背景
PolarDB 的云原生存算分离架构, 具备低廉的数据存储、高效扩展弹性、高速多机并行计算能力、高速数据搜索和处理; PolarDB与计算算法结合, 将实现双剑合璧, 推动业务数据的价值产出, 将数据变成生产力.
本文将介绍PolarDB 开源版通过pg_similarity实现17种文本相似搜索 - token归一切分, 根据文本相似度检索相似文本.
测试环境为macos+docker, polardb部署请参考:
pg_similarity for PolarDB
pg_similarity支持17种相似算法
- L1 Distance (as known as City Block or Manhattan Distance);
- Cosine Distance;
- Dice Coefficient;
- Euclidean Distance;
- Hamming Distance;
- Jaccard Coefficient;
- Jaro Distance;
- Jaro-Winkler Distance;
- Levenshtein Distance;
- Matching Coefficient;
- Monge-Elkan Coefficient;
- Needleman-Wunsch Coefficient;
- Overlap Coefficient;
- Q-Gram Distance;
- Smith-Waterman Coefficient;
- Smith-Waterman-Gotoh Coefficient;
- Soundex Distance.
以上大多数相似算法支持索引操作. 详见: https://github.com/eulerto/pg_similarity
需要注意
- token切分归一化的算法由参数设置, 如果你的数据写入时参数是a, 那么写入的文本会按a来切分, 如果未来又改成了b, 那么未来的切分和之前的切分算法可能不一样, 当然如果业务允许也OK.
- 在比对文本相似性时亦如此.
部署pg_similarity for PolarDB
1、下载并编译
git clone --depth 1 https://github.com/eulerto/pg_similarity.git
cd pg_similarity/
USE_PGXS=1 make
USE_PGXS=1 make install export PGHOST=127.0.0.1
[postgres@67e1eed1b4b6 pg_similarity]$ USE_PGXS=1 make installcheck
/home/postgres/tmp_basedir_polardb_pg_1100_bld/lib/pgxs/src/makefiles/../../src/test/regress/pg_regress --inputdir=./ --bindir='/home/postgres/tmp_basedir_polardb_pg_1100_bld/bin' --dbname=contrib_regression test1 test2 test3 test4
(using postmaster on 127.0.0.1, default port)
============== dropping database "contrib_regression" ==============
DROP DATABASE
============== creating database "contrib_regression" ==============
CREATE DATABASE
ALTER DATABASE
============== running regression test queries ==============
test test1 ... ok
test test2 ... ok
test test3 ... ok
test test4 ... ok
==========================================================
All 4 tests passed.
POLARDB:
All 4 tests, 0 tests in ignore, 0 tests in polar ignore.
========================================================== 2、加载pg_similarity插件
postgres=# create database db1;
CREATE DATABASE
postgres=# \c db1
You are now connected to database "db1" as user "postgres".
db1=# create extension pg_similarity ;
CREATE EXTENSION 3、pg_similarity插件会新增一些函数和操作符, 用于相似搜索.
db1=# \df
List of functions
Schema | Name | Result data type | Argument data types | Type
--------+-------------------------+------------------+-------------------------------------------------------------------------------+------
public | block | double precision | text, text | func
public | block_op | boolean | text, text | func
public | cosine | double precision | text, text | func
public | cosine_op | boolean | text, text | func
public | dice | double precision | text, text | func
public | dice_op | boolean | text, text | func
public | euclidean | double precision | text, text | func
public | euclidean_op | boolean | text, text | func
public | gin_extract_query_token | internal | internal, internal, smallint, internal, internal, internal, internal | func
public | gin_extract_value_token | internal | internal, internal, internal | func
public | gin_token_consistent | boolean | internal, smallint, internal, integer, internal, internal, internal, internal | func
public | hamming | double precision | bit varying, bit varying | func
public | hamming_op | boolean | bit varying, bit varying | func
public | hamming_text | double precision | text, text | func
public | hamming_text_op | boolean | text, text | func
public | jaccard | double precision | text, text | func
public | jaccard_op | boolean | text, text | func
public | jaro | double precision | text, text | func
public | jaro_op | boolean | text, text | func
public | jarowinkler | double precision | text, text | func
public | jarowinkler_op | boolean | text, text | func
public | lev | double precision | text, text | func
public | lev_op | boolean | text, text | func
public | matchingcoefficient | double precision | text, text | func
public | matchingcoefficient_op | boolean | text, text | func
public | mongeelkan | double precision | text, text | func
public | mongeelkan_op | boolean | text, text | func
public | needlemanwunsch | double precision | text, text | func
public | needlemanwunsch_op | boolean | text, text | func
public | overlapcoefficient | double precision | text, text | func
public | overlapcoefficient_op | boolean | text, text | func
public | qgram | double precision | text, text | func
public | qgram_op | boolean | text, text | func
public | smithwaterman | double precision | text, text | func
public | smithwaterman_op | boolean | text, text | func
public | smithwatermangotoh | double precision | text, text | func
public | smithwatermangotoh_op | boolean | text, text | func
public | soundex | double precision | text, text | func
public | soundex_op | boolean | text, text | func
(39 rows)
db1=# \do
List of operators
Schema | Name | Left arg type | Right arg type | Result type | Description
--------+------+---------------+----------------+-------------+-------------
public | ~!! | text | text | boolean |
public | ~!~ | text | text | boolean |
public | ~## | text | text | boolean |
public | ~#~ | text | text | boolean |
public | ~%% | text | text | boolean |
public | ~** | text | text | boolean |
public | ~*~ | text | text | boolean |
public | ~++ | text | text | boolean |
public | ~-~ | text | text | boolean |
public | ~== | text | text | boolean |
public | ~=~ | text | text | boolean |
public | ~?? | text | text | boolean |
public | ~@@ | text | text | boolean |
public | ~@~ | text | text | boolean |
public | ~^^ | text | text | boolean |
public | ~|| | text | text | boolean |
public | ~~~ | text | text | boolean |
(17 rows) 4、pg_similarity的常用配置, 我们只需将pg_similarity配置到shared_preload_libraries即可开始测试.
