Computed Column（计算列）是自SQL Server 2005开始就有的特性。计算列的定义是一个表达式。表达式可以是非计算列，常量，函数间的组合。但是不可以是子查询。

计算列数据固化

默认情况下计算列的数据是存储在磁盘上，仅当计算列被查询引用是才进行实时计算。只在计算列在定义是添加了PERSISTED关键词是才将数据固化。

计算列上创建索引或者作为分区函数的引用列

计算列上是运行创建索引和作为分区函数的引用列。但是必须指定PERSISTED关键词。

用法其实很简单。那么这里有些问题。使用计算列的代价到底有多大？

INSERT发生时使用计算列和非计算列的性能区别

这里做一个测试。首先创建好两张表

IF EXISTS(SELECT * FROM sys.tables WHERE name = 'computed_column_test_computed')

BEGIN

    DROP TABLE dbo.computed_column_test_computed

END

IF EXISTS(SELECT * FROM sys.tables WHERE name = 'computed_column_test_noncomputed')

BEGIN

    DROP TABLE dbo.computed_column_test_noncomputed

END

CREATE TABLE dbo.computed_column_test_computed

(

    dttm DATETIME,

    dttm_year AS YEAR(dttm) PERSISTED,

    dttm_month AS MONTH(dttm) PERSISTED,

    dttm_nextday AS DATEADD(DAY,1,dttm) PERSISTED,

    dttm_previousday AS DATEADD(DAY,-1,dttm) PERSISTED,

    dttm_week AS DATEPART(ww,dttm),

    dttm_monthname AS CASE DATEPART(mm, dttm)

                        WHEN 1 THEN 'January'

                        WHEN 2 THEN 'February'

                        WHEN 3 THEN 'March'

                        WHEN 4 THEN 'April'

                        WHEN 5 THEN 'May'

                        WHEN 6 THEN 'June'

                        WHEN 7 THEN 'July'

                        WHEN 8 THEN 'August'

                        WHEN 9 THEN 'September'

                        WHEN 10 THEN 'October'

                        WHEN 11 THEN 'November'

                        WHEN 12 THEN 'December'

                      END PERSISTED,

    dttm_quarter AS DATEPART(qq,dttm) PERSISTED

)

CREATE TABLE dbo.computed_column_test_noncomputed

(

    dttm DATETIME,

    dttm_year SMALLINT,

    dttm_month SMALLINT,

    dttm_nextday DATETIME,

    dttm_previousday DATETIME,

    dttm_week INT,

    dttm_monthname VARCHAR(30),

    dttm_quarter VARCHAR(30)

)

然后开启IO和TIME的统计信息开关，然后分别对两张表进行数据插入。

SET STATISTICS TIME ON

SET STATISTICS IO ON

INSERT dbo.computed_column_test_computed(

dttm

)

SELECT DATEADD(SECOND, Num, GETDATE())

FROM dbo.Numbers

WHERE Num <= 1000000

INSERT dbo.computed_column_test_noncomputed(dttm ,

    dttm_year ,

    dttm_month ,

    dttm_nextday ,

    dttm_previousday ,

    dttm_week ,

    dttm_monthname ,

    dttm_quarter)

SELECT     GETDATE(),

        YEAR(GETDATE()) PERSISTED,

        MONTH(GETDATE()) PERSISTED,

        DATEADD(DAY,1,GETDATE()) ,

        DATEADD(DAY,-1,GETDATE()),

        DATEPART(ww,GETDATE()),

        CASE DATEPART(mm, GETDATE())

        WHEN 1 THEN 'January'

        WHEN 2 THEN 'February'

        WHEN 3 THEN 'March'

        WHEN 4 THEN 'April'

        WHEN 5 THEN 'May'

        WHEN 6 THEN 'June'

        WHEN 7 THEN 'July'

        WHEN 8 THEN 'August'

        WHEN 9 THEN 'September'

        WHEN 10 THEN 'October'

        WHEN 11 THEN 'November'

        WHEN 12 THEN 'December'

        END,

    DATEPART(qq,GETDATE())

FROM dbo.Numbers

WHERE Num <= 1000000

我的例子里面分别进行10万行、30万行、50万行和100万行数据的插入测试。一共有7个计算列。每个例子测试两次。

 SQL Server Execution Times:

   CPU time = 0 ms,  elapsed time = 0 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 100840, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 164, physical reads 2, read-ahead reads 162, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 515 ms,  elapsed time = 1564 ms.

(100000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 1327 ms.

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 100833, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 164, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 265 ms,  elapsed time = 759 ms.

(100000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 5 ms.

 SQL Server Execution Times:

   CPU time = 0 ms,  elapsed time = 0 ms.

