PostgreSQL 11 相似图像搜索插件 imgsmlr 性能测试与优化 1 - 单机单表 (4亿图像)-阿里云开发者社区

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PostgreSQL 11 相似图像搜索插件 imgsmlr 性能测试与优化 1 - 单机单表 (4亿图像)

简介: 标签 PostgreSQL , imgsmlr , GiST , 图像特征值 , 小波转换 背景 imgsmlr是PostgreSQL的一款支持以图搜图的插件, https://github.com/postgrespro/imgsmlr 这个插件新增了: 1、几种图像特征值数据类型, 2、图像特征值相似算子, 3、图像特征值相似排序索引支持, 4、图像相似排序的索引(通过扩展GiST索引接口实现)支持, 5、png,gif等图像格式特征值提取函数。

标签

PostgreSQL , imgsmlr , GiST , 图像特征值 , 小波转换


背景

imgsmlr是PostgreSQL的一款支持以图搜图的插件,

https://github.com/postgrespro/imgsmlr

这个插件新增了:

1、几种图像特征值数据类型,

2、图像特征值相似算子,

3、图像特征值相似排序索引支持,

4、图像相似排序的索引(通过扩展GiST索引接口实现)支持,

5、png,gif等图像格式特征值提取函数。

图像特征值为64*64的16个区域经过小波转换后的16个浮点数。

在数据量(图片数)非常庞大时,输入一个图片特征值,搜索相似度排行前N的图片,性能如何呢?如何优化呢?

接下来的3篇文档将分别介绍如下三种场景的图像特征值搜索性能以及优化思路:

1、单机单表

2、单机分区表(使用DBLINK 异步调用)

3、citus,多机,sharding 表

附加 - rum 相似查询性能(以及存在的问题)

《PostgreSQL 相似搜索插件介绍大汇总 (rum,pg_trgm,smlar,imgsmlr,pg_similarity) (rum,gin,gist)》

相似查询分元素重叠类,向量类。RUM为第一种元素重叠度类的相似搜索技术。

相似排序,走索引,性能如何,有什么需要优化的点?

git clone https://github.com/postgrespro/rum  
cd rum  
. /var/lib/pgsql/.bash_profile  
USE_PGXS=1 make  
USE_PGXS=1 make install  
create extension rum;  

1、生成随机浮点数组的UDF接口

create or replace function gen_rand_float4(int,int) returns float4[] as $$  
  select array(select (random()*$1)::float4 from generate_series(1,$2));  
$$ language sql strict;  

2、建表,索引

create unlogged table t_rum(id int primary key, arr float4[]);  
create index idx_t_rum_1 on t_rum using rum(arr);  

4、写入随机浮点数数组

vi test.sql  
\set id random(1,2000000000)  
insert into t_rum values (:id, gen_rand_float4(10,16)) on conflict(id) do nothing;  
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 64 -j 64 -t 10000000  
postgres=# select * from t_rum limit 2;  
    id     |                                                                   arr                                                                     
-----------+-----------------------------------------------------------------------------------------------------------------------------------------  
 182025544 | {5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}  
  51515704 | {0.123099,9.26626,0.00549683,9.01483,0.911669,3.44338,4.55135,4.65002,0.820029,9.66546,1.93433,3.00254,1.28121,8.99883,1.85269,6.39579}  
(2 rows)  
  
  
postgres=# select count(*) from t_rum;  
  count    
---------  
 3244994  
(1 row)  

5、使用rum提供的数组相似搜索(元素重叠率计算)

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from t_rum order by arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}' limit 1;  
                                                                                     QUERY PLAN                                                                                       
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 Limit  (cost=22435.67..22435.68 rows=1 width=97) (actual time=12527.447..12527.450 rows=1 loops=1)  
   Output: id, arr, ((arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}'::real[]))  
   Buffers: shared hit=50450  
   ->  Sort  (cost=22435.67..29469.15 rows=3244994 width=97) (actual time=12527.445..12527.446 rows=1 loops=1)  
         Output: id, arr, ((arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}'::real[]))  
         Sort Key: ((t_rum.arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}'::real[]))  
         Sort Method: top-N heapsort  Memory: 25kB  
         Buffers: shared hit=50450  
         ->  Seq Scan on public.t_rum  (cost=0.00..8368.72 rows=3244994 width=97) (actual time=0.054..11788.483 rows=3244994 loops=1)  
               Output: id, arr, (arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}'::real[])  
               Buffers: shared hit=50447  
 Planning Time: 0.115 ms  
 Execution Time: 12527.498 ms  
(13 rows)  

