PostgreSQL模糊查询相关学习参考

PostgreSQL大数据量快速模糊检索实践_postgresql 模糊查询-CSDN博客文章浏览阅读1.5k次，点赞20次，收藏25次。注意： 本文内容于 2024-08-18 23:50:33 创建，可能不会在此平台上进行更新。。_postgresql 模糊查询https://blog.csdn.net/qq_30460361/article/details/141371128https://blog.csdn.net/qq_30460361/article/details/141371128https://blog.csdn.net/qq_30460361/article/details/141371128https://blog.csdn.net/qq_30460361/article/details/141371128在 PostGIS 中进行千万级空间数据的空间查询和关键字查询_postgis空间查询-CSDN博客文章浏览阅读1k次，点赞12次，收藏21次。在给定的计算机配置下，通过合理的表结构设计、字符串处理、索引创建以及查询策略，可以较为高效地对千万级空间数据进行空间查询和关键字查询。创建表、更新字符串和创建索引的过程相对耗时较长，但一旦索引创建完成，实际查询操作非常迅速。同时，也验证了 PostGIS 在处理复杂空间查询和关键字查询方面的强大能力。本测试在探究在有限的计算机配置下，如何高效地对千万级的空间数据进行空间查询和关键字查询。通过实际操作和测试，评估不同查询策略的性能，为处理大规模空间数据提供可行的解决方案。_postgis空间查询https://blog.csdn.net/eqmaster/article/details/142382839https://blog.csdn.net/eqmaster/article/details/142382839https://blog.csdn.net/eqmaster/article/details/142382839https://blog.csdn.net/eqmaster/article/details/142382839利用pg_trgm的gist和gin索引加速字符匹配查询_gin索引 前缀匹配-CSDN博客文章浏览阅读5k次。pg_trgm是用来做相似度匹配的，在一些情况下也可以拿来代替全文检索做字符匹配。从大量数据中通过字符串的匹配查找数据的关键是索引，对字符串的精确相等匹配，前缀匹配(like 'x%')和后缀匹配(like '%x')可以使用btree索引,对中缀匹配(like '%x%')和正则表达式匹配就可以用pg_trgm的索引了。下面用一个例子说明一下。1.环境CentOS 6.5_gin索引 前缀匹配https://blog.csdn.net/rudygao/article/details/49247605https://blog.csdn.net/rudygao/article/details/49247605https://blog.csdn.net/rudygao/article/details/49247605https://blog.csdn.net/rudygao/article/details/49247605PostgreSQL（二） 索引介绍 索引扫描方式（gin索引 pg_trgm模糊查询索引原理）_gin索引扫描-CSDN博客文章浏览阅读3.3k次，点赞2次，收藏18次。1.索引的意义1.1索引的优点创建索引能够加快对表的查询，排序，以及唯一约束的作用。索引能够提供给优化器更好的值分布统计信息。1.2索引的缺点创建索引会增加数据库的存储空间，在计算数据库的容量大小时需要计算表和索引的总空间大小。在创建完索引之后的表，执行插入、更新和删除操作时，索引需要更新，故耗时会成倍增加。2.索引管理2.1创建索引创建索引时，不能包括schema模式名，因为索引默认被创建在其基表所在的模式中，创..._gin索引扫描https://blog.csdn.net/qq_35260875/article/details/106084392https://blog.csdn.net/qq_35260875/article/details/106084392https://blog.csdn.net/qq_35260875/article/details/106084392https://blog.csdn.net/qq_35260875/article/details/106084392PostgreSQL pg_trgm扩展安装 模糊查询 使用原理_postgresql15模糊查询扩展包-CSDN博客文章浏览阅读7.2k次，点赞2次，收藏15次。1.pg_trgm安装（1）安装btree_gin和pg_trgm# 需要先进入pg源码包中su - postgrescd contrib/pg_trgm/make && make install安装pg_trgm扩展时需要安装btree_gin才可以使用cd contrib/btree_gin/make && make install（2）创建扩展 安装的扩展默认都是在pg_catalog这个schema下面。也..._postgresql15模糊查询扩展包https://blog.csdn.net/qq_35260875/article/details/106148664https://blog.csdn.net/qq_35260875/article/details/106148664https://blog.csdn.net/qq_35260875/article/details/106148664https://blog.csdn.net/qq_35260875/article/details/106148664postgresql系列之并行查询_postgresql 关闭并行执行-CSDN博客文章浏览阅读6.5k次，点赞2次，收藏14次。本文是《postgresql实战》的读书笔记，感兴趣可以参考该书对应章节一、并行查询postgresql在9.6开始支持并行查询，但支持的范围非常有限，在postgresql10得到进一步了增强。1.1 并行查询相关参数参数描述max_work_processer(integer)设置系统支持的最大后台进程，默认值为8，此参数调整后需要重启数据库才生效max_p..._postgresql 关闭并行执行https://blog.csdn.net/qq_31156277/article/details/84261112https://blog.csdn.net/qq_31156277/article/details/84261112https://blog.csdn.net/qq_31156277/article/details/84261112https://blog.csdn.net/qq_31156277/article/details/84261112

