菜鸟末端轨迹 - 电子围栏(解密支撑每天251亿个包裹的数据库) - 阿里云RDS PostgreSQL最佳实践
背景
菜鸟末端轨迹项目中涉及的一个关键需求，面面判断。
在数据库中存储了一些多边形记录，约几百万到千万条记录，例如一个小区，在地图上是一个多边形。
不同的快递公司，会有各自不同的多边形划分方法（每个网点负责的片区(多边形)，每个快递员负责的片区(多边形)）。
用户在寄件时，根据用户的位置，查找对应快递公司负责这个片区的网点、或者负责该片区的快递员。
一、需求
1、在数据库中存储了一些静态的面信息，代表小区、园区、写字楼等等。所有的面不相交。
2、为了支持不同的业务类型，对一个地图，可能划分为不同的多边形组成。
例如不同的快递公司，会有各自不同的多边形划分方法（网点负责的片区(多边形)，某个快递员负责的片区(多边形)）。
因此在一张地图上，有多个图层，每个图层的多边形划分方法可能不一样。
3、快速的根据快递公司、客户的位置，求包含这个点的多边形（即得到对应快递公司负责这个片区的网点、或者负责该片区的快递员）。
二、架构设计
用到阿里云的RDS PostgreSQL，以及PG提供的PostGIS插件。
我们需要用到PostGIS的函数有两个
http://postgis.net/docs/manual-2.3/ST_Within.html
1、ST_within
ST_Within — Returns true if the geometry A is completely inside geometry B
boolean ST_Within(geometry A, geometry B);
Returns TRUE if geometry A is completely inside geometry B. For this function to make sense, the source geometries must both be of the same coordinate projection, having the same SRID. It is a given that if ST_Within(A,B) is true and ST_Within(B,A) is true, then the two geometries are considered spatially equal.
This function call will automatically include a bounding box comparison that will make use of any indexes that are available on the geometries. To avoid index use, use the function _ST_Within.
-- a circle within a circle
SELECT ST_Within(smallc,smallc) As smallinsmall,
ST_Within(smallc, bigc) As smallinbig,
ST_Within(bigc,smallc) As biginsmall,
ST_Within(ST_Union(smallc, bigc), bigc) as unioninbig,
ST_Within(bigc, ST_Union(smallc, bigc)) as biginunion,
ST_Equals(bigc, ST_Union(smallc, bigc)) as bigisunion
FROM
(
SELECT ST_Buffer(ST_GeomFromText('POINT(50 50)'), 20) As smallc,
ST_Buffer(ST_GeomFromText('POINT(50 50)'), 40) As bigc) As foo;
-- Result
smallinsmall | smallinbig | biginsmall | unioninbig | biginunion | bigisunion
--------------+------------+------------+------------+------------+------------
t | t | f | t | t | t
(1 row)
2、ST_Contains
ST_Contains — Returns true if and only if no points of B lie in the exterior of A, and at least one point of the interior of B lies in the interior of A.
boolean ST_Contains(geometry geomA, geometry geomB);
Returns TRUE if geometry B is completely inside geometry A. For this function to make sense, the source geometries must both be of the same coordinate projection, having the same SRID. ST_Contains is the inverse of ST_Within. So ST_Contains(A,B) implies ST_Within(B,A) except in the case of invalid geometries where the result is always false regardless or not defined.
This function call will automatically include a bounding box comparison that will make use of any indexes that are available on the geometries. To avoid index use, use the function _ST_Contains.
-- A circle within a circle
SELECT ST_Contains(smallc, bigc) As smallcontainsbig,
ST_Contains(bigc,smallc) As bigcontainssmall,
ST_Contains(bigc, ST_Union(smallc, bigc)) as bigcontainsunion,
ST_Equals(bigc, ST_Union(smallc, bigc)) as bigisunion,
ST_Covers(bigc, ST_ExteriorRing(bigc)) As bigcoversexterior,
ST_Contains(bigc, ST_ExteriorRing(bigc)) As bigcontainsexterior
FROM (SELECT ST_Buffer(ST_GeomFromText('POINT(1 2)'), 10) As smallc,
ST_Buffer(ST_GeomFromText('POINT(1 2)'), 20) As bigc) As foo;
-- Result
smallcontainsbig | bigcontainssmall | bigcontainsunion | bigisunion | bigcoversexterior | bigcontainsexterior
------------------+------------------+------------------+------------+-------------------+---------------------
f | t | t | t | t | f
-- Example demonstrating difference between contains and contains properly
SELECT ST_GeometryType(geomA) As geomtype, ST_Contains(geomA,geomA) AS acontainsa, ST_ContainsProperly(geomA, geomA) AS acontainspropa,
ST_Contains(geomA, ST_Boundary(geomA)) As acontainsba, ST_ContainsProperly(geomA, ST_Boundary(geomA)) As acontainspropba
FROM (VALUES ( ST_Buffer(ST_Point(1,1), 5,1) ),
( ST_MakeLine(ST_Point(1,1), ST_Point(-1,-1) ) ),
( ST_Point(1,1) )
) As foo(geomA);
geomtype | acontainsa | acontainspropa | acontainsba | acontainspropba
--------------+------------+----------------+-------------+-----------------
ST_Polygon | t | f | f | f
ST_LineString | t | f | f | f
ST_Point | t | t | f | f
三、DEMO与性能
1 PG内置几何类型 面点搜索 压测
为了简化测试，采样PG内置的几何类型进行测试，用法与PostGIS是类似的。
1、创建测试表
postgres=# create table po(id int, typid int, po polygon);
CREATE TABLE
2、创建分区表或分区索引
create extension btree_gist;
create index idx_po_1 on po using gist(typid, po);
3、创建空间排他约束，可选
如果要求单个typid内的po不重叠，可以创建空间排他约束
create table tbl_po(id int, typid int, po polygon)
PARTITION BY LIST (typid);
CREATE TABLE tbl_po_1
PARTITION OF tbl_po (
EXCLUDE USING gist (po WITH &&)
) FOR VALUES IN (1);
...
