數學與MySQL

The Original post: http://anothermysqldba.blogspot.com/2016/04/math-with-mysql.html

我以為我張貼了這個很久以前...哦....

我們都知道，數學是所有生命的基本方面，如果沒有超越世界各地使用的通用語言。 MySQL的，像所有的數據庫，可以幫助您與數學的許多方面。

下面是功能列表： https://dev.mysql.com/doc/refman/5.6/en/mathematical-functions.html

下面是一些簡單的例子，讓你開始。

• 二次公式AX ^ 2 + BX + C = 0


# 2x^2 – 4x – 3 = 0.
SET @a= 1;
SET @b= 3;
SET @c= -4;
SET @XX = ( -(@b) - SQRT( POW(@b,2) -4 * @a * @c) / POW(@a,2) ) ;
SET @YY = ( -(@b) + SQRT( POW(@b,2) -4 * @a * @c) / POW(@a,2) ) ;
SET @XXX = MOD(@YY, @XX);

SELECT @XX / @XXX as X;
+------+
| X |
+------+
| -4 |
+------+
SELECT @YY / @XXX as X ;
+------+
| X |
+------+
| 1 |
+------+


• 勾股定理（記住幾何101）：A ^ 2 + B ^ 2 = C ^ 2


SET @A = 14;
SET @B = 48;
SELECT @C := SQRT(POW(@A,2) + POW(@B,2) );
+-------------------------------------+
| @C := SQRT(POW(@A,2) + POW(@B,2) ) |
+-------------------------------------+
| 50 |
+-------------------------------------+

這樣解決了C和你當然使用這個求解的為好。


SELECT @A := SQRT(POW(@C,2) - POW(@B,2)) ;
+-----------------------------------+
| @A := SQRT(POW(@C,2) - POW(@B,2)) |
+-----------------------------------+
| 14 |
+-----------------------------------+

• 對數和它的身份登錄XY =日誌X +記錄Y

http://www.businessinsider.com/the-17-equations-that-changed-the-world-2012-7#the-logarithm-and-its-identities-2


SET @X = 2;
SET @Y = 3;
SELECT concat(log(@X * @Y) ,' = ', log(@X) + log(@Y) ) as "logarithm and its identities" ;
+---------------------------------------+
| logarithm and its identities |
+---------------------------------------+
| 1.791759469228055 = 1.791759469228055 |
+---------------------------------------+

• 歐拉公式為多面體：F - E + V = 2

http://www.businessinsider.com/the-17-equations-that-changed-the-world-2012-7#eulers-formula-for-polyhedra-6


SET @V = 4; # Vertices
SET @E = 6; # Edges
SET @F = 4; # Faces
SELECT @V - @E + @F as Tetrahedron;

SET @V = 8; # Vertices
SET @E = 12; # Edges
SET @F = 6; # Faces
SELECT @V - @E + @F as Hexahedron;

SET @V = 12; # Vertices
SET @E = 30; # Edges
SET @F = 20; # Faces
SELECT @V - @E + @F as Icosahedron;

SET @V = 12; # Vertices
SET @E = 30; # Edges
SET @F = 20; # Faces
SELECT @V - @E + @F as Icosahedron;

• 相對論電子商務愛因斯坦的相對論= mc ^ 2


SET @lbs = 190; # lbs
SET @lb2gram = 453.6; # 1 lbs = 453.6g
SET @lbstograms := @lbs * @lb2gram / 1;
SET @m := @lbstograms * 1 / 1000;
SET @c := POW(3.00 * POW(10,8), 2 );
SELECT @E := @m * @c ;
+----------------+
| @E := @m * @c |
+----------------+
| 7.75656e18 |
+----------------+

• 1 = 0.9999 .....


