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技术 2022年11月21日
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首先我们建立一个测试用员工表

---创建一个测试的员工表---
create table Employee(
EmployeeNo int primary key, --员工编号
EmployeeName nvarchar(50) null, --员工名称
CreateUser nvarchar(50) null, --创建人
CreateDate datetime null, --创建时间
)

执行后结果:

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” alt=”” width=”382″ height=”93″ />

那么假如我们要批量插入10000条数据,应该怎么办?

这里有四种方法(普通循环,事务循环、批量插入、cte插入)

1、普通循环插入(while)

/*******************************************
***普通循环(插入数据10000,执行时间:1283毫秒)
********************************************/--开启开关(记录sql语句各个阶段所消耗的时间)---
set statistics time on;
--声明两个变量---
declare @Index int;
declare @Timer datetime;
--对两个变量进行赋值----
set @Index = 1;
set @Timer = GETDATE();
--当循环小于1000次执行添加语句---
while @Index <=10000
begin
--执行添加的语句--
insert into Employee(EmployeeNo,EmployeeName,CreateUser,CreateDate)
values(@Index,'员工'+ cast(@Index as CHAR(5)),'system',GETDATE())
--设置循环次数加1
set @Index = @Index+1
end
--获取执行的毫秒数--
select DATEDIFF(MS,@Timer,GETDATE()) as '执行时间(毫秒)'
--关闭开关(记录SQL语句各阶段所消耗的时间)
set statistics time off;

执行普通循环插入10000条数据,大概需要1200多毫秒,结果如图所示

aaarticlea/png;base64,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” alt=”” />

2、事务循环插入

/*******************************************
***事务循环(插入数据1000,执行时间:460毫秒)
********************************************/--开启事务--
begin tran;
--开启开关(记录sql语句各个阶段所消耗的时间)---
set statistics time on;
--声明两个变量---
declare @Index int;
declare @Timer Datetime;
--对两个变量进行赋值----
set @Index=1;
set @Timer = GETDATE();
--当循环小于1000次执行添加语句---
while @Index <=10000
begin
--执行添加的语句--
insert into Employee(EmployeeNo,EmployeeName,CreateUser,CreateDate)
values(@Index,'员工'+ cast(@Index as CHAR(5)),'system',GETDATE())
--设置循环次数加1
set @Index = @Index+1
end
--获取执行的毫秒数--
select DATEDIFF(MS,@Timer,GETDATE()) as '执行时间(毫秒)'
set statistics time off;
--提交事务--
commit;

执行事务循环插入10000条数据,大概需要400多毫秒,结果如下所示:

aaarticlea/png;base64,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” alt=”” />

3、批量插入

/*******************************************
***批量插入(插入数据10000,执行时间:33毫秒)
********************************************/--开启开关(记录sql语句各个阶段所消耗的时间)--
set statistics time on;
--声明一个时间变量---
declare @Timer datetime;
---对时间变量进行赋值---
set @Timer = GETDATE();
---执行批量操作的sql语句---
insert Employee(EmployeeNo,EmployeeName,CreateUser,CreateDate)
select top(10000) EmployeeNo=ROW_NUMBER() over( order by c1.[object_id]),'员工','system',GETDATE()
from sys.columns as c1 cross join sys.columns as c2
order by c1.object_id
--获取执行的毫秒数--
select DATEDIFF(MS, @Timer, GETDATE()) AS [执行时间(毫秒)];
--关闭开关(记录SQL语句各阶段所消耗的时间)--
SET STATISTICS TIME OFF;

执行批量插入10000条数据,大概只要33毫秒,结果如图所示:

aaarticlea/png;base64,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” alt=”” />

4、CTE插入

--/*******************************************
--***CTE插入(插入数据10000,执行时间:40毫秒)
--********************************************/
--开启开关(记录sql语句各个阶段所消耗的时间)--
set statistics time on;
--声明一个时间变量并赋值--
declare @Timer datetime = GETDATE();
---将要添加10000条语句组合成CTE模块---
;with CTE(EmployeeNo,EmployeeName,CreateUser,CreateDate) as (
select top(10000) EmployeeNo = ROW_NUMBER() over (order by C1.[OBJECT_ID]), '员工', 'system', GETDATE()
from SYS.COLUMNS as C1 cross join SYS.COLUMNS as C2
order by C1.[OBJECT_ID]
)
--执行CTE插入语句---
insert Employee select EmployeeNo,EmployeeName,CreateUser,CreateDate from CTE;
--获取执行的毫秒数--
select DATEDIFF(MS, @Timer, GETDATE()) as [执行时间(毫秒)];
---关闭开关(记录sql语句各个阶段所消耗的时间)---
set statistics time off;

执行插入10000条数据,大概需要40毫秒,结果如图所示:

aaarticlea/png;base64,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” alt=”” />

最后我们查看一下,批量插入10000条数据的员工表

sql server中的大数据的批量操作(批量插入,批量删除)

小结:

1)按执行时间,效率依次为:CTE和批量插入效率相当,速度最快,事务插入次之,单循环插入速度最慢;

2)单循环插入速度最慢是由于INSERT每次都有日志,事务插入大大减少了写入日志次数,批量插入只有一次日志,CTE的基础是CLR,善用速度是最快的

那么,假如我们要批量删除我们插入的数据,怎么办呢?