CREATE TABLE tbl_po_20
PARTITION OF tbl_po (
EXCLUDE USING gist (po WITH &&)
) FOR VALUES IN (20);
查看某分区表的空间排他约束如下
postgres=# \d tbl_po_1
Table "postgres.tbl_po_1"
Column | Type | Collation | Nullable | Default
--------+---------+-----------+----------+---------
id | integer | | |
typid | integer | | |
po | polygon | | |
Partition of: tbl_po FOR VALUES IN (1)
Indexes:
"tbl_po_1_po_excl" EXCLUDE USING gist (po WITH &&)
4、写入1000万多边形测试数据
insert into po select id, random()*20, polygon('(('||x1||','||y1||'),('||x2||','||y2||'),('||x3||','||y3||'))') from (select id, 180-random()*180 x1, 180-random()*180 x2, 180-random()*180 x3, 90-random()*90 y1, 90-random()*90 y2, 90-random()*90 y3 from generate_series(1,10000000) t(id)) t;
5、测试面点判断性能
查询包含point(1,1)的多边形，响应时间0.57毫秒。
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from po where typid=1 and po @> polygon('((1,1),(1,1),(1,1))') limit 1;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------
Limit (cost=0.42..1.76 rows=1 width=93) (actual time=0.551..0.551 rows=1 loops=1)
Output: id, typid, po
Buffers: shared hit=74
-> Index Scan using idx_po_1 on postgres.po (cost=0.42..673.48 rows=503 width=93) (actual time=0.550..0.550 rows=1 loops=1)
Output: id, typid, po
Index Cond: ((po.typid = 1) AND (po.po @> '((1,1),(1,1),(1,1))'::polygon))
Rows Removed by Index Recheck: 17
Buffers: shared hit=74
Planning time: 0.090 ms
Execution time: 0.572 ms
(10 rows)
6、压测
vi test.sql
\set x random(-180,180)
\set y random(-90,90)
\set typid random(1,20)
select * from po where typid=:typid and po @> polygon('((:x,:y),(:x,:y),(:x,:y))') limit 1;
pgbench -M simple -n -r -P 1 -f ./test.sql -c 64 -j 64 -T 100
transaction type: ./test.sql
scaling factor: 1
query mode: simple
number of clients: 64
number of threads: 64
duration: 100 s
number of transactions actually processed: 29150531
latency average = 0.220 ms
latency stddev = 0.140 ms
tps = 291487.813205 (including connections establishing)
tps = 291528.228634 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set x random(-180,180)
0.001 \set y random(-90,90)
0.000 \set typid random(1,20)
0.223 select * from po where typid=:typid and po @> polygon('((:x,:y),(:x,:y),(:x,:y))') limit 1;
惊不惊喜、意不意外
TPS：29万 ，平均响应时间：0.2毫秒
2 PostGIS空间数据库 面点搜索 压测
阿里云 RDS PostgreSQL，HybridDB for PostgreSQL 已经内置了PostGIS空间数据库插件，使用前创建插件即可。
create extension postgis;
1、建表
postgres=# create table po(id int, typid int, po geometry);
CREATE TABLE
2、创建空间索引
postgres=# create extension btree_gist;
postgres=# create index idx_po_1 on po using gist(typid, po);
3、写入1000万多边形测试数据
postgres=# insert into po
select
id, random()*20,
ST_PolygonFromText('POLYGON(('||x1||' '||y1||','||x2||' '||y2||','||x3||' '||y3||','||x1||' '||y1||'))')
from
(
select id, 180-random()*180 x1, 180-random()*180 x2, 180-random()*180 x3, 90-random()*90 y1, 90-random()*90 y2, 90-random()*90 y3 from generate_series(1,10000000) t(id)
) t;
4、测试面点判断性能
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from po where typid=1 and st_within(ST_PointFromText('POINT(1 1)'), po) limit 1;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------
Limit (cost=0.42..4.21 rows=1 width=40) (actual time=0.365..0.366 rows=1 loops=1)
Output: id, typid, po
Buffers: shared hit=14
