MYSQL中提供了不少內置的函數,如下:html
CHAR_LENGTH(str) 返回值爲字符串str 的長度,長度的單位爲字符。一個多字節字符算做一個單字符。 對於一個包含五個二字節字符集, LENGTH()返回值爲 10, 而CHAR_LENGTH()的返回值爲5。 eg: mysql> select char_length('zhang') -> ; +----------------------+ | char_length('zhang') | +----------------------+ | 5 | +----------------------+ 1 row in set (0.00 sec) CONCAT(str1,str2,...) 字符串拼接 若有任何一個參數爲NULL ,則返回值爲 NULL。 mysql> select concat('zz','l') -> ; +------------------+ | concat('zz','l') | +------------------+ | zzl | +------------------+ 1 row in set (0.01 sec) CONCAT_WS(separator,str1,str2,...) 字符串拼接(自定義鏈接符) CONCAT_WS()不會忽略任何空字符串。 (然而會忽略全部的 NULL)。 mysql> select CONCAT_WS('**','zzl','cyy'); +-----------------------------+ | CONCAT_WS('**','zzl','cyy') | +-----------------------------+ | zzl**cyy | +-----------------------------+ 1 row in set (0.00 sec) CONV(N,from_base,to_base) 進制轉換 mysql> SELECT CONV('a',16,2); 表示將 a 由16進制轉換爲2進制字符串表示 +----------------+ | CONV('a',16,2) | +----------------+ | 1010 | +----------------+ 1 row in set (0.01 sec) mysql> SELECT CONV('10',8,2); 表示將 a 由8進制轉換爲2進制字符串表示 +----------------+ | CONV('10',8,2) | +----------------+ | 1000 | +----------------+ 1 row in set (0.00 sec) FORMAT(X,D) 將數字X 的格式寫爲'#,###,###.##',以四捨五入的方式保留小數點後 D 位, 並將結果以字符串的形式返回。若 D 爲 0, 則返回結果不帶有小數點,或不含小數部分。 eg: mysql> SELECT FORMAT(89333322.31,5); +-----------------------+ | FORMAT(89333322.31,5) | +-----------------------+ | 89,333,322.31000 | +-----------------------+ 1 row in set (0.00 sec) INSERT(str,pos,len,newstr) 在str的指定位置插入字符串 pos:要替換位置其實位置 len:替換的長度 newstr:新字符串 特別的: 若是pos超過原字符串長度,則返回原字符串 若是len超過原字符串長度,則由新字符串徹底替換 mysql> select insert('zhang','1','1','Z') -> ; +-----------------------------+ | insert('zhang','1','1','Z') | +-----------------------------+ | Zhang | +-----------------------------+ 1 row in set (0.01 sec) INSTR(str,substr) 返回字符串 str 中子字符串的第一個出現位置。 mysql> select instr('zhang','an') -> ; +---------------------+ | instr('zhang','an') | +---------------------+ | 3 | +---------------------+ 1 row in set (0.01 sec) LEFT(str,len) 返回字符串str 從開始的len位置的子序列字符。 mysql> select left('zhang',8) -> ; +-----------------+ | left('zhang',8) | +-----------------+ | zhang | +-----------------+ 1 row in set (0.00 sec) mysql> select left('zhang',3); +-----------------+ | left('zhang',3) | +-----------------+ | zha | +-----------------+ 1 row in set (0.00 sec) LOWER(str) 變小寫 mysql> select LOWER('ZHAng'); +----------------+ | LOWER('ZHAng') | +----------------+ | zhang | +----------------+ 1 row in set (0.00 sec) UPPER(str) 變大寫 mysql> select UPPER('ZHAng'); +----------------+ | UPPER('ZHAng') | +----------------+ | ZHANG | +----------------+ 1 row in set (0.00 sec) SUBSTRING(str,pos,len) 獲取字符串子序列 mysql> select substring('zhang','3','2') -> ; +----------------------------+ | substring('zhang','3','2') | +----------------------------+ | an | +----------------------------+ 1 row in set (0.00 sec) LOCATE(substr,str,pos) 獲取子序列索引位置 mysql> select locate('f','zhangfddadadafff','1'); +------------------------------------+ | locate('f','zhangfddadadafff','1') | +------------------------------------+ | 6 | +------------------------------------+ 1 row in set (0.00 sec) REPEAT(str,count) 返回一個由重複的字符串str 組成的字符串,字符串str的數目等於count 。 若 count <= 0,則返回一個空字符串。 若str 或 count 爲 NULL,則返回 NULL 。 mysql> select repeat('zhang',3) -> ; +-------------------+ | repeat('zhang',3) | +-------------------+ | zhangzhangzhang | +-------------------+ 1 row in set (0.01 sec) mysql> select repeat('zhang',2) -> ; +-------------------+ | repeat('zhang',2) | +-------------------+ | zhangzhang | +-------------------+ 1 row in set (0.00 sec) REPLACE(str,from_str,to_str) 返回字符串str 以及全部被字符串to_str替代的字符串from_str 。 