GIS业务,附近查找性能优化

标签

PostgreSQL , PostGIS , KNN , order sort 优化

背景

空间数据中对临近点的检索使用非常常见, 例如以经纬度为坐标点, 检索离这个点1公里范围内的其他点的信息.

最近有网友问到这样的问题，如何优化呢.

ps 现在的版本可以直接支持，不需要使用子查询来支持了。

《PostgreSQL 百亿地理位置数据 近邻查询性能》

正文

本文将以Postgis为例, 举一个简单的例子, 利用gist 索引加速检索.

测试表 :

create table cust_jw        
(        
 dz varchar(300),        
 jwd geometry        
);        

测试数据, 取自经纬度信息网站.

insert into cust_jw values ('杭州', ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163));          
insert into cust_jw values ('北京', ST_Transform(ST_GeomFromText('POINT(116.46 39.92)', 4326), 2163));          
insert into cust_jw values ('南京', ST_Transform(ST_GeomFromText('POINT(118.78 32.04)', 4326), 2163));          
insert into cust_jw values ('南宁', ST_Transform(ST_GeomFromText('POINT(108.33 22.84)', 4326), 2163));          
insert into cust_jw values ('贵阳', ST_Transform(ST_GeomFromText('POINT(106.71 26.57)', 4326), 2163));          
insert into cust_jw values ('南昌', ST_Transform(ST_GeomFromText('POINT(115.89 28.68)', 4326), 2163));          
insert into cust_jw values ('余杭', ST_Transform(ST_GeomFromText('POINT(120.3 30.43)', 4326), 2163));          

创建gist索引 :

create index idx_cust_jw_1 on cust_jw using gist(jwd);          

这个索引方法支持包含<->两个几何类型的距离排序和&&两个几何类型相交.

详见pg_amop , pg_am, pg_operator, pg_opfamily等系统表.

以下SQL查出北京到杭州的直线距离, 单位米 :

SELECT ST_Distance(          
ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163),          
ST_Transform(ST_GeomFromText('POINT(116.46 39.92)', 4326), 2163)          
);          
   st_distance              
------------------          
 1256521.71432098          
(1 row)          

以下SQL 查出表中距离ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163)这个点20公里的坐标.

函数使用方法参考postgis手册.

digoal=# select *,ST_Distance(jwd, ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163)) from cust_jw where jwd && ST_Buffer(ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163), 20000, 10);          
  dz  |                        jwd                         |   st_distance              
------+----------------------------------------------------+------------------          
 杭州 | 0101000020730800004C94087D5D4F54C173AA7759E8FB5D41 |                0          
 余杭 | 0101000020730800000E6E5A20494854C121FC688DA9EF5D41 | 14483.9823187612          
(2 rows)          
Time: 1.335 ms          

前面已经说了, 这个索引访问方法支持&&操作符, <->操作符.

digoal=# explain select *,ST_Distance(jwd, ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163)) from cust_jw where jwd && ST_Buffer(ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163), 20000, 10);                                                                                                                   
                          QUERY PLAN                                                                                                          
                                                                                                                                              
                                                                                                                                              
                                                                                                                                              
                                                                                                                                              
                                                                         
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
------------------------------------------------------------------------------------------------------------------------------------          
---------------------------------------------------------------          
 Index Scan using idx_cust_jw_1 on cust_jw  (cost=0.14..3.41 rows=1 width=548)          
   Index Cond: (jwd && '01030000207308000001000000290000004C94087DD53B54C173AA7759E8FB5D411122F50B133C54C154295A2DDAF85D41D751B134CA          
3C54C1F4F2B643DFF55D41B6BBAE74F63D54C10FB6A0650AF35D41CDDC4767903F54C1D331586C6DF05D4124855AF48D4154C14B9BC9D018EE5D41AC1BE98FE24354          
C1F4F2B6431BEC5D41E89F31897F4654C1DDD11D5181EA5D41CDDC4767544954C1FE67201155E95D412D13EB504F4C54C1383864E89DE85D414C94087D5D4F54C173          
AA775960E85D416B1526A96B5254C1383864E89DE85D41CB4BC992665554C1FE67201155E95D41B088DF703B5854C1DDD11D5181EA5D41EC0C286AD85A54C1F4F2B6          
431BEC5D4174A3B6052D5D54C14B9BC9D018EE5D41CB4BC9922A5F54C1D331586C6DF05D41E26C6285C46054C10FB6A0650AF35D41C1D65FC5F06154C1F4F2B643DF          
F55D4187061CEEA76254C154295A2DDAF85D414C94087DE56254C173AA7759E8FB5D4187061CEEA76254C1922B9585F6FE5D41C1D65FC5F06154C1F261386FF1015E          
41E26C6285C46054C1D79E4E4DC6045E41CB4BC9922A5F54C11323974663075E4174A3B6052D5D54C19BB925E2B7095E41EC0C286AD85A54C1F261386FB50B5E41B0          
88DF703B5854C10983D1614F0D5E41CB4BC992665554C1E8ECCEA17B0E5E416B1526A96B5254C1AE1C8BCA320F5E414C94087D5D4F54C173AA7759700F5E412D13EB          
504F4C54C1AE1C8BCA320F5E41CDDC4767544954C1E8ECCEA17B0E5E41E89F31897F4654C10983D1614F0D5E41AC1BE98FE24354C1F261386FB50B5E4124855AF48D          
4154C19BB925E2B7095E41CDDC4767903F54C11323974663075E41B6BBAE74F63D54C1D79E4E4DC6045E41D751B134CA3C54C1F261386FF1015E411122F50B133C54          
C1922B9585F6FE5D414C94087DD53B54C173AA7759E8FB5D41'::geometry)          
(2 rows)          
Time: 1.218 ms          