-> Index Scan using idx_po_1 on public.po (cost=0.42..64.92 rows=17 width=40) (actual time=0.364..0.364 rows=1 loops=1)
Output: id, typid, po
Index Cond: ((po.typid = 1) AND (po.po ~ '0101000000000000000000F03F000000000000F03F'::geometry))
Filter: _st_contains(po.po, '0101000000000000000000F03F000000000000F03F'::geometry)
Rows Removed by Filter: 1
Buffers: shared hit=14
Planning time: 0.201 ms
Execution time: 0.389 ms
(11 rows)
postgres=# select id,typid,st_astext(po) from po where typid=1 and st_within(ST_PointFromText('POINT(1 1)'), po) limit 5;
id | typid | st_astext
---------+-------+--------------------------------------------------------------------------------------------------------------------------------------------------------
9781228 | 1 | POLYGON((0.295946141704917 0.155529817566276,16.4715472329408 56.1022255802527,172.374844718724 15.4784881789237,0.295946141704917 0.155529817566276))
704428 | 1 | POLYGON((173.849076312035 77.8871315997094,167.085936572403 23.9897218951955,0.514283403754234 0.844541620463133,173.849076312035 77.8871315997094))
5881120 | 1 | POLYGON((104.326644698158 44.4173073163256,3.76680867746472 76.8664212757722,0.798425730317831 0.138536808080971,104.326644698158 44.4173073163256))
1940693 | 1 | POLYGON((0.774057107046247 0.253543308936059,126.49553722702 22.7823389600962,8.62134614959359 56.176855028607,0.774057107046247 0.253543308936059))
3026739 | 1 | POLYGON((0.266327261924744 0.406031627207994,101.713274326175 38.6256391229108,2.88589236326516 15.3229149011895,0.266327261924744 0.406031627207994))
(5 rows)
5、压测
vi test.sql
\setrandom x -180 180
\setrandom y -90 90
\setrandom typid 1 20
select * from po where typid=:typid and st_within(ST_PointFromText('POINT(:x :y)'), po) limit 1;
pgbench -M simple -n -r -P 1 -f ./test.sql -c 64 -j 64 -T 120
transaction type: Custom query
scaling factor: 1
query mode: simple
number of clients: 64
number of threads: 64
duration: 120 s
number of transactions actually processed: 23779817
latency average: 0.321 ms
latency stddev: 0.255 ms
tps = 198145.452614 (including connections establishing)
tps = 198160.891580 (excluding connections establishing)
statement latencies in milliseconds:
0.002615 \setrandom x -180 180
0.000802 \setrandom y -90 90
0.000649 \setrandom typid 1 20
0.316816 select * from po where typid=:typid and st_within(ST_PointFromText('POINT(:x :y)'), po) limit 1;
惊不惊喜、意不意外
TPS：19.8万 ，平均响应时间：0.32毫秒
四、技术点
1、空间排他约束
这个约束可以用于强制记录中的多边形不相交。例如地图这类严谨数据，绝对不可能出现两个多边形相交的，否则就有领土纷争了。
PostgreSQL就是这么严谨，意不意外。
-- 例子
CREATE TABLE tbl_po_1
PARTITION OF tbl_po (
EXCLUDE USING gist (po WITH &&)
) FOR VALUES IN (1);
2、分区表
本例中不同的快递公司，对应不同的图层，每个快递公司根据网点、快递员负责的片区(多边形)划分为多个多边形。
使用LIST分区，每个分区对应一家快递公司。
3、空间索引
GiST空间索引，支持KNN、包含、相交、上下左右等空间搜索。
效率极高。
4、空间分区索引
《分区索引的应用和实践 - 阿里云RDS PostgreSQL最佳实践》
5、面面、点判断
面面判断或面点判断是本例的主要需求，用户在寄包裹时，根据用户位置在数据库的一千万多边形中找出覆盖这个点的多边形。
五、云端产品
六、类似场景、案例
《PostgreSQL 物流轨迹系统数据库需求分析与设计 - 包裹侠实时跟踪与召回》
七、小结
菜鸟末端轨迹项目中涉及的一个关键需求，面面判断。
在数据库中存储了一些多边形记录，约几百万到千万条记录，例如一个小区，在地图上是一个多边形。
不同的快递公司，会有各自不同的多边形划分方法（网点负责的片区(多边形)，某个快递员负责的片区(多边形)）。
用户在寄件时，根据用户的位置，查找对应快递公司负责这个片区的网点、或者负责该片区的快递员。
使用阿里云RDS PostgreSQL，用户存放约1千万的多边形数据，单库实现了每秒29万的处理请求，单次请求平均响应时间约0.2毫秒。
惊不惊喜、意不意外。
八、参考
http://postgis.net/docs/manual-2.3/ST_Within.html
《分区索引的应用和实践 - 阿里云RDS PostgreSQL最佳实践》