批量删除有3中方法(循环删除、批量删除、truncate 删除)

1、循环删除

--/*******************************************
--***循环删除(删除数据10000,执行时间:20毫秒)
--********************************************/
set statistics time on;
--声明一个时间变量---
declare @Timer datetime = GETDATE();
--删除语句--
delete from Employee
--获取执行的毫秒数--
select DATEDIFF(MS, @Timer, GETDATE()) as [执行时间(毫秒)];
set statistics time off;

删除10000条数据,所需的时间大概为20毫秒,如下所示:

aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAALYAAABeCAIAAAABhXm8AAAYIklEQVR4nO2de1QTZ97HEdt/7HvU97SeukW29pzdnp7tutbdFtt6W30tWpWyvSlaWy14oa5oRWyRrToIykVuQSVyCYFgJAaRAAkKATGEa7gkRBACJCQBArmRCwnKJXneP2aZDoGEqBEQ53t+//xmnjzPZJ4PM9/8fGZ00g8YYi8nP2huGUSpqKiourq6tbWVQqH09fVpn0EymUyK6UWWk21EyGRybW0tF9NLLFuICIXCXEwvvWwh8iz3F0xzRhgimKYQhgimKYQhgmkK2UJEo9EoXw5pNJqZnojZK1uIyOVywcshuVw+0xMxezUFIh0dHUaj8fHcldFo7OjowBCxoSkQEQqFIyMjYO5qZGREKBR2dnbOdA3TMZLJZBgiDhaMiFgsnunJdYBEIpFYLJ5diIyazF2qwU6FYdIQyQ0PxYbmTstQaIanEwLbghGZeKNRKpUymUwmk3VNkEQiecZ/t3pO6u7unnWIaIzDv2Q0HkqsmzR+vFL3ydG6fxyyjMS8HtvTZjabTSaTxUaTyWQ2mx0AxXhZQ6S1tfX+/fulpaV0Oj0fpby8PPINSnU1x+Ez8eyavYiEFMhjy4yxZcbYe8ZYgtE/0Xgo0Xgo0XjwmvH7OOPe2HGxwb91SkRYLJa7u7tCoUC2KBQKd3d3FovlMDTGZA2R6mphYeHDgYEBnU6HbNRoNBKJJD6HlltR6fCZeHbNXkQCMtuCssVB2eKgLHFQsvhoonhnhHhL0OTxyU5+YsYUiOTl5b311ls9Pb836+npeeutt/Ly8ixa5ubmHrCp+Pj4p0OkvFx092473GZoaEhpHBEPAKHWVC7QBCfkZTHKHT4Tz67ZiIjeOBye0XitoJ3MlpDZkgxWZzytIbW4PYEuCSNLQkmi4zGccwRBGFly8boYSu348VzDPz/PSUxstDFntbW1ERERgYGBWq22oKAgISEhISEhLCxs0aJFP/30E5ymp6cbDAYAAARBb7/99k9j+vDDDy1SDw+PZ0HEZDJptVqZTNYillWJ+ovaBjOKugPP5mRlOQyR0tLSrKwsGw34fP61a9fs+akyGxExGoczMhrFYi3y19bQ0KDRaOD08ePH5eXlSqXSbDaPjIyo1XoSiff112eTkm5b61AgEEAQ5O/vPzw8zOVyvb293dzc3NzcPvjgg1dfffXdd9+F023btimVShiRdevW1Yzp4MGDFumzIJJLb+3VavkPHlRU1JeVVRWW1V0vEaakVv7yy42nQESpVLLZbIlEotVqJRIJm81WKpVarTYwMHDr1q1wm/r6+paWFqRxSUlJSUlJaGjo66+/TqVSS8YL7gqt2YyIxmw2m83mx48fV1ZWqlQqOH306FFRUVFvb+/w8IjRaOztVZFIvJ07D6akpFjr0MPD4+zZs6Ojo11dXcuWLcvPz4e3W7vRQBA0b968+WOaN2+eRfosiCTerCNwOLSCAhKpIjW1MD2dnJ5OTksnBwYSsrIqNFotHHaqtbXVxcUlMzNTo9HcvHnTxcWlublZo9EgiKjV6s8///z06dNw46VLlzo7O8+3IicnJwqFYjHE7ETkEYVCr63ltrW18fn82tpaFotVXV1dV1dXV1dXU1PDYDBu364tKKhlMouzsxnJyTVTIrJ06dJVq1Zt3Ljx3r17Wq12SkTWr1/fMKbDhw9bpM+CSB6jVWU0anU6tdqgUunU6n5Zn+pWZVces7agXJDL76