 SQL Server Execution Times:

   CPU time = 0 ms,  elapsed time = 0 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 100840, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 164, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 391 ms,  elapsed time = 411 ms.

(100000 row(s) affected)

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 100833, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 164, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 234 ms,  elapsed time = 273 ms.

(100000 row(s) affected)

 SQL Server Execution Times:

   CPU time = 0 ms,  elapsed time = 0 ms.

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 6 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 302521, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 487, physical reads 1, read-ahead reads 330, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 1250 ms,  elapsed time = 1986 ms.

(300000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 74 ms.

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 302499, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 487, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 813 ms,  elapsed time = 966 ms.

(300000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 14 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 302521, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 487, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 1156 ms,  elapsed time = 1709 ms.

(300000 row(s) affected)

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 302499, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 487, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 734 ms,  elapsed time = 3089 ms.

(300000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 8 ms, elapsed time = 8 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 504201, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 809, physical reads 0, read-ahead reads 315, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 2031 ms,  elapsed time = 2080 ms.

(500000 row(s) affected)

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 504166, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 809, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 1297 ms,  elapsed time = 2915 ms.

(500000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 0 ms.

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 4 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 504201, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 809, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 1984 ms,  elapsed time = 3540 ms.

(500000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 2 ms.

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 504166, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 809, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 1266 ms,  elapsed time = 1341 ms.

(500000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 13 ms, elapsed time = 13 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 1008359, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 1615, physical reads 0, read-ahead reads 791, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 4500 ms,  elapsed time = 9110 ms.

(1000000 row(s) affected)

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 1008333, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 1615, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 2531 ms,  elapsed time = 3903 ms.

(1000000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 0 ms.

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 3 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 1008359, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 1615, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 3797 ms,  elapsed time = 6559 ms.

(1000000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 3 ms.

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 1008333, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 1615, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 2422 ms,  elapsed time = 3895 ms.

(1000000 row(s) affected)

可以看到确实使用computed column会对性能有一定的影响。当计算列数量越多的情况下性能的影响越大。但是当计算列数量很少的情况下，影响或者说差别其实很小很小。以我做的实验为例，讲计算列数量减少到只有3个，数据量依旧停留在100万行的情况，两者的性能差异其实已经很小了。

SQL Server parse and compile time:

   CPU time = 0 ms, elapsed time = 4 ms.

 SQL Server Execution Times:

   CPU time = 0 ms,  elapsed time = 0 ms.

 SQL Server Execution Times:

   CPU time = 0 ms,  elapsed time = 0 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 1005813, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 1615, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 3203 ms,  elapsed time = 5058 ms.

(1000000 row(s) affected)

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 1005319, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 1615, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 2281 ms,  elapsed time = 4747 ms.

(1000000 row(s) affected)

SQL Server parse and compile time:

   CPU time = 16 ms, elapsed time = 28 ms.

Table 'computed_column_test_computed'. Scan count 0, logical reads 1005813, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 1615, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 2984 ms,  elapsed time = 3512 ms.

(1000000 row(s) affected)

Table 'computed_column_test_noncomputed'. Scan count 0, logical reads 1005319, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Table 'Numbers'. Scan count 1, logical reads 1615, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

 SQL Server Execution Times:

   CPU time = 2672 ms,  elapsed time = 2859 ms.

(1000000 row(s) affected)

那么总结下，计算列的使用原则我认为是在表中计算列的数量本身不多，而且一次性数据行插入量不大，计算逻辑固定，计算复杂度大的情况下，推荐使用计算列。

比如像DimDate这种表，表中可能有非常多的属性列用于表示当前日期的一些额外属性，比如下一天的日期，前一天的日期等等。用计算列是一个很好的选择。

参考：

Computed Columns

SQL Server ->> Computed Column（计算列）的更多相关文章

  1. SQL Server中的标识列

    一.标识列的定义以及特点 SQL Server中的标识列又称标识符列,习惯上又叫自增列. 该种列具有以下三种特点: .列的数据类型为不带小数的数值类型 .在进行插入(Insert)操作时,该列的值是由 ...

  2. SQL Server自动化运维系列——关于邮件通知那点事（.Net开发人员的福利）

    需求描述 在我们的生产环境中,大部分情况下需要有自己的运维体制,包括自己健康状态的检测等.如果发生异常,需要提前预警的,通知形式一般为发邮件告知. 邮件作为一种非常便利的预警实现方式,在及时性和易用性 ...

  3. SQL Server自动化运维系列——监控跑批Job运行状态（Power Shell）

    需求描述 在我们的生产环境中,大部分情况下需要有自己的运维体制,包括自己健康状态的检测等.如果发生异常,需要提前预警的,通知形式一般为发邮件告知. 在上一篇文章中已经分析了SQL SERVER中关于邮 ...

  4. SQL SERVER将某一列字段中的某个值替换为其他的值 分类： MSSQL 2014-11-05 13:11 67人阅读 评论(0) 收藏

    SQL SERVER将某一列字段中的某个值替换为其他的值 UPDATE 表名 SET 列名 = REPLACE(列名 ,'贷','袋') SQL SERVER"函数 replace 的参数 ...

  5. SQL Server 2016：内存列存储索引

    作者 Jonathan Allen,译者 谢丽 SQL Server 2016的一项新特性是可以在“内存优化表(Memory Optimized Table)”上添加“列存储索引(Columnstor ...

  6. SQL Server ->> ColumnStore Index（列存储索引）

    Columnstored index是SQL Server 2012后加入的重大特性,数据不再以heap或者B Tree的形式存储(row level)存储在每一个数据库文件的页里面,而是以列为单位存 ...

  7. Sql Server中的标识列(自增长字段)

    一.标识列的定义以及特点 SQL Server中的标识列又称标识符列,习惯上又叫自增列.该种列具有以下三种特点: 1.列的数据类型为不带小数的数值类型2.在进行插入(Insert)操作时,该列的值是由 ...

  8. SQL Server 2012 自动增长列，值跳跃问题

    介绍 从 SQL Server 2012 版本开始, 当SQL Server 实例重启之后,表格的自动增长列的值会发生跳跃,而具体的跳跃值的大小是根据增长列的数据类型而定的.如果数据类型是 整型(in ...

  9. 为SQL Server表中的列添加/修改/删除注释属性（sp_addextendedproperty、sp_updateextendedproperty、sp_dropextendedproperty）

    本篇基本完全参考:sql--sp_addextendedproperty和sp_updateextendedproperty (Transact-SQL) 三个存储过程用法一样,以sp_addexte ...

随机推荐

  1. python学习 条件控制

    if - else  if 条件a : 内容a else : 内容 非a if - elif - else  if 条件a : 内容a elif 条件b: 内容b-a else : 内容 非(a∪b) ...

  2. 关于DES加密之选择更新版

    数据加密算法(Data Encryption Algorithm,DEA)是一种对称加密算法,很可能是使用最广泛的密钥系统,特别是在保护金融数据的安全中,最初开发的DEA是嵌入硬件中的.通常,自动取款 ...

  3. 换个角度看Salesforce之基础配置学习笔记(二)

    1. 登录后无法使用Developer Console? 先找到当前登录用户的Profie,然后勾选Profile中的View All Data(Modify All Data)即可: 2. Pers ...

  4. erlang随笔3--OTP

    OTP最核心的概念就是行为.一个行为封装了某种常见的行为模式.可以把这些行为理解为某种应用程序框架.可以通过回调模块来 定制这些框架.OTP依靠行为引用了容错,扩容和动态代码升级等特性.所以在写回调模 ...

  5. 解决chrome浏览器对于自动填充的input表单添加的默认的淡黄色背景问题 && 一般的浏览器input和button的高度不一致问题

    解决chrome浏览器对于自动填充的input表单添加的默认的淡黄色背景问题 如果我们把一个表单设置位 autofocus ,这时这个表单在获取焦点后就会产生淡黄色的背景,我们就是使用!importa ...

  6. Robot Framework（AutoItLibrary安装）

    RobotFramework下安装AutoItLibrary库 1.安装pythonwin32 在下载地址:http://sourceforge.net/projects/pywin32/files/ ...

  7. 在W3C SCHOOL网站上发现一个关于Schema的错误

    原地址是http://www.w3school.com.cn/schema/schema_complex_empty.asp 下面这个例子是不正确的 xmlspy报错. 因为<xs:restri ...

  8. 转 Unity企业级支持案例与分析

    Unity大中华区技术支持总监张黎明以“Unity企业级支持案例与分析”为主题进行了分享. 以下为演讲实录: 张黎明:非常感谢大家来参加今年的Unite,其实我现在看到有的朋友已经不是第一次来参加Un ...

  9. JavaScript中的异步 macrotask 和 microtask

    看过很多setTimeout.Promise执行顺序的面试题,一直不明白为啥都是异步操作,Promise就牛×些呢?直到了解了macrotask和micromask才恍然大悟... 先来一道面试题助助 ...

  10. .netCore2.0 配置文件

    之前的asp.net 的配置文件都是xml格式,而.netCore的配置文件则采用Json键值对的格式来存储,具体获取如下 var config = new ConfigurationBuilder( ...