mysql> select replace('zhangzhanling','ling','zhan') -> ; +----------------------------------------+ | replace('zhangzhanling','ling','zhan') | +----------------------------------------+ | zhangzhanzhan | +----------------------------------------+ 1 row in set (0.00 sec) REVERSE(str) 返回字符串 str ,順序和字符順序相反。 mysql> select reverse('zhang') -> ; +------------------+ | reverse('zhang') | +------------------+ | gnahz | +------------------+ 1 row in set (0.01 sec) RIGHT(str,len) 從字符串str 開始,返回從後邊開始len個字符組成的子序列 mysql> select right('zhang','3') -> ; +--------------------+ | right('zhang','3') | +--------------------+ | ang | +--------------------+ 1 row in set (0.00 sec) SPACE(N) 返回一個由N空格組成的字符串。 mysql> select space(4) -> ; +----------+ | space(4) | +----------+ | | +----------+ 1 row in set (0.00 sec) 不帶有len 參數的格式從字符串str返回一個子字符串,起始於位置 pos。帶有len參數的格式從字符串str返回一個長度同len字符相同的子字符串,起始於位置 pos。 使用 FROM的格式爲標準 SQL 語法。也可能對pos使用一個負值。倘若這樣,則子字符串的位置起始於字符串結尾的pos 字符,而不是字符串的開頭位置。在如下格式的函數中能夠對pos 使用一個負值。 SUBSTRING(str,pos) , mysql> SELECT SUBSTRING('zhangzhanling',5); +------------------------------+ | SUBSTRING('zhangzhanling',5) | +------------------------------+ | gzhanling | +------------------------------+ 1 row in set (0.00 sec) SUBSTRING(str FROM pos) mysql> SELECT SUBSTRING('zhangzhanling' from 5); +-----------------------------------+ | SUBSTRING('zhangzhanling' from 5) | +-----------------------------------+ | gzhanling | +-----------------------------------+ 1 row in set (0.00 sec) SUBSTRING(str,pos,len) , mysql> SELECT SUBSTRING('zhangzhanling',4,5); +--------------------------------+ | SUBSTRING('zhangzhanling',4,5) | +--------------------------------+ | ngzha | +--------------------------------+ 1 row in set (0.00 sec) SUBSTRING(str FROM pos FOR len) mysql> SELECT SUBSTRING('zhangzhanling' from -4 for 2); +------------------------------------------+ | SUBSTRING('zhangzhanling' from -4 for 2) | +------------------------------------------+ | li | +------------------------------------------+ 1 row in set (0.01 sec)
更多的請參照:mysql
1.查看自定義函數功能是個否開啓:ios
mysql> show variables like '%func%'; +---------------------------------+-------+ | Variable_name | Value | +---------------------------------+-------+ | log_bin_trust_function_creators | OFF | +---------------------------------+-------+ 1 row in set, 12 warnings (0.02 sec) mysql> SET GLOBAL log_bin_trust_function_creators=1; 開啓自定義函數功能 Query OK, 0 rows affected (0.00 sec) mysql> show variables like '%func%'; +---------------------------------+-------+ | Variable_name | Value | +---------------------------------+-------+ | log_bin_trust_function_creators | ON | +---------------------------------+-------+ 1 row in set, 12 warnings (0.01 sec) 注:SET GLOBAL log_bin_trust_function_creators=1; 關閉自定義函數功能
2.基本語法:sql
delimiter 自定義符號 -- 若是函數體只有一條語句, begin和end能夠省略, 同時delimiter也能夠省略 create function 函數名(形參列表) returns 返回類型 -- 注意是retruns begin 函數體 -- 函數內定義的變量如:set @x = 1; 變量x爲全局變量,在函數外面也可使用 返回值 end 自定義符號 delimiter ;
3.建立自定義函數示例:數據庫
mysql> delimiter $$ mysql> create function my(a int, b int) returns int -> begin -> return a + b; -> end -> $$ Query OK, 0 rows affected (0.00 sec) mysql> delimiter ;
4.刪除函數:vim
mysql> drop function my; Query OK, 0 rows affected (0.02 sec)
5.執行函數:服務器
mysql> select my(11,23); +-----------+ | my(11,23) | +-----------+ | 34 | +-----------+ 1 row in set (0.01 sec)
爲何用索引?
在咱們的生產環境中,通常讀(查詢)寫(插入,更新,刪除)的比例能佔到1:10甚至更多,所以對查詢語句的優化是很是重要的,這裏就必須用索引嘍。
索引是什麼?
索引是數據庫中專門用於幫助用戶快速查詢數據的一種數據結構,相似與字典中的目錄,查找字典內容時能夠根據目錄查找到數據的存放位置目錄,而後直接獲取。
索引的好處是什麼?