SELECT SUM(.9/(9/10));
+----------------+
| SUM(.9/(9/10)) |
+----------------+
| 1.00000 |
+----------------+

快速密碼生成與MySQL

Original post http://anothermysqldba.blogspot.com/2015/07/quick-password-generation-with-mysql.html

 

這裡是一個快速而簡單的方法來生成使用MySQL密碼的應用程序。 
該查詢將創建一個大寫和小寫的長度和值隨機生成的密碼。 


SELECT CONCAT(UCASE(SUBSTRING( SUBSTRING(MD5(RAND()), FLOOR( 1 + (RAND() * 5)) , FLOOR( 8 + (RAND() * 15 )) ) , FLOOR( 1 + (RAND() * 4 )) , FLOOR( 4 + (RAND() * 12 )) )), 
SUBSTRING('!@#$%^&*', FLOOR( 1 + (RAND() * 4)) , FLOOR( 1 + (RAND() * 8)) ), 
SUBSTRING( SUBSTRING(MD5(RAND()), FLOOR( 1 + (RAND() * 5)) , FLOOR( 8 + (RAND() * 15 )) ) , 5)) as password \G 

該查詢將採取隨機值用於動態口令及第一部分上情況下，它在適用時則第二半它增加了一些符號那麼剩下的是在較低的情況下，因為MD5會自動導入。 你當然可以調整任何符號，你會喜歡。 

結果的實施例： 


CREATE TABLE `generated` ( 
`password` varchar(255) NOT NULL 
) ENGINE=InnoDB ; 

MariaDB [(none)]> INSERT INTO test.generated SELECT CONCAT(UCASE(SUBSTRING( SUBSTRING(MD5(RAND()), FLOOR( 1 + (RAND() * 5)) , FLOOR( 8 + (RAND() * 15 )) ) , FLOOR( 1 + (RAND() * 4 )) , FLOOR( 4 + (RAND() * 12 )) )), SUBSTRING('!@#$%^&*', FLOOR( 1 + (RAND() * 4)) , FLOOR( 1 + (RAND() * 8)) ),SUBSTRING( SUBSTRING(MD5(RAND()), FLOOR( 1 + (RAND() * 5)) , FLOOR( 8 + (RAND() * 15 )) ) , 5)) as password; 

MariaDB [(none)]> select * from test.generated; 
+-----------------------------------+ 
| password | 
+-----------------------------------+ 
| 7232E1D9$%^&*f7de22c1b14f15c8a6 | 
| DEE22B7F7#$%541adcdd1b8e2 | 
| 2C4BB81001@#$%^&d0d9a2126 | 
| 941D3B92D@#$%^a4b7be92 | 
| 1CF2563254FC@#a79bb5 | 
| 2AE86E7D6!@#$%^&*4ca03f2e8 | 
| 074DD8D3#$e70a1 | 
| B2DDC!@#$%^47a252f79 | 
| 3A6D0A#$%^&*01ce9278a2a | 
| E9FB4CD16E19!739db9faa1616505c | 
| 1ED7A2E1379B4!@#$%ebe60b | 
| E9B57D71DB1@#$%^e9a4f8c2e94bf3d35 | 
| C97982!@#$8b5c534653c06 | 
| DE8998057C8!@#$%^8977a807 | 
| 7C7172EB1AE5$%^&*79d2b27 | 
| B52CE71@#$%^&*4508 | 
| AB17714F!@#$%^&203ba4ff80f8a6 | 
| 558D@#$04087871e29ff54 | 
| 27A78E8EF#$%^b8cee8628d81593 | 
| 315F2EC4#ad9913ec0595c | 
+-----------------------------------+ 



斐波那契序列步驟

Original post: http://anothermysqldba.blogspot.com/2015/06/fibonacci-sequence-procedure.html
 
那麼它已經一段時間，因為我張貼，抱歉。 今天，我在看電視節​​目邊緣的重播，當沃爾特引用的斐波那契數列我得到了側履帶與MySQL選項這一點。 

現在這個職位已經存在： 

• http://slava-technical.blogspot.com/2010/10/fibonacci-numbers-built-with-sql.html

所以我把這個職位，並擴展了它一點點，結果是，你可以調用和斐波那契數列，你是在後返回範圍的過程。 

該過程如下： 


delimiter // 
CREATE PROCEDURE `Fibonacci`(IN POS INT, IN RANG INT, IN LIMTED INT) 
BEGIN 
select FORMAT(Fibonacci,0) AS Fibonacci from ( 
select @f0 Fibonacci, @fn:=@f1+@f0, @f0:=@f1, @f1:=@fn 
from (select @f0:=0, @f1:=1, @fn:=1) x, 
information_schema.STATISTICS p limit LIMTED) y LIMIT POS, RANG; 
END// 
delimiter ; 

