使MySQL查询区分大小写的实现方法

1、一种方法是可以设置表或行的collation，使其为binary或case

sensitive。在MySQL中，对于Column

Collate其约定的命名方法如下：

*_bin:

表示的是binary

case

sensitive

collation，也就是说是区分大小写的

*_cs:

case

sensitive

collation，区分大小写

*_ci:

case

insensitive

collation，不区分大小写

###########

#

Start

binary

collation

example

###########

mysql>

create

table

case_bin_test

(word

VARCHAR(10))

CHARACTER

SET

latin1

COLLATE

latin1_bin

Query

OK,

0

rows

affected

(0.02

sec)

mysql>

INSERT

INTO

case_bin_test

VALUES

('Frank'),('Google'),('froogle'),('flickr'),('FlicKr')

Query

OK,

5

rows

affected

(0.00

sec)

Records:

5

Duplicates:

0

Warnings:

0

mysql>

SELECT

*

FROM

case_bin_test

WHERE

word

LIKE

'f%'

+---------+

|

word

|

+---------+

|

froogle

|

|

flickr

|

+---------+

2

rows

in

set

(0.00

sec)

mysql>

SELECT

*

FROM

case_bin_test

WHERE

word

LIKE

'F%'

+---------+

|

word

|

+---------+

|

Frank

|

|

FlicKr

|

+---------+

4

rows

in

set

(0.00

sec)

###########

#

End

###########

2、另外一种方法

###########

#

Start

case

sensitive

collation

example

###########

mysql>

create

table

case_cs_test

(word

VARCHAR(10))

CHARACTER

SET

latin1

COLLATE

latin1_general_cs

Query

OK,

0

rows

affected

(0.08

sec)

mysql>

INSERT

INTO

case_cs_test

VALUES

('Frank'),('Google'),('froogle'),('flickr'),('FlicKr')

Query

OK,

5

rows

affected

(0.00

sec)

Records:

5

Duplicates:

0

Warnings:

0

mysql>

SELECT

*

FROM

case_cs_test

WHERE

word

LIKE

'F%'

+---------+

|

word

|

+---------+

|

Frank

|

|

FlicKr

|

+---------+

4

rows

in

set

(0.00

sec)

mysql>

SELECT

*

FROM

case_cs_test

WHERE

word

LIKE

'f%'

+---------+

|

word

|

+---------+

|

froogle

|

|

flickr

|

+---------+

2

rows

in

set

(0.00

sec)

###########

#

end

###########

3、还有一种方法就是在查询时指定collation

mysql>

create

table

case_test

(word

VARCHAR(10))

CHARACTER

SET

latin1

Query

OK,

0

rows

affected

(0.01

sec)

mysql>

INSERT

INTO

case_test

VALUES

('Frank'),('Google'),('froogle'),('flickr'),('FlicKr')

Query

OK,

7

rows

affected

(0.01

sec)

Records:

7

Duplicates:

0

Warnings:

0

mysql>

SELECT

*

FROM

case_test

WHERE

word

LIKE

'f%'

+---------+

|

word

|

+---------+

|

Frank

|

|

froogle

|

|

flickr

|

|

FlicKr

|

+---------+

6

rows

in

set

(0.01

sec)

mysql>

SELECT

*

FROM

case_test

WHERE

word

LIKE

'F%'

+---------+

|

word

|

+---------+

|

Frank

|

|

froogle

|

|

flickr

|

|

FlicKr

|

+---------+

6

rows

in

set

(0.01

sec)

mysql>

SELECT

*

FROM

case_test

WHERE

word

COLLATE

latin1_bin

LIKE

'F%'

+---------+

|

word

|

+---------+

|

Frank

|

|

FlicKr

|

+---------+

4

rows

in

set

(0.05

sec)

mysql>

SELECT

*

FROM

case_test

WHERE

word

COLLATE

latin1_bin

LIKE

'f%'

+---------+

|

word

|

+---------+

|

froogle

|

|

flickr

|

+---------+

2

rows

in

set

(0.00

sec)

mysql>

SELECT

*

FROM

case_test

WHERE

word

LIKE

'f%'

COLLATE

latin1_bin

+---------+

|

word

|

+---------+

|

froogle

|

|

flickr

|

+---------+

2

rows

in

set

(0.00

sec)

mysql>

SELECT

*

FROM

case_test

WHERE

word

LIKE

'F%'

COLLATE

latin1_bin

+---------+

|

word

|

+---------+

|

Frank

|

|

FlicKr

|

+---------+

4

rows

in

set

(0.01

sec)

mysql>

SELECT

*

FROM

case_test

WHERE

word

LIKE

'F%'

COLLATE

latin1_general_cs

+---------+

|

word

|

+---------+

|

Frank

|

|

FlicKr

|

+---------+

4

rows

in

set

(0.04

sec)

南大通用，人大金仓

谈及数据库的发展历史，就不得不提及三位数据库领域的开拓者，分别是Frank、Micheal和JimGray，他们为数据库理论奠定了坚实的基础，都获得了图灵奖。

早在1972年，Micheal最早提出了Ingres数据库，于2014年获得图灵奖，Ingres数据库最后分化衍生为Sybase与Postgres两部分。

其中Postgres数据库有大量分析函数，适用于分析型事务，尤其是OLAP。1972年，埃里森在硅谷开发了Oracle数据库，再到1983年IBM开发了DB2数据库，同年Tdata诞生，直到1995年MySQL数据库诞生。

