openGauss每日一练第20天 openGauss全文检索
1.tsvector
–把一个字符串按照空格进行分词，分词的顺序是按照长短和字母排序的, 自动去掉分词中重复的词条
omm=# tsvector
‘Fat’ ‘Rats’ ‘The’
(1 row)
–词条位置常量也可以放到词汇中
omm=# SELECT ‘a:1 fat:2 cat:3 sat:4 on:5 a:6 mat:7 and:8 ate:9 a:10 fat:11 rat:12’::tsvector;
tsvector
‘a’:1,6,10 ‘and’:8 ‘ate’:9 ‘cat’:3 ‘fat’:2,11 ‘mat’:7 ‘on’:5 ‘rat’:12 ‘sat’:4
(1 row)
–拥有位置的词汇甚至可以用一个权来标记，反映文档结构，这个权可以是A，B，C或D。默认的是D，因此输出中不会出现
tsvector
‘a’:1A ‘cat’:5 ‘fat’:2B,4C
(1 row)
–to_tsvector函数对这些单词进行规范化处理, 罗列出词条并连同它们文档中的位置
omm=# SELECT to_tsvector(‘english’, ‘The Fat Rats’);
to_tsvector
‘fat’:2 ‘rat’:3
(1 row)
2.tsquery
omm=# SELECT ‘fat & rat’::tsquery;
tsquery
‘fat’ & ‘rat’
(1 row)
–规范化转为tsquery类型
omm=# SELECT to_tsquery(‘Fat:ab & Cats’);
to_tsquery
‘fat’:AB & ‘cat’
(1 row)
3.基本文本匹配
–全文检索基于匹配算子@@，当一个tsvector匹配到一个tsquery时，则返回true, tsvector和tsquery两种数据类型可以任意排序。
result
t
(1 row)
omm=# SELECT ‘fat & cow’::tsquery @@ ‘a fat cat sat on a mat and ate a fat rat’::tsvector AS RESULT;
result
f
(1 row)
omm=# SELECT to_tsvector(‘fat cats ate fat rats’) @@ to_tsquery(‘fat & rat’) AS RESULT;
result
t
(1 row)
– to_tsvector和to_tsquery标准化处理
omm=# SELECT to_tsvector(‘fat cats ate fat rats’) @@ to_tsquery(‘fat & cow’) AS RESULT;
result
f
(1 row)
4.分词器
–查看所有分词器
omm=# \dF
List of text search configurations
Schema | Name | Description
------------±-----------±--------------------------------------
pg_catalog | danish | configuration for danish language
pg_catalog | dutch | configuration for dutch language
pg_catalog | english | configuration for english language
pg_catalog | finnish | configuration for finnish language
pg_catalog | french | configuration for french language
pg_catalog | german | configuration for german language
pg_catalog | hungarian | configuration for hungarian language
pg_catalog | italian | configuration for italian language
pg_catalog | ngram | ngram configuration
pg_catalog | norwegian | configuration for norwegian language
pg_catalog | portuguese | configuration for portuguese language
pg_catalog | simple | simple configuration
pg_catalog | spanish | configuration for spanish language
pg_catalog | pound | pound configuration
pg_catalog | romanian | configuration for romanian language
pg_catalog | russian | configuration for russian language
–More-- pg_catalog | swedish | configuration for swedish language
pg_catalog | turkish | configuration for turkish language
pg_catalog | zhparser | zhparser configuration
–查看默认分词器
omm=# show default_text_search_config;
default_text_search_config
pg_catalog.english
(1 row)
5.表和索引
omm=# CREATE SCHEMA tsearch;
CREATE SCHEMA
CREATE TABLE TABLE tsearch.pgweb(id int, body text, title text, last_mod_date date);omm=#
omm=#
omm=# CREATE TABLE TABLE tsearch.pgweb(id int, body text, title text, last_mod_date date);
ERROR: syntax error at or near “TABLE”
LINE 1: CREATE TABLE TABLE tsearch.pgweb(id int, body text, title te…
^
omm=# ERROR: relation “pgweb” already exists text, title text, last_mod_date date);omm=#
omm=#
omm=# INSERT INTO tsearch.pgweb VALUES(1, ‘China, officially the People’‘s Republic of China(PRC), located in Asia, is the world’‘s most populous state.’, ‘China’, ‘2010-1-1’);
INSERT 0 1
omm=# INSERT INTO tsearch.pgweb VALUES(2, ‘America is a rock band, formed in England in 1970 by multi-instrumentalists Dewey Bunnell, Dan Peek, and Gerry Beckley.’, ‘America’, ‘2010-1-1’);
INSERT 0 1
omm=#