你会发现,走了索引,但是并不快。扫描了大量(50447)的索引PAGE。

原因是我们没有管它的阈值,导致扫描了大量的index BLOCK。默认的阈值为0.5,太低了。

postgres=# show rum.array_similarity_threshold  
postgres-# ;  
 rum.array_similarity_threshold   
--------------------------------  
 0.5  
(1 row)  

调成0.9,只输出90%以上相似(重叠度)的数组。性能瞬间暴增,扫描的数据块也变少了。

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from t_rum where arr % '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}' order by arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}' limit 1;  
                                                                                  QUERY PLAN                                                                                    
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 Limit  (cost=1.54..1.56 rows=1 width=97) (actual time=0.664..0.664 rows=0 loops=1)  
   Output: id, arr, ((arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}'::real[]))  
   Buffers: shared hit=128 read=40  
   ->  Index Scan using idx_t_rum_1 on public.t_rum  (cost=1.54..87.65 rows=3245 width=97) (actual time=0.662..0.662 rows=0 loops=1)  
         Output: id, arr, (arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}'::real[])  
         Index Cond: (t_rum.arr % '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}'::real[])  
         Order By: (t_rum.arr <=> '{5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444}'::real[])  
         Buffers: shared hit=128 read=40  
 Planning Time: 0.184 ms  
 Execution Time: 0.691 ms  
(10 rows)  

元素重叠度相似搜索优化

1、调整阈值,阶梯化解题。

《PostgreSQL 相似搜索设计与性能 - 地址、QA、POI等文本 毫秒级相似搜索实践》

实际上图像特征值近似搜索,也有优化的空间,接下来进入正题。

部署imgsmlr (on PG 11)

1、假设yum安装的PG 11

2、克隆源码

yum install -y git  
git clone https://github.com/postgrespro/imgsmlr  
cd imgsmlr  

3、修改头文件

vi imgsmlr.h  
  
// 追加  
#ifndef FALSE  
#define FALSE   (0)  
#endif  
  
  
#ifndef TRUE  
#define TRUE    (!FALSE)  
#endif  

4、安装依赖的图像转换包

yum install -y gd-devel  

5、编译安装IMGSMLR插件

. /var/lib/pgsql/.bash_profile  
USE_PGXS=1 make  
USE_PGXS=1 make install  

单节点 单表图像搜索 (4亿图像)

1、创建生成随机图像特征值signature的UDF。

create or replace function gen_rand_img_sig(int) returns signature as $$  
  select ('('||rtrim(ltrim(array(select (random()*$1)::float4 from generate_series(1,16))::text,'{'),'}')||')')::signature;  
$$ language sql strict;  
postgres=# select * from gen_rand_img_sig(10);  
                                                                         gen_rand_img_sig                                                                           
------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 (6.744310, 5.105020, 0.087113, 3.808010, 8.129480, 2.834540, 2.495250, 0.940481, 0.033208, 6.583490, 2.840330, 1.422440, 6.683830, 0.080847, 8.327730, 2.471430)  
(1 row)  
  
postgres=# select * from gen_rand_img_sig(10);  
                                                                         gen_rand_img_sig                                                                           
------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 (3.013650, 6.170690, 0.601905, 2.692030, 1.268540, 7.803740, 9.757770, 5.537750, 0.391753, 4.440790, 1.201580, 5.501380, 6.166980, 0.240686, 9.768680, 2.911290)  
(1 row)  

2、建表,建图像特征值索引

create table t_img_sig (id int primary key, sig signature);  
create index idx_t_img_sig_1 on t_img_sig using gist(sig);  