1.索引能夠加快查詢速度,可是並非索引越多越好。數據結構
2.mysql中的primary key,unique,聯合惟一也都是索引,這些索引除了加速查找之外,還有約束的功能運維
若是mysql數據庫添加太多的索引,磁盤的iostat磁盤使用率會持續很高,甚至長時間達到100%。ide
只有加速查找的功能
eg:
建立表 + 索引 mysql> create table in1( -> nid int not null auto_increment primary key, -> name varchar(32) not null, -> email varchar(64) not null, -> extra text, -> index ix_name (name) -> ); Query OK, 0 rows affected (0.05 sec) 建立索引 mysql> create index int_name on in1(nid); Query OK, 0 rows affected (0.04 sec) Records: 0 Duplicates: 0 Warnings: 0 查看索引 mysql> show index from in1; +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+ | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+ | in1 | 0 | PRIMARY | 1 | nid | A | 0 | NULL | NULL | | BTREE | | | | in1 | 1 | ix_name | 1 | name | A | 0 | NULL | NULL | | BTREE | | | | in1 | 1 | int_name | 1 | nid | A | 0 | NULL | NULL | | BTREE | | | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+ 3 rows in set (0.01 sec) 刪除索引 mysql> drop index int_name on in1; Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 查看是否刪除成功 mysql> show index from in1; +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+ | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+ | in1 | 0 | PRIMARY | 1 | nid | A | 0 | NULL | NULL | | BTREE | | | | in1 | 1 | ix_name | 1 | name | A | 0 | NULL | NULL | | BTREE | | | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+ 2 rows in set (0.00 sec) 注意:對於建立索引時若是是BLOB 和 TEXT 類型,必須指定length。 create index ix_extra on in1(extra(32));
主鍵索引 PRIMARY KEY:加速查找+約束(不爲空、不能重複)
惟一索引 UNIQUE:加速查找+約束(不能重複)
建立表 mysql> create table in2( -> nid int not null auto_increment primary key, -> name varchar(32) not null, -> email varchar(64) not null, -> extra text, -> unique ix_name (name) -> ); Query OK, 0 rows affected (0.03 sec) 建立惟一索引 mysql> create unique index nid_name on in2(nid); Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0
加速查詢 和 惟一約束(不可含null)
第一種建立方式: mysql> create table in3( -> nid int not null auto_increment primary key, -> name varchar(32) not null, -> email varchar(64) not null, -> extra text, -> index ix_name (name) -> ); Query OK, 0 rows affected (0.03 sec) 第二種建立方式: mysql> create table in4( -> nid int not null auto_increment, -> name varchar(32) not null, -> email varchar(64) not null, -> extra text, -> primary key(nid), -> index ix_name (name) -> ); Query OK, 0 rows affected (0.03 sec) 刪除主鍵索引 mysql> alter table in4 modify nid int, drop primary key; Query OK, 0 rows affected (0.05 sec) Records: 0 Duplicates: 0 Warnings: 0 增長主鍵索引 mysql> alter table in4 add primary key(name); Query OK, 0 rows affected (0.05 sec) Records: 0 Duplicates: 0 Warnings: 0
簡單的講是將n個列組合成一個索引
PRIMARY KEY(id,name):聯合主鍵索引
UNIQUE(id,name):聯合惟一索引
INDEX(id,name):聯合普通索引
mysql> create table in5( -> nid int not null auto_increment primary key, -> name varchar(32) not null, -> email varchar(64) not null, -> extra text -> ); PRIMARY KEY(id,name):聯合主鍵索引 mysql> create index ix_name_email on in5(nid,name); Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 UNIQUE(id,name):聯合惟一索引 mysql> alter table in5 add unique index(nid,name); Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 INDEX(id,name):聯合普通索引 mysql> create index ix_name on in5(name,email); Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0
建立表 mysql> create table s1( -> id int, -> name varchar(20), -> gender char(6), -> email varchar(50) -> ); Query OK, 0 rows affected (0.03 sec) 建立存儲過程,實現批量插入記錄 mysql> delimiter $$ 聲明存儲過程的結束符號 :$$ mysql> create procedure auto_insert1() -> BEGIN -> declare i int default 1; -> while(i<3000000)do -> insert into s1 values(i,'zzl','man',concat('zzl',i,'@wsdashi.com')); -> set i=i+1; -> end while; -> END #$$結束 Query OK, 0 rows affected (0.01 sec) mysql> delimiter ;從新聲明分號爲結束符號 查看存儲過程 mysql> show create procedure auto_insert1\G *************************** 1. row *************************** Procedure: auto_insert1 sql_mode: ONLY_FULL_GROUP_BY,STRICT_TRANS_TABLES,NO_ZERO_IN_DATE,NO_ZERO_DATE,ERROR_FOR_DIVISION_BY_ZERO,NO_AUTO_CREATE_USER,NO_ENGINE_SUBSTITUTION Create Procedure: CREATE DEFINER=`root`@`localhost` PROCEDURE `auto_insert1`() BEGIN declare i int default 1; while(i<3000000)do insert into s1 values(i,'zzl','man',concat('zzl',i,'@wsdashi.com')); set i=i+1; end while; END character_set_client: gbk collation_connection: gbk_chinese_ci Database Collation: latin1_swedish_ci 1 row in set (0.00 sec) 調用存儲過程 mysql> call auto_insert1();
mysql> call auto_insert1();
Query OK, 1 row affected (6 hours 30 min 52.60 sec)
mysql> select * from s1 where id=3500000; Empty set (1.63 sec)
時間1.63 sec
mysql> create index s1_id on s1(id); Query OK, 0 rows affected (7.54 sec) Records: 0 Duplicates: 0 Warnings: 0