[postgres@67e1eed1b4b6 pg_similarity]$ cat pg_similarity.conf.sample
#-----------------------------------------------------------------------
# postgresql.conf
#-----------------------------------------------------------------------
# the former needs a restart every time you upgrade pg_similarity and
# the later needs that you create a $libdir/plugins directory and move
# pg_similarity.so to it (it doesn't require a restart; just open a new
# connection).
#shared_preload_libraries = 'pg_similarity'
# - or -
#local_preload_libraries = 'pg_similarity'
#-----------------------------------------------------------------------
# pg_similarity
#-----------------------------------------------------------------------
# - Block -
#pg_similarity.block_tokenizer = 'alnum' # alnum, camelcase, gram, or word
#pg_similarity.block_threshold = 0.7 # 0.0 .. 1.0
#pg_similarity.block_is_normalized = true
# - Cosine -
#pg_similarity.cosine_tokenizer = 'alnum'
#pg_similarity.cosine_threshold = 0.7
#pg_similarity.cosine_is_normalized = true
# - Dice -
#pg_similarity.dice_tokenizer = 'alnum'
#pg_similarity.dice_threshold = 0.7
#pg_similarity.dice_is_normalized = true
# - Euclidean -
#pg_similarity.euclidean_tokenizer = 'alnum'
#pg_similarity.euclidean_threshold = 0.7
#pg_similarity.euclidean_is_normalized = true
# - Hamming -
#pg_similarity.hamming_threshold = 0.7
#pg_similarity.hamming_is_normalized = true
# - Jaccard -
#pg_similarity.jaccard_tokenizer = 'alnum'
#pg_similarity.jaccard_threshold = 0.7
#pg_similarity.jaccard_is_normalized = true
# - Jaro -
#pg_similarity.jaro_threshold = 0.7
#pg_similarity.jaro_is_normalized = true
# - Jaro -
#pg_similarity.jaro_threshold = 0.7
#pg_similarity.jaro_is_normalized = true
# - Jaro-Winkler -
#pg_similarity.jarowinkler_threshold = 0.7
#pg_similarity.jarowinkler_is_normalized = true
# - Levenshtein -
#pg_similarity.levenshtein_threshold = 0.7
#pg_similarity.levenshtein_is_normalized = true
# - Matching Coefficient -
#pg_similarity.matching_tokenizer = 'alnum'
#pg_similarity.matching_threshold = 0.7
#pg_similarity.matching_is_normalized = true
# - Monge-Elkan -
#pg_similarity.mongeelkan_tokenizer = 'alnum'
#pg_similarity.mongeelkan_threshold = 0.7
#pg_similarity.mongeelkan_is_normalized = true
# - Needleman-Wunsch -
#pg_similarity.nw_threshold = 0.7
#pg_similarity.nw_is_normalized = true
# - Overlap Coefficient -
#pg_similarity.overlap_tokenizer = 'alnum'
#pg_similarity.overlap_threshold = 0.7
#pg_similarity.overlap_is_normalized = true
# - Q-Gram -
#pg_similarity.qgram_tokenizer = 'qgram'
#pg_similarity.qgram_threshold = 0.7
#pg_similarity.qgram_is_normalized = true
# - Smith-Waterman -
#pg_similarity.sw_threshold = 0.7
#pg_similarity.sw_is_normalized = true
# - Smith-Waterman-Gotoh -
#pg_similarity.swg_threshold = 0.7
#pg_similarity.swg_is_normalized = true 5、测试相似搜索, 导入测试数据
[postgres@67e1eed1b4b6 ~]$ cd pg_similarity/
[postgres@67e1eed1b4b6 pg_similarity]$ psql
psql (11.9)
Type "help" for help.
