PostgreSQL 多维空间几何对象 相交、包含 高效率检索实践 - cube

7 minute read

背景

多维空间对象的几何运算,高效率检索实践。

例如我们在数据库中存储了多维几何对象,可以使用lower, upper的数组来表达,例如3维度对象:

CUBE  
[  
xmin1  
ymin1  
zmin1  
,  
xmax1  
ymax1  
zmax1  
]  

在介绍CUBE类型前,我们可以使用6个字段(xmin,xmax,ymin,ymax,zmin,zmax)来表达一个立方体。

包含和相交查询

在介绍CUBE类型前,我们如果使用6个字段来表达立方体,那么相交,包含分别如何标示呢?

包含:

(xmin1 <= xmin2 and xmax1 >= xmax2)  
and  
(ymin1 <= ymin2 and ymax1 >= ymax2)  
and  
(zmin1 <= zmin2 and zmax1 >= zmax2)  

相交:

每个坐标都相交,注意任意坐标相交的方位有

-----  
   -----    
  
或  
  
   -----    
------  
  
或  
  
--------  
  ---   
  
或  
  
---  
   ---  
  
或  
  
---  
---  
  
或  
  
   ---  
---  

每条边都有相交即CUBE相交,表达如下

((xmin1 >= xmin2 and xmin1 <= xmax2) or (xmax1 >= xmin2 and xmax1 <= xmax2) or (xmin1 <= xmin2 and xmax1 >= xmax2))  
and  
((ymin1 >= ymin2 and ymin1 <= ymax2) or (ymax1 >= ymin2 and ymax1 <= ymax2) or (ymin1 <= ymin2 and ymax1 >= ymax2))  
and  
((zmin1 >= zmin2 and zmin1 <= zmax2) or (zmax1 >= zmin2 and zmax1 <= zmax2) or (zmin1 <= zmin2 and zmax1 >= zmax2))  

使用6个字段的空间计算性能

1、创建测试表

create table test1 (  
  id int primary key,   
  x_min int,   
  y_min int,   
  z_min int,  
  x_max int,  
  y_max int,  
  z_max int  
);  

2、写入100万记录

insert into test1 select id, x, y, z, x+1+(random()*100)::int, y+1+(random()*100)::int, z+1+(random()*100)::int   
from (select id, (random()*1000)::int x, (random()*1000)::int y, (random()*1000)::int z from generate_series(1,1000000) t(id)) t ;  

记录如下

postgres=# select * from test1 limit 10;  
 id | x_min | y_min | z_min | x_max | y_max | z_max   
----+-------+-------+-------+-------+-------+-------  
  1 |    37 |   367 |   948 |    93 |   372 |   989  
  2 |   994 |   543 |   596 |  1031 |   613 |   617  
  3 |   399 |   616 |   897 |   444 |   624 |   959  
  4 |   911 |   624 |    67 |  1007 |   705 |    84  
  5 |   286 |   560 |   882 |   334 |   632 |   936  
  6 |   370 |   748 |   897 |   403 |   779 |   992  
  7 |   723 |   292 |   484 |   756 |   358 |   503  
  8 |   514 |    48 |   792 |   556 |    98 |   879  
  9 |    17 |   400 |   485 |    26 |   435 |   514  
 10 |   240 |   631 |   841 |   253 |   642 |   897  
(10 rows)  

3、包含查询

select * from test1 where   
(x_min <= 37 and x_max >= 93)  
and  
(y_min <= 367 and y_max >= 372)  
and  
(z_min <= 948 and z_max >= 989);  
  
  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from test1 where   
(x_min <= 37 and x_max >= 93)  
and  
(y_min <= 367 and y_max >= 372)  
and  
(z_min <= 948 and z_max >= 989);  
                                                                         QUERY PLAN                                                                            
-------------------------------------------------------------------------------------------------------------------------------------------------------------  
 Seq Scan on public.test1  (cost=0.00..13220.05 rows=539 width=28) (actual time=0.024..79.397 rows=15 loops=1)  
   Output: id, x_min, y_min, z_min, x_max, y_max, z_max  
   Filter: ((test1.x_min <= 37) AND (test1.x_max >= 93) AND (test1.y_min <= 367) AND (test1.y_max >= 372) AND (test1.z_min <= 948) AND (test1.z_max >= 989))  
   Rows Removed by Filter: 999985  
   Buffers: shared hit=1835  
 Planning Time: 0.103 ms  
 Execution Time: 79.421 ms  
(7 rows)  
  
Time: 79.947 ms  
  
  
   id   | x_min | y_min | z_min | x_max | y_max | z_max   
--------+-------+-------+-------+-------+-------+-------  
      1 |    37 |   367 |   948 |    93 |   372 |   989  
 104882 |    17 |   327 |   924 |   111 |   389 |  1012  
 178185 |    31 |   315 |   897 |   104 |   380 |   990  
 228661 |     9 |   363 |   934 |   101 |   394 |  1001  
 275030 |    21 |   334 |   912 |   102 |   379 |  1012  
 405290 |    10 |   356 |   911 |   102 |   435 |   996  
 586417 |    35 |   362 |   930 |   128 |   454 |  1016  
 594367 |    23 |   312 |   943 |   112 |   395 |  1017  
 622753 |    11 |   365 |   916 |    93 |   427 |   995  
 645719 |    32 |   309 |   918 |    94 |   377 |  1015  
 757900 |    34 |   339 |   905 |    98 |   430 |   998  
 784203 |    36 |   344 |   945 |    95 |   390 |  1035  
 824046 |    23 |   367 |   946 |   115 |   423 |  1021  
 878257 |    37 |   339 |   948 |   123 |   398 |  1033  
 914020 |    26 |   358 |   918 |   109 |   379 |  1019  
(15 rows)  
  
Time: 80.269 ms  

4、相交查询

select * from test1 where   
((x_min >= 37 and x_min <= 93) or (x_max >= 37 and x_max <= 93) or (x_min <= 37 and x_max >= 93))  
and  
((y_min >= 367 and y_min <= 372) or (y_max >= 367 and y_max <= 372) or (y_min <= 367 and y_max >= 372))  
and  
((z_min >= 948 and z_min <= 989) or (z_max >= 948 and z_max <= 989) or (z_min <= 948 and z_max >= 989))  
;  
  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from test1 where   
((x_min >= 37 and x_min <= 93) or (x_max >= 37 and x_max <= 93) or (x_min <= 37 and x_max >= 93))  
and  
((y_min >= 367 and y_min <= 372) or (y_max >= 367 and y_max <= 372) or (y_min <= 367 and y_max >= 372))  
and  
((z_min >= 948 and z_min <= 989) or (z_max >= 948 and z_max <= 989) or (z_min <= 948 and z_max >= 989))  
;  
                       QUERY PLAN                                                                                                                          
 Seq Scan on public.test1  (cost=0.00..39229.87 rows=4364 width=28) (actual time=0.026..119.539 rows=483 loops=1)  
   Output: id, x_min, y_min, z_min, x_max, y_max, z_max  
   Filter: ((((test1.x_min >= 37) AND (test1.x_min <= 93)) OR ((test1.x_max >= 37) AND (test1.x_max <= 93)) OR ((test1.x_min <= 37) AND (test1.x_max >= 93))) AND (((test1.y_min >= 367) AND (test1.y_min <= 372)) OR ((test1.y_max >= 367) AND (test1.y_max <= 372)) OR ((test1.y_min <= 367) AND (test1.y_max >= 372))) AND (((test1.z_min >= 948) AND (test1.z_min <= 989)) OR ((test1.z_max >= 948) AND (test1.z_max <= 989)) OR ((test1.z_min <= 948) AND (test1.z_max >= 989))))  
   Rows Removed by Filter: 999517  
   Buffers: shared hit=1835  
 Planning Time: 0.135 ms  
 Execution Time: 119.621 ms  
(7 rows)  
  
Time: 120.283 ms  

cube 类型

cube的多维体表达方法如下

It does not matter which order the opposite corners of a cube are entered in.

The cube functions automatically swap values if needed to create a uniform “lower left — upper right” internal representation.

When the corners coincide, cube stores only one corner along with an “is point” flag to avoid wasting space.

1、创建 cube 插件

create extension cube;  

2、创建测试表

create table test2 (  
  id int primary key,  
  cb cube  
);  

3、将数据导入test2 cube表

insert into test2 select id, cube(array[x_min,y_min,z_min], array[x_max,y_max,z_max]) from test1;  

4、给CUBE类型创建gist索引

create index idx_test2_cb on test2 using gist(cb);  

5、包含查询性能

explain (analyze,verbose,timing,costs,buffers) select * from test2 where cb @> cube '[(37,367,948), (93,372,989)]';  
  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from test2 where cb @> cube '[(37,367,948), (93,372,989)]';  
                                                           QUERY PLAN                                                              
---------------------------------------------------------------------------------------------------------------------------------  
 Index Scan using idx_test2_cb on public.test2  (cost=0.25..20.65 rows=1000 width=60) (actual time=0.154..0.247 rows=15 loops=1)  
   Output: id, cb  
   Index Cond: (test2.cb @> '(37, 367, 948),(93, 372, 989)'::cube)  
   Buffers: shared hit=26  
 Planning Time: 0.196 ms  
 Execution Time: 0.269 ms  
(6 rows)  
  
postgres=# \timing  
Timing is on.  
postgres=# select * from test2 where cb @> cube '[(37,367,948), (93,372,989)]';  
   id   |               cb                  
--------+---------------------------------  
      1 | (37, 367, 948),(93, 372, 989)  
 228661 | (9, 363, 934),(101, 394, 1001)  
 586417 | (35, 362, 930),(128, 454, 1016)  
 824046 | (23, 367, 946),(115, 423, 1021)  
 914020 | (26, 358, 918),(109, 379, 1019)  
 104882 | (17, 327, 924),(111, 389, 1012)  
 594367 | (23, 312, 943),(112, 395, 1017)  
 645719 | (32, 309, 918),(94, 377, 1015)  
 784203 | (36, 344, 945),(95, 390, 1035)  
 275030 | (21, 334, 912),(102, 379, 1012)  
 757900 | (34, 339, 905),(98, 430, 998)  
 878257 | (37, 339, 948),(123, 398, 1033)  
 405290 | (10, 356, 911),(102, 435, 996)  
 622753 | (11, 365, 916),(93, 427, 995)  
 178185 | (31, 315, 897),(104, 380, 990)  
(15 rows)  
  
Time: 0.685 ms  

6、相交查询性能

select * from test2 where cb && cube '[(37,367,948), (93,372,989)]';  
  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from test2 where cb && cube '[(37,367,948), (93,372,989)]';  
                                                            QUERY PLAN                                                              
----------------------------------------------------------------------------------------------------------------------------------  
 Index Scan using idx_test2_cb on public.test2  (cost=0.25..76.66 rows=5000 width=60) (actual time=0.086..0.943 rows=483 loops=1)  
   Output: id, cb  
   Index Cond: (test2.cb && '(37, 367, 948),(93, 372, 989)'::cube)  
   Buffers: shared hit=505  
 Planning Time: 0.085 ms  
 Execution Time: 1.011 ms  
(6 rows)  
  
Time: 1.506 ms  

7、除此以外,CUBE还支持很多的几何计算操作符,也可以做包含点的查询。

https://www.postgresql.org/docs/devel/static/cube.html

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from test2 where cb @> cube '(37,367,948)';
                                                            QUERY PLAN                                                            
----------------------------------------------------------------------------------------------------------------------------------
 Index Scan using idx_test2_cb on public.test2  (cost=0.25..20.65 rows=1000 width=60) (actual time=0.153..0.420 rows=107 loops=1)
   Output: id, cb
   Index Cond: (test2.cb @> '(37, 367, 948)'::cube)
   Buffers: shared hit=121
 Planning Time: 0.077 ms
 Execution Time: 0.448 ms
(6 rows)

Time: 0.893 ms

优化

如果SQL请求返回的记录数非常多,建议流式返回,同时建议根据BLOCK设备的随机IO能力设置正确的random_page_cost参数。

《PostgreSQL 10 参数模板 - 珍藏级》

流式返回例子

postgres=# begin;
BEGIN
postgres=# declare cur1 cursor for select * from test2 where cb && cube '[(37,367,948), (93,372,989)]';
DECLARE CURSOR
postgres=# \timing
Timing is on.
postgres=# fetch 10 from cur1;
   id   |               cb               
--------+--------------------------------
  41724 | (65, 363, 939),(87, 425, 980)
 115087 | (72, 362, 977),(97, 454, 1005)
 235266 | (74, 362, 958),(133, 457, 994)
 489571 | (51, 362, 970),(101, 393, 989)
 655616 | (77, 359, 932),(79, 455, 1026)
 786710 | (73, 358, 942),(160, 374, 960)
      1 | (37, 367, 948),(93, 372, 989)
   6441 | (48, 368, 949),(88, 426, 964)
  59620 | (29, 364, 939),(60, 452, 997)
 153554 | (22, 367, 959),(75, 374, 997)
(10 rows)

Time: 0.297 ms
postgres=# end;
COMMIT
Time: 0.138 ms

如果是SSD盘,建议random_page_cost设置为1.1-1.3

alter system set random_page_cost=1.3;
select pg_reload_conf();

小结

使用cube插件,我们在对多维几何空间对象进行查询时,可以使用GIST索引,性能非常棒。

在100万空间对象的情况下,性能提升了100倍。

PS, test1表(分字段表达)即使使用BTREE索引,效果也不好,因为多字段的范围检索,初级索引是要全扫描的,以前有一个智能DNS的例子类似,使用GIST提升了20多倍性能。

《PostgreSQL 黑科技 range 类型及 gist index 20x+ speedup than Mysql index combine query》

使用CUBE插件,我们还可以用来计算多维对象的向量相似性,按向量相似性排序。参考末尾连接。

参考

《PostgreSQL 相似人群圈选,人群扩选,向量相似 使用实践》

《PostgreSQL 黑科技 range 类型及 gist index 20x+ speedup than Mysql index combine query》

《通过空间思想理解GiST索引的构造》

https://www.postgresql.org/docs/devel/static/cube.html

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