高级模糊查询的实现 | Pigsty如何在PostgreSQL中实现比较复杂的模糊查询逻辑？https://pigsty.cc/zh/blog/dev/fuzzymatch/#%E9%AB%98%E7%BA%A7%E6%A8%A1%E7%B3%8A%E6%9F%A5%E8%AF%A2https://pigsty.cc/zh/blog/dev/fuzzymatch/#%E9%AB%98%E7%BA%A7%E6%A8%A1%E7%B3%8A%E6%9F%A5%E8%AF%A2https://pigsty.cc/zh/blog/dev/fuzzymatch/#%E9%AB%98%E7%BA%A7%E6%A8%A1%E7%B3%8A%E6%9F%A5%E8%AF%A2中文模糊查询性能优化 by PostgreSQL trgm - Digoal.Zhou’s Blog背景前模糊，后模糊，前后模糊，正则匹配都属于文本搜索领域常见的需求。PostgreSQL在文本搜索领域除了全文检索，还有trgm是一般数据库没有的，可能很多人没有听说过。对于前模糊和后模糊，PG则与其他数据库一样，可以使用btree来加速。后模糊可以使用反转函数的函数索引来加速。对于前后模糊和正则匹配，则可以使用trgm，TRGM是一个非常强的插件，对这类文本搜索场景性能提升非常有效，100万左右的数据量，性能提升有500倍以上。ascii字符模糊查询\正则匹配的例子生成100万数据，测试模糊查询的性能create extension pg_trgm; postgres=# create table tbl (id int, info text); CREATE TABLE postgres=# insert into tbl select generate_series(1,1000000), md5(random()::text); INSERT 0 1000000 postgres=# create index idx_tbl_1 on tbl using gin(info gin_trgm_ops); CREATE INDEX postgres=# select * from tbl limit 10; id | info ----+---------------------------------- 1 | dc369f84738f7fa4dc38c364cef817d0 2 | 4912b0b16670c4f2390d44ae790b9809 3 | eb442b00bf3b5bc6863d004a2c8fa3bb 4 | 0b4b8a8ad0cdf2e6870afbb94813eba4 5 | 661e895ee982ec4d9f944b10adffb897 6 | 09c4e7476d4bdfc1ccbdfe92ba0fdbdf 7 | 8b6e442faed938d066dda5e552100277 8 | e5cdeca599d5068a8d3bb6ce9f370827 9 | ddbbfbeaa9199219b7c909fb395d9a69 10 | 96f254f64df1ec43bb0cb4801222c919 (10 rows) postgres=# select * from tbl where info ~ '670c4f2'; id | info ----+---------------------------------- 2 | 4912b0b16670c4f2390d44ae790b9809 (1 row) Time: 2.668 ms postgres=# explain analyze select * from tbl where info ~ '670c4f2'; QUERY PLAN --------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on tbl (cost=28.27..138.43 rows=100 width=37) (actual time=1.957..1.958 rows=1 loops=1) Recheck Cond: (info ~ '670c4f2'::text) Heap Blocks: exact=1 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..28.25 rows=100 width=0) (actual time=1.939..1.939 rows=1 loops=1) Index Cond: (info ~ '670c4f2'::text) Planning time: 0.342 ms Execution time: 1.989 ms (7 rows) 不使用TRGM优化的情况下，需要1657毫秒.postgres=# set enable_bitmapscan=off; SET Time: 0.272 ms postgres=# select * from tbl where info ~ 'e770044a'; id | info ----+---------------------------------- 6 | 776c3cdf5fa818a324ef3e770044a488 (1 row) Time: 1657.231 ms 对于ascii字符，使用pg_trgm后性能提升非常明显。一、中文支持( 适用于小于9.3的版本 )PostgreSQL 9.3开始，pg_trgm支持wchar，如果你用的是9.3以前的版本，那么需要转换一下，把文本转换为bytea即可。