您可以致電此並傳遞任何值和範圍後，你是。 

所以，如果你想第5值序列和下一個值（ 從0開始 ） 


> CALL Fibonacci(5,2,100); 
+-----------+ 
| Fibonacci | 
+-----------+ 
| 5 | 
| 8 | 
+-----------+ 

所以，如果你想在30號值序列和下一個值（ 從0開始 ） 


> CALL Fibonacci(30,2,100); 
+-----------+ 
| Fibonacci | 
+-----------+ 
| 832,040 | 
| 1,346,269 | 
+-----------+ 

所以，如果你想150價值序列和下一個值（ 從0開始 ） 


> CALL Fibonacci(150,2,1000); 
+--------------------------------------------+ 
| Fibonacci | 
+--------------------------------------------+ 
| 9,969,216,677,189,305,000,000,000,000,000 | 
| 16,130,531,424,904,583,000,000,000,000,000 | 
+--------------------------------------------+ 

所以，你的想法。 現在，您也可以擴大戰果的範圍內，如果你想超過2只更改過程調用的第二個值。 


> CALL Fibonacci(0,10,100); 
+-----------+ 
| Fibonacci | 
+-----------+ 
| 0 | 
| 1 | 
| 1 | 
| 2 | 
| 3 | 
| 5 | 
| 8 | 
| 13 | 
| 21 | 
| 34 | 
+-----------+ 

> CALL Fibonacci(30,5,100); 
+-----------+ 
| Fibonacci | 
+-----------+ 
| 832,040 | 
| 1,346,269 | 
| 2,178,309 | 
| 3,524,578 | 
| 5,702,887 | 
+-----------+ 

無論如何，希望有人發現它有用和信用查詢的基礎確實去了原來的職位。

查找/從字符串中解析字符串

Original post: http://anothermysqldba.blogspot.com/2014/12/findparse-string-from-within-string.html

所以，我注意到了一些不同的問題，並張貼關於解析字符串了另一個字符串最近。 雖然一些解決方案包括創建新的功能等也可以在某些情況下，一個單一的查詢內完成。 

例如，讓我們說，我們正在尋找拉出從URL域。 我會盡量細講，為什麼和如何工作的。 
我們有如下表。 


CREATE TABLE `parse_example` ( 
`id` int(11) NOT NULL AUTO_INCREMENT, 
`urldemo` varchar(150) NOT NULL, 
PRIMARY KEY (`id`) 
) ENGINE=InnoDB; 
+----+----------------------------+ 
| id | urldemo | 
+----+----------------------------+ 
| 1 | http://www.mysql.com/ | 
| 2 | http://www.percona.com/ | 
| 3 | https://tools.percona.com/ | 
| 4 | https://mariadb.com/ | 
| 5 | http://planet.mysql.com/ | 
| 6 | http://dev.mysql.com/doc/ | 
+----+----------------------------+ 

這個例子的目的是無視http：//或https：//開頭和任何的.COM之後。 所以我們用locate找到位置。 

在.COM引用是容易的，因為那是恆定的，所以我們可以與啟動。 


SELECT LOCATE('.com', urldemo), urldemo FROM parse_example; 
+-------------------------+----------------------------+ 
| LOCATE('.com', urldemo) | urldemo | 
+-------------------------+----------------------------+ 
| 17 | http://www.mysql.com/ | 
| 19 | http://www.percona.com/ | 
| 22 | https://tools.percona.com/ | 
| 16 | https://mariadb.com/ | 
| 20 | http://planet.mysql.com/ | 
| 17 | http://dev.mysql.com/doc/ | 
+-------------------------+----------------------------+ 

OK，所以我們要刪除的/，什麼地方是什麼？ 


SELECT LOCATE('.com', urldemo) as start, LOCATE('.com', urldemo) +4 as end, SUBSTRING(urldemo FROM LOCATE('.com', urldemo) + 4 ) AS resulting , urldemo FROM parse_example; 
+-------+-----+-----------+----------------------------+ 
| start | end | resulting | urldemo | 
+-------+-----+-----------+----------------------------+ 
| 17 | 21 | / | http://www.mysql.com/ | 
| 19 | 23 | / | http://www.percona.com/ | 
| 22 | 26 | / | https://tools.percona.com/ | 
| 16 | 20 | / | https://mariadb.com/ | 
| 20 | 24 | / | http://planet.mysql.com/ | 
| 17 | 21 | /doc/ | http://dev.mysql.com/doc/ | 
+-------+-----+-----------+----------------------------+

