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PostgreSQL數(shù)據(jù)庫中如何保證LIKE語句的效率(推薦)

更新時間：2021年03月08日 08:30:57 作者：瀚高PG實驗室

這篇文章主要介紹了PostgreSQL數(shù)據(jù)庫中如何保證LIKE語句的效率,本文給大家介紹的非常詳細(xì)，對大家的學(xué)習(xí)或工作具有一定的參考借鑒價值，需要的朋友可以參考下

在任何數(shù)據(jù)庫中使用LIKE語句往往都是令人頭疼的一件事，因為不少用戶發(fā)現(xiàn)LIKE語句效率極低，查看執(zhí)行計劃后發(fā)現(xiàn)原來沒有走索引，那么在Postgresql數(shù)據(jù)中LIKE語句的執(zhí)行效率又是怎樣的呢？我們又該如何提高LIKE語句的執(zhí)行效率呢？

實驗環(huán)境

數(shù)據(jù)庫環(huán)境： PostgreSQL 12.3 X86_64

創(chuàng)建虛擬環(huán)境：

postgres=# create database testdb01 owner highgo;
CREATE DATABASE
postgres=# \c testdb01 highgo
 
 
testdb01=# create table testliketb01 (userid int primary key,username varchar(20),password varchar(60),description text);
CREATE TABLE

為何保證測試效果更直觀，我們使用隨機(jī)數(shù)據(jù)填充一下該表

testdb01=# insert into testliketb01 select generate_series(1,500000),split_part('張三,李四,王五,小明,小紅',',',(random()*(5-1)+1)::int),md5((random()*(5-1)+1)::varchar),split_part('highgo,highgo02,highgo03',',',(random()*(3-1)+1)::int);

至此，虛擬數(shù)據(jù)創(chuàng)建完畢。

testdb01=# select * from testliketb01 limit 10;
userid | username |             password             | description
--------+----------+----------------------------------+-------------
      1 | 王五     | 4f2bca371b42abd1403d5c20c4542dff | highgo
      2 | 李四     | 2a978c605188770c5ed162889fff189e | highgo02
      3 | 李四     | f5d129ab728b72ac6f663fe544bc7c16 | highgo
      4 | 小明     | 53134fa1022c58e65168b6aa1fbe5e39 | highgo02
      5 | 王五     | 2cf9abb2a8b676a626fa2c317d401ed8 | highgo02
      6 | 王五     | 2247a0cfda1f2819554d6e8e454622eb | highgo02
      7 | 張三     | 59dfdc680c17533dfba1c72c9ce0bf76 | highgo02
      8 | 王五     | 87db4258236a3826259dcc3e7cb5fc63 | highgo02
      9 | 王五     | baaf7a2f7027df9aaeb665121432b6e2 | highgo02
     10 | 王五     | 2f8fb36b3227c795b111b9bd5b031a76 | highgo02
(10 rows)
此時數(shù)據(jù)庫的狀態(tài)：
testdb01=# \l+ testdb01
                                                List of databases
   Name   | Owner  | Encoding |   Collate   |    Ctype    | Access privileges | Size  | Tablespace | Description
----------+--------+----------+-------------+-------------+-------------------+-------+------------+-------------
testdb01 | highgo | UTF8     | en_US.UTF-8 | en_US.UTF-8 |                   | 59 MB | pg_default |
(1 row)

簡單LIKE語句查詢：

testdb01=# explain analyze select * from testliketb01 where username like '王%';
                                                      QUERY PLAN                                                       
-----------------------------------------------------------------------------------------------------------------------
Seq Scan on testliketb01  (cost=0.00..11405.00 rows=125350 width=52) (actual time=0.014..177.571 rows=124952 loops=1)
   Filter: ((username)::text ~~ '王%'::text)
   Rows Removed by Filter: 375048
Planning Time: 0.121 ms
Execution Time: 190.554 ms
(5 rows)