注:若是在生產環境下面,在已經有的數據中,建立索引的時候,會鎖表,用戶不能使用該表,因此通常這樣的操做要晚上作
mysql> select * from s1 where id=35000000; Empty set (0.01 sec)
時間0.01 sec
1. 必定是爲搜索條件的字段建立索引,好比select * from s1 where id = 222;就須要爲id加上索引,若是id加上索引查詢其餘的字段是無論用的
2. 在表中已經有大量數據的狀況下,建索引會很慢,且佔用硬盤空間,建完後查詢速度加快
注意:在生產環境中,一個新的功能上線,須要建表,站在運維的角度,必定要多問如下開發,這個表是否有大量的讀操做,若是有的話,須要開發明確怎麼查詢的,從而建索引,若是讀的少,寫的多,可根據狀況不建索引,索引過多會消耗磁盤利用率的。
等於:指定要找2000這個id號,在索引樹中能夠快速的查找 mysql> select count(*) from s1 where id=2000; +----------+ | count(*) | +----------+ | 1 | +----------+ 1 row in set (0.00 sec) 大於:會利用索引樹,沒有指定那個id,而是指定了一個範圍,這個範圍包含大於2000的id,則mysql會拿着2001去搜索樹中找一次,而後2002在找,一次類推,總體下來,和整表掃描沒啥區別 mysql> select count(*) from s1 where id>2000; +----------+ | count(*) | +----------+ | 2997999 | +----------+ 1 row in set (1.21 sec) 若是範圍小的話,查詢速度仍然是很快的。 mysql> select count(*) from s1 where id>2000 and id<3000; +----------+ | count(*) | +----------+ | 999 | +----------+ 1 row in set (0.01 sec) 不等於:不等於2000,範圍很大,查詢很慢。 mysql> select count(*) from s1 where id != 2000; +----------+ | count(*) | +----------+ | 2999998 | +----------+ 1 row in set (1.20 sec) 等於2000,就一個數,則查詢很快。 mysql> select count(*) from s1 where id = 2000; +----------+ | count(*) | +----------+ | 1 | +----------+ 1 row in set (0.00 sec) between ...and... 範圍大的,查詢依然仍是很慢的 mysql> select count(*) from s1 where id between 1 and 3000000; +----------+ | count(*) | +----------+ | 2999999 | +----------+ 1 row in set (1.30 sec) 範圍小的,查詢是快的 mysql> select count(*) from s1 where id between 1 and 2; +----------+ | count(*) | +----------+ | 2 | +----------+ 1 row in set (0.00 sec) like:前面帶%號查詢比後面帶%或者等於特定值的要慢 mysql> select count(*) from s1 where id like '1000dd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (1.09 sec) mysql> select count(*) from s1 where id like '1000dd%'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (1.08 sec) mysql> select count(*) from s1 where id like '%1000'; +----------+ | count(*) | +----------+ | 300 | +----------+ 1 row in set (1.14 sec)
查看下錶結構 mysql> desc s1; +--------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+-----+---------+-------+ | id | int(11) | YES | MUL | NULL | | | name | varchar(20) | YES | | NULL | | | gender | char(6) | YES | | NULL | | | email | varchar(50) | YES | | NULL | | +--------+-------------+------+-----+---------+-------+ 4 rows in set (0.02 sec) 刪除id的索引 mysql> drop index s1_id on s1; Query OK, 0 rows affected (0.03 sec) Records: 0 Duplicates: 0 Warnings: 0 查看下是否刪除成功 mysql> desc s1; +--------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+-----+---------+-------+ | id | int(11) | YES | | NULL | | | name | varchar(20) | YES | | NULL | | | gender | char(6) | YES | | NULL | | | email | varchar(50) | YES | | NULL | | +--------+-------------+------+-----+---------+-------+ 4 rows in set (0.00 sec) 查看一個name等於dddd的個數有多少,速度是慢的 mysql> select count(*) from s1 where name='dddd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (1.77 sec) 建立name的索引 mysql> create index s1_name on s1(name); Query OK, 0 rows affected (8.35 sec) Records: 0 Duplicates: 0 Warnings: 0 查詢速度明顯提高不少 mysql> select count(*) from s1 where name='dddd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.01 sec) 查詢name爲zzl的字段,速度再一次變慢 mysql> select count(*) from s1 where name='zzl'; +----------+ | count(*) | +----------+ | 2999999 | +----------+ 1 row in set (1.05 sec) 爲什麼是這種狀況呢? 咱們編寫存儲過程爲表s1批量添加記錄,name字段的值均爲zzl,也就是說name這個字段的區分度很低 利用b+樹的結構,查詢的速度與樹的高度成反比,要想將樹的高低控制的很低, 須要保證:在某一層內數據項均是按照從左到右,從小到大的順序依次排開,即左1<左2<左3<... 而對於區分度低的字段,沒法找到大小關係,由於值都是相等的,毫無疑問,還想要用b+樹存放這些等值的數據, 只能增長樹的高度,字段的區分度越低,則樹的高度越高。極端的狀況,索引字段的值都同樣,那麼b+樹幾乎成了一根棍。 本例中就是這種極端的狀況,name字段全部的值均爲'zzl' 因此得出,爲區分度低的字段創建索引,索引樹的高度會很高。 1:若是條件是name='dddd',那麼確定是能夠第一時間判斷出'dddd'是不在索引樹中的(由於樹中全部的值均爲'zzl’),因此查詢速度很快 2:若是條件正好是name='zzl',查詢時,咱們永遠沒法從樹的某個位置獲得一個明確的範圍,只能往下找,在往下找,在在往下找。。。這與全表掃描的IO次數沒有多大區別,因此速度很慢
查看錶結構
mysql> desc s1;
+--------+-------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+--------+-------------+------+-----+---------+-------+
| id | int(11) | YES | | NULL | |
| name | varchar(20) | YES | MUL | NULL | |
| gender | char(6) | YES | | NULL | |
| email | varchar(50) | YES | | NULL | |
+--------+-------------+------+-----+---------+-------+
4 rows in set (0.00 sec)
刪除原有的索引
mysql> drop index s1_name on s1;
Query OK, 0 rows affected (0.02 sec)
Records: 0 Duplicates: 0 Warnings: 0
確認刪除
mysql> desc s1;
+--------+-------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+--------+-------------+------+-----+---------+-------+
| id | int(11) | YES | | NULL | |
| name | varchar(20) | YES | | NULL | |
| gender | char(6) | YES | | NULL | |