這給了我們我們的最終位置，我只把現場別名使結果更容易執行。 

現在，經過HTTP和HTTPS整理其實是很容易的，以及它們都具有：//之後他們，所以我們只需要第二的位置/字符串中。 


SELECT LOCATE('/', urldemo) as first, LOCATE('/', urldemo) +1 as second, urldemo 
FROM parse_example; 
+-------+--------+----------------------------+ 
| first | second | urldemo | 
+-------+--------+----------------------------+ 
| 6 | 7 | http://www.mysql.com/ | 
| 6 | 7 | http://www.percona.com/ | 
| 7 | 8 | https://tools.percona.com/ | 
| 7 | 8 | https://mariadb.com/ | 
| 6 | 7 | http://planet.mysql.com/ | 
| 6 | 7 | http://dev.mysql.com/doc/ | 
+-------+--------+----------------------------+ 

這些查詢只是顯示了最終的查詢的不同方面會做。 因此，讓我們把它放在一起。 


SELECT 
TRIM(TRAILING SUBSTRING(urldemo FROM LOCATE('.com', urldemo) + 4 ) 
FROM SUBSTRING(urldemo FROM LOCATE('/', urldemo) + 2 ) ) AS parsed_domain , 
urldemo as original_url 
FROM parse_example; 
+-------------------+----------------------------+ 
| parsed_domain | original_url | 
+-------------------+----------------------------+ 
| www.mysql.com | http://www.mysql.com/ | 
| www.percona.com | http://www.percona.com/ | 
| tools.percona.com | https://tools.percona.com/ | 
| mariadb.com | https://mariadb.com/ | 
| planet.mysql.com | http://planet.mysql.com/ | 
| dev.mysql.com | http://dev.mysql.com/doc/ | 
+-------------------+----------------------------+ 

現在，希望可以幫助您能夠解析出任何你需要的。 本實施例被限制在一個網址。 但是，由於功能的一些例子已經在這裡是我的，你可以用它來解析任何你需要的功能的例子。 


CREATE FUNCTION PARSE_STRING(delimiterA VARCHAR(50), delimiterB VARCHAR(50), passed_string VARCHAR(255) ) 
RETURNS VARCHAR(255) DETERMINISTIC 
RETURN 
TRIM(TRAILING SUBSTRING(passed_string FROM LOCATE(delimiterB, passed_string) ) 
FROM SUBSTRING(passed_string FROM LOCATE(delimiterA, passed_string) + CHAR_LENGTH(delimiterA) ) ) ; 

SELECT PARSE_STRING('//','.com', urldemo) FROM parse_example; 
+------------------------------------+ 
| PARSE_STRING('//','.com', urldemo) | 
+------------------------------------+ 
| www.mysql | 
| www.percona | 
| tools.percona | 
| mariadb | 
| planet.mysql | 
| dev.mysql | 
+------------------------------------+ 

從全名外地拉一個姓氏： 


SELECT PARSE_STRING('John ','', 'John Smith') ; 
+----------------------------------------+ 
| PARSE_STRING('John ','', 'John Smith') | 
+----------------------------------------+ 
| Smith | 
+----------------------------------------+ 

拉頭名 


SELECT PARSE_STRING('',' Smith', 'John Smith') ; 
+-----------------------------------------+ 
| PARSE_STRING('',' Smith', 'John Smith') | 
+-----------------------------------------+ 
| John | 
+-----------------------------------------+ 

授予的名稱例子，你需要知道的分隔符值。 但是，這只是一個例子，你可以建立在。

一個MySQL分區和SUBPARTITION示例

Original post: http://anothermysqldba.blogspot.com/2014/12/a-mysql-partition-and-subpartition.html

因此，這是一個如何建立一個分區和MySQL的一個SUBPARTITION只是一個簡單的例子。 這裡的概念是，你必須在一個時間字段無數值表中的數據。 你可能有分佈在很多年（最有可能的，你做的）數據。 所以這個分區數據的一種方法是通過一年來排序，但隨後也即每年分區內按月份排序。 

