以下两个查询组件的性能比较如何?
下喜欢
... LOWER(description) LIKE '%abcde%' ...
我喜欢
... description iLIKE '%abcde%' ...
答案取决于许多因素,例如 Postgres 版本、编码和区域设置 - 尤其是
LC_COLLATE
。
裸表达式
lower(description) LIKE '%abc%'
通常比 description ILIKE '%abc%'
快一点,并且比等效正则表达式 description ~* 'abc'
快一点。这对于顺序扫描很重要,其中必须对每个测试行评估表达式。
但是对于像您在答案中演示的大表,肯定会使用索引。对于任意模式(不仅仅是左锚定),我建议使用附加模块
pg_trgm
建立三元组索引。然后我们讨论毫秒而不是秒,并且上述表达式之间的差异无效。
GIN 和 GiST 索引(使用
gin_trgm_ops
或 gist_trgm_ops
运算符类)支持 LIKE
(~~
)、ILIKE
(~~*
)、~
、~*
(以及更多变体)一样。通过 description
上的 trigram GIN 索引(通常比 GiST 大,但读取速度更快),您的查询将使用 description ILIKE 'case_insensitive_pattern'
。
相关:
Postgres 中模式匹配的基础知识:
当使用所述三元组索引时,通常更实用:
description ILIKE '%abc%'
或者使用不区分大小写的正则表达式运算符(不带
%
通配符):
description ~* 'abc'
(description)
上的索引不支持对 lower(description)
的查询,例如:
lower(description) LIKE '%abc%'
反之亦然。
对于
lower(description)
exclusively 上的谓词,表达式索引是稍微更好的选择。
在所有其他情况下,最好使用
(description)
上的索引,因为它支持 区分大小写和 - 不敏感的谓词。
根据我的测试(每个查询的十),
LOWER
LIKE
比17%
快约iLIKE
。
解释
我创建了一百万行,其中包含一些随机混合文本数据:
require 'securerandom'
inserts = []
1000000.times do |i|
inserts << "(1, 'fake', '#{SecureRandom.urlsafe_base64(64)}')"
end
sql = "insert into books (user_id, title, description) values #{inserts.join(', ')}"
ActiveRecord::Base.connection.execute(sql)
验证行数:
my_test_db=# select count(id) from books ;
count
---------
1000009
(是的,我有来自其他测试的九个额外行 - 不是问题。)
示例查询和结果:
my_test_db=# SELECT "books".* FROM "books" WHERE "books"."published" = 'f'
my_test_db=# and (LOWER(description) LIKE '%abcde%') ;
id | user_id | title | description | published
---------+---------+-------+----------------------------------------------------------------------------------------+------
1232322 | 1 | fake | 5WRGr7oCKABcdehqPKsUqV8ji61rsNGS1TX6pW5LJKrspOI_ttLNbaSyRz1BwTGQxp3OaxW7Xl6fzVpCu9y3fA | f
1487103 | 1 | fake | J6q0VkZ8-UlxIMZ_MFU_wsz_8MP3ZBQvkUo8-2INiDIp7yCZYoXqRyp1Lg7JyOwfsIVdpPIKNt1uLeaBCdelPQ | f
1817819 | 1 | fake | YubxlSkJOvmQo1hkk5pA1q2mMK6T7cOdcU3ADUKZO8s3otEAbCdEcmm72IOxiBdaXSrw20Nq2Lb383lq230wYg | f
“下喜欢”的结果
my_test_db=# EXPLAIN ANALYZE SELECT "books".* FROM "books" WHERE "books"."published" = 'f' and (LOWER(description) LIKE '%abcde%') ;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------
Seq Scan on books (cost=0.00..32420.14 rows=1600 width=117) (actual time=938.627..4114.038 rows=3 loops=1)
Filter: ((NOT published) AND (lower(description) ~~ '%abcde%'::text))
Rows Removed by Filter: 1000006
Total runtime: 4114.098 ms
iLIKE 的结果
my_test_db=# EXPLAIN ANALYZE SELECT "books".* FROM "books" WHERE "books"."published" = 'f' and (description iLIKE '%abcde%') ;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------
Seq Scan on books (cost=0.00..29920.11 rows=100 width=117) (actual time=1147.612..4986.771 rows=3 loops=1)
Filter: ((NOT published) AND (description ~~* '%abcde%'::text))
Rows Removed by Filter: 1000006
Total runtime: 4986.831 ms
数据库信息泄露
Postgres 版本:
my_test_db=# select version();
version
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
PostgreSQL 9.2.4 on x86_64-apple-darwin12.4.0, compiled by i686-apple-darwin11-llvm-gcc-4.2 (GCC) 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00), 64-bit
排序规则设置:
my_test_db=# select datcollate from pg_database where datname = 'my_test_db';
datcollate
-------------
en_CA.UTF-8
表定义:
my_test_db=# \d books
Table "public.books"
Column | Type | Modifiers
-------------+-----------------------------+-------------------------------------------------------
id | integer | not null default nextval('books_id_seq'::regclass)
user_id | integer | not null
title | character varying(255) | not null
description | text | not null default ''::text
published | boolean | not null default false
Indexes:
"books_pkey" PRIMARY KEY, btree (id)
在我的 Rails 项目中。
ILIKE
几乎比 LOWER LIKE
快 10 倍,我在 GIN
列上添加了 entities.name
索引
> Entity.where("LOWER(name) LIKE ?", name.strip.downcase).limit(1).first
Entity Load (2443.9ms) SELECT "entities".* FROM "entities" WHERE (lower(name) like 'baidu') ORDER BY "entities"."id" ASC LIMIT $1 [["LIMIT", 1]]
> Entity.where("name ILIKE ?", name.strip).limit(1).first
Entity Load (285.0ms) SELECT "entities".* FROM "entities" WHERE (name ilike 'Baidu') ORDER BY "entities"."id" ASC LIMIT $1 [["LIMIT", 1]]
# explain analyze SELECT "entities".* FROM "entities" WHERE (name ilike 'Baidu') ORDER BY "entities"."id" ASC LIMIT 1;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=3186.03..3186.04 rows=1 width=1588) (actual time=7.812..7.812 rows=1 loops=1)
-> Sort (cost=3186.03..3187.07 rows=414 width=1588) (actual time=7.811..7.811 rows=1 loops=1)
Sort Key: id
Sort Method: quicksort Memory: 26kB
-> Bitmap Heap Scan on entities (cost=1543.21..3183.96 rows=414 width=1588) (actual time=7.797..7.805 rows=1 loops=1)
Recheck Cond: ((name)::text ~~* 'Baidu'::text)
Rows Removed by Index Recheck: 6
Heap Blocks: exact=7
-> Bitmap Index Scan on index_entities_on_name (cost=0.00..1543.11 rows=414 width=0) (actual time=7.787..7.787 rows=7 loops=1)
Index Cond: ((name)::text ~~* 'Baidu'::text)
Planning Time: 6.375 ms
Execution Time: 7.874 ms
(12 rows)
GIN 索引对于提高
ILIKE
性能确实有帮助