| email | varchar(50) | YES | | NULL | |
+--------+-------------+------+-----+---------+-------+
4 rows in set (0.00 sec)
沒有建索引以前,查詢速度是很慢點
mysql> select count(*) from s1 where name='zzl' and gender='man' and email='137@wsdashi.com'
-> ;
+----------+
| count(*) |
+----------+
| 0 |
+----------+
1 row in set (1.73 sec)
建立聯合索引
mysql> create index s1_name on s1(name,gender,email);
Query OK, 0 rows affected (15.21 sec)
Records: 0 Duplicates: 0 Warnings: 0
查詢速度加快
mysql> select count(*) from s1 where name='zzl' and gender='man' and email='137@wsdashi.com'
-> ;
+----------+
| count(*) |
+----------+
| 0 |
+----------+
1 row in set (0.00 sec)
隨意的還位置,查詢速度不變
mysql> select count(*) from s1 where name='zzl' and email='137@wsdashi.com' and gender='man';
+----------+
| count(*) |
+----------+
| 0 |
+----------+
1 row in set (0.00 sec)
可是若是是兩個的話,查詢速度是慢的
mysql> select count(*) from s1 where name='zzl' and email='137@wsdashi.com' ;
+----------+
| count(*) |
+----------+
| 0 |
+----------+
1 row in set (2.00 sec)
是一個的話,查詢速度也是慢的
mysql> select count(*) from s1 where name='zzl' ;
+----------+
| count(*) |
+----------+
| 2999999 |
+----------+
1 row in set (1.91 sec)
查看錶結構 mysql> desc s1; +--------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+-----+---------+-------+ | id | int(11) | YES | | NULL | | | name | varchar(20) | YES | MUL | NULL | | | gender | char(6) | YES | | NULL | | | email | varchar(50) | YES | | NULL | | +--------+-------------+------+-----+---------+-------+ 4 rows in set (0.01 sec) 刪除原有的索引 mysql> drop index s1_name on s1; Query OK, 0 rows affected (0.03 sec) Records: 0 Duplicates: 0 Warnings: 0 建立id索引 mysql> create index s1_name on s1(id); Query OK, 0 rows affected (7.63 sec) Records: 0 Duplicates: 0 Warnings: 0 查看錶結構 mysql> desc s1; +--------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+-----+---------+-------+ | id | int(11) | YES | MUL | NULL | | | name | varchar(20) | YES | | NULL | | | gender | char(6) | YES | | NULL | | | email | varchar(50) | YES | | NULL | | +--------+-------------+------+-----+---------+-------+ 4 rows in set (0.01 sec) 查詢id的速度是至關快的,由於id有索引。 mysql> select count(*) from s1 where id=4000; +----------+ | count(*) | +----------+ | 1 | +----------+ 1 row in set (0.00 sec) 索引id字段參與了計算,沒法拿到一個明確的值去索引樹中查找,因此查詢速度是比較慢的 mysql> select count(*) from s1 where id*2=4000; +----------+ | count(*) | +----------+ | 1 | +----------+ 1 row in set (1.02 sec)
1、and與or的邏輯 條件1 and 條件2:全部條件都成立纔算成立,但凡要有一個條件不成立則最終結果不成立 條件1 or 條件2:只要有一個條件成立則最終結果就成立 2、and的工做原理 條件: a = 10 and b = 'ddd' and c > 3 and d =4 索引: 製做聯合索引(d,a,b,c) 工做原理: 對於連續多個and:mysql會按照聯合索引,從左到右的順序找一個區分度高的索引字段(這樣即可以快速鎖定很小的範圍),加速查詢,即按照d—>a->b->c的順序 3、or的工做原理 條件: a = 10 or b = 'ddd' or c > 3 or d =4 索引: 製做聯合索引(d,a,b,c) 工做原理: 對於連續多個or:mysql會按照條件的順序,從左到右依次判斷,即a->b->c->d
eg:
mysql> desc s1; +--------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+-----+---------+-------+ | id | int(11) | YES | MUL | NULL | | | name | varchar(20) | YES | | NULL | | | gender | char(6) | YES | | NULL | | | email | varchar(50) | YES | | NULL | | +--------+-------------+------+-----+---------+-------+ 4 rows in set (0.00 sec) name字段添加索引,可是改字段的區分度比較低 mysql> create index s1name on s1(name); Query OK, 0 rows affected (9.00 sec) Records: 0 Duplicates: 0 Warnings: 0 name='ddd'能夠很快的從索引樹中區分出該字段不存在,於是速度會很快 mysql> select count(*) from s1 where name='ddd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.00 sec) gender是非索引字段的,可是,name='ddd'不成立的話,就不用管gender的條件了呢,至關於只有name='ddd'速度仍是很快的; mysql> select count(*) from s1 where name='ddd' and gender='man' -> ; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.00 sec) 在左邊條件成立可是索引字段的區分度低的狀況下(name與gender均屬於這種狀況), 會依次往右找到一個區分度高的索引字段,加速查詢 mysql> select count(*) from s1 where name='ddd' and gender='man'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.00 sec) mysql> select count(*) from s1 where name='zzl' and gender='man'; +----------+ | count(*) | +----------+ | 2999999 | +----------+ 1 row in set (20.95 sec) mysql> create index s1_gender on s1(gender); Query OK, 0 rows affected (11.72 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> select count(*) from s1 where name='zzl' and gender='man'; +----------+ | count(*) | +----------+ | 2999999 | +----------+ 1 row in set (3.74 sec) mysql> select count(*) from s1 where name='zzl' and gender='xxx'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.01 sec) mysql> select count(*) from s1 where name='zzl' and gender='man'; +----------+ | count(*) | +----------+ | 2999999 | +----------+ 1 row in set (3.01 sec) mysql> select count(*) from s1 where name='zzl' and