omm=# INSERT INTO tsearch.pgweb VALUES(3, ‘England is a country that is part of the United Kingdom. It shares land borders with Scotland to the north and Wales to the west.’, ‘England’,‘2010-1-1’);
omm=# INSERT 0 1
omm=#
omm=# SELECT id, body, title FROM tsearch.pgweb WHERE to_tsvector(body) @@ to_tsquery(‘america’);
id | body
| title
----±------------------------------------------------------------------------------
------------------------------------------±--------
2 | America is a rock band, formed in England in 1970 by multi-instrumentalists De
wey Bunnell, Dan Peek, and Gerry Beckley. | America
(1 row)
omm=# SELECT title FROM tsearch.pgweb WHERE to_tsvector(title || ’ ’ || body) @@ to_tsquery(‘china & asia’);
title
China
(1 row)
omm=# CREATE INDEX pgweb_idx_1 ON tsearch.pgweb USING gin(to_tsvector(‘english’, body));
CREATE INDEX
omm=#
omm=#
omm=# body));CREATE INDEX pgweb_idx_3 ON tsearch.pgweb USING gin(to_tsvector('englisle || ’ ’ ||
omm(#
CREATE INDEX
omm=#
omm=#
omm=# \d+ tsearch.pgweb
Table “tsearch.pgweb”
Column | Type | Modifiers | Storage | Stats target | Description
---------------±--------±----------±---------±-------------±------------
id | integer | | plain | |
body | text | | extended | |
title | text | | extended | |
last_mod_date | date | | plain | |
Indexes:
“pgweb_idx_1” gin (to_tsvector(‘english’::regconfig, body)) TABLESPACE pg_default
“pgweb_idx_3” gin (to_tsvector(‘english’::regconfig, (title || ’ '::text) || body)) TABLESPACE pg_default
Has OIDs: no
Options: orientation=row, compression=no
6.清理数据
omm=# drop schema tsearch cascade;
NOTICE: drop cascades to table tsearch.pgweb
DROP SCHEMA
课后作业
1.用tsvector @@ tsquery和tsquery @@ tsvector完成两个基本文本匹配
omm=# SELECT ‘good good study day day up’::tsvector;
tsvector
‘day’ ‘good’ ‘study’ ‘up’
(1 row)
omm=# SELECT ‘you & live’::tsquery;
tsquery
‘you’ & ‘live’
(1 row)
omm=# SELECT ‘good good study day day up’::tsvector @@ ‘study & day’::tsquery AS RESULT;
result
t
(1 row)
omm=# SELECT ‘you & live’::tsquery @@ ‘Cease to struggle and you cease to live’::tsvector AS RESULT;
result
f
(1 row)
2.创建表且至少有两个字段的类型为 text类型，在创建索引前进行全文检索
omm=# create table c
omm-# (
omm(# id integer,
omm(# content1 text,
omm(# content2 text
omm(# );
CREATE TABLE
omm=# insert into c values
omm-# (1,‘good good study day day up’,‘hello’),
omm-# (2,‘Cease to struggle and you cease to live’,‘world’);
INSERT 0 2
omm=# select * from c;
id | content1 | content2
----±----------------------------------------±---------
1 | good good study day day up | hello
2 | Cease to struggle and you cease to live | world
(2 rows)
omm=# select * from c where to_tsvector(content1) @@ to_tsquery(‘good’);
id | content1 | content2
----±---------------------------±---------
1 | good good study day day up | hello
(1 row)
omm=# select * from c where to_tsvector(content1 || ’ ’ || content2) @@ to_tsquery(‘cease & world’);
id | content1 | content2
----±----------------------------------------±---------
2 | Cease to struggle and you cease to live | world
(1 row)
3.创建GIN索引
omm=# create index content1_idx on c using gin(to_tsvector(‘english’, content1));
CREATE INDEX
omm=# \d c
Table “public.c”
Column | Type | Modifiers
----------±--------±----------
id | integer |
content1 | text |
content2 | text |
Indexes:
“content1_idx” gin (to_tsvector(‘english’::regconfig, content1)) TABLESPACE pg_default
4.清理数据
omm=# drop table c;
DROP TABLE