而如今的Oracle于2009年收购了MySQL，这样一来就同时拥有了Oracle和开源的MySQL两套数据库，MySQL的创始人在离开后又开发了一套数据库MariaDB，现在国内有很多银行，像亿联银行等新的银行都在使用MariaDB。

JSON (JavaScriptObject Notation) 是一种轻量级的数据交换格式，主要用于传送数据。JSON采用了独立于语言的文本格式，类似XML，但是比XML简单，易读并且易编写。对机器来说易于解析和生成，并且会减少网络带宽的传输。由于JSON格式可以解耦javascript客户端应用与Restful服务器端的方法调用，因而在互联网应用中被大量使用。

JSON的格式非常简单：名称/键值。之前MySQL版本里面要实现这样的存储，要么用VARCHAR要么用TEXT大文本。 MySQL5.7发布后，专门设计了JSON数据类型以及关于这种类型的检索以及其他函数解析。我们先看看MySQL老版本的JSON存取。

示例表结构：

CREATE TABLE json_test(

id INT,

person_desc TEXT

)ENGINE INNODB

我们来插入一条记录：

INSERT INTO json_test VALUES (1,'{

"programmers": [{

"firstName": "Brett",

"lastName": "McLaughlin",

"email": "aaaa"

}, {

"firstName": "Jason",

"lastName": "Hunter",

"email": "bbbb"

}, {

"firstName": "Elliotte",

"lastName": "Harold",

"email": "cccc"

}],

"authors": [{

"firstName": "Isaac",

"lastName": "Asimov",

"genre": "sciencefiction"

}, {

"firstName": "Tad",

"lastName": "Williams",

"genre":"fantasy"

}, {

"firstName": "Frank",

"lastName": "Peretti",

"genre": "christianfiction"

}],

"musicians": [{

"firstName": "Eric",

"lastName": "Clapton",

"instrument": "guitar"

}, {

"firstName": "Sergei",

"lastName": "Rachmaninoff",

"instrument": "piano"

}]

}')

那一般我们遇到这样来存储JSON格式的话，只能把这条记录取出来交个应用程序，由应用程

来解析。如此一来，JSON又和特定的应用程序耦合在一起，其便利性的优势大打折扣。

现在到了MySQL5.7，可以支持对JSON进行属性的解析，我们重新修改下表结构：

ALTER TABLE json_test MODIFY person_desc json

先看看插入的这行JSON数据有哪些KEY：

mysql>SELECT id,json_keys(person_desc) as "keys" FROM json_test\G

*************************** 1. row***************************

id: 1

keys: ["authors", "musicians","programmers"]

1 row in set (0.00 sec)

我们可以看到，里面有三个KEY，分别为authors，musicians，programmers。那现在找一

KEY把对应的值拿出来：

mysql>SELECT json_extract(AUTHORS,'$.lastName[0]') AS 'name', AUTHORS FROM

->(

->SELECT id,json_extract(person_desc,'$.authors[0][0]') AS "authors" FROM json_test

->UNION ALL

->SELECT id,json_extract(person_desc,'$.authors[1][0]') AS "authors" FROM json_test

->UNION ALL

->SELECT id,json_extract(person_desc,'$.authors[2][0]') AS "authors" FROM json_test

->) AS T1

->ORDER BY NAME DESC\G

*************************** 1. row***************************

name:"Williams"

AUTHORS: {"genre": "fantasy","lastName": "Williams", "firstName":"Tad"}

*************************** 2. row***************************

name:"Peretti"

AUTHORS: {"genre":"christianfiction", "lastName": "Peretti","firstName":

"Frank"}*************************** 3. row***************************

name:"Asimov"

AUTHORS: {"genre": "sciencefiction","lastName": "Asimov", "firstName":"Isaac"}

3 rows in set (0.00 sec)

现在来把详细的值罗列出来：

mysql>SELECT

->json_extract(AUTHORS,'$.firstName[0]') AS "firstname",

->json_extract(AUTHORS,'$.lastName[0]')AS "lastname",

->json_extract(AUTHORS,'$.genre[0]') AS"genre"

->FROM

->(

->SELECT id,json_extract(person_desc,'$.authors[0]')AS "authors" FROM json

_test

->) AS T\G

*************************** 1. row***************************

firstname: "Isaac"

lastname:"Asimov"

genre:"sciencefiction"

1 row in set (0.00 sec)

我们进一步来演示把authors 这个KEY对应的所有对象删掉。

mysql>UPDATE json_test

->SET person_desc =json_remove(person_desc,'$.authors')\G

Query OK, 1 row affected (0.01 sec)

Rows matched: 1 Changed: 1  Warnings: 0

查找下对应的KEY，发现已经被删除掉了。

mysql>SELECT json_contains_path(person_desc,'all','$.authors')as authors_exists FROM

json_test\G

*************************** 1. row***************************

authors_exists: 0

1 row in set (0.00 sec)

总结下，虽然MySQL5.7开始支持JSON数据类型，但是我建议如果要使用的话，最好是把这的值取出来，然后在应用程序段来计算。毕竟数据库是用来处理结构化数据的，大量的未预先定义schema的json解析，会拖累数据库的性能。

---