U+eEB98IDX0WHPb2ClUllVVbVnz57Y2FipVJqfn79u3bqCggIYkY6Ojk2bNiUkJAgEAu0YIuHh4WWT6fbt22+88caLgsgglZrd0iJQq9VyuVwqlZaXl7e3t8MVBbG4i0qtZLEETU1CbrPgbk1LGunurl0/2ECksLAwOTnZz88PsauwId29e/eCBQvc3d0tHCgEQX/84x8Rc/r3v//dIn0WRPLzH/b39+v1er1eL5cPCcSjFUIjtU73UKLqUujblcbGnj5iPhe6SM7OLrazUnL79m0Wi6XVamUyWUpKSlNTE4wIksLNYEQ2bdr0w2T65ptvFixY8KIg8igzs1gqlcPp0NAQfKMBAJjN5oGBwVu3qtrauuRaQ3v/UHX34HVKtpfXbhuIgPF2FQBAp9N/Gq+EhASkscVeC7tq0fhJEcnJ4YvF4ra2NoFAwON1llZIqKxWEkfcpRkECgVo5hq5rAxi7ZkzpNDQVBrtnlwut/YvxhaWk8/nx4xpy5Yt77//PpKWlpZqxxD57LPPvCeTl5fXC4TIUEZGlUDQMzAwoNfrVWr1vaoqkUSiUunkcl1XV3d+fgmX29YoVLA7VKUPu0nkm15eXjYQEQgEaMup0Wi6u7trxksgECCNLXZZ2FVYXC53eNhqPdcGIjTaA6lUyuPx6uvr6+vrORzOPTabUFsr1mpBSwvIIQ3ejqFRqqm5JccDYr777nxpabVCoZr0LN+5c2fjxo05OTmwx6RQKK+++uqKFSv+MV6LFi2C/zZgREJDQ0smE5VKff31118MRAwGQ3p6enFxcVVVFYvFKikrI3Bqs4qKbty4n55eQaMxUlJqEpPqCITCtHQyMe16UlLSzp07bSDi6emJNmX5+fkTDSly70A3dnJycnZ2RuwqnMK7XF1d0SUW+xFB6iK/f9/h4YzGRrFWC8xmYDaNDg9LJJLDkREfehz68MPD7lsChEKptRMN29WbN29qtVoKhbJ06dLm5mb1eG3ZsgWNyFywqzAiAoHAYDAMDAwolBpSFo/fLJXJFFKpXCTqvHEj9y6nk83TF7EVGffariWnfvXVcRs/ekUiEXp5vk6ngw0pssXX1xdBBGlcVFS0ZMkSHA7n6+u7fv16JIX3NjU1Pd1V5O7ddrPZbDQaO3WjUs1gb/8AVzVM5DWKtVrwkAXSQsyxsUNq9aWcUycu/cfvdJr7vwOEEquIKJXK6upqqVSqHbuKvPfeeyvGa+HChWhEwsPD2ZMpJyfnhbGrMCKdnZ1wOjj4ODeXLRR26fV6hdbQLNPdoGSXVnXf5w/Tag3XK3qvJRO//DIgMdHyh4mFeDwe7Dd5PB4EQWjLaZHCjWHPIZFI4L1Go/HGjRsBAQG5ubm2B7IHEb1e363WtysNTcqhKtlwan6jWKYFSgloZJurqx/pdOX8gusl+UEJ2e5eAcJOq4igRaFQFi1aFBISEj9ef/3rX9GIzAW7CiNSX18vlUo7OroaG3tu3WKVlbU0NLTVcJvuVDdlkG/SaM1ZBe1ERm1qXiU+Mdk2InA5NSIiAjabfD4fgqAVK1YkjGn79u0IIkhjxNuiAUpISIAgCF2KfQpE4Opql0rXrByq6wNVrcOpaY1isRaIxeB+kakiT93bU9qQm1GVF5Sa7e5u60YDKysrq7S0lEKhLFy48Ny5czHj9f7778PfRSqVnjhxwsKfWrjXiooKi85nLyJEIpFAyIiPJ1+4cDs0tCA0lBYWRoqIuBIZGR0ZGX3xIjUkJOX8mHbs8LVxo/H393dzc/P390e2QBC0ePFitzG5uLggEEzaGH2NSUpKQpdinw4RlUrVrByqk4NKkZ5exScQKyQSNWCzzRdOP750uKvlwTX6bxG5MUEJUyACF0z37t2Lw+GmtKtIKbalpaW+vl4oFLq7u1+5cgVde30xqqsGg4FEIu3atevjj79avXrPmjX/dHPz+fTTz8fSNWvWrFm9es/HH3+1BiUCgWCtw4kKDg729PS0ltpubI9sIHLnTvvo6KhSqWpSPq7tMGfXlR2t/mfS9USptBuYzUOPBvt6ZQKBoLKy8tatiqAgW4hoNJrm5mYLu9ra2mrRbOvWregbDYVCQZfnt23bdvr0aaQruFCL/vhsRMRkMqnV6s7OToFAJBCI29raBQJJW1vHWNrW1tYmEIgFAlEbSnDhxE719vaKRCJrqe3G9sgGIllZXB6vKTubf72iKYP7kFDHwVXfTr1Olkq7AeuWOdhrMOxHfgUrABdwMjzaNiIMBmPt2rV0Oh2xq0+BSGNjo0AgQLras2dPTEwM+uOzEZE5IBuIRF0tPH4p+lRgpM/FqD3R0Xuio71j4xJTSVJpd3fjg8rraffI+Nu3KkNwGWE40pkzKTYQaWpqIhAIvb29cDoREYlEcvTo0WXLlgUGBpaXl3t7e8fExDQ1NZWVlYWEhHyP0saNG+HPIjwhwhB5LrKGSEtLTxa9JpRIvJacHJGaGkwkBhOJocQ0QlpGZuaduGRmQFTu8UjScX/S8eOkEycS9u8/Z49dhVVZWenv7w9fUWBJpVJ/f38fHx8qlWqxt7Ky0me84L0xMTFUKhXdLYbIc5E1RCaVQqG4c6fo1Kn4774LmRhHjsRIJD0OnyH7hSHyXPREiMxyYYg8F8GIdHR0CF98tbe3t7W1Obzblx0RuH7q8L+8uaQnQMRoHOwQirF42eIJEJHJ+rJzGMySspJS9nQGs6TsbtG94nvTOujLFkXFrEJm6aS7ngwROoM5+OjRNN8LHj161NfXNzo6Os3jvlTS6/VqtXrSXRgimADAEME0peYsIhwOh8FgWEsnlcFgSEtLQ9a4PJ1sjMtgMK5OprS0NBtrEpDPcjgc2206OzvRXVmkT605i8iUC44spNFoCgsL3d3d2Wx2d3d39XihV8WityMPj1kbKDk52cfHp6GhYWho6OTJk6sn6N1330U9gvrIqFF0VHdwqjkWB7B27doDBw5MPCr4GTY4TUlJ2b59O7K8YcLDrcOjQ/2SBrFCY3yiecIQAQAAk8mUm5vr6uoqk8kAAMHBwa+MF7KQwMPDw9nZGd7o5OQ06fM7FgPl5+cjPU/UuIk0tzXmEfbO/2HhKwtfsUOenp4ymczV1fWVV15xdna2GBfpeXR01GQyASDX9uQELjtGyOUJnuRFChgiAACAx+P37dvX3NwML2Xt6+vjjReykMDDw8PX15fH4zGZzCVLltiDiE6nQ3qeKDQiWj6lOO3MiWRJBYfPs0MikWh4eLi5uZnH46GX7lr07Ofnh8fjARgZNXTJiAF7wzPjWBIbZ9VCcxMRHA63atUqV1dXf39/jUaDpMjSPRqNhm4MQRCTydRoNP7+/jwez8aIHh4eEAQB1FOANBoNvSgQPS4AgEaj4XA4uOeJj73QaDQUIp01yVeu/DucJAOGUQB/dtKHZby9vSceJBpNHA6H7pnJZEIQhMPhwOgjICu4dBp3lVzWafdJnpuI4PH4jz76aPny5fBzsEh65MiRI0eOLF++HJ5mpDGdTu/s7MThcMeOHQsPD7dhKiciQqfTj6CEHheMzRzceNu2beiW8GH8joiCQbgQc+RIlgAA+LRCEIQcM6J9+/a99tpryNULtthXr17dvn37ihUr0AeJvj6NAWQCQFV8/uekBAJDAezU3EQE2LzRINOMqLu7OyoqCj6hFqZy8eLFLi4uq1evhp2gh4cHbBvpdPobb7wx5Y0GjYhFY4uJHK6MPh8T/W0SH3GgBw4cWLt2rYVFhceNioqC7apSqdy+ffvq1atdXFwWL16MHKQVRAAAgJ/0bXTM+ehKe98ZNpcR2bFjB3yEo6OjZ8+etYbI6OgoBEHz58+3eLnNpI0ntavwKzCQca0hQqPRRlDasWMHeiJluaci4yJP5crgxvPnz7cwp87Ozk5OTk5OTkiKDIT+giMjI1988UVwcDAaEfTSXWQgO0/yXEZk4cKFmzdvVigUfn5+b775pjVE/Pz8IAiKj4+3E5GJdvX+/fvwQMAmIu+8885KlBYuXGgDkfj4eAtz6uvr6+TktGTJEiaTaeFPkS+oUCg2b96clpbW19eHRqSvrw9x3BgiAIzZ1ZUrV2ZmZhqNRiaT6enpaQ0RJpPZ0NAw8RVZkzae6EUAAFKpFI/Hw+jYQOTYsWMElFauXDkpIkajMTMzUyqVWthVCIIIBAL8jQAADQ0NTCYTOSonJydXV1dfX188Hi+VSsH430qwa4YbY4gAMGZXkami0+kQBP36669EIhFtOdF6FkTQ6VN7EW3x+YuxUQfIvz8tzGAw/j2m5cuXb9u27cqYkBKwwWAgEom//vorbJODgoK0Y29e4fP5cAp/fTweD29vJx+Iir14vlhr3zmeo4iAyfwpMhlwqtFo4KIn3MZ+RCa1