以下是你可以用考慮一個例子。 

考慮到測試表。 你的表當然有更多的領域。 


CREATE TABLE `t1` ( 
`id` int(11) NOT NULL AUTO_INCREMENT, 
`date_time` datetime DEFAULT NOW(), 
PRIMARY KEY (`id`) 
) ENGINE=InnoDB; 

首先，我將填充測試表隨機值的DATE_TIME領域。 


delimiter // 
CREATE PROCEDURE populate_t1( IN rowsofdata INT ) 
BEGIN 

SET @A = 1; 
SET @B = 25 - @A; 

WHILE rowsofdata > 0 DO 
SELECT FLOOR( @A + (RAND() * @B )) INTO @randvalue; 
INSERT INTO t1 
SELECT NULL, NOW() - INTERVAL @randvalue MONTH; 
SET rowsofdata = rowsofdata - 1; 
END WHILE; 
END// 
delimiter ; 
call populate_t1(1000); 

檢查，看看我結束了什麼樣的價值觀為： 


> SELECT COUNT(*) FROM t1 WHERE date_time BETWEEN '2012-01-01 00:00:00' AND '2013-01-01 00:00:00'\G 
*************************** 1. row *************************** 
COUNT(*): 43 
1 row in set (0.00 sec) 

> SELECT COUNT(*) FROM t1 WHERE date_time BETWEEN '2013-01-01 00:00:00' AND '2014-01-01 00:00:00'\G 
*************************** 1. row *************************** 
COUNT(*): 529 
1 row in set (0.00 sec) 

> SELECT COUNT(*) FROM t1 WHERE date_time BETWEEN '2014-01-01 00:00:00' AND NOW() \G
*************************** 1. row *************************** 
COUNT(*): 428 
1 row in set (0.00 sec) 

現在，我可以改變表，所以我可以通過分區添加我的分區，然後測試值計數。 


ALTER TABLE t1 DROP PRIMARY KEY, ADD PRIMARY KEY (`id`,`date_time`), LOCK=SHARED; 
ALTER TABLE t1 
PARTITION BY RANGE( YEAR(date_time) ) 
SUBPARTITION BY HASH(MONTH(date_time) ) ( 

PARTITION p2012 VALUES LESS THAN (2013) ( 
SUBPARTITION dec_2012, 
SUBPARTITION jan_2012, 
SUBPARTITION feb_2012, 
SUBPARTITION mar_2012, 
SUBPARTITION apr_2012, 
SUBPARTITION may_2012, 
SUBPARTITION jun_2012, 
SUBPARTITION jul_2012, 
SUBPARTITION aug_2012, 
SUBPARTITION sep_2012, 
SUBPARTITION oct_2012, 
SUBPARTITION nov_2012 
), 

PARTITION p2013 VALUES LESS THAN (2014) ( 
SUBPARTITION dec_2013, 
SUBPARTITION jan_2013, 
SUBPARTITION feb_2013, 
SUBPARTITION mar_2013, 
SUBPARTITION apr_2013, 
SUBPARTITION may_2013, 
SUBPARTITION jun_2013, 
SUBPARTITION jul_2013, 
SUBPARTITION aug_2013, 
SUBPARTITION sep_2013, 
SUBPARTITION oct_2013, 
SUBPARTITION nov_2013 

), 
PARTITION p2014 VALUES LESS THAN (2015) ( 
SUBPARTITION dec_2014, 
SUBPARTITION jan_2014, 
SUBPARTITION feb_2014, 
SUBPARTITION mar_2014, 
SUBPARTITION apr_2014, 
SUBPARTITION may_2014, 
SUBPARTITION jun_2014, 
SUBPARTITION jul_2014, 
SUBPARTITION aug_2014, 
SUBPARTITION sep_2014, 
SUBPARTITION oct_2014, 
SUBPARTITION nov_2014 
), 

PARTITION pmax VALUES LESS THAN MAXVALUE ( 
SUBPARTITION dec_max, 
SUBPARTITION jan_max, 
SUBPARTITION feb_max, 
SUBPARTITION mar_max, 
SUBPARTITION apr_max, 
SUBPARTITION may_max, 
SUBPARTITION jun_max, 
SUBPARTITION jul_max, 
SUBPARTITION aug_max, 
SUBPARTITION sep_max, 
SUBPARTITION oct_max, 
SUBPARTITION nov_max 
) 
); 

我型我秀創建表非常不同了。 


> show create table t1; 
CREATE TABLE `t1` ( 
`id` int(11) NOT NULL AUTO_INCREMENT, 
`date_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP, 
PRIMARY KEY (`id`,`date_time`) 
) ENGINE=InnoDB AUTO_INCREMENT=1001 DEFAULT CHARSET=latin1 
/*!50100 PARTITION BY RANGE ( YEAR(date_time)) 
SUBPARTITION BY HASH (MONTH(date_time)) 
(PARTITION p2012 VALUES LESS THAN (2013) 
(SUBPARTITION dec_2012 ENGINE = InnoDB, 
SUBPARTITION jan_2012 ENGINE = InnoDB, 
SUBPARTITION feb_2012 ENGINE = InnoDB, 
SUBPARTITION mar_2012 ENGINE = InnoDB, 
SUBPARTITION apr_2012 ENGINE = InnoDB, 
SUBPARTITION may_2012 ENGINE = InnoDB, 
SUBPARTITION jun_2012 ENGINE = InnoDB, 
SUBPARTITION jul_2012 ENGINE = InnoDB, 
SUBPARTITION aug_2012 ENGINE = InnoDB, 
SUBPARTITION sep_2012 ENGINE = InnoDB, 
SUBPARTITION oct_2012 ENGINE = InnoDB, 
SUBPARTITION nov_2012 ENGINE = InnoDB), 
PARTITION p2013 VALUES LESS THAN (2014) 
(SUBPARTITION dec_2013 ENGINE = InnoDB, 
SUBPARTITION jan_2013 ENGINE = InnoDB, 
SUBPARTITION feb_2013 ENGINE = InnoDB, 
SUBPARTITION mar_2013 ENGINE = InnoDB, 
SUBPARTITION apr_2013 ENGINE = InnoDB, 
SUBPARTITION may_2013 ENGINE = InnoDB, 
SUBPARTITION jun_2013 ENGINE = InnoDB, 
SUBPARTITION jul_2013 ENGINE = InnoDB, 
SUBPARTITION aug_2013 ENGINE = InnoDB, 
SUBPARTITION sep_2013 ENGINE = InnoDB, 
SUBPARTITION oct_2013 ENGINE = InnoDB, 
SUBPARTITION nov_2013 ENGINE = InnoDB), 
PARTITION p2014 VALUES LESS THAN (2015) 
(SUBPARTITION dec_2014 ENGINE = InnoDB, 
SUBPARTITION jan_2014 ENGINE = InnoDB, 
SUBPARTITION feb_2014 ENGINE = InnoDB, 
SUBPARTITION mar_2014 ENGINE = InnoDB, 
SUBPARTITION apr_2014 ENGINE = InnoDB, 
SUBPARTITION may_2014 ENGINE = InnoDB, 
SUBPARTITION jun_2014 ENGINE = InnoDB, 
SUBPARTITION jul_2014 ENGINE = InnoDB, 
SUBPARTITION aug_2014 ENGINE = InnoDB, 
SUBPARTITION sep_2014 ENGINE = InnoDB, 
SUBPARTITION oct_2014 ENGINE = InnoDB, 
SUBPARTITION nov_2014 ENGINE = InnoDB), 
PARTITION pmax VALUES LESS THAN MAXVALUE 
(SUBPARTITION dec_max ENGINE = InnoDB, 
SUBPARTITION jan_max ENGINE = InnoDB, 
SUBPARTITION feb_max ENGINE = InnoDB, 
SUBPARTITION mar_max ENGINE = InnoDB, 
SUBPARTITION apr_max ENGINE = InnoDB, 
SUBPARTITION may_max ENGINE = InnoDB, 
SUBPARTITION jun_max ENGINE = InnoDB, 
SUBPARTITION jul_max ENGINE = InnoDB, 
SUBPARTITION aug_max ENGINE = InnoDB, 
SUBPARTITION sep_max ENGINE = InnoDB, 
SUBPARTITION oct_max ENGINE = InnoDB, 
SUBPARTITION nov_max ENGINE = InnoDB)) 

所以，我們還能指望我們的價值預期？ 


> SELECT count(*) FROM t1 PARTITION (p2012) \G 
*************************** 1. row *************************** 
count(*): 43 
> SELECT count(*) FROM t1 PARTITION (p2013) \G 
*************************** 1. row *************************** 
count(*): 529 
> SELECT count(*) FROM t1 PARTITION (p2014) \G 
*************************** 1. row *************************** 
count(*): 428 

到目前為止好，所有的價值觀匹配了我們所收到的數量。 所以我們也可以算或每子分區中進行選擇。 


> SELECT * FROM t1 PARTITION (dec_2012) limit 5; 
+-----+---------------------+ 
| id | date_time | 
+-----+---------------------+ 
| 59 | 2012-12-19 00:59:57 | 
| 68 | 2012-12-19 00:59:58 | 
| 93 | 2012-12-19 00:59:59 | 
| 105 | 2012-12-19 00:59:59 | 
| 111 | 2012-12-19 00:59:59 | 
+-----+---------------------+ 

> SELECT * FROM t1 PARTITION (jan_2013) limit 5; 
+-----+---------------------+ 
| id | date_time | 
+-----+---------------------+ 
| 6 | 2013-01-19 00:59:55 | 
| 29 | 2013-01-19 00:59:56 | 
| 55 | 2013-01-19 00:59:57 | 
| 79 | 2013-01-19 00:59:58 | 
| 100 | 2013-01-19 00:59:59 | 
+-----+---------------------+ 

> SELECT * FROM t1 PARTITION (jan_2014) limit 5; 
+-----+---------------------+ 
| id | date_time | 
+-----+---------------------+ 
| 16 | 2014-01-19 00:59:55 | 
| 190 | 2014-01-19 01:00:04 | 
| 191 | 2014-01-19 01:00:04 | 
| 229 | 2014-01-19 01:00:05 | 
| 234 | 2014-01-19 01:00:06 | 
+-----+---------------------+ 

> SELECT * FROM t1 PARTITION (jun_2014) limit 5; 
+-----+---------------------+ 
| id | date_time | 
+-----+---------------------+ 
| 13 | 2014-06-19 00:59:55 | 
| 189 | 2014-06-19 01:00:04 | 
| 221 | 2014-06-19 01:00:05 | 
| 222 | 2014-06-19 01:00:05 | 
| 238 | 2014-06-19 01:00:06 | 
+-----+---------------------+ 

> SELECT * FROM t1 PARTITION (dec_2013) limit 5; 
+-----+---------------------+ 
| id | date_time | 
+-----+---------------------+ 
| 50 | 2013-12-19 00:59:57 | 
| 74 | 2013-12-19 00:59:58 | 
| 98 | 2013-12-19 00:59:59 | 
| 107 | 2013-12-19 00:59:59 | 
| 167 | 2013-12-19 01:00:02 | 
+-----+---------------------+ 

這是偉大的，方便的，但是，當2015年或2016年左右出現什麼情況？ 所有這些數據將在PMAX分區。 那麼，我們如何P2014和Pmax的在增加一個新的分區？ 

如果您在PMAX沒有數據，你可以刪除它並添加一個新的分區到年底。 但它也很容易重新組織的分區。 這將需要的PMAX分區，改變成我們新的分區。 


ALTER TABLE t1 REORGANIZE PARTITION pmax INTO ( 
PARTITION p2015 VALUES LESS THAN (2016) ( 
SUBPARTITION dec_2015, 
SUBPARTITION jan_2015, 
SUBPARTITION feb_2015, 
SUBPARTITION mar_2015, 
SUBPARTITION apr_2015, 
SUBPARTITION may_2015, 
SUBPARTITION jun_2015, 
SUBPARTITION jul_2015, 
SUBPARTITION aug_2015, 
SUBPARTITION sep_2015, 
SUBPARTITION oct_2015, 
SUBPARTITION nov_2015 
), 
PARTITION pmax VALUES LESS THAN MAXVALUE ( 
SUBPARTITION dec_max, 
SUBPARTITION jan_max, 
SUBPARTITION feb_max, 
SUBPARTITION mar_max, 
SUBPARTITION apr_max, 
SUBPARTITION may_max, 
SUBPARTITION jun_max, 
SUBPARTITION jul_max, 
SUBPARTITION aug_max, 
SUBPARTITION sep_max, 
SUBPARTITION oct_max, 
SUBPARTITION nov_max 
) 
); 

希望這可以幫助，好運。