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Postgresql运算符性能:列表与子查询

in-operator postgresql-performance postgresql-9.5 postgresql

12

Radu Gancea · 技术社区 · 8 年前

对于大约700个ID的列表,查询性能比传递返回这些700 ID的子查询慢20倍以上。应该是相反的。

e、 g.(第一次查询时间不到400毫秒,后一次查询时间为9600毫秒)

select date_trunc('month', day) as month, sum(total)
from table_x
where y_id in (select id from table_y where prop = 'xyz') 
and day between '2015-11-05' and '2016-11-04' 
group by month

在我的机器上比直接传递数组快20倍:

select date_trunc('month', day) as month, sum(total) 
from table_x
where y_id in (1625, 1871, ..., 1640, 1643, 13291, 1458, 13304, 1407, 1765) 
and day between '2015-11-05' and '2016-11-04' 
group by month

您知道问题是什么,或者如何优化并获得相同的性能吗?

2 回复 | 直到 8 年前

1

16

Clodoaldo Neto 8 年前

区别在于简单过滤器与哈希连接:

explain analyze
select i 
from t
where i in (500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600);
                                              QUERY PLAN                                                                                                                                                                                                                        
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Seq Scan on t  (cost=0.00..140675.00 rows=101 width=4) (actual time=0.648..1074.567 rows=101 loops=1)
   Filter: (i = ANY ('{500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600}'::integer[]))
   Rows Removed by Filter: 999899
 Planning time: 0.170 ms
 Execution time: 1074.624 ms

explain analyze
select i
from t
where i in (select i from r);
                                                    QUERY PLAN                                                     
-------------------------------------------------------------------------------------------------------------------
 Hash Semi Join  (cost=3.27..17054.40 rows=101 width=4) (actual time=0.382..240.389 rows=101 loops=1)
   Hash Cond: (t.i = r.i)
   ->  Seq Scan on t  (cost=0.00..14425.00 rows=1000000 width=4) (actual time=0.030..117.193 rows=1000000 loops=1)
   ->  Hash  (cost=2.01..2.01 rows=101 width=4) (actual time=0.074..0.074 rows=101 loops=1)
         Buckets: 1024  Batches: 1  Memory Usage: 12kB
         ->  Seq Scan on r  (cost=0.00..2.01 rows=101 width=4) (actual time=0.010..0.035 rows=101 loops=1)
 Planning time: 0.245 ms
 Execution time: 240.448 ms

要获得相同的性能,请加入阵列:

explain analyze
select i
from
    t
    inner join
    unnest(
        array[500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600]::int[]
    ) u (i) using (i)
;
                                                      QUERY PLAN                                                       
-----------------------------------------------------------------------------------------------------------------------
 Hash Join  (cost=2.25..18178.25 rows=100 width=4) (actual time=0.267..243.768 rows=101 loops=1)
   Hash Cond: (t.i = u.i)
   ->  Seq Scan on t  (cost=0.00..14425.00 rows=1000000 width=4) (actual time=0.022..118.709 rows=1000000 loops=1)
   ->  Hash  (cost=1.00..1.00 rows=100 width=4) (actual time=0.063..0.063 rows=101 loops=1)
         Buckets: 1024  Batches: 1  Memory Usage: 12kB
         ->  Function Scan on unnest u  (cost=0.00..1.00 rows=100 width=4) (actual time=0.028..0.041 rows=101 loops=1)
 Planning time: 0.172 ms
 Execution time: 243.816 ms

或使用 values 语法:

explain analyze
select i 
from t
where i = any (values (500),(501),(502),(503),(504),(505),(506),(507),(508),(509),(510),(511),(512),(513),(514),(515),(516),(517),(518),(519),(520),(521),(522),(523),(524),(525),(526),(527),(528),(529),(530),(531),(532),(533),(534),(535),(536),(537),(538),(539),(540),(541),(542),(543),(544),(545),(546),(547),(548),(549),(550),(551),(552),(553),(554),(555),(556),(557),(558),(559),(560),(561),(562),(563),(564),(565),(566),(567),(568),(569),(570),(571),(572),(573),(574),(575),(576),(577),(578),(579),(580),(581),(582),(583),(584),(585),(586),(587),(588),(589),(590),(591),(592),(593),(594),(595),(596),(597),(598),(599),(600))
;
                                                      QUERY PLAN                                                       
-----------------------------------------------------------------------------------------------------------------------
 Hash Semi Join  (cost=2.53..17053.65 rows=101 width=4) (actual time=0.279..239.888 rows=101 loops=1)
   Hash Cond: (t.i = "*VALUES*".column1)
   ->  Seq Scan on t  (cost=0.00..14425.00 rows=1000000 width=4) (actual time=0.022..117.199 rows=1000000 loops=1)
   ->  Hash  (cost=1.26..1.26 rows=101 width=4) (actual time=0.059..0.059 rows=101 loops=1)
         Buckets: 1024  Batches: 1  Memory Usage: 12kB
         ->  Values Scan on "*VALUES*"  (cost=0.00..1.26 rows=101 width=4) (actual time=0.002..0.027 rows=101 loops=1)
 Planning time: 0.242 ms
 Execution time: 239.933 ms

2

NiVeR 8 年前

尝试将临界线更改为:

where y_id = any (values (1625, 1871, ..., 1640, 1643, 13291, 1458, 13304, 1407, 1765) )