3、写入约4亿随机图像特征值

vi testsig.sql  
\set id random(1,2000000000)  
insert into t_img_sig values (:id, gen_rand_img_sig(10)) on conflict(id) do nothing;  
pgbench -M prepared -n -r -P 1 -f ./testsig.sql -c 32 -j 32 -t 20000000  
postgres=# select * from t_img  limit 10;  
    id     |                                                                               sig                                                                                  
-----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------  
  47902935 | (5.861920, 1.062770, 8.318020, 2.205840, 0.202951, 6.956610, 1.413190, 2.898480, 8.961630, 6.377800, 1.110450, 6.684520, 2.286290, 7.850760, 1.832650, 0.074348)  
 174656795 | (2.165030, 0.183753, 9.913950, 9.208260, 5.165660, 6.603510, 2.008380, 8.117910, 2.358590, 5.466330, 9.139280, 8.893700, 4.664190, 9.361670, 9.016990, 2.271000)  
  96186891 | (9.605980, 4.395920, 4.336720, 3.174360, 8.706960, 0.155107, 9.408940, 4.531100, 2.783530, 5.681780, 9.792380, 6.428320, 2.983760, 9.733290, 7.635160, 7.035780)  
  55061667 | (7.567960, 5.874530, 5.222040, 5.638520, 3.488960, 8.770750, 7.054610, 7.239630, 9.202280, 9.465020, 4.079080, 5.729770, 0.475227, 8.434800, 6.873730, 5.140080)  
  64659434 | (4.860650, 3.984440, 3.009900, 5.116680, 6.489150, 4.224800, 0.609752, 8.731120, 6.577390, 8.542540, 9.096120, 8.976700, 8.936000, 2.836270, 7.186250, 6.264300)  
  87143098 | (4.801570, 7.870150, 0.939599, 3.666670, 1.102340, 5.819580, 6.511330, 6.430760, 0.584531, 3.024190, 6.255460, 8.823820, 5.076960, 0.181344, 8.137380, 1.230360)  
 109245945 | (7.541850, 7.201460, 6.858400, 2.605210, 1.283090, 7.525200, 4.213240, 8.413760, 9.707390, 1.916970, 1.719320, 1.255280, 9.006780, 4.851420, 2.168250, 5.997360)  
   4979218 | (8.463000, 4.051410, 9.057320, 1.367980, 3.344340, 7.032640, 8.583770, 1.873090, 5.524810, 0.187254, 5.783270, 6.141040, 2.479410, 6.406450, 9.371700, 0.050690)  
  72846137 | (7.018560, 4.039150, 9.114800, 2.911170, 5.531180, 8.557330, 6.739050, 0.103649, 3.691390, 7.584640, 8.184180, 0.599390, 9.037130, 4.090610, 4.369770, 6.480000)  
  36813995 | (4.643480, 8.704640, 1.073880, 2.665530, 3.298300, 9.244280, 5.768050, 0.887555, 5.990350, 2.991390, 6.186550, 6.464940, 6.187140, 0.150242, 2.123070, 2.932270)  
(10 rows)  
  
Time: 58.101 ms  

写入约4.39亿图像特征值。

postgres=# select count(*) from t_img_sig;  
   count     
-----------  
 438924137  
(1 row)  

4、输入一个图像特征值,搜索与之最相似的图像。

explain (analyze,verbose,timing,costs,buffers) select * from t_img_sig order by sig <-> '(5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444)' limit 1;  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from t_img_sig where signature_distance(sig,'(5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444)') > 0.9 order by sig <-> '(5.07998,6.80827,5.42024,2.53619,4.10843,0.532198,4.33886,9.60262,6.68369,8.01305,9.60298,8.087,1.25819,6.54424,6.04902,5.3444)' limit 1;  
                                                                                                                          QUERY PLAN                                                                                                          
                    
