背景

本文主要要展示的是PostgreSQL在位置信息近邻（KNN）查询方面的性能。

测试类型point，索引类型GiST。

（PostGIS同样支持KNN查询，性能和本文的测试差不多）

测试数据量大于100亿。

结果

64个并发，随机选点，单次KNN查询请求的平均响应时间为0.848毫秒。

测试环境和优化请参考

http://blog.163.com/digoal@126/blog/static/16387704020160941345888/

创建测试表

postgres=# create table tbl_point(id serial8, poi point);

CREATE TABLE

postgres=# \d tbl_point

Table "benchmarksql.tbl_point"

Column | Type | Modifiers

--------+--------+--------------------------------------------------------

id | bigint | not null default nextval('tbl_point_id_seq'::regclass)

poi | point |

postgres=# alter sequence tbl_point_id_seq cache 10000;

ALTER SEQUENCE

生成测试数据：

point的x和y的取值范围都是-50000到50000，所以一共可以生成100亿个不同的point。

和测试数据量相符。

vi test.sql

insert into tbl_point(poi) select point(trunc(100000*(0.5-random())), trunc(100000*(0.5-random()))) from generate_series(1,10000);

使用pgbench每秒约插入233万位置信息。

pgbench -M prepared -n -r -f ./test.sql -P 1 -c 96 -j 96 -T 1100

tps = 233.018365 (including connections establishing)

tps = 233.150940 (excluding connections establishing)

数据量

postgres=# select count(*) from tbl_point;

count

------------

2532820000

(1 row)

当前表大小：

postgres=# \dt+

在point类型上创建GiST索引

postgres=# create index idx_tbl_point on tbl_point using gist(poi) with (buffering=on);

postgres=# \d+ tbl_point

Table "benchmarksql.tbl_point"

--------+--------+--------------------------------------------------------+---------+--------------+-------------

Indexes:

"idx_tbl_point" gist (poi) WITH (buffering='on')

索引大小：

\di+

新建完索引后，插入性能会下降，现在每秒约插入55万条位置信息。

pgbench -M prepared -n -r -f ./test.sql -P 1 -c 96 -j 96 -T 100

transaction type: Custom query

scaling factor: 1

query mode: prepared

number of clients: 96

number of threads: 96

duration: 100 s

number of transactions actually processed: 5587

latency average: 1726.947 ms

latency stddev: 118.223 ms

tps = 55.390665 (including connections establishing)

tps = 55.419003 (excluding connections establishing)

statement latencies in milliseconds:

1726.946947 insert into tbl_point(poi) select point(trunc(100000*(0.5-random())), trunc(100000*(0.5-random()))) from generate_series(1,10000);

持续插入100亿记录

pgbench -M prepared -n -r -f ./test.sql -P 1 -c 64 -j 64 -T 13600

表

500GB

索引

720GB

knn检索例子

postgres=# select *,poi <-> point(1000,1000) dist from tbl_point where poi <-> point(1000,1000) < 100 order by poi <-> point(1000,1000) limit 10;

id | poi | dist

------------+-------------+------------------

399588621 | (1000,999) | 1

1030719903 | (1001,999) | 1.4142135623731

2698052191 | (1001,1001) | 1.4142135623731

3291219762 | (999,999) | 1.4142135623731

2757190006 | (1002,1000) | 2

2862610530 | (998,1001) | 2.23606797749979

3450459141 | (998,1001) | 2.23606797749979

3124756442 | (1002,1001) | 2.23606797749979

3105439886 | (1001,998) | 2.23606797749979

473144305 | (998,1002) | 2.82842712474619

(10 rows)

KNN执行计划

排序和检索都走了GiST索引。

例如，一个查询如下，扫描了16个数据块，在shared buffer命中8个，读8个块(可能在OS CACHE或直接读block dev)。

postgres=# explain (analyze,verbose,buffers,timing,costs) select *,poi <-> point(10090,10090) dist from tbl_point where poi <-> point(10090,10090) < 100 order by poi <-> point(10090,10090) limit 10;

QUERY PLAN

----------------------------------------------------------------------------------------------------------------------------------------------------------------

Limit (cost=0.56..13.15 rows=10 width=24) (actual time=0.469..1.309 rows=10 loops=1)

Output: id, poi, ((poi <-> '(10090,10090)'::point))

Buffers: shared hit=8 read=8 dirtied=1

-> Index Scan using idx_tbl_point on benchmarksql.tbl_point (cost=0.56..1510464450.86 rows=1199422376 width=24) (actual time=0.468..1.306 rows=10 loops=1)

Output: id, poi, (poi <-> '(10090,10090)'::point)

Order By: (tbl_point.poi <-> '(10090,10090)'::point)

Filter: ((tbl_point.poi <-> '(10090,10090)'::point) < '100'::double precision)

Buffers: shared hit=8 read=8 dirtied=1

Planning time: 0.084 ms

Execution time: 1.347 ms

(10 rows)

KNN检索压力测试

测试在100亿地理位置数据中，postgresql 的knn查询性能。

测试脚本如下，随机生成一个point，然后查找这个point附近距离为100以内，按距离排序，取出1条。

vi test.sql

\setrandom x -50000 50000

\setrandom y -50000 50000

select * from tbl_point where poi <-> point(:x,:y) <100 order by poi <-> point(:x,:y) limit 1;