转换为bytea前，效率是不高的，如下。postgres=# explain analyze select * from tbl where info ~ '中国'; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------ Bitmap Heap Scan on tbl (cost=149.62..151.82 rows=2 width=37) (actual time=8.624..8.624 rows=0 loops=1) Recheck Cond: (info ~ '中国'::text) Rows Removed by Index Recheck: 10103 Heap Blocks: exact=156 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..149.61 rows=2 width=0) (actual time=1.167..1.167 rows=10103 loops=1) Index Cond: (info ~ '中国'::text) Planning time: 0.244 ms Execution time: 8.657 ms (8 rows) Time: 9.388 ms 从执行计划来分析，中文虽然走索引，但是它是没有正确的使用token的，所以都放到recheck了。还不如全表扫描postgres=# set enable_bitmapscan=off; SET postgres=# explain analyze select * from tbl where info ~ '中国'; QUERY PLAN ------------------------------------------------------------------------------------------------ Seq Scan on tbl (cost=0.00..399.75 rows=2 width=37) (actual time=6.899..6.899 rows=0 loops=1) Filter: (info ~ '中国'::text) Rows Removed by Filter: 10103 Planning time: 0.213 ms Execution time: 6.921 ms (5 rows) Time: 7.593 ms 中文bytea化，支持pg_trgm索引你可以用PostgreSQL的函数索引和bytea化(转换成ascii码)来实现这块的功能例如postgres=# select text(textsend(info)) from tbl limit 10; text ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \xe7abbde69b8ce7b5a4e8b197e5afa9e58c88e991a6e7b18ce5b495e8a79fe7ae8ee882bce7a283e7af9de8a086e7ac8de59e81e5a6bae9bcb6e6ba9fe981bbe4bda8e7928de98ab0e5a18de697b5e79fabe9b0a5e9b0a5 \xe5aa8ee69ab5e58996e892b0e89484e587b0e8bcbce69f80e79eb8e89390e7baa8e79f93e582b6e98f81e9a18ee9b48ee9ba8ce784a6e8b5a2e5a797e9a3b5e5a4aee986b1e9919de6b19ce9bdb9e6bbb6e8b5bde8b5bd \xe7b4a4e5b2b3e7ac96e79481e78dbce5b28ae6b9b6e88dafe5aebce4bcbde8a3a3e4be98e78e93e5848ae4b888e5b0b5e5aeaee9aeb2e99982e59a98e6b0b2e583b3e9b799e893a5e5ba89e8949fe7868ee78cbde78cbd \xe797a3e4b991e8baaee9ae88e69db5e78c99e9a8abe9bd80e7bd98e8b3bae89cb5e799bbe78d89e990a7e5b989e6a484e6a1a1e6939ce9b490e890b4e9a5abe6b392e58a9be5adaae9b895e89985e8a79ee8b889e8b889 \xe687a4e9b795e58094e9b0a6e6a58ee4bd80e6898ae6bdbee7828de788bde79897e8be83e59b93e7908ae9879be7b093e89eaae6a3bce792bee59e9ae8b5abe7a89fe9b6aae99bbae9a18fe6b3abe7b7aae89282e89282 \xe996b8e5a4b7e6b2b7e8a397e6a898e58a94e6a4a5e586b3e9b8b5e5ba98e99ba4e99c90e6be90e88d94e99dade89892e594abe59d98e5a7afe592a0e58c9be59590e8a299e7bb86e9abace7a5bee881bde793a7e793a7 \xe795aee7bba4e4bc86e7b29ae780b2e7bd9fe8a9bee8bf97e68486e5a4bde8a79ee6bf8be98cb8e8b6bfe4bb8ae88ba3e8ba98e6acb8e6aa94e59ab5e697bfe78b96e6859be7afb9e9bb85e799a7e798a3e6a982e6a982 \xe98987e7828be585ace9808ce5959be6b4a0e582ade59fbfe7b18ee792b9e8bd87e8849ce89d98e4b8b4e7af9ce6abb3e98a8ce89490e897bde59ea7e8a5a8e98a94e7848be59abae5bb9be890b6e58188e6acb8e6acb8 \xe7898de88880e89abfe99dbfe5bab9e5b387e8b3a7e8a0bfe9a4a7e5aa9be6a18ee68ca7e9b2b2e58b8de6a088e6a4abe5a481e58297e4bb90e5b780e786b4e6958de58bb4e78884e9ae98e9909ae8b19be984a8e984a8 \xe6b4a8e8b99ee6b789e8bfb9e9b69de9b0a6e9b7bde59fbae6a886e793a1e691ace9a185e5bba1e699a5e9bcace78598e9adaee9b199e59eb5e897b6e88f92e69caee8b9ade8beade4bdbae5b3b6e599b9e7bea1e7bea1 (10 rows) Time: 0.457 ms 对bytea文本创建gin索引，需要创建一个immutable函数。请务必使用时保证创建索引、查询是客户端的编码一致，即查询与存储的编码一致才能命中结果哦。postgres=# create or replace function textsend_i (text) returns bytea as $$ select textsend($1); $$ language sql strict immutable; CREATE FUNCTION postgres=# drop index idx_tbl_1 ; DROP INDEX Time: 10.179 ms postgres=# create index idx_tbl_1 on tbl using gin(text(textsend_i(info)) gin_trgm_ops); CREATE INDEX 使用了bytea的gin索引后，性能提升非常明显，数据量越多，性能表现越好。postgres=# set enable_bitmapscan=on; postgres=# explain analyze select * from tbl where text(textsend_i(info)) ~ ltrim(text(textsend_i('中国')), '\x'); QUERY PLAN ---------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on tbl (cost=369.28..504.93 rows=100 width=37) (actual time=0.099..0.099 rows=0 loops=1) Recheck Cond: ((textsend_i(info))::text ~ 'e4b8ade59bbd'::text) -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..369.25 rows=100 width=0) (actual time=0.097..0.097 rows=0 loops=1) Index Cond: ((textsend_i(info))::text ~ 'e4b8ade59bbd'::text) Planning time: 0.494 ms Execution time: 0.128 ms (6 rows) postgres=# select * from tbl limit 10; id | info ----+------------------------------------------------------------ 1 | 竽曌絤豗審匈鑦籌崕觟箎肼碃篝蠆笍垁妺鼶溟遻佨璍銰塍旵矫鰥鰥 2 | 媎暵剖蒰蔄凰輼柀瞸蓐纨矓傶鏁顎鴎麌焦赢姗飵央醱鑝汜齹滶赽赽 3 | 紤岳笖甁獼岊湶药宼伽裣侘玓儊丈尵宮鮲陂嚘氲僳鷙蓥庉蔟熎猽猽 4 | 痣乑躮鮈杵猙騫齀罘賺蜵登獉鐧幉椄桡擜鴐萴饫泒力孪鸕虅觞踉踉 5 | 懤鷕倔鰦楎佀扊潾炍爽瘗较囓琊釛簓螪棼璾垚赫稟鶪雺顏泫緪蒂蒂 6 | 閸夷沷裗樘劔椥决鸵庘雤霐澐荔靭蘒唫坘姯咠匛啐袙细髬祾聽瓧瓧 7 | 畮绤伆粚瀲罟詾迗愆夽觞濋錸趿今苣躘欸檔嚵旿狖慛篹黅癧瘣橂橂 8 | 鉇炋公逌啛洠傭埿籎璹轇脜蝘临篜櫳銌蔐藽垧襨銔焋嚺廛萶偈欸欸 9 | 牍舀蚿靿庹峇賧蠿餧媛桎挧鲲勍栈椫夁傗仐巀熴敍勴爄鮘鐚豛鄨鄨 10 | 洨蹞淉迹鶝鰦鷽基樆瓡摬顅廡晥鼬煘魮鱙垵藶菒朮蹭辭佺島噹羡羡 (10 rows) postgres=# explain analyze select * from tbl where text(textsend_i(info)) ~ ltrim(text(textsend_i('坘')), '\x'); QUERY PLAN ---------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on tbl (cost=149.88..574.79 rows=320 width=37) (actual time=0.063..0.063 rows=0 loops=1) Recheck Cond: ((textsend_i(info))::text ~ 'e59d98'::text) -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..149.80 rows=320 width=0) (actual time=0.061..0.061 rows=0 loops=1) Index Cond: ((textsend_i(info))::text ~ 'e59d98'::text) Planning time: 0.303 ms Execution time: 0.087 ms (6 rows) postgres=# select * from tbl where text(textsend_i(info)) ~ ltrim(text(textsend_i('坘')), '\x'); id | info ------+------------------------------------------------------------ 6 | 閸夷沷裗樘劔椥决鸵庘雤霐澐荔靭蘒唫坘姯咠匛啐袙细髬祾聽瓧瓧 432 | 飒莭鮊鍥?