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 Limit  (cost=0.48..0.51 rows=1 width=72) (actual time=4094.810..4094.812 rows=1 loops=1)  
   Output: id, sig, ((sig <-> '(5.079980, 6.808270, 5.420240, 2.536190, 4.108430, 0.532198, 4.338860, 9.602620, 6.683690, 8.013050, 9.602980, 8.087000, 1.258190, 6.544240, 6.049020, 5.344400)'::signature))  
   Buffers: shared hit=205999  
   ->  Index Scan using idx_t_img_sig_1 on public.t_img_sig  (cost=0.48..5361351.06 rows=146395778 width=72) (actual time=4094.808..4094.808 rows=1 loops=1)  
         Output: id, sig, (sig <-> '(5.079980, 6.808270, 5.420240, 2.536190, 4.108430, 0.532198, 4.338860, 9.602620, 6.683690, 8.013050, 9.602980, 8.087000, 1.258190, 6.544240, 6.049020, 5.344400)'::signature)  
         Order By: (t_img_sig.sig <-> '(5.079980, 6.808270, 5.420240, 2.536190, 4.108430, 0.532198, 4.338860, 9.602620, 6.683690, 8.013050, 9.602980, 8.087000, 1.258190, 6.544240, 6.049020, 5.344400)'::signature)  
         Filter: (signature_distance(t_img_sig.sig, '(5.079980, 6.808270, 5.420240, 2.536190, 4.108430, 0.532198, 4.338860, 9.602620, 6.683690, 8.013050, 9.602980, 8.087000, 1.258190, 6.544240, 6.049020, 5.344400)'::signature) > '0.9'::double precision)  
         Buffers: shared hit=205999  
 Planning Time: 0.073 ms  
 Execution Time: 4194.485 ms  
(10 rows)  

性能与瓶颈

性能:4.39亿图像特征值,以图搜图约4.2秒。

瓶颈:

1、扫描了大量的索引页(205999)。

优化思路

1、压缩精度,比如使用3位小数。据用户说有10倍性能提升。

精度优化如下,使用新的生成图像特征值的函数,使用3位小数。

create or replace function gen_rand_img_sig3(int) returns signature as $$  
  select ('('||rtrim(ltrim(array(select trunc((random()*$1)::numeric,3) from generate_series(1,16))::text,'{'),'}')||')')::signature;  
$$ language sql strict;  

例子如下

postgres=# select gen_rand_img_sig3(10);  
                                                                        gen_rand_img_sig3                                                                           
------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 (2.984000, 3.323000, 4.083000, 6.292000, 5.008000, 9.029000, 6.208000, 1.141000, 1.796000, 9.257000, 1.397000, 1.235000, 7.157000, 3.745000, 0.112000, 7.723000)  
(1 row)  

2、使用分区表+dblink异步接口并行调用。(内核层面直接支持imgsmlr gist index scan并行更好)

下一篇介绍

3、使用citus sharding。多机,提高整体计算能力。 (因为扫描大量索引页,即使CPU没有瓶颈,将来内存带宽也会成为瓶颈。多机可以解决这个问题。)

下一篇介绍

4、内核层面,支持维度更低的signature,现在是16片,比如支持降低到4片,性能也可以提升。

精度现象

1、当有记录可以完全匹配时,扫描少量INDEX PAGE。

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from  t_img_sig order by sig <-> '(3.727000, 2.594000, 0.185000, 3.996000, 6.450000, 7.126000, 5.499000, 1.540000, 8.239000, 6.262000, 2.053000, 2.566000, 4.522000, 6.929000, 1.582000, 2.179000)'::signature limit 1;
                                                                                                     QUERY PLAN                                                                                                      
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Limit  (cost=0.48..0.49 rows=1 width=72) (actual time=1.596..1.598 rows=1 loops=1)
   Output: id, sig, ((sig <-> '(3.727000, 2.594000, 0.185000, 3.996000, 6.450000, 7.126000, 5.499000, 1.540000, 8.239000, 6.262000, 2.053000, 2.566000, 4.522000, 6.929000, 1.582000, 2.179000)'::signature))
   Buffers: shared hit=125
   ->  Index Scan using t_img_sig1_sig_idx on public.t_img_sig  (cost=0.48..7318159.22 rows=785457848 width=72) (actual time=1.594..1.595 rows=1 loops=1)
         Output: id, sig, (sig <-> '(3.727000, 2.594000, 0.185000, 3.996000, 6.450000, 7.126000, 5.499000, 1.540000, 8.239000, 6.262000, 2.053000, 2.566000, 4.522000, 6.929000, 1.582000, 2.179000)'::signature)
         Order By: (t_img_sig.sig <-> '(3.727000, 2.594000, 0.185000, 3.996000, 6.450000, 7.126000, 5.499000, 1.540000, 8.239000, 6.262000, 2.053000, 2.566000, 4.522000, 6.929000, 1.582000, 2.179000)'::signature)
         Buffers: shared hit=125
 Planning Time: 0.072 ms
 Execution Time: 1.621 ms
(9 rows)

