带有子查询的PostgreSQL IN运算符性能较差 [英] PostgreSQL IN operator with subquery poor performance
问题描述
为什么"IN"运算符与子查询一起使用时这么慢?
Why is the "IN" operator so slow when used with subquery?
select *
from view1
where id in (1,2,3,4,5,6,7,8,9,10)
order by somedata;
在9毫秒内执行.
select *
from view1
where id in (select ext_id
from aggregate_table
order by somedata limit 10)
order by somedata;
在25000毫秒内执行，并且似乎在视图(view1)上使用顺序扫描，而不是像在第一次查询中一样，对子查询返回的主键进行索引扫描.
executes in 25000ms and seems to use sequential scan on the view (view1) instead of index scan on primary keys returned by subquery as in does in first query.
子查询select ext_id from aggregate_table order by somedata limit 10在0.1毫秒内执行
The subquery select ext_id from aggregate_table order by somedata limit 10 executes in 0.1ms
所以第二个查询的速度慢是由于对view1的顺序扫描造成的，该视图是一个视图
包含三个UNIONS，每个UNION中大约包含三个JOINS.第一个UNION包含约100万行，其他则少得多.与具有约10万行的表联接.不过，那并不是很重要，我只是想了解IN运算符的行为.
so the slowness of the second query is caused by sequential scan on view1 which is a view
containing three UNIONS and about three JOINS in each UNION. The first UNION contains about a 1M rows, others much less. Joins with tables with some 100K rows. That's not so relevant, though, I just wanted to understand the IN operator behaviour.
我要完成的工作是获取子查询(一组主键)的结果，并仅使用它们从复杂视图(view1)中选择数据.
What I'm trying to accomplish is to take the result of subquery (a set of primary keys) and select the data from a complex view (view1) using just them.
我也不能使用
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.我只想通过主键从视图中选择10个结果，然后仅对这些结果进行排序.
because I do not want to sort the big join by somedata. I just want to select 10 results from the view by primary keys and then sort only these.
问题是为什么当我显式列出这些键时，IN运算符的执行速度会很快，而使用返回完全相同的键集的快速子查询时，为什么IN运算符会这么慢?
The question is why does IN operator perform fast when I explicitly list these keys and so slow when I use a fast subquery that returns the exact same set of keys?
根据要求进行分析
第一个查询-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
第二次查询-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
推荐答案
似乎我终于找到了解决方案:
Seems that I have finally found a solution:
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;
在阐述@Dukeling的想法之后:
After elaborating @Dukeling's idea:
我怀疑(1,2,3,4,5,6,7,8,9,10)中的id可以优化并 (select ...)中的id不能在哪里，原因是 (1,2,3,4,5,6,7,8,9,10)是一个常数表达式，而select是 不是.
I suspect where id in (1,2,3,4,5,6,7,8,9,10) can be optimised and where id in (select ...) can't, the reason being that (1,2,3,4,5,6,7,8,9,10) is a constant expression, while the select is not.
并将它们定位在更快的查询计划中
and locating these in faster query plan
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，现在使用
this works even faster than the first query in the question, about 1.2ms, and now it uses
Recheck Cond: (id = ANY ($1))
Index Cond: (id = ANY ($1))
和位图在计划中扫描.
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