q7BtZLPZNhCBPSa6YDrOVLakRV6K2XXhvg4A+LWh6CLv2rVrYdcMi81m2x4XFux8fXx8kpOTAQAAmAF4XBm95zIuMq3F3pM89xFB7BtS5fT09IQgiEajoZmwH5Enqq7CKVIDtdB4U1mfHxxz7osrxWYAn0d0kXfevHlnz56FfS7cs8lkQn9xi3FNJtPIyIhUKnV1dUVe+gXACDALaaciSYT8ertP8hxHBG3fkCqnSCSCIOidd955OkSeqLoKp0gN1ELjTeWgtCgpO/o/ITVANwTA+CLvhg0b3nzzzZUrVyK+GI/H+/n5WRsXj8evXLlyw4YN9+/f1+l0/906pAc1Eft+S7p4+8Gg3Sd5biKClFPR9g2thoaGY8eO2UCEx+PBJtHV1XWitwUAIKYS3o7UT1etWmVn4R+Hw3l7e2/evBkZ97GCXUWNOeFFrVIZQ3A4tFe9dOmSp6enk5PTggULvLy8rA2EHIaFtwUAAKAzqqqoXifOpZbcleomO6LJNTcRwePxsMtD2zcLIW7OWopYxSlXEQAAtFptUFAQ3B4xhgAABoOBTqc6SEU3vyorKIenHYwc2wuLz+ej3au1gZDDmOyY9YNaXk5QVhW/2+7KKgBzFRFMDhSGCKYphCGCaQo5DJF8elF/v2ZwejUwMCASiX7++eeamppbt27FxMT09vaeOHECSx2Ywv+9nQMQyaMXyuUKg8EwnYgYDIZOsXTnoZOZdysvpWQdD44ta5J+fRBLHZb+8htUUVFh7QLzNIjYeAHh89Dw8HBPnzKKWpbEkpC5OiwcHjt9/JKSkqyd/xcDkdbWVpdlrgHx1Bk/m3MyHnapUZUVS70YiPTIVfG5nJTy7hk/m3My/rX38NWrV62d/xcDEZFY8uW+Ixcp5TN+NudkJN3ItfH8jmMRGQWgm0+v4XE6n6i0NyUisF0Nz6qa9BtmcHovpyUcD4naHxi1P4R0OK0lldNP5uqSGaUhuKj9gVH7A6P240pDGF0zPhmzMy6nUafBro481su7H7A4NVcC1vqHQHl1DkXEhl3NqOlJzL3r84PbR5988KcVq9522/rOLlxIgSy1SnIh8jfPTX9evuLDP6342x82BXwbWZZUO/PzMQtjeuyqWlSRHPrxq6+/9sr8eVv3OhoRG3Y1lV4Enfzsf3eTAqhCMlccR4z/7tM/bI5oDaPcOPhz2OZ9mThufwb3wcl9Pt//fNm/aObnYxbG9NjV0SGDuq+9tomffHpDwAWHX0Ws29XrnN7Ue/zIQmlKtZrM7U+m5f12ZN0/TpdDceE7T178KKA4naslc1XBAR57T57xoc78fMzCmE67+hiAkiseZ2McjYjddlV2NTPtiOdf1p3nBoWc2hUQuimMB++KDdu/J+Dc19ekMz4fszCm064+L0Q6xdJdhwOs2VUk0orvnwk99emaHT+kiYIvHtt96vz2OAG862rcuBQLdFwm3py26urzQqSnTxl1k2W7uprOagqLC9+1z/v9fZlhZfK4OAwRe2PXgWPTVl19XohMXV2t778ce2zLV/tX/EjBcXUZXN3VuOO7T4WgEdlz6vyOuLYZn49ZGNNZXX1uVxGb1dWMKsmV6MPrvzy+5Zes88zudK6OzNWRSBe/CbjgdqYCbhN2xnNvQNBeknrG52MWxly3q+UPk1LjNn6y6/9Okk/ShERkez7hhxNh63xzr3E117mi0757vj8RcTR/5udjFsYct6tERuH5o6sX/c/qD77x/yrw0r7ASz+eueJ3ve3q3bJfzgZv89z/dWDED4FBmzz9dp+9Fc6e+fmYhTGddnUIgLpbAcmZKeV2P+NjFyI27GpSXuGvvm5/+8jtz3/7b7y32n0DVBlWJL9MvHzwu7Ht310+SHw445MxO2M67epzEWxXl7m6nsIWAzyfeChVYYsBsLAV2GIALKaI6bSrz0X2V1exeLqYTrv6XGRndRWLp445YlddXV39Y29cr+8nN2hItaobXG1GnRpLHZI63q7q9fqBaZRer1epVBwO51/f7obCojNzGKs+Xs992LF7/yEsdUjqfeCQw+xqPqNIp9cPzZCKiorq6upEIhH8HyJjqQNTLpfrGERm5IFNTDMrDBFMUwhDBNMUwhDBNIUwRDBNIQwRTFMIQwTTFMIQwTSF/h9xjcsk3yeh3wAAAABJRU5ErkJggg==” alt=”” />