2、当修改少量内容,少量值完全匹配,其他值不完全匹配时,扫描的INDEX PAGE增加。

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from  t_img_sig order by sig <-> '(3.727000, 2.594000, 0.185000, 3.996000, 6.450000, 7.126000, 5.499000, 1.540000, 8.239000, 6.262000, 2.053000, 2.566000, 4.522000, 6.929000, 1.582000, 2.179001)'::signature limit 1;
                                                                                                     QUERY PLAN                                                                                                      
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Limit  (cost=0.48..0.49 rows=1 width=72) (actual time=7.051..7.052 rows=1 loops=1)
   Output: id, sig, ((sig <-> '(3.727000, 2.594000, 0.185000, 3.996000, 6.450000, 7.126000, 5.499000, 1.540000, 8.239000, 6.262000, 2.053000, 2.566000, 4.522000, 6.929000, 1.582000, 2.179001)'::signature))
   Buffers: shared hit=454
   ->  Index Scan using t_img_sig1_sig_idx on public.t_img_sig  (cost=0.48..7324626.56 rows=786152016 width=72) (actual time=7.049..7.049 rows=1 loops=1)
         Output: id, sig, (sig <-> '(3.727000, 2.594000, 0.185000, 3.996000, 6.450000, 7.126000, 5.499000, 1.540000, 8.239000, 6.262000, 2.053000, 2.566000, 4.522000, 6.929000, 1.582000, 2.179001)'::signature)
         Order By: (t_img_sig.sig <-> '(3.727000, 2.594000, 0.185000, 3.996000, 6.450000, 7.126000, 5.499000, 1.540000, 8.239000, 6.262000, 2.053000, 2.566000, 4.522000, 6.929000, 1.582000, 2.179001)'::signature)
         Buffers: shared hit=454
 Planning Time: 0.074 ms
 Execution Time: 7.076 ms
(9 rows)

3、当大量修改值,不能完全匹配时,需要扫描大量INDEX PAGE。

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from  t_img_sig order by sig <-> '(7.727000, 3.594000, 1.185000, 4.996000, 6.950000, 7.129000, 5.429000, 1.520000, 8.219000, 6.222000, 2.013000, 2.536000, 4.532000, 6.939000, 1.538000, 2.178000)'::signature limit 1;
                                                                                                     QUERY PLAN                                                                                                      
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Limit  (cost=0.47..0.48 rows=1 width=72) (actual time=2528.890..2528.891 rows=1 loops=1)
   Output: id, sig, ((sig <-> '(7.727000, 3.594000, 1.185000, 4.996000, 6.950000, 7.129000, 5.429000, 1.520000, 8.219000, 6.222000, 2.013000, 2.536000, 4.532000, 6.939000, 1.538000, 2.178000)'::signature))
   Buffers: shared hit=121510
   ->  Index Scan using t_img_sig1_sig_idx on public.t_img_sig  (cost=0.47..1361409.21 rows=146121007 width=72) (actual time=2528.887..2528.888 rows=1 loops=1)
         Output: id, sig, (sig <-> '(7.727000, 3.594000, 1.185000, 4.996000, 6.950000, 7.129000, 5.429000, 1.520000, 8.219000, 6.222000, 2.013000, 2.536000, 4.532000, 6.939000, 1.538000, 2.178000)'::signature)
         Order By: (t_img_sig.sig <-> '(7.727000, 3.594000, 1.185000, 4.996000, 6.950000, 7.129000, 5.429000, 1.520000, 8.219000, 6.222000, 2.013000, 2.536000, 4.532000, 6.939000, 1.538000, 2.178000)'::signature)
         Buffers: shared hit=121510
 Planning Time: 0.092 ms
 Execution Time: 2582.558 ms
(9 rows)

具体原因可以参考

https://github.com/postgrespro/imgsmlr/blob/master/imgsmlr_idx.c

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

参考

https://github.com/postgrespro/imgsmlr

《PostgreSQL 相似搜索插件介绍大汇总 (rum,pg_trgm,smlar,imgsmlr,pg_similarity) (rum,gin,gist)》

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