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[转] Usefull Sql

morphis — Tue, 30 Oct 2007 09:40:00 GMT

SQL语句先前写的时候，很容易把一些特�D�的用法忘记�Q�我�Ҏ(gu��)��整理�?ji��n)一下SQL语句操作�?br /> 一、基��
1、说明：(x��)创徏数据�?br /> CREATE DATABASE database-name
2、说明：(x��)删除数据�?br /> drop database dbname
3、说明：(x��)备䆾sql server
--- 创徏备䆾数据�?device
USE master
EXEC sp_addumpdevice 'disk', 'testBack', 'c:\mssql7backup\MyNwind_1.dat'
--- 开�?备䆾
BACKUP DATABASE pubs TO testBack
4、说明：(x��)创徏新表
create table tabname(col1 type1 [not null] [primary key],col2 type2 [not null],..)
�Ҏ(gu��)��已有的表创徏新表�Q?
A�Q�create table tab_new like tab_old (使用旧表创徏新表)
B�Q�create table tab_new as select col1,col2... from tab_old definition only
5、说明：(x��)删除新表
drop table tabname
6、说明：(x��)增加一个列
Alter table tabname add column col type
注：(x��)列增加后��不能删除。DB2中列加上后数据类型也不能改变�Q�唯一能改变的是增加varchar�c�d��的长度�?br /> 7、说明：(x��)��d��主键�Q?Alter table tabname add primary key(col)
说明�Q�删除主键：(x��) Alter table tabname drop primary key(col)
8、说明：(x��)创徏索引�Q�create [unique] index idxname on tabname(col....)
删除索引�Q�drop index idxname
注：(x��)索引是不可更改的�Q�想更改必须删除重新建�?br /> 9、说明：(x��)创徏视图�Q�create view viewname as select statement
删除视图�Q�drop view viewname
10、说明：(x��)几个��单的基本的sql语句
选择�Q�select * from table1 where 范围
插入�Q�insert into table1(field1,field2) values(value1,value2)
删除�Q�delete from table1 where 范围
更新�Q�update table1 set field1=value1 where 范围
查找�Q�select * from table1 where field1 like '%value1%' ---like的语法很�_�֦��Q�查资料!
排序�Q�select * from table1 order by field1,field2 [desc]
��L��Q�select count as totalcount from table1
求和�Q�select sum(field1) as sumvalue from table1
�q�_��Q�select avg(field1) as avgvalue from table1
最大：(x��)select max(field1) as maxvalue from table1
最��：(x��)select min(field1) as minvalue from table1
11、说明：(x��)几个高��查询�q�算�?br /> A�Q?UNION �q�算�W?
UNION �q�算�W�通过�l�合其他两个�l�果表（例如 TABLE1 �?TABLE2�Q��ƈ消去表中��M��重复行而派生出一个结果表。当 ALL �?UNION 一起��用时�Q�即 UNION ALL�Q�，不消除重复行。两�U�情况下�Q�派生表的每一行不是来�?TABLE1 ��是来自 TABLE2�?
B�Q?EXCEPT �q�算�W?
EXCEPT �q�算�W�通过包括所有在 TABLE1 中但不在 TABLE2 中的行�ƈ消除所有重复行而派生出一个结果表。当 ALL �?EXCEPT 一起��用时 (EXCEPT ALL)�Q�不消除重复行�?
C�Q?INTERSECT �q�算�W?br /> INTERSECT �q�算�W�通过只包�?TABLE1 �?TABLE2 中都有的行�ƈ消除所有重复行而派生出一个结果表。当 ALL �?INTERSECT 一起��用时 (INTERSECT ALL)�Q�不消除重复行�?
注：(x��)使用�q�算词的几个查询�l�果行必��L��一致的�?
12、说明：(x��)使用外连�?
A、left outer join�Q?
左外�q�接�Q�左�q�接�Q�：(x��)�l�果集几包括�q�接表的匚w��行，也包括左�q�接表的所有行�?
SQL: select a.a, a.b, a.c, b.c, b.d, b.f from a LEFT OUT JOIN b ON a.a = b.c
B�Q�right outer join:
叛_��q�接(双��?�Q�结果集既包括连接表的匹配连接行�Q�也包括双��接表的所有行�?
C�Q�full outer join�Q?
全外�q�接�Q�不仅包括符可��接表的匹配行�Q�还包括两个�q�接表中的所有记录�?br /> 二、提�?br /> 1、说明：(x��)复制�?只复制结�?源表名：(x��)a 新表名：(x��)b) (Access可用)
法一�Q�select * into b from a where 1<>1
法二�Q�select top 0 * into b from a
2、说明：(x��)拯��?拯��数据,源表名：(x��)a 目标表名�Q�b) (Access可用)
insert into b(a, b, c) select d,e,f from b;
3、说明：(x��)跨数据库之间表的拯��(具体数据使用�l�对路径) (Access可用)
insert into b(a, b, c) select d,e,f from b in '具体数据�? where 条�g
例子�Q?.from b in '"&Server.MapPath(".")&"\data.mdb" &"' where..
4、说明：(x��)子查�?表名1�Q�a 表名2�Q�b)
select a,b,c from a where a IN (select d from b ) 或�? select a,b,c from a where a IN (1,2,3)
5、说明：(x��)昄��文章、提交�h和最后回复时�?br /> select a.title,a.username,b.adddate from table a,(select max(adddate) adddate from table where table.title=a.title) b
6、说明：(x��)外连接查�?表名1�Q�a 表名2�Q�b)
select a.a, a.b, a.c, b.c, b.d, b.f from a LEFT OUT JOIN b ON a.a = b.c
7、说明：(x��)在线视图查询(表名1�Q�a )
select * from (SELECT a,b,c FROM a) T where t.a > 1;
8、说明：(x��)between的用�?between限制查询数据范围时包括了(ji��n)边界�?not between不包�?br /> select * from table1 where time between time1 and time2
select a,b,c, from table1 where a not between 数�? and 数�?
9、说明：(x��)in 的��用方�?br /> select * from table1 where a [not] in ('�?','�?','�?','�?')
10、说明：(x��)两张兌��表，删除主表中已�l�在副表中没有的信息
delete from table1 where not exists ( select * from table2 where table1.field1=table2.field1 )
11、说明：(x��)四表联查问题�Q?br /> select * from a left inner join b on a.a=b.b right inner join c on a.a=c.c inner join d on a.a=d.d where .....
12、说明：(x��)日程安排提前五分钟提�?
SQL: select * from 日程安排 where datediff('minute',f开始时�?getdate())>5
13、说明：(x��)一条sql 语句搞定数据库分��?br /> select top 10 b.* from (select top 20 主键字段,排序字段 from 表名 order by 排序字段 desc) a,表名 b where b.主键字段 = a.主键字段 order by a.排序字段
14、说明：(x��)�?0条记�?br /> select top 10 * form table1 where 范围
15、说明：(x��)选择在每一�l�b值相同的数据中对应的a最大的记录的所有信�?�c�M��q�样的用法可以用于论坛每月排行榜,每月热销产品分析,按科目成�l�排�?�{�等.)
select a,b,c from tablename ta where a=(select max(a) from tablename tb where tb.b=ta.b)
16、说明：(x��)包括所有在 TableA 中但不在 TableB和TableC 中的行�ƈ消除所有重复行而派生出一个结果表
(select a from tableA ) except (select a from tableB) except (select a from tableC)
17、说明：(x��)随机取出10条数�?br /> select top 10 * from tablename order by newid()
18、说明：(x��)随机选择记录
select newid()
19、说明：(x��)删除重复记录
Delete from tablename where id not in (select max(id) from tablename group by col1,col2,...)
20、说明：(x��)列出数据库里所有的表名
select name from sysobjects where type='U'
21、说明：(x��)列出表里的所有的
select name from syscolumns where id=object_id('TableName')
22、说明：(x��)列示type、vender、pcs字段�Q�以type字段排列�Q�case可以方便地实现多重选择�Q�类似select 中的case�?br /> select type,sum(case vender when 'A' then pcs else 0 end),sum(case vender when 'C' then pcs else 0 end),sum(case vender when 'B' then pcs else 0 end) FROM tablename group by type
昄��l�果�Q?br /> type vender pcs
�?sh��)�?A 1
�?sh��)�?A 1
光盘 B 2
光盘 A 2
手机 B 3
手机 C 3
23、说明：(x��)初始化表table1
TRUNCATE TABLE table1
24、说明：(x��)选择�?0�?5的记�?br /> select top 5 * from (select top 15 * from table order by id asc) table_别名 order by id desc
三、技�?br /> 1�?=1�Q?=2的��用，在SQL语句�l�合时用的较�?br /> "where 1=1" 是表�C�选择全部   "where 1=2"全部不选，
如：(x��)
if @strWhere !=''
begin
set @strSQL = 'select count(*) as Total from [' + @tblName + '] where ' + @strWhere
end
else
begin
set @strSQL = 'select count(*) as Total from [' + @tblName + ']'
end
我们可以直接写成
set @strSQL = 'select count(*) as Total from [' + @tblName + '] where 1=1 安定 '+ @strWhere
2、收�~�数据库
--重徏索引
DBCC REINDEX
DBCC INDEXDEFRAG
--收羃数据和日�?br /> DBCC SHRINKDB
DBCC SHRINKFILE
3、压�~�数据库
dbcc shrinkdatabase(dbname)
4、�{�U�L��据库�l�新用户以已存在用户权限
exec sp_change_users_login 'update_one','newname','oldname'
go
5、检查备份集
RESTORE VERIFYONLY from disk='E:\dvbbs.bak'
6、修复数据库
ALTER DATABASE [dvbbs] SET SINGLE_USER
GO
DBCC CHECKDB('dvbbs',repair_allow_data_loss) WITH TABLOCK
GO
ALTER DATABASE [dvbbs] SET MULTI_USER
GO
7、日志清�?br /> SET NOCOUNT ON
DECLARE @LogicalFileName sysname,
        @MaxMinutes INT,
        @NewSize INT

USE     tablename             -- 要操作的数据库名
SELECT @LogicalFileName = 'tablename_log', -- 日志文�g�?br /> @MaxMinutes = 10,               -- Limit on time allowed to wrap log.
        @NewSize = 1                  -- 你想讑֮�的日志文件的大小(M)
-- Setup / initialize
DECLARE @OriginalSize int
SELECT @OriginalSize = size
FROM sysfiles
WHERE name = @LogicalFileName
SELECT 'Original Size of ' + db_name() + ' LOG is ' +
        CONVERT(VARCHAR(30),@OriginalSize) + ' 8K pages or ' +
        CONVERT(VARCHAR(30),(@OriginalSize*8/1024)) + 'MB'
FROM sysfiles
WHERE name = @LogicalFileName
CREATE TABLE DummyTrans
(DummyColumn char (8000) not null)

DECLARE @Counter   INT,
        @StartTime DATETIME,
        @TruncLog VARCHAR(255)
SELECT @StartTime = GETDATE(),
        @TruncLog = 'BACKUP LOG ' + db_name() + ' WITH TRUNCATE_ONLY'
DBCC SHRINKFILE (@LogicalFileName, @NewSize)
EXEC (@TruncLog)
-- Wrap the log if necessary.
WHILE     @MaxMinutes > DATEDIFF (mi, @StartTime, GETDATE()) -- time has not expired
      AND @OriginalSize = (SELECT size FROM sysfiles WHERE name = @LogicalFileName)
      AND (@OriginalSize * 8 /1024) > @NewSize
BEGIN -- Outer loop.
    SELECT @Counter = 0
    WHILE ((@Counter < @OriginalSize / 16) AND (@Counter < 50000))
      BEGIN -- update
        INSERT DummyTrans VALUES ('Fill Log')
        DELETE DummyTrans
        SELECT @Counter = @Counter + 1
      END
    EXEC (@TruncLog)
END
SELECT 'Final Size of ' + db_name() + ' LOG is ' +
        CONVERT(VARCHAR(30),size) + ' 8K pages or ' +
        CONVERT(VARCHAR(30),(size*8/1024)) + 'MB'
FROM sysfiles
WHERE name = @LogicalFileName
DROP TABLE DummyTrans
SET NOCOUNT OFF
8、说明：(x��)更改某个�?br /> exec sp_changeobjectowner 'tablename','dbo'
9、存储更改全部表
CREATE PROCEDURE dbo.User_ChangeObjectOwnerBatch
@OldOwner as NVARCHAR(128),
@NewOwner as NVARCHAR(128)
AS
DECLARE @Name   as NVARCHAR(128)
DECLARE @Owner as NVARCHAR(128)
DECLARE @OwnerName as NVARCHAR(128)
DECLARE curObject CURSOR FOR
select 'Name'   = name,
'Owner'   = user_name(uid)
from sysobjects
where user_name(uid)=@OldOwner
order by name
OPEN curObject
FETCH NEXT FROM curObject INTO @Name, @Owner
WHILE(@@FETCH_STATUS=0)
BEGIN
if @Owner=@OldOwner
begin
set @OwnerName = @OldOwner + '.' + rtrim(@Name)
exec sp_changeobjectowner @OwnerName, @NewOwner
end
-- select @name,@NewOwner,@OldOwner
FETCH NEXT FROM curObject INTO @Name, @Owner
END
close curObject
deallocate curObject
GO

10、SQL SERVER中直接��@环写入数�?br /> declare @i int
set @i=1
while @i<30
begin
insert into test (userid) values(@i)
set @i=@i+1
end
��记存储�q�程中经常用到的本周�Q�本月，本年函数
Dateadd(wk,datediff(wk,0,getdate()),-1)
Dateadd(wk,datediff(wk,0,getdate()),6)
Dateadd(mm,datediff(mm,0,getdate()),0)
Dateadd(ms,-3,dateadd(mm,datediff(m,0,getdate())+1,0))
Dateadd(yy,datediff(yy,0,getdate()),0)
Dateadd(ms,-3,DATEADD(yy, DATEDIFF(yy,0,getdate())+1, 0))
上面的SQL代码只是一个时间段
Dateadd(wk,datediff(wk,0,getdate()),-1)
Dateadd(wk,datediff(wk,0,getdate()),6)
��是表示本周旉��D?
下面的SQL的条仉��?��是查询旉��D�在本周范围内的:
Where Time BETWEEN Dateadd(wk,datediff(wk,0,getdate()),-1) AND Dateadd(wk,datediff(wk,0,getdate()),6)
而在存储�q�程�?
select @begintime = Dateadd(wk,datediff(wk,0,getdate()),-1)
select @endtime = Dateadd(wk,datediff(wk,0,getdate()),6)

morphis 2007-10-30 17:40 发表评论

Ruby on Rails学习(f��n) (一)

morphis — Sat, 24 Mar 2007 15:08:00 GMT

　　“从书籍销售情况就可以看出那种技术是当前最��行的技术，具体的数据我不太记得�?ji��n)，只记得JAVA书籍的销售量是增长了(ji��n)4%�Q�C#增长�?6%�Q�Python增长�?0%�Q�Perl下降4%�Q�而Ruby书籍的销售量增长�?552%�Q�没错，我没��打��数点）(j��)”，原文在这。虽然Java书籍销售量基数肯定是远�q�大于Ruby书籍�Q�但是如此之大的增长量还是引起了(ji��n)我的好奇�Q�到底是什么东西能使相关科技书以�q�么大销售量增长?

什么是Ruby on Rails

让我们先来看一张图片：(x��)

看完�q�张囄��Q�我�?j��)里充满疑惑�Q�难道Ruby + Rails真的能够有这么好吗？

�?j��)里有这么几个疑问�?x��)

�Q�、Ruby是谁开发的�Q?/div>

�Q�、Ruby是什么？

�Q�、Rails是什么？

�Q�、Ruby on Rails与目前已�l�有的开发语�a�相比有什么优点？��Z��么要使用它？

�Q�、Ruby on Rails�E�_��吗？效率高吗�Q�能够承受大数据量的讉K��吗？

�Q�、Ruby on Rails有长�q�的发展前景吗？

让我们一个一个的解开�q�些疑问�Q?/div>

�Q��?i>松本行弘"Matz"(Matsumoto Yukihiro)是Ruby语言的发明�h�Q�他�?993�q��v便开始着手Ruby的研发工作。他一直想发明一�U�语�a��Q��你既能进行高效开发又能��n受编�E�的快乐�?993�q?�?4日Ruby诞生�?ji��n)�?995�q?2月Matz推出�?ji��n)Ruby的第一个版本Ruby 0.95。不久Ruby便凭借其独特的魅力横扫日本，�怿�在不久的��来�Q�Ruby��走向世界�?/i>Ruby是日本�h发明的，�q�点让我很不是滋呻I��Z��是很奇怪的�Q�美国，�Ƨ洲比我们强�q�能接受�Q�而日本比我们强我��?

�Q�、Ruby是一�U�有着��清晰语法的纯面向对象的编�E�语�a��Q�它能够让编�E�变得有��和优雅�Q�这点在后面的内容中��实得到印证�Q�。Ruby成功的组合了(ji��n)Smalltalk的优雅以�?qi��ng)Python的易用性，�q�有Perl的实用主义。Ruby��h��于９�Q�年代的日本�Q�在�q�去的几�q�时间里随着更多的英语资料的出现变得更加的流行�?/div>
�Q�、Rails是一个用来开发数据库后台的WEB应用的开源框架�?/div>
�Q�、到目前为止我发现Ruby on Rails最大的优点��是在于��单！RoR的核�?j��)思想��是“更��的�~�程�Q�更��单的配置�Q��?/div>

安装和配�|�非常简单，不象Java需要安装运行环境，安装应用服务器，然后再进行一大堆的配�|�。在安装上Ruby和Perl很象�Q�只需要装一个简单的解释环境?y��u)��可以�?ji��n)�Q�和Perl很象的地方很多，例如正则表达式的支持�Q�。RoR避免�?ji��n)繁杂的XML配置文�g�Q�一个Rails应用�E�序只需要简单编�E�就可以通过影射和发现配�|�好所有的东西。你的应用程序和数据库里已经包含�?ji��n)所有Rails需要的东西�?/div>

�~�码��单，很多代码都是可以自动生成�Q�可以自动生成MVC�Q�可以自动生成框架、Web服务。甚至你只要写上一行代码就可以实现以前使用Java上百行代码的工作量，比其他开发工具速度快１�Q�倍！。当然越��的�~�程量就意味着��少的bug�?/div>

�Q�、关于稳定性目前还没有很全面的数据�Q�暂时还不太清楚。关于效率，从相关的资料上可以看刎ͼ�(x��)有�h说RoR的性能和开发效率比java的struts+spring+hibernate�l�典搭配�q�要�?5%-30%�?/div>
�Q�、RoR目前发展势头强劲�Q�在使用RoR的过�E�中你将�?x��)发现它已经具备了(ji��n)作为WEB开发语�a�的本质。如果它能够以简单�ؓ(f��)�ȝ��理念�l�箋发展�Q�相信它?y��u)��象当年的PHP和Linux在网�l�上引�v新的一轮革命�?/div>

囄��和部分内容�{自：(x��)

http://www.duduwolf.com/cmd.asp?act=tb&id=273

http://www.neokeen.com/mornlee/2005/07/06/1120663087531.html

http://dev.csdn.net/article/73751.shtm

morphis 2007-03-24 23:08 发表评论

Comet: Low Latency Data for the Browser

morphis — Tue, 20 Mar 2007 07:25:00 GMT
http://alex.dojotoolkit.org/?p=545
http://www.cnblogs.com/allenyoung/archive/2006/08/24/485140.html

An old web technology is slowly being resurrected from the depths of history. Browser features that have gone untouched for years are once again being employed to bring better responsiveness to UIs. Servers are learning to cope with a new way of doing things. And I’m not talking about Ajax.

New services like Jot Live and Meebo are built with a style of data transmission that is neither traditional nor Ajax. Their brand of low-latency data transfer to the browser is unique, and it is becoming ever-more common. Lacking a better term, I’ve taken to calling this style of event-driven, server-push data streaming “Comet�? It doesn’t stand for anything, and I’m not sure that it should. There is much confusion about how these techniques work, and so using pre-existing definitions and names is as likely to get as much wrong as it would get right.

Defining Comet

For a new term to be useful, at a minimum we need some examples of the technology, a list of the problems being solved, and properties which distinguish it from other techniques. As with Ajax, these aren’t hard to find. A short list of example applications includes:

GMail’s GTalk integration

Jot Live

Renkoo

cgi:irc

Meebo

So what makes these apps special? What makes them different from other things that might at first glance appear similar? Fundamentally, they all use long-lived HTTP connections to reduce the latency with which messages are passed to the server. In essence, they do not poll the server occasionally. Instead the server has an open line of communication with which it can push data to the client.

From the perspective of network activity, we can modify JJG’s original Ajax diagram to illustrate how Comet differs:

As is illustrated above, Comet applications can deliver data to the client at any time, not only in response to user input. The data is delivered over a single, previously-opened connection. This approach reduces the latency for data delivery significantly.

The architecture relies on a view of data which is event driven on both sides of the HTTP connection. Engineers familiar with SOA or message oriented middleware will find this diagram to be amazingly familiar. The only substantive change is that the endpoint is the browser.

While Comet is similar to Ajax in that it’s asynchronous, applications that implement the Comet style can communicate state changes with almost negligible latency. This makes it suitable for many types of monitoring and multi-user collaboration applications which would otherwise be difficult or impossible to handle in a browser without plugins.

Why Is Comet Better For Users?

Regular Ajax improves the responsiveness of a UI for a single user, but at the cost of allowing the context to go “stale�?for long-lived pages. Changes to data from others users is lost until a user refreshes the whole page. An application can alternately return to the “bad old days�?and maintain some sort of state mechanism by which it tells client about changes since the last time they’ve communicated. The user has to either wait until they preform some action which would kick off a request to see the updated state from other users (which might impact the action they wanted to preform!) or request changes from the server at some interval (called “polling�?. Since the web is inherently multi-user, it’s pretty obvious that regular Ajax imposes usability and transparency hurdles for users. Applications that employ the Comet technique can avoid this problem by pushing updates to all clients as they happen. UI state does not go out of sync and everyone using an application can easily understand what their changes will mean for other users. Ajax improves single-user responsiveness. Comet improves application responsiveness for collaborative, multi-user applications and does it without the performance headaches associated with intermittent polling.

But Does It Scale?

New server software is often required to make applications built using Comet scale, but the patterns for event-driven IO on the server side are becoming better distributed. Even Apache will provide a Comet-ready worker module in the upcoming 2.2 release. Until then, tools like Twisted, POE, Nevow, mod_pubsub, and other higher-level event-driven IO abstractions are making Comet available to developers on the bleeding edge. Modern OSes almost all now support some sort of kernel-level event-driven IO system as well. I’ve even heard that Java’s NIO packages will start to take advantage of them in a forthcoming release. These tools are quietly making the event-driven future a reality. This stuff will scale, and most of the tools are in place already.

I’ll be giving a more on this topic at ETech and describing the various techniques that Comet applications can employ to push data from the server to the client. As always, I’ll post the slides here as well.

The future of the read-write web is multi-user. There is life after Ajax.

Endnotes

First, a word on terminology and its importance. “Ajax�?was coined to describe background request/response data transfer. Many of us had worked on solutions to do exactly this, but it wasn’t until a simple name and accompanying description were provided that it was possible for people not directly building applications to describe what it was they liked about it. Common terminology acts not only as a shortcut in discussions between technical folks, but also as a bridge for those who may not be able to give a technical rundown of exactly how it works.

As with Ajax, those of us who build technology are now faced with another communication challenge. We have a hard problem for which solutions are available (and have been for some time) but no way to communicate about them. Terminology is again the missing link. Today, keeping an HTTP connection open for doing low-latency data transfer to the browser has no digestible name. When I describe a cool new hack, there’s nothing to associate it with. When people say “how the hell did they do that?�? we don’t have a compact answer. Therefore, in the spirit of improved communication (and not technology invention), I’m proposing a new name for this stuff.

Next, for those who are network-level programmers or are familiar with sockets and/or basic TCP/IP programming, you will probably scoff at the concept of web applications finally getting this kind of datagram packet support. Fair enough. It is however interesting to note that while more responsive UIs have been available on a variety of platforms to date, the Web has “won�?the broad majority of market share for most classes of applications in which the browser provides enough native (non-plugin) support to make the performance and/or UI feasible. Comet may be a new name for an old set of concepts wrapped in some pretty grotty hacks, but that in no way diminishes the market impact it will have (and is already having).

Lastly, as current Dojo users might expect, Dojo already supports Comet via dojo.io.bind(). More than a year ago we designed the API with Comet in mind. In the next couple of weeks I’ll be showing how bind’s pluggable transport layer can be combined with Dojo’s event topic mechanism to provide message delivery on top of a message bus.

morphis 2007-03-20 15:25 发表评论