2、批量删除

/*******************************************
***批量删除(删除数据10000,执行时间:23毫秒)
********************************************/set statistics time on;
declare @Timer datetime = GETDATE();SET ROWCOUNT 10000;
while 1 = 1
begin
--开启事务--
begin tran
--执行删除--
delete from Employee;
--提交事务--
commit;
IF @@ROWCOUNT = 0
break;
end
set ROWCOUNT 0;
--获取执行的毫秒数---
select DATEDIFF(MS, @Timer, GETDATE()) as [执行时间(毫秒)];
set statistics time off;

删除10000条数据,所需的时间大概为23毫秒,如下所示:

aaarticlea/png;base64,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” alt=”” />

3、truncate删除

--/*******************************************
--***truncate删除(删除数据10000,执行时间:3毫秒)
--********************************************/
set statistics time on;
--声明一个时间变量--
declare @Timer datetime = getdate();
--执行truncate语句--
truncate table Employee
---获取执行的毫秒数---
select DATEDIFF(MS, @Timer, GETDATE()) as [执行时间(毫秒)]
set statistics time off

删除10000条数据,所需的时间大概为3毫秒,如下所示:

aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMIAAABuCAIAAAD6XVvzAAAYPklEQVR4nO2de1QTZ97HEe0/9hz0nNZTt8iu/adn37frWnetdrdeal8XrUrZ7bYWrbsq3tAKVkq3ylYdhKqgQMMqkUsIgkgAgUASUAJCCJBwDxGEBHIbQsjVXEhAAsm8f8wyHQKJEcIlMJ/z/PObPJmZzPMx883DzOjR3NwsFApNJpNhwBj/n5SnHZ2DKMrKythsdldXF4lEksvlOjsolcry8vKSkpKSkhIWizVpn66urmaMBYpH87Q16u/vZ7FYZ8+ePXLkyLlz59LT0zGNFhsu0KimpubIGCQSyd43FqbRAma6GpWUlISFhcEOFRUVgSCIabQImZZGFArlxx9/PHLkyOnTpwsKCkQikT2HMI0WNtPSiEqlXrp0CdYoPz9fKBRiGi1OpntSKy0tRU5qZDJZIpFgGi1CXBCxa2trjx49ikXsxYwLNJLL5Ww2G/7BHxISQiQSMY0WGy7QSKfTKZXKioqK0tLS0tJSNpuNabTYcI1GzoBptIDBNMJwAZhGGC4A0wjDBcyeRjKZDMRYoDirUVZWVmNjYysGxmQ4pZFAICjCwLCPUxpN51yGsRjANMJwAZhGGC4A0wjDBTilkVarVS0OtFrtXI+IW+KURgqFgrc4UCgUcz0ibomzGvX09JhMphcLF5PJ1NPTg2k0NZzVSCAQjIyMQAuXkZERgUAgEonmekLYNchkMkyjOQDWSCwWz7UALkAoFIrFYvfTaNRi7VUPipTGSZtQYXwmNnaIbJtSa55NURwDazTxpKZSqWQymUwm652ARCKZ5t8ZZwipVOqWGmlN5n9ltp1Mapq0Hb3d9KezTX88aduSivscD63VarVYLDYLLRaL1Wp1gTjjsadRV1dXVVVVZWUllUqloCguLs56QGKzG2ZztJzEvTWKLFHEV5viq03xT0zxBFNokulkkulkkunEXdM/fjYdih/Xtod2vVQjBoPh6+urVCqRJUql0tfXl8FguEyfMexpxGYLHj9+NjAwoNfrkYVarVYikSQUkotq62ZztJzEvTUKy+aH54vD88XheeLwFPHZJPH+aPGu8Mnbn/ZzkzJfolFxcfHbb7/d1/dLt76+vrfffru4uNimZ1FR0XGHJCQkTE2jmhrho0fdcJ/h4WGVaUQ8AAl0lhqeNiKxOI9WM5uj5STuqpHBZL6R2Xa3pDuLKcliSjIZogRyS1p5dyJVcj1LEpUhPBfXcIXAu54luXZfDKT1HL3S8vGnhUlJbQ7GtbGxMTo6+sKFCzqdrqSkJDExMTEx8fr16ytWrDh9+jRc3rt3z2g0QhAEAMBvfvOb02Ns3LjRpvTz85uORhaLRafTyWSyTrGMJXxexh/MLJNeuFyYl+cyjSorK/Py8hx04HK5d+/edeYnmLtqZDKZMzPbxGId8q+2paVFq9XC5YsXL2pqalQqldVqHRkZ0WgMGRmcv//9cnJygb0V8ng8AABCQ0PNZnNra2tgYOCmTZs2bdr0/vvvv/baa++++y5c7tmzR6VSwRpt3bq1fowTJ07YlNPRqIja1a/TcZ8+ra1trq5mPa5uul8hSE2r+9e/HkxBI5VKxWQy4ftCJRIJk8lUqVQ6ne7ChQu7d++G+zQ3N3d2diKdKyoqKioqoqKi3njjjdzc3IrxTLzF1N010lqtVqvV+uLFi7q6OrVaDZdDQ0NlZWX9/f1m84jJZOrvV2dkcPbvP5GammpvhX5+fpcvXx4dHe3t7V2zZg2FQoGX2zupAQCwZMmSpWMsWbLEppyORkk5TYSGBnJJSUZGbVra43v3su7dy0q/l3XhAiEvr1ar08HNSbq6ury9vbOzs7VabU5Ojre3d0dHh1arRTTSaDSffvrpxYsX4c6rV6/29PRcagcPD4+Jt5i6r0ZDJBK1sbGVz+dzudzGxkYGg8Fms5uampqamurr62k0WkFBY0lJI51enp9PS0mpf6lGq1ev3rBhw44dO548eaLT6V6q0bZt21rGOHXqlE05HY2KaV1qk0mn12s0RrVar9E8l8nVD+t6i+mNJTW8Im5/7tOnuU+fcnp6nPn9r1KpWCzWwYMH4+PjQRCkUChbt24tKSmBNerp6fnkk08SExN5PJ5uTKMbN25UT0ZBQcGbb765kDQazM3N7+zkaTQahUIBgmBNTU13dzc84yIW9+bm1jEYvPZ2QWsH71F9Z3rGo6+++qcDjR4/fpySkhIcHIxEbDhEHzhwYPny5b6+vjapGQCAX//610ig/sMf/mBTTkcjCuXZ8+fPDQaDwWBQKIZ54tFagSm3Sf9Mou5VGrpVprY+OZHSClzLys8vd3ImqaCggMFg6HQ6mUyWmpra3t4Oa4SUcDdYo08++eSfk/HFF18sX758IWk0lJ1dDoIKuBweHoZPahAEWa3WgYHBhw9ZfH6vQmfsfj7Mlg7eJ+UHBBxwoBE0PmJDEESlUk+PJzExEels86pNxLbp/KoaFRZyxWIxn8/n8XgcjqiyVpLL6MpoEPdqByGlEupoNbUyMomNly5lREWlkclPFAqFvSsFbGIyl8uNG2PXrl3vvfceUlZWVurGNPrLX/4SOBkBAQELTKPhzEwWj9c3MDBgMBjUGs0TFksokajVeoVC39srpVAqWlv5bQIls0dd+UyakZUTEBDgQCMej4eOyVqtViqV1o+Hx+MhnW1esonYMK2trWaz3XlzBxqRyU9BEORwOPDNNA0NDU+YTEJjo1ingzo7ocKMwYI4MomdW1RxLizu66+vVlaylUr1pIe7tLR0x44dhYWFcC4mkUivvfbaunXr/jieFStWwP9+YI2ioqIqJiM3N/eNN95YOBoZjcZ79+6Vl5ezWCwGg1FRXU1oaMwrK3vwoOrevVoymZaaWp+U3EQgPE6/l0VMv5+cnLx//34HGvn7+6ODJIVCmRiikfMUurOHh4enpycSseESfsnHxwc9BeW8Rsi80S+f12zObGsT63SQ1QpZLaNms0QiORUTvdHv5MaNp3x3hQkEjp465+3tnZOTo9PpSCTS6tWrOzo6NOPZtWsXWqPFErFhjXg8ntFoHBgYUKq0GXkcbgcokylBUCEUih48KHrUIGJyDGVMZeYT/t2UtM8/P+fgB79QKETfv6LX6+EQjSwJCgpCNEI6l5WVrVq1CofDBQUFbdu2DSnhV9vb26f2bfToUbfVajWZTCL9KKgd7H8+0Ko2EzltYp0OesaA0iOt8fHDGs3Nwu/P3/x38MV032/CBBK7GqlUKjabDYKgbuzb6Le//e268Xh5eaE1unHjBnMyCgsLF1TEhjUSiURwOTj4oqiIKRD0GgwGpc7YIdM/IOVXsqRVXDO50Xi/tv9uCvFvfwtLSrL9wWUDh8OBMzKHwwEAAB2TbUq4M5yBJBIJ/KrJZHrw4EFYWFhRUZHjDTmjkcFgkGoM3Spju2qYJTOnUdrEMh2kkkBtTCubPaTX13BL7ldQwhPzfQPCBCK7GqEhkUgrVqyIjIxMGM/vfvc7tEaLJWLDGjU3N4Mg2NPT29bW9/Aho7q6s6WFX9/aXspuz8zKIZM78kq6ibTGtOI6fFKKY43gaevo6Gg4IHO5XAAA1q1blzjG3r17EY2QzkgeR0uWmJgIAAB6ynsKGsGz2L1qfYdquEkOsbrMaeltYrEOEouhqjJLbbGmv6+ypSiTVRyelu/r6+ikBpOXl1dZWUkikby8vK5cuRI3nvfeew/+LCAInj9/3iZT2yTu2tpam5W7t0ZEIpFAyExIyPrpp4KoqJKoKPL16xnR0bdjYmJjYmKvXcuNjEy9Osa+fUEOTmqhoaGbNm0KDQ1FlgAAsHLlyk1jeHt7I6JM2hn9XZWcnIye8p6aRmq1ukM13KSA6oQGKotLINZKJBqIybT+dPHFzVO9nU/vUn+MLooLT3yJRvDE9KFDh3A43EsjNjLl3dnZ2dzcLBAIfH19b9++jZ7jXjiz2EajMSMj46uvvvrww883bz740Ucfb9p07M9//nSs/Oijjz7avPnghx9+/hEKAoFgb4UTiYiI8Pf3t1c67uwMDjQqLe0eHR1VqdTtqheNPdb8puqz7I+T7yeBoBSyWoeHBuX9Mh6PV1dX9/BhbXi4I420Wm1HR4dNxO7q6rLptnv3bvRJjUQiof9UsmfPnosXLyKrgifE0W93V40sFotGoxGJRDyekMcT8/ndPJ6Ez+8ZK/l8Pp/HE/N4Qj4KeGLJSfr7+4VCob3ScWdncKBRXl4rh9Oen8+9X9ue2fqM0NSAYxek3c8CQSnEeGiNCBi8fpRbywjDhX13I9axRjQabcuWLVQqFYnYU9Cora2Nx+Mhqzp48GBcXBz67e6q0QLAgUa37jw+dzP2+wsxx67dOhgbezA2NjD+56S0DBCUStue1t1Pf5KFL3hYF4nLvI7LuHQp1YFG7e3tBAKhv78fLidqJJFIzp49u