笩妳琈笈慻儘轴轧坘碠郎蚿呙偓鍹脆鼺蹔谕蚱畨縫鱳鱳 934 | 咓僨復圼峷奁扉羰滵樞韴迬猰優鰸獤溅躐瓜抵権纀懶粯坘蚲纾鴁鴁 3135 | 倣稽蛯巭瘄皮蓈睫柨苧眱賴髄猍乱歖痐坘恋顎东趥谓鰪棩剔烱茟茟 3969 | 崴坘螏顓碴鵰邰欴苄蛨簰瘰膪菷栱镘衟齘觊诀忮繈憘痴峣撋梆澝澝 4688 | 围豁啖顫诬呅尥腥缾郸熛枵焐篯坘僇矟銘隨譼鎶舰肳礞婛轲蠟慕慕 6121 | 窳研稼旅唣疚褣鬾韨赑躽坘浒攁舑遬鳴滴抓嗠捒铗牜欘質丛姤騖騖 6904 | 飘稘輔鬄枠舶婬儁噈坘裎姖爙炃苖隽斓堯鈶摙蚼疁兗快鐕鎒墩譭譭 8854 | 叒鐲唬鞩泍糕懜坘戚靥鎿鋂炿尟汜阢甌鲖埁顔胳邉謾宱肦劰責戆戆 9104 | 鵬篱爯俌坘柉誵孀漴纞錀澁摫螭芄餜爹綅俆逨哒猈珢輿廄陲欗缷缷 9404 | 民坘謤齏隽紽峐荟頩胯頴傳蠂枯滦榦陠帡疃鈶遽艌瘧蒭嗍龞瓈嚍嚍 9727 | 夃坘慫逹壪泵偉鸶揺雠倴矸虠覾芽齏遬儂錞鐴焑劽疁擯蛛倞瑫菰菰 (12 rows) 二、中文支持( 适用于大于等于9.3的版本 )pg_trgm支持中文的前提条件：数据库的collate和ctype都不能为C。例如这些数据库，Collate, Ctype = C的，pg_trgm都不支持wchar(含中文)。postgres=# \l+ List of databases Name | Owner | Encoding | Collate | Ctype | Access privileges | Size | Tablespace | Description --------------------+----------+-----------+------------+------------+-----------------------+---------+------------+-------------------------------------------- contrib_regression | postgres | UTF8 | C | C | | 9313 kB | pg_default | db | postgres | SQL_ASCII | C | C | | 7359 kB | pg_default | db1 | postgres | EUC_CN | C | C | | 7351 kB | pg_default | postgres | postgres | UTF8 | C | C | | 1686 MB | pg_default | default administrative connection database template0 | postgres | UTF8 | C | C | =c/postgres +| 7225 kB | pg_default | unmodifiable empty database | | | | | postgres=CTc/postgres | | | template1 | postgres | UTF8 | C | C | =c/postgres +| 7225 kB | pg_default | default template for new databases | | | | | postgres=CTc/postgres | | | test | postgres | UTF8 | en_US.UTF8 | en_US.UTF8 | | 7415 kB | pg_default | test01 | postgres | UTF8 | C | C | | 1621 MB | pg_default | (8 rows) 例子1，不支持wchar的情况(collate,ctype=C)postgres=# \c db1 You are now connected to database "db1" as user "postgres". db1=# create extension pg_trgm; CREATE EXTENSION db1=# select show_trgm('你好'); show_trgm ----------- {} (1 row) 例子2，支持wchar的情况(collate,ctype<>C)db1=# \c test You are now connected to database "test" as user "postgres". test=# select show_trgm('你好'); show_trgm ------------------------- {0xcf7970,0xf98da8,IgR} (1 row) 创建数据库时，指定Collate, Ctype，例子。postgres=# create database test02 with template template0 lc_collate "zh_CN.UTF8" lc_ctype "zh_CN.UTF8" encoding 'UTF8'; CREATE DATABASE postgres=# \l+ test02 List of databases Name | Owner | Encoding | Collate | Ctype | Access privileges | Size | Tablespace | Description --------+----------+----------+------------+------------+-------------------+---------+------------+------------- test02 | postgres | UTF8 | zh_CN.UTF8 | zh_CN.UTF8 | | 7225 kB | pg_default | (1 row) 中文模糊查询加速前面讲了，数据库前提(collate,ctype<>C)例子1 (GIN索引)postgres=# \c test02 You are now connected to database "test02" as user "postgres". test02=# create extension pg_trgm; CREATE EXTENSION test02=# create table test(id int, info