2021年12月20日摸鱼打卡

[a17603877842]

学习目标

学习openGauss全文检索

openGauss提供了两种数据类型用于支持全文检索。tsvector类型表示为文本搜索优化的文件格式，tsquery类型表示文本查询

课程学习

连接数据库

#第一次进入等待15秒
#数据库启动中...
su - omm
gsql -r

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

SELECT 'The Fat Rats'::tsvector;

–词条位置常量也可以放到词汇中

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;

–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

SELECT 'a:1A fat:2B,4C cat:5D'::tsvector;

–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

SELECT to_tsvector('english', 'The Fat Rats');

2.tsquery

SELECT 'fat & rat'::tsquery;

–规范化转为tsquery类型

SELECT to_tsquery('Fat:ab & Cats');

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。

SELECT 'a fat cat sat on a mat and ate a fat rat'::tsvector @@ 'cat & rat'::tsquery AS RESULT;
SELECT 'fat & cow'::tsquery @@ 'a fat cat sat on a mat and ate a fat rat'::tsvector AS RESULT;

– to_tsvector和to_tsquery标准化处理

SELECT to_tsvector('fat cats ate fat rats') @@ to_tsquery('fat & rat') AS RESULT;
SELECT to_tsvector('fat cats ate fat rats') @@ to_tsquery('fat & cow') AS RESULT;

4.分词器

–查看所有分词器

\dF

–查看默认分词器

show default_text_search_config;

5.表和索引

CREATE SCHEMA tsearch;
CREATE TABLE tsearch.pgweb(id int, body text, title text, last_mod_date date);
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 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 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');

–将body字段中包含america的行打印出来

SELECT id, body, title FROM tsearch.pgweb WHERE to_tsvector(body) @@ to_tsquery('america');

–检索出在title或者body字段中包含china和asia的行

SELECT title FROM tsearch.pgweb WHERE to_tsvector(title || ' ' || body) @@ to_tsquery('china & asia');

–为了加速文本搜索，可以创建GIN索引(指定english配置来解析和规范化字符串)

CREATE INDEX pgweb_idx_1 ON tsearch.pgweb USING gin(to_tsvector('english', body));

–连接列的索引

CREATE INDEX pgweb_idx_3 ON tsearch.pgweb USING gin(to_tsvector('english', title || ' ' ||
body));

–查看索引定义

\d+ tsearch.pgweb

6.清理数据

drop schema tsearch cascade;

课程作业

1.用tsvector @@ tsquery和tsquery @@ tsvector完成两个基本文本匹配

SELECT 'a fat cat sat on a mat and ate a fat rat'::tsvector @@ 'cat & rat'::tsquery AS RESULT;
SELECT 'fat & cow'::tsquery @@ 'a fat cat sat on a mat and ate a fat rat'::tsvector AS RESULT;

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

CREATE SCHEMA tsearch;
CREATE TABLE tsearch.pgweb(id int, body text, title text, last_mod_date date);
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 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 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');
​
SELECT id, body, title FROM tsearch.pgweb WHERE to_tsvector(body) @@ to_tsquery('america');
​
SELECT title FROM tsearch.pgweb WHERE to_tsvector(title || ' ' || body) @@ to_tsquery('china & asia');

3.创建GIN索引

CREATE INDEX pgweb_idx_1 ON tsearch.pgweb USING gin(to_tsvector('english', body));
CREATE INDEX pgweb_idx_3 ON tsearch.pgweb USING gin(to_tsvector('english', title || ' ' ||
body));
​
\d+ tsearch.pgweb

4.清理数据

drop schema tsearch cascade;