gender='man' and id=333; +----------+ | count(*) | +----------+ | 1 | +----------+ 1 row in set (0.01 sec) mysql> select count(*) from s1 where name='zzl' and gender='man' and id>333; +----------+ | count(*) | +----------+ | 2999666 | +----------+ 1 row in set (18.17 sec) mysql> select count(*) from s1 where name='zzl' and gender='xxx' and id>333; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.02 sec) mysql> select count(*) from s1 where name='zzl' and -> gender='xxx' and id>222; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.00 sec) mysql> select count(*) from s1 where name='zzl' and -> gender='man' and id>222; +----------+ | count(*) | +----------+ | 2999777 | +----------+ 1 row in set (21.25 sec) 當前面三個條件都成立的時候,都沒法用索引達到加速的目的,name和gender是由於區分度低,第三個id由於範圍太大了,第四個email的區分度很高,可是沒有添加索引,因此該語句查詢速度是很是的低的 mysql> select count(*) from s1 where name='zzl' and -> gender='man' and id> 222 and email='dddd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (22.87 sec) 給email字段添加索引 mysql> create index s1_email on s1(email); Query OK, 0 rows affected (16.55 sec) Records: 0 Duplicates: 0 Warnings: 0 添加上email字段的索引後,索引明顯的提高 mysql> select count(*) from s1 where name='zzl' and -> gender='man' and id> 222 and email='dddd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.02 sec) 通過分析,在條件爲name='zzl' and gender='man' and id>222 and email='dddd'的狀況下,咱們徹底不必爲前三個條件的字段加索引,由於只能用上email字段的索引, 前三個字段的索引反而會下降咱們的查詢效率 驗證: mysql> select count(*) from s1 where name='zzl' and -> gender='man' and id> 222 and email='dddd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.02 sec) mysql> desc s1; +--------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+-----+---------+-------+ | id | int(11) | YES | MUL | NULL | | | name | varchar(20) | YES | MUL | NULL | | | gender | char(6) | YES | MUL | NULL | | | email | varchar(50) | YES | MUL | NULL | | +--------+-------------+------+-----+---------+-------+ 4 rows in set (0.01 sec) mysql> drop index s1_gender on s1; Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> drop index s1name on s1; Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> drop index s1_name on s1; Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> desc s1; +--------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+-----+---------+-------+ | id | int(11) | YES | | NULL | | | name | varchar(20) | YES | | NULL | | | gender | char(6) | YES | | NULL | | | email | varchar(50) | YES | MUL | NULL | | +--------+-------------+------+-----+---------+-------+ 4 rows in set (0.01 sec) mysql> select count(*) from s1 where name='zzl' and -> gender='man' and id> 222 and email='dddd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.00 sec) 刪掉索引後時間是0.00不刪是0.02,同時也論證了不是索引越多越好的哦
對於組合索引mysql會一直向右匹配直到遇到範圍查詢(>、<、between、like)就中止匹配(指的是範圍大了,有索引速度也慢),好比a = 1 and b = 2 and c > 3 and d = 4 若是創建(a,b,c,d)順序的索引,d是用不到索引的,若是創建(a,b,d,c)的索引則均可以用到,a,b,d的順序能夠任意調整。
mysql> drop index s1_email on s1; Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 創建索引的時候,沒有將範圍寫到最後面,查詢速度慢 mysql> create index ddd on s1(id,name,gender,email); Query OK, 0 rows affected (16.29 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> select count(*) from s1 where name='zzl' and gender='man' and id > 222 and email='dddd'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (2.27 sec) 更改查詢的位置,有些許的提高,可是提高不大 mysql> select count(*) from s1 where name='zzl' and gender='man' and email='dddd' and id>222; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (2.10 sec) 刪除剛纔的索引 mysql> drop index ddd on s1; Query OK, 0 rows affected (0.03 sec) Records: 0 Duplicates: 0 Warnings: 0 把查詢範圍的,放到最後 mysql> create index ddd on s1(name,gender,email,id); Query OK, 0 rows affected (17.44 sec) Records: 0 Duplicates: 0 Warnings: 0 查詢速度顯著提高 mysql> select count(*) from s1 where name='zzl' and gender='man' and email='dddd' and id>222; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.01 sec)
- 使用函數 select * from s1 where reverse(email) = '123@wsdashi.com'; - 類型不一致 若是列是字符串類型,傳入條件是必須用引號引發來,否則... select * from s1 where email = 999; #排序條件爲索引,則select字段必須也是索引字段,不然沒法命中 - order by select name from s1 order by email desc; 當根據索引排序時候,select查詢的字段若是不是索引,則速度仍然很慢 select email from s1 order by email desc; 特別的:若是對主鍵排序,則仍是速度很快: select * from s1 order by nid desc; - 組合索引最左前綴 若是組合索引爲:(name,email) name and email -- 命中索引 name -- 命中索引 email -- 未命中索引 - count(1)或count(列)代替count(*)在mysql中沒有差異了 - create index xxxx on tb(title(19)) #text類型,必須制定長度
1 避免使用select * 2 count(1)或count(列) 代替 count(*) 3 建立表時儘可能時 char 代替 varchar 4 表的字段順序固定長度的字段優先 5 組合索引代替多個單列索引(常用多個條件查詢時) 6 儘可能使用短索引 7 使用鏈接(JOIN)來代替子查詢(Sub-Queries) 8 連表時注意條件類型需一致 9 索引散列值(重複少)不適合建索引,例:性別不適合