测试结果

单次请求的平均响应时间为0.848毫秒。

pgbench -M prepared -n -r -f ./test.sql -P 1 -c 64 -j 64 -T 100

transaction type: Custom query

scaling factor: 1

query mode: prepared

number of clients: 64

number of threads: 64

duration: 100 s

number of transactions actually processed: 7418337

latency average: 0.858 ms

latency stddev: 0.564 ms

tps = 74151.604194 (including connections establishing)

tps = 74184.255934 (excluding connections establishing)

statement latencies in milliseconds:

0.007518 \setrandom x -50000 50000

0.002193 \setrandom y -50000 50000

0.847847 select * from tbl_point where poi <-> point(:x,:y) <100 order by poi <-> point(:x,:y) limit 1;

因为数据量较大，内存加索引超过1TB，远远超越了内存大小，要做到0.858毫秒的响应，得益于AliFlash pci-E SSD卡的性能，单次请求平均0.01毫秒完成。队列等待0.19毫秒。

avg-cpu: %user %nice %system %iowait %steal %idle

69.54 0.00 24.11 5.87 0.00 0.47

Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s avgrq-sz avgqu-sz await svctm %util

dfa 0.00 0.00 26100.00 2096.00 417600.00 33536.00 16.00 5.05 0.18 0.03 98.00

dfb 0.00 0.00 26150.00 2038.00 418400.00 32600.00 16.00 5.01 0.18 0.03 98.40

dfc 0.00 0.00 25931.00 2026.00 414896.00 32384.00 16.00 6.15 0.22 0.04 99.70

dm-0 0.00 0.00 78178.00 6160.00 1250848.00 98520.00 16.00 16.73 0.19 0.01 101.00

其他优化手段1

1. http://blog.163.com/digoal@126/blog/static/16387704020137610534650/

当请求的数据距离不在给予范围内时，被扫描的GiST索引PAGE会被放大，所以优化手段可以先order by limit，然后再过滤举例满足条件的。

例子

这个查询可能要跑很久很久才能出结果，并且结果可能是0记录。

explain (analyze,verbose,buffers,timing,costs) select *,poi <-> point(10090,10090000) dist from tbl_point where poi <-> point(10090,10090000) < 100 order by poi <-> point(10090,10090000) limit 10;

优化手段，先order by limit，然后再过滤举例满足条件的。

postgres=# explain (analyze,verbose,buffers,timing,costs) select * from (select *,poi <-> point(10090,10090000) dist from tbl_point order by poi <-> point(10090,10090000) limit 1000 ) t where poi <-> point(10090,10090000) < 100 limit 10;

QUERY PLAN

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Limit (cost=0.56..13.51 rows=10 width=32) (actual time=3.769..3.769 rows=0 loops=1)

Output: t.id, t.poi, t.dist

Buffers: shared hit=1174

-> Subquery Scan on t (cost=0.56..432.00 rows=333 width=32) (actual time=3.769..3.769 rows=0 loops=1)

Output: t.id, t.poi, t.dist

Filter: ((t.poi <-> '(10090,10090000)'::point) < '100'::double precision)

Rows Removed by Filter: 1000

Buffers: shared hit=1174

-> Limit (cost=0.56..417.00 rows=1000 width=24) (actual time=0.106..3.596 rows=1000 loops=1)

Output: tbl_point.id, tbl_point.poi, ((tbl_point.poi <-> '(10090,10090000)'::point))

Buffers: shared hit=1174

-> Index Scan using idx_tbl_point on benchmarksql.tbl_point (cost=0.56..1498470227.10 rows=3598267127 width=24) (actual time=0.105..3.505 rows=1000 loops=1)

Output: tbl_point.id, tbl_point.poi, (tbl_point.poi <-> '(10090,10090000)'::point)

Order By: (tbl_point.poi <-> '(10090,10090000)'::point)

Buffers: shared hit=1174

Planning time: 0.069 ms

Execution time: 3.793 ms

(17 rows)

PostGIS也是这么玩的：

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

其他优化手段2

更小气的玩法如下，这是为节约资源优化到极致的方法，使用游标解决以上问题，最多多一个PAGE的扫描。

digoal=# do language plpgsql $$

declare

v_rec record;

v_limit int := 1000;

begin

set 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

[参考]

1. http://www.postgresql.org/docs/9.5/static/gist-intro.html

PostgreSQL 百亿地理位置数据近邻查询性能

背景

结果

测试环境和优化请参考

创建测试表

生成测试数据：

在point类型上创建GiST索引

持续插入100亿记录

knn检索例子

KNN执行计划

KNN检索压力测试

测试结果

其他优化手段1

其他优化手段2

关系型数据库

热门文章

最新文章

相关产品

相关课程

相关电子书

相关实验场景

推荐镜像

PostgreSQL 百亿地理位置数据 近邻查询性能

背景

结果

测试环境和优化请参考

创建测试表

生成测试数据：

在point类型上创建GiST索引

持续插入100亿记录

knn检索例子

KNN执行计划

KNN检索压力测试

测试结果

其他优化手段1

其他优化手段2

关系型数据库

热门文章

最新文章

相关产品

相关课程

相关电子书

相关实验场景

推荐镜像

PostgreSQL 百亿地理位置数据近邻查询性能