postgres=# CREATE TABLE simtst (a text);
CREATE TABLE
postgres=#
postgres=# INSERT INTO simtst (a) VALUES
postgres-# ('Euler Taveira de Oliveira'),
postgres-# ('EULER TAVEIRA DE OLIVEIRA'),
postgres-# ('Euler T. de Oliveira'),
postgres-# ('Oliveira, Euler T.'),
postgres-# ('Euler Oliveira'),
postgres-# ('Euler Taveira'),
postgres-# ('EULER TAVEIRA OLIVEIRA'),
postgres-# ('Oliveira, Euler'),
postgres-# ('Oliveira, E. T.'),
postgres-# ('ETO');
INSERT 0 10
postgres=#
postgres=# \copy simtst FROM 'data/similarity.data'
COPY 2999 6、测试相似搜索, 创建gin索引
https://github.com/eulerto/pg_similarity/blob/master/pg_similarity--1.0.sql
以下操作符支持索引检索
CREATE OPERATOR CLASS gin_similarity_ops
FOR TYPE text USING gin
AS
OPERATOR 1 ~++, -- block
OPERATOR 2 ~##, -- cosine
OPERATOR 3 ~-~, -- dice
OPERATOR 4 ~!!, -- euclidean
OPERATOR 5 ~??, -- jaccard
-- OPERATOR 6 ~%%, -- jaro
-- OPERATOR 7 ~@@, -- jarowinkler
-- OPERATOR 8 ~==, -- lev
OPERATOR 9 ~^^, -- matchingcoefficient
-- OPERATOR 10 ~||, -- mongeelkan
-- OPERATOR 11 ~#~, -- needlemanwunsch
OPERATOR 12 ~**, -- overlapcoefficient
OPERATOR 13 ~~~, -- qgram
-- OPERATOR 14 ~=~, -- smithwaterman
-- OPERATOR 15 ~!~, -- smithwatermangotoh
-- OPERATOR 16 ~*~, -- soundex
FUNCTION 1 bttextcmp(text, text),
FUNCTION 2 gin_extract_value_token(internal, internal, internal),
FUNCTION 3 gin_extract_query_token(internal, internal, int2, internal, internal, internal, internal),
FUNCTION 4 gin_token_consistent(internal, int2, internal, int4, internal, internal, internal, internal),
STORAGE text; postgres=# create index on simtst using gin (a gin_similarity_ops);
CREATE INDEX 6、测试相似搜索, 使用索引根据相似性高速锁定目标数据.
可以根据threshold调整目标数据, 大于等于它的相似度才会被返回.
相似度threadshold设置越大, 范围越收敛, 性能越好.
可以放到函数中设置threadshold, 分阶段返回.
postgres=# show pg_similarity.cosine_tokenizer;
pg_similarity.cosine_tokenizer
--------------------------------
alnum
(1 row)
postgres=# show pg_similarity.cosine_threshold;
pg_similarity.cosine_threshold
--------------------------------
0.7
(1 row)
postgres=# show pg_similarity.cosine_is_normalized;
pg_similarity.cosine_is_normalized
------------------------------------
on
(1 row)
postgres=# select *, cosine(a, 'hello') from simtst where a ~## 'hello' limit 10;
a | cosine
---+--------
(0 rows)
postgres=# select *, cosine(a, 'EULER TAVEIRA DE OLIVEI') from simtst where a ~## 'EULER TAVEIRA DE OLIVEI' limit 10;
a | cosine
---------------------------+--------
EULER TAVEIRA DE OLIVEIRA | 0.75
(1 row)
postgres=# explain select *, cosine(a, 'EULER TAVEIRA DE OLIVEI') from simtst where a ~## 'EULER TAVEIRA DE OLIVEI' limit 10;
QUERY PLAN
----------------------------------------------------------------------------------
Limit (cost=36.02..44.29 rows=3 width=40)
-> Bitmap Heap Scan on simtst (cost=36.02..44.29 rows=3 width=40)
Recheck Cond: (a ~## 'EULER TAVEIRA DE OLIVEI'::text)
-> Bitmap Index Scan on simtst_a_idx (cost=0.00..36.02 rows=3 width=0)
Index Cond: (a ~## 'EULER TAVEIRA DE OLIVEI'::text)
(5 rows)
postgres=# set pg_similarity.cosine_threshold=0.75;
SET
postgres=# select *, cosine(a, 'EULER TAVEIRA DE OLIVEI') from simtst where a ~## 'EULER TAVEIRA DE OLIVEI' limit 10;
a | cosine
---------------------------+--------
EULER TAVEIRA DE OLIVEIRA | 0.75
(1 row)
postgres=# set pg_similarity.cosine_threshold=0.76;
SET
postgres=# select *, cosine(a, 'EULER TAVEIRA DE OLIVEI') from simtst where a ~## 'EULER TAVEIRA DE OLIVEI' limit 10;
a | cosine
---+--------
(0 rows) 