組合索引時指對錶上的多個列合起來作一個索引。組合索引的建立方法與單個索引的建立方法同樣,不一樣之處在僅在於有多個索引列,以下
mysql> create table s2( -> a int, -> b int, -> primary key(a), -> key id_a_b(a,b) -> ); Query OK, 0 rows affected (0.04 sec)
那麼什麼時候須要使用組合索引呢?在討論這個問題以前,先來看一下組合索引內部的結果。從本質上來講,組合索引就是一棵B+樹,不一樣的是組合索引的鍵值得數量不是1,而是>=2。接着來討論兩個整型列組成的組合索引,假定兩個鍵值得名稱分別爲a、b如圖
能夠看到這與咱們以前看到的單個鍵的B+樹並無什麼不一樣,鍵值都是排序的,經過葉子結點能夠邏輯上順序地讀出全部數據,就上面的例子來講,即(1,1),(1,2),(2,1),(2,4),(3,1),(3,2),數據按(a,b)的順序進行了存放。
所以,對於查詢select * from table where a=xxx and b=xxx, 顯然是可使用(a,b) 這個聯合索引的,對於單個列a的查詢select * from table where a=xxx,也是可使用(a,b)這個索引的。
但對於b列的查詢select * from table where b=xxx,則不可使用(a,b) 索引,其實你不難發現緣由,葉子節點上b的值爲一、二、一、四、一、2顯然不是排序的,所以對於b列的查詢使用不到(a,b) 索引
組合索引的第二個好處是在第一個鍵相同的狀況下,已經對第二個鍵進行了排序處理,例如在不少狀況下應用程序都須要查詢某個用戶的購物狀況,並按照時間進行排序,最後取出最近三次的購買記錄,這時使用組合索引能夠幫咱們避免多一次的排序操做,由於索引自己在葉子節點已經排序了,以下
準備數據表 mysql> create table buy_log( -> userid int unsigned not null, -> buy_date date -> ); Query OK, 0 rows affected (0.03 sec) mysql> mysql> insert into buy_log values -> (1,'2009-01-01'), -> (2,'2009-01-01'), -> (3,'2009-01-01'), -> (1,'2009-02-01'), -> (3,'2009-02-01'), -> (1,'2009-03-01'), -> (1,'2009-04-01'); Query OK, 7 rows affected (0.00 sec) Records: 7 Duplicates: 0 Warnings: 0 mysql> mysql> alter table buy_log add key(userid); Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> alter table buy_log add key(userid,buy_date); Query OK, 0 rows affected (0.03 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> show create table buy_log; +---------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Table | Create Table | +---------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | buy_log | CREATE TABLE `buy_log` ( `userid` int(10) unsigned NOT NULL, `buy_date` date DEFAULT NULL, KEY `userid` (`userid`), KEY `userid_2` (`userid`,`buy_date`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1 | +---------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 1 row in set (0.01 sec) 能夠看到possible_keys在這裏有兩個索引能夠用,分別是單個索引userid與聯合索引userid_2,可是優化器最終選擇了使用的key是userid由於該索引的葉子節點包含單個鍵值,因此理論上一個頁能存放的記錄應該更多 mysql> explain select * from buy_log where userid=2; +----+-------------+---------+------------+------+-----------------+--------+---------+-------+------+----------+-------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+---------+------------+------+-----------------+--------+---------+-------+------+----------+-------+ | 1 | SIMPLE | buy_log | NULL | ref | userid,userid_2 | userid | 4 | const | 1 | 100.00 | NULL | +----+-------------+---------+------------+------+-----------------+--------+---------+-------+------+----------+-------+ 1 row in set, 1 warning (0.01 sec) 假定要取出userid爲1的最近3次的購買記錄,用的就是聯合索引userid_2了,由於在這個索引中,在userid=1的狀況下,buy_date都已經排序好了 mysql> explain select * from buy_log where userid=1 order by buy_date desc limit 3; +----+-------------+---------+------------+------+-----------------+----------+---------+-------+------+----------+--------------------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+---------+------------+------+-----------------+----------+---------+-------+------+----------+--------------------------+ | 1 | SIMPLE | buy_log | NULL | ref | userid,userid_2 | userid_2 | 4 | const | 4 | 100.00 | Using where; Using index | +----+-------------+---------+------------+------+-----------------+----------+---------+-------+------+----------+--------------------------+ 1 row in set, 1 warning (0.00 sec)
InnoDB存儲引擎支持覆蓋索引(covering index,或稱索引覆蓋),即從輔助索引中就能夠獲得查詢記錄,而不須要查詢彙集索引中的記錄。
使用覆蓋索引的一個好處是:輔助索引不包含整行記錄的全部信息,故其大小要遠小於彙集索引,所以能夠減小大量的IO操做
注意:覆蓋索引技術最先是在InnoDB Plugin中完成並實現,這意味着對於InnoDB版本小於1.0的,或者MySQL數據庫版本爲5.0如下的,InnoDB存儲引擎不支持覆蓋索引特性
對於InnoDB存儲引擎的輔助索引而言,因爲其包含了主鍵信息,所以其葉子節點存放的數據爲(primary key1,priamey key2,...,key1,key2,...)eg:
select age from s1 where id=123 and name = 'zzl'; #id字段有索引,可是name字段沒有索引,該sql命中了索引,但未覆蓋,須要去彙集索引中再查找詳細信息。 重要的是:索引字段覆蓋了全部,那全程經過索引來加速查詢以及獲取結果就ok了 mysql> desc s1; +--------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+-----+---------+-------+ | id | int(11) | NO | | NULL | | | name | varchar(20) | YES | | NULL | | | gender | char(6) | YES | | NULL | | | email | varchar(50) | YES | | NULL | | +--------+-------------+------+-----+---------+-------+ rows in set (0.21 sec) mysql> explain select name from s1 where id=1000; #沒有任何索引 +----+-------------+-------+------------+------+---------------+------+---------+------+---------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+------+---------------+------+---------+------+---------+----------+-------------+ | 1 | SIMPLE | s1 | NULL | ALL | NULL | NULL | NULL | NULL | 2688336 | 10.00 | Using where | +----+-------------+-------+------------+------+---------------+------+---------+------+---------+----------+-------------+ row in set, 1 warning (0.00 sec) mysql> create index idx_id on s1(id); #建立索引 Query OK, 0 rows affected (4.16 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> explain select name from s1 where id=1000; #命中輔助索引,可是未覆蓋索引,還須要從彙集索引中查找name +----+-------------+-------+------------+------+---------------+--------+---------+-------+------+----------+-------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+------+---------------+--------+---------+-------+------+----------+-------+ | 1 | SIMPLE | s1 | NULL | ref | idx_id | idx_id | 4 | const | 1 | 100.00 | NULL | +----+-------------+-------+------------+------+---------------+--------+---------+-------+------+----------+-------+ row in set, 1 warning (0.08 sec) mysql> explain select id from s1 where id=1000; #在輔助索引中就找到了所有信息,Using index表明覆蓋索引 +----+-------------+-------+------------+------+---------------+--------+---------+-------+------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+------+---------------+--------+---------+-------+------+----------+-------------+ | 1 | SIMPLE | s1 | NULL | ref | idx_id | idx_id | 4 | const | 1 | 100.00 | Using index | +----+-------------+-------+------------+------+---------------+--------+---------+-------+------+----------+-------------+ row in set, 1 warning (0.03 sec)
innodb存儲引擎並不會選擇經過查詢彙集索引來進行統計。因爲buy_log表有輔助索引,而輔助索引遠小於彙集索引,選擇輔助索引能夠減小IO操做,故優化器的選擇如上key爲userid輔助索引
對於(a,b)形式的聯合索引,通常是不能夠選擇b中所謂的查詢條件。但若是是統計操做,而且是覆蓋索引,則優化器仍是會選擇使用該索引,以下
#聯合索引userid_2(userid,buy_date),通常狀況,咱們按照buy_date是沒法使用該索引的,但特殊狀況下:查詢語句是統計操做,且是覆蓋索引,則按照buy_date當作查詢條件時,也可使用該聯合索引 mysql> explain select count(*) from buy_log where buy_date >= '2011-01-01' and buy_date < '2011-02-01'; +----+-------------+---------+-------+---------------+----------+---------+------+------+--------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+---------+-------+---------------+----------+---------+------+------+--------------------------+ | 1 | SIMPLE | buy_log | index | NULL | userid_2 | 8 | NULL | 7 | Using where; Using index | +----+-------------+---------+-------+---------------+----------+---------+------+------+--------------------------+ 1 row in set (0.00 sec)
詳細的參考:https://dev.mysql.com/doc/refman/5.5/en/explain-output.html 執行計劃:通常狀況下是這樣的 all < index < range < index_merge < ref_or_null < ref < eq_ref < system/const id,email 慢: select * from userinfo3 where name='alex' explain select * from userinfo3 where name='alex' type: ALL(全表掃描) select * from userinfo3 limit 1; 快: select * from userinfo3 where email='alex' type: const(走索引)
1.若是運行真的是很是的慢,須要設置SQL_NO_CACHE 2.where條件單表查,鎖定最小返回記錄表。這句話的意思是把查詢語句的where都應用到表中返回的記錄數最小的表開始查起,單表每一個字段分別查詢,看哪一個字段的區分度最高 3.explain查看執行計劃,是否與1預期一致(從鎖定記錄較少的表開始查詢) 4.order by limit 形式的sql語句讓排序的表優先查 5.瞭解業務方使用場景 6.加索引時參照建索引的幾大原則 7.觀察結果,不符合預期繼續從0分析
慢日誌 - 執行時間 > 10 - 未命中索引 - 日誌文件路徑 配置: - 內存 show variables like '%query%'; show variables like '%queries%'; set global 變量名 = 值 - 配置文件 mysqld --defaults-file='E:\xunyou\mysql-5.7.16-winx64\mysql-5.7.16-winx64\my-default.ini' my.conf內容: slow_query_log = ON slow_query_log_file = D:/.... 注意:修改配置文件以後,須要重啓服務
MySQL日誌管理 ======================================================== 錯誤日誌: 記錄 MySQL 服務器啓動、關閉及運行錯誤等信息 二進制日誌: 又稱binlog日誌,以二進制文件的方式記錄數據庫中除 SELECT 之外的操做 查詢日誌: 記錄查詢的信息 慢查詢日誌: 記錄執行時間超過指定時間的操做 中繼日誌: 備庫將主庫的二進制日誌複製到本身的中繼日誌中,從而在本地進行重放 通用日誌: 審計哪一個帳號、在哪一個時段、作了哪些事件 事務日誌或稱redo日誌: 記錄Innodb事務相關的如事務執行時間、檢查點等 ======================================================== 1、bin-log 1. 啓用 # vim /etc/my.cnf [mysqld] log-bin[=dir\[filename]] # service mysqld restart 2. 暫停 //僅當前會話 SET SQL_LOG_BIN=0; SET SQL_LOG_BIN=1; 3. 查看 查看所有: # mysqlbinlog mysql.000002 按時間: # mysqlbinlog mysql.000002 --start-datetime="2012-12-05 10:02:56" # mysqlbinlog mysql.000002 --stop-datetime="2012-12-05 11:02:54" # mysqlbinlog mysql.000002 --start-datetime="2012-12-05 10:02:56" --stop-datetime="2012-12-05 11:02:54" 按字節數: # mysqlbinlog mysql.000002 --start-position=260 # mysqlbinlog mysql.000002 --stop-position=260 # mysqlbinlog mysql.000002 --start-position=260 --stop-position=930 4. 截斷bin-log(產生新的bin-log文件) a. 重啓mysql服務器 b. # mysql -uroot -p123 -e 'flush logs' 5. 刪除bin-log文件 # mysql -uroot -p123 -e 'reset master' 2、查詢日誌 啓用通用查詢日誌 # vim /etc/my.cnf [mysqld] log[=dir\[filename]] # service mysqld restart 3、慢查詢日誌 啓用慢查詢日誌 # vim /etc/my.cnf [mysqld] log-slow-queries[=dir\[filename]] long_query_time=n # service mysqld restart MySQL 5.6: slow-query-log=1 slow-query-log-file=slow.log long_query_time=3 查看慢查詢日誌 測試:BENCHMARK(count,expr) SELECT BENCHMARK(50000000,2*3);