2bNmgsXLtTU1AQGBsbFxbW3t1dXV0dGRv4DxY4dO+D3Is4hYBrNGfY06uzsy6PWRxGJd1NSotPSIojECCIxiphOSM/Mzi79OYUedqvoXEzGudCMc+cyzp9PPHLkijMRG6auri40NBRE/dcGIAiGhoYeO3YsNzfX5tW6urpj44FfjYuLy83NRa8W02jOsKfRpCiVytLSsu+/T/j668iJ7cyZOImkb6ZHzgGYRnPGK2k0z8E0mjNgjXp6egTuT3d3N5/Pn80tYhr9F3ieejb/BS8kpqKRyTTYIxBjDWtIm4pGMpk8v5BGr6iuqGTOZqNXVD8qe1L+ZFY3uthaWTnjMb3yVd81RY2oNPrg0NAsn3eGhobkcvno6Ogsb3dRYTAYNBrNq74L0whjHJhGGC5gUWvU0NBAo9HslZNiNBrT09ORa6SmhoPt0mi0O5ORnp7u4HoV5L0NDQ2O+4hEIvSqbMops6g1eulFbTZotdrHjx/7+voymUypVMoeD/oqb/Ry5AZOextKSUk5duxYS0vL8PDwd999t3kC7777Lup28iGTVtnD7mlgN9jswJYtW44fPz5xr+D7SOEyNTV17969yKUvE25UN48OP5e0iJVa0yuNE6aRsxpZLJaioiIfHx+ZTAZBUERExLLxIBeZ+Pn5eXp6wgs9PDwmvT/OZkMUCgVZ80TGDbaV31ZMOLT0n17LvJY5gb+/v0wm8/HxWbZsmaenp812kTWPjo5aLBYIUuj6Ci+sCSEUcXiv8gAWTCNnNcLj8YcPH+7o6IAvzZbL5ZzxIBeZ+Pn5BQUFcTgcOp2+atUqZzTS6/XImieC1kjHJZWnXzqfIqlt4HKcQCgUms3mjo4ODoeDvhTdZs3BwcF4PB6CRkaNvTJi2KEb2T8zJA6Oqg2LVyMcDrdhwwYfH5/Q0FCtVouUyGWmZDIZ3RkAADqdrtVqQ0NDORyOgy36+fkBAACh7tYlk8noC1jR24UgiEwm43A4eM0Tbysjk8kojUT1Kbdvf3MjQwYZRyH4vZPejBYYGDhxJ9H64nA49JrpdDoAADgcDhodgmQlNy/i7mRVi5w+yItXIzwe/8EHH6xduxa+7x0pz5w5c+bMmbVr18IqIJ2pVKpIJMLhcCEhITdu3HAQhCdqRKVSz6BAbxcaG1248549e9A94d34RSMljfBT3JkzeTwIgg8rAADIPiMcPnz49ddfR74F4Z8Fd+7c2bt377p169A7if6eG5PMAkHq8qvfJicSaErISRavRpDDkxqiAoJUKr116xZ80G2C8MqVK729vTdv3gynVz8/PzjqUqnUN99886UnNbRGNp1tBttcF3s1LvbLZC6Smo8fP75lyxabWA1v99atW3DEVqlUe/fu3bx5s7e398qVK5GdtKMRBEEQN/nL2LirsXXOPh9xsWu0b98+eA9HR0cvX75sT6PR0VEAAJYuXWrzAK5JO08aseHH6yDbtacRmUweQbFv3z70YMuKvo/5Oeb7IhnceenSpTaB2tPT08PDw8PDAymRDaE/4MjIyGeffRYREYHWCH0pOrIhJw/yYtfIy8tr586dSqUyODj4rbfesqdRcHAwAAAJCQlOajQxYldVVcEbghxq9M4776xH4eXl5UCjhIQEm0AdFBTk4eGxatUqOp1uk6mRD6hUKnfu3Jmeni6Xy9EayeVy5FcCppEtjiP2+vXrs7OzTSYTnU739/e3pxGdTm9paZn4OMBJO0/MRhAEgSCIx+NhvRxoFBISQkCxfv36STUymUzZ2dkgCNpEbAAACAQC/IkgCGppaaHT6cheeXh4+Pj4BAUF4fF4EASh8b8B4aQPd8Y0ssVxxEaGk0qlAgDwww8/EIlEdExGMx2N0OWUs5Gu/Oq1+FvHs355OgCNRvtmjLVr1+7Zs+f2GMhUu9FoJBKJP/zwAxztw8PDdWNPfuJyuXAJf3w8Hg8v7846fiv+2tVynXPHeBFrBE2WqZEBg0utVgtPLsN9nNdo0ogNR10mk+lAIzgXoyemxwXhzvSYm3Ff/VSlhyD4kc3oyfQtW7bASR+GyWQ63i4MnNaPHTuWkpICQRAEWSHoRV3swf/gYtI7nT3ImEbjIicym+zv7w8AAJlMRnvjvEavNIsNl8hcsw3jg3AzJSLuyme3y60QfBzRk+lLliy5fPkynM3hNVssFvQHt9muxWIZGRkBQdDHxwd5wCEEjUBWAfn7mAwCpdnpg4xpNC5yIrPJQqEQAIB33nlnahq90iw2XCJzzTaMD8KDYFlyfuy/I+sh/TAEjZ9M3759+1tvvbV+/Xoky+Px+ODgYHvbxePx69ev3759e1VVlV6v/+/SYQNUH334x+RrBU8HnT7Ii1cjZNoaHTnRtLS0hISEONCIw+HAwdbHx2diHocgCAnC8HJknnrDhg1O/hEGh8MFBgbu3LkT2e4LJZOVG3c+IJelNkXicOh8ffPmTX9/fw8Pj+XLlwcEBNjbELIbNnkcgiAI0pvUrNyA81fSKh6B+sn2aHIWr0Z4PB5OpujIaQOSQO2VSLx96RUmEATpdLrw8HC4PxJmIQii0Wjo8mU7qZRyWXnhhRzdYMzYqzBcLheduO1tCNmNyfbZMKjjFIbnsbhSp2ewIWgxa4ThQjCNMFwAphGGC5hVjSjUsufPtYOzy8DAgFAo/Pbbb+vr6x8+fBgXF9ff33/+/HmsdGEJ/1ews6RRMfWxQqE0Go2zqