以前弄过类似,去除相同信息的方法,现在找不到了,不过今天又花一些时间给弄出来了,记录一下
还是先上代码吧 ,可以先看代码如下:
ALTER procedure [dbo].[PROC_ITEMMASTER_GETUNIQUE] @PAGEINDEX INT,@uid int,@itemnumber varchar(50)
AS
begin tran --开始事务
drop table [ItemMaster].[dbo].[testim] --删除表
--把不重复记录转存到testim中
select * into [ItemMaster].[dbo].[testim] from [ItemMaster].[dbo].[dat_item_master] where item_uid in(select min(item_uid) as item_uid from [ItemMaster].[dbo].[dat_item_master] group by item_number) and status=0
select top 10 * from [ItemMaster].[dbo].[testim] where item_uid not in (select top (10*(@PAGEINDEX-1)) item_uid from [ItemMaster].[dbo].[testim])
and owneruid=@uid and item_number like @itemnumber+'%'
--判断是否出错
if @@error0
begin
rollback tran --出错则回滚
end
else
begin --否则提前事务
commit tran
end
我的数据是这样的:因为item_uid是标识列,item_number有重复的,
我想过滤成这样:
顺带说几个在编程的时候遇到的小问题
1.程序 出现 Could not find stored procedure 找不到这个存储过程
因为我的程序数据库有四个,而默认连接是A,但实际要执行B库里的存储过程,导致出错,
解决办法1:可在A里面建个一样的存储过程2:在执行连接的时候,替换下数据库就行了
2. asp.net/C# 将存储过程中返回的数据集,填充到dataset/datatable
代码如下:
SqlConnection conn = new SqlConnection(ConfigurationManager.ConnectionStrings["SolutionSQLServer"].ToString());
SqlCommand cmd = new SqlCommand("Test",conn);
cmd.CommandType = CommandType.StoredProcedure;
cmd.Parameters.Add("@MaxId", SqlDbType.Int).Value = 12000;
SqlDataAdapter sda = new SqlDataAdapter(cmd);
DataTable dt = new DataTable();
sda.Fill(dt);
在这感谢 http://www.cnblogs.com/liujuncm5/archive/2009/08/31/1557569.html
3.在存储过程里面,写SQL语句不能动态不加order by 功能
比如
代码如下:
--·@new_orderby 是传入参数,不能这样写
select top (10*(2-1)) item_uid from testim order by @new_orderby
--执行这个的时候,SQL会出现 The SELECT item identified by the ORDER BY number 1 contains a variable as part
of the expression identifying a column position. Variables are only allowed when
ordering by an expression referencing a column name.
不过我找到解决办法,不过很麻烦,
(第二个回答用 ' sql '进行连接)
(用case end 也行)
4. select into 和 insert into select 两种复制文句 (这里感谢)
1.
语句形式为:
2.
语句形式为:
。
5.顺便复习下常用的SQL方法语句
代码如下:
declare @name varchar(200) --声明变量
set @name='abcd;def' --赋值
print 'exec len :'+Convert(varchar(10),Len(@name)) --convert(type,value)转换,Len(value)获取大小
print 'exec charindex:'+Convert(varchar(10),CharIndex('e',@name))--CharIndex(find,value) 在value中查找find的位置
print 'not replace:'+@name
print 'exec replace:'+Replace(@name,';','') --用replace替换
print 'exec substring:'+Substring(@name,0,3)--用substring截取
print @@RowCount --返回上一行代码受影响的行数
作者:

Stored procedures are precompiled SQL statements in MySQL for improving performance and simplifying complex operations. 1. Improve performance: After the first compilation, subsequent calls do not need to be recompiled. 2. Improve security: Restrict data table access through permission control. 3. Simplify complex operations: combine multiple SQL statements to simplify application layer logic.

The working principle of MySQL query cache is to store the results of SELECT query, and when the same query is executed again, the cached results are directly returned. 1) Query cache improves database reading performance and finds cached results through hash values. 2) Simple configuration, set query_cache_type and query_cache_size in MySQL configuration file. 3) Use the SQL_NO_CACHE keyword to disable the cache of specific queries. 4) In high-frequency update environments, query cache may cause performance bottlenecks and needs to be optimized for use through monitoring and adjustment of parameters.

The reasons why MySQL is widely used in various projects include: 1. High performance and scalability, supporting multiple storage engines; 2. Easy to use and maintain, simple configuration and rich tools; 3. Rich ecosystem, attracting a large number of community and third-party tool support; 4. Cross-platform support, suitable for multiple operating systems.

The steps for upgrading MySQL database include: 1. Backup the database, 2. Stop the current MySQL service, 3. Install the new version of MySQL, 4. Start the new version of MySQL service, 5. Recover the database. Compatibility issues are required during the upgrade process, and advanced tools such as PerconaToolkit can be used for testing and optimization.

MySQL backup policies include logical backup, physical backup, incremental backup, replication-based backup, and cloud backup. 1. Logical backup uses mysqldump to export database structure and data, which is suitable for small databases and version migrations. 2. Physical backups are fast and comprehensive by copying data files, but require database consistency. 3. Incremental backup uses binary logging to record changes, which is suitable for large databases. 4. Replication-based backup reduces the impact on the production system by backing up from the server. 5. Cloud backups such as AmazonRDS provide automation solutions, but costs and control need to be considered. When selecting a policy, database size, downtime tolerance, recovery time, and recovery point goals should be considered.

MySQLclusteringenhancesdatabaserobustnessandscalabilitybydistributingdataacrossmultiplenodes.ItusestheNDBenginefordatareplicationandfaulttolerance,ensuringhighavailability.Setupinvolvesconfiguringmanagement,data,andSQLnodes,withcarefulmonitoringandpe

Optimizing database schema design in MySQL can improve performance through the following steps: 1. Index optimization: Create indexes on common query columns, balancing the overhead of query and inserting updates. 2. Table structure optimization: Reduce data redundancy through normalization or anti-normalization and improve access efficiency. 3. Data type selection: Use appropriate data types, such as INT instead of VARCHAR, to reduce storage space. 4. Partitioning and sub-table: For large data volumes, use partitioning and sub-table to disperse data to improve query and maintenance efficiency.

TooptimizeMySQLperformance,followthesesteps:1)Implementproperindexingtospeedupqueries,2)UseEXPLAINtoanalyzeandoptimizequeryperformance,3)Adjustserverconfigurationsettingslikeinnodb_buffer_pool_sizeandmax_connections,4)Usepartitioningforlargetablestoi


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