結(jié)論：LIKE查詢沒有走索引創(chuàng)建普通索引： testdb01=# create index idx_testliketb01_username on testliketb01(username); CREATE INDEX 執(zhí)行三遍：analyze testliketb01 ; 重新執(zhí)行LIKE語句，發(fā)現(xiàn)還是沒有走索引創(chuàng)建包含operator class的索引： testdb01=# create index idx_testliketb01_username on testliketb01(username varchar_pattern_ops); CREATE INDEX 執(zhí)行三遍：analyze testliketb01 ;

testdb01=# explain analyze select * from testliketb01 where username like '王%';
                                                                   QUERY PLAN                                                                    
-------------------------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on testliketb01  (cost=2665.26..9387.14 rows=125350 width=52) (actual time=31.383..94.745 rows=124952 loops=1)
   Filter: ((username)::text ~~ '王%'::text)
   Heap Blocks: exact=5155
   ->  Bitmap Index Scan on idx_testliketb01_username  (cost=0.00..2633.92 rows=125350 width=0) (actual time=29.730..29.730 rows=124952 loops=1)
         Index Cond: (((username)::text ~>=~ '王'::text) AND ((username)::text ~<~ '玌'::text))
Planning Time: 0.111 ms
Execution Time: 107.030 ms
(7 rows)

結(jié)論：在創(chuàng)建完普通索引并收集統(tǒng)計信息后數(shù)據(jù)庫在執(zhí)行LIKE語句時有可能仍然無法使用索引。在創(chuàng)建完帶有操作類的索引收集完統(tǒng)計信息后，執(zhí)行LIKE語句可以看到正常使用索引，且執(zhí)行效率有了不小提升。

PS：operator class是Postgresql新版中創(chuàng)建索引的新選項，旨在通過制定索引的操作類可以更精準(zhǔn)的收集統(tǒng)計信息。

為了更精準(zhǔn)的收集統(tǒng)計信息，我們也可以在初始化或者創(chuàng)建數(shù)據(jù)庫時將Collate設(shè)置為"C"，這也是Postgresql數(shù)據(jù)中常用的優(yōu)化手段。我們來測試一下將Collate設(shè)置為"C"的效果：

testdb01=# create database testdb02 with TEMPLATE template0  LC_COLLATE='C'  LC_CTYPE ='C' owner highgo;
CREATE DATABASE
 
 
testdb02=# \l+ testdb02
                                           List of databases
   Name   | Owner  | Encoding | Collate | Ctype | Access privileges | Size  | Tablespace | Description
----------+--------+----------+---------+-------+-------------------+-------+------------+-------------
testdb02 | highgo | UTF8     | C       | C     |                   | 59 MB | pg_default |
(1 row)
 
 
testdb02=# create index idx_testliketb01_username on testliketb01(username);
CREATE INDEX
testdb02=# analyze testliketb01 ;
ANALYZE
testdb02=# analyze testliketb01 ;
ANALYZE
testdb02=# analyze testliketb01 ;
ANALYZE
testdb02=#  explain analyze select * from testliketb01 where username like '王%';
                                                                   QUERY PLAN                                                                    
-------------------------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on testliketb01  (cost=2680.26..9410.67 rows=126033 width=52) (actual time=35.262..99.052 rows=124992 loops=1)
   Filter: ((username)::text ~~ '王%'::text)
   Heap Blocks: exact=5155
   ->  Bitmap Index Scan on idx_testliketb01_username  (cost=0.00..2648.75 rows=126033 width=0) (actual time=33.920..33.920 rows=124992 loops=1)
         Index Cond: (((username)::text >= '王'::text) AND ((username)::text < '玌'::text))
Planning Time: 0.276 ms
Execution Time: 111.578 ms
(7 rows)

結(jié)論：創(chuàng)建數(shù)據(jù)庫時將Collate設(shè)置為"C"，即便索引為普通索引，LIKE語句也可以使用索引提升查詢效率。