以下SQL 按距离排序.

digoal=# select *,ST_Distance(jwd, ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163)) from cust_jw order by jwd <-> ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163);          
  dz  |                        jwd                         |   st_distance              
------+----------------------------------------------------+------------------          
 杭州 | 0101000020730800004C94087D5D4F54C173AA7759E8FB5D41 |                0          
 余杭 | 0101000020730800000E6E5A20494854C121FC688DA9EF5D41 | 14483.9823187612          
 南京 | 0101000020730800000FFE5AD1D62653C16F4F972A10635E41 | 321491.591341196          
 南昌 | 010100002073080000B2744BA1FE5253C10975D1494AA25F41 | 503843.306221247          
 北京 | 0101000020730800006EBBB0F1AB0E4FC17207C71D44525E41 | 1256521.71432098          
 南宁 | 01010000207308000030806B3882F451C18E3F38DCBB686141 |  1409624.7420143          
 贵阳 | 01010000207308000082EA89026EE14FC1D6A3AD6E9E786141 | 1732521.31784296          
(7 rows)          
Time: 0.598 ms          

通过以下方法强制排序走索引 :

digoal=# set enable_seqscan=off;          
SET          
Time: 0.109 ms          
digoal=# explain select *,ST_Distance(jwd, ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163)) from cust_jw order by jwd <-> ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163);          
                                      QUERY PLAN                                                
--------------------------------------------------------------------------------------          
 Index Scan using idx_cust_jw_1 on cust_jw  (cost=0.14..54.44 rows=140 width=548)          
   Order By: (jwd <-> '0101000020730800004C94087D5D4F54C173AA7759E8FB5D41'::geometry)          
(2 rows)          

以下为进一步的优化, 如果点比较密集的话, 这种方法比较好.

digoal=# select * from (select *,ST_Distance(jwd, ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163)) AS dist from cust_jw order by jwd <-> ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163) limit 1000) t where dist<15000;          
  dz  |                        jwd                         |       dist                 
------+----------------------------------------------------+------------------          
 杭州 | 0101000020730800004C94087D5D4F54C173AA7759E8FB5D41 |                0          
 余杭 | 0101000020730800000E6E5A20494854C121FC688DA9EF5D41 | 14483.9823187612          
(2 rows)          
Time: 0.634 ms          

极致优化

进一步优化, 使用游标, 可以将数据扫描降到极限. (前提是for循环中的sql order by使用了索引)

digoal=# do language plpgsql $$          
declare          
  v_rec record;          
  v_limit int := 1000;          
begin          
  set local enable_seqscan=off;  -- 强制索引, 因为扫描行数够就退出.          
  for v_rec in select *,ST_Distance(jwd, ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163)) AS dist from cust_jw order by jwd <-> ST_Transform(ST_GeomFromText('POINT(120.19 30.26)', 4326), 2163) loop          
    if v_limit <=0 then           
      raise notice '已经取足数据';          
      return;          
    end if;          
    if v_rec.dist > 20000 then           
      raise notice '满足条件的点已输出完毕';          
      return;          
    else          
      raise notice 'do someting, v_rec:%', v_rec;          
    end if;          
    v_limit := v_limit -1;          
  end loop;          
end;          
$$;          
NOTICE:  do someting, v_rec:(杭州,0101000020730800004C94087D5D4F54C173AA7759E8FB5D41,0)          
NOTICE:  do someting, v_rec:(余杭,0101000020730800000E6E5A20494854C121FC688DA9EF5D41,14483.9823187612)          
NOTICE:  满足条件的点已输出完毕          
DO          

使用这种方法最多扫描比需求结果多1行.

函数化

select * from 
(
select *,
  ST_Distance (ST_Transform ($1, 26986), ST_Transform (jwd, 26986) ) as dist 
  from cust_jw 
  where dz='杭州'
  order by ST_Transform (pos, 26986) <-> ST_Transform ($1, 26986) limit 200
) t
where
distance2Center <= 2000.0;


create or replace function ff(geometry, float8, int) returns setof record as $$                                                        
declare
  v_rec record;
  v_limit int := $3;
begin
  set local enable_seqscan=off;   -- 强制索引, 扫描行数够就退出.
  for v_rec in 
    select *,
    ST_Distance ( ST_Transform ($1, 26986), ST_Transform (jwd, 26986) ) as dist 
    from cust_jw 
    order by ST_Transform (jwd, 26986) <-> ST_Transform ($1, 26986)
  loop
    if v_limit <=0 then
      raise notice '已经取足数据';
      return;
    end if;
    if v_rec.dz='杭州' and v_rec.distance2Center > $2 then
      raise notice '满足条件的点已输出完毕';
      return;
    elsif v_rec.dz='杭州' then
      raise notice 'do someting, v_rec:%', v_rec;
      return next v_rec;
    else
      NULL;
    end if;
    v_limit := v_limit -1;
  end loop;
end;
$$ language plpgsql strict volatile;
  
select * from ff(ST_GeomFromText ('POINT(114.111618652344 28.332331814237)', 4326),2000.0,1) as t(dz varchar,jwd geometry,dist float8);

SRID

如果需要转换SRID，那么请使用表达式索引，例如ST_Transform (pos, 26986).

参考

1. http://www.ximizi.com/jingweidu.php

2. http://postgis.net/docs/manual-2.0/ST_Distance_Sphere.html

3. http://postgis.net/docs/manual-2.0/ST_Buffer.html

4. http://postgis.net/docs/manual-2.0/ST_Transform.html

5. http://postgis.net/docs/manual-2.0/ST_GeomFromText.html

6. http://postgis.net/docs/manual-2.0/geometry_distance_centroid.html