text); CREATE TABLE test02=# insert into test values (1,'你好，我是中国人'); INSERT 0 1 test02=# create index idx_test_1 on test using gin(info gin_trgm_ops); CREATE INDEX test02=# set enable_seqscan=off; SET test02=# explain (analyze,verbose,timing,costs,buffers) select * from test where info ~ '北京天安门'; QUERY PLAN ------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on public.test (cost=5.20..6.51 rows=1 width=36) (actual time=0.075..0.075 rows=0 loops=1) Output: id, info Recheck Cond: (test.info ~ '北京天安门'::text) -- 说明索引已过滤了 Buffers: shared hit=4 -> Bitmap Index Scan on idx_test_1 (cost=0.00..5.20 rows=1 width=0) (actual time=0.070..0.070 rows=0 loops=1) Index Cond: (test.info ~ '北京天安门'::text) Buffers: shared hit=4 Planning time: 0.174 ms Execution time: 0.107 ms (9 rows) test02=# explain (analyze,verbose,timing,costs,buffers) select * from test where info ~ '1'; QUERY PLAN -------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on public.test (cost=13.01..14.32 rows=1 width=36) (actual time=0.052..0.052 rows=0 loops=1) Output: id, info Recheck Cond: (test.info ~ '1'::text) Rows Removed by Index Recheck: 1 -- 命中索引（与TOKEN有关）, 通过recheck过滤成功 Heap Blocks: exact=1 Buffers: shared hit=4 -> Bitmap Index Scan on idx_test_1 (cost=0.00..13.01 rows=1 width=0) (actual time=0.040..0.040 rows=1 loops=1) Index Cond: (test.info ~ '1'::text) Buffers: shared hit=3 Planning time: 0.157 ms Execution time: 0.076 ms (11 rows) test02=# explain (analyze,verbose,timing,costs,buffers) select * from test where info ~ '你好'; QUERY PLAN -------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on public.test (cost=13.00..14.31 rows=1 width=36) (actual time=0.052..0.052 rows=1 loops=1) Output: id, info Recheck Cond: (test.info ~ '你好'::text) -- 命中索引 Heap Blocks: exact=1 Buffers: shared hit=4 -> Bitmap Index Scan on idx_test_1 (cost=0.00..13.00 rows=1 width=0) (actual time=0.040..0.040 rows=1 loops=1) Index Cond: (test.info ~ '你好'::text) Buffers: shared hit=3 Planning time: 0.156 ms Execution time: 0.077 ms (10 rows) test02=# select * from test where info ~ '1'; id | info ----+------ (0 rows) test02=# select * from test where info ~ '你好'; id | info ----+------------------ 1 | 你好，我是中国人 (1 row) test02=# select * from test where info ~ '北京天安门'; id | info ----+------ (0 rows) 例子2 (GiST索引)test02=# create index idx_test_2 on test using gist(info gist_trgm_ops); CREATE INDEX test02=# drop index idx_test_1; DROP INDEX test02=# explain (analyze,verbose,timing,costs,buffers) select * from test where info ~ '你好'; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------- Index Scan using idx_test_2 on public.test (cost=0.12..2.74 rows=1 width=36) (actual time=0.081..0.082 rows=1 loops=1) Output: id, info Index Cond: (test.info ~ '你好'::text) Buffers: shared hit=2 Planning time: 0.134 ms Execution time: 0.121 ms (6 rows) test02=# explain (analyze,verbose,timing,costs,buffers) select * from test where info ~ '1'; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------- Index Scan using idx_test_2 on public.test (cost=0.12..2.74 rows=1 width=36) (actual time=0.079..0.079 rows=0 loops=1) Output: id, info Index Cond: (test.info ~ '1'::text) Rows Removed by Index Recheck: 1 Buffers: shared hit=2 Planning time: 0.068 ms Execution time: 0.107 ms (7 rows) test02=# explain (analyze,verbose,timing,costs,buffers) select * from test where info ~ '北京天安门'; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------- Index Scan using idx_test_2 on public.test (cost=0.12..2.74 rows=1 width=36) (actual time=0.102..0.102 rows=0 loops=1) Output: id, info Index Cond: (test.info ~ '北京天安门'::text) Buffers: shared hit=1 Planning time: 0.067 ms Execution time: 0.130 ms (6 rows) gist与gin选哪个如果过滤条件返回的结果集非常大(比如万行+)，并且你需要limit返回，建议gist。如果过滤条件返回的结果集很小，建议GIN。三、非精确模糊匹配，使用相似度排序输出使用gist索引，根据相似度排序返回结果，这种方法可能输出非精确匹配的结果。例如postgresql, 与 gersql 可能相似度很高会排在前面。 而用户可能并不需要它。例子CREATE TABLE test_trgm (t text); CREATE INDEX trgm_idx ON test_trgm USING GIN (t gin_trgm_ops); SELECT t, t <-> 'word' AS dist FROM test_trgm ORDER BY dist LIMIT 10; 或者 SELECT t FROM test_trgm ORDER BY t <-> 'word' desc LIMIT 10; 如果列包含中文，同样可以使用前面的immutable函数索引代替之 注意事项因为pg_trgm以3个连续的字符作为TOKEN，当你查询的词是1个或者2个字符时，效果不好。(头部匹配至少提供1个字符，尾部匹配至少提供2个字符，例如 ‘^a’, ‘ab$’，这样才能保证至少能匹配到TOKEN，使用倒排优化。)建议查询至少3个字符的情况。如果有1个字符或者2个字符模糊查询的场景，怎么办？可以将字符串按连续的1个，2个字符，切分成数组，再对这个数组建立gin索引，查找array @> {目标词}即可。参考有兴趣还可以再参考以下文章。如何用PostgreSQL解决一个人工智能语义去重的小问题https://yq.aliyun.com/articles/25899PostgreSQL 百亿数据 秒级响应 正则及模糊查询https://yq.aliyun.com/articles/7444PostgreSQL 1000亿数据量 正则匹配 速度与激情https://yq.aliyun.com/articles/7549《PostgreSQL 9.3 pg_trgm imporve support multi-bytes char and gist,gin index for reg-exp search》其他注意事项当提供的词语过短（例如小于3），或者提供的是热词（覆盖率较大）时，可能导致recheck严重。原理参考,第一重过滤时，过多的token命中，而且组合后的BLOCK都复合条件导致。《电商内容去重\内容筛选应用(实时识别转载\盗图\侵权?) - 文本、图片集、商品集、数组相似判定的优化和索引技术》解决办法，或者说评估方法, 如果评估出来row过多，可以调整输入参数CREATE FUNCTION count_estimate(query text) RETURNS INTEGER AS $func$ DECLARE rec record; ROWS INTEGER; BEGIN FOR rec IN EXECUTE 'EXPLAIN ' || query LOOP ROWS := SUBSTRING(rec."QUERY PLAN" FROM ' rows=([[:digit:]]+)'); EXIT WHEN ROWS IS NOT NULL; END LOOP; RETURN ROWS; END $func$ LANGUAGE plpgsql;digoal’s 大量PostgreSQL文章入口https://billtian.github.io/digoal.blog/2016/05/06/02.htmlhttps://billtian.github.io/digoal.blog/2016/05/06/02.htmlhttps://billtian.github.io/digoal.blog/2016/05/06/02.html

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