能，原因是(1,2,3,4,5,6,7,8,9,10)是常量表达式，而select则不是。
怎么样：
WITH myCTE AS
(
SELECT ext_id
FROM aggregate_table
ORDER BY somedata
LIMIT 10
)
SELECT *
FROM myCTE
LEFT JOIN table1
ON myCTE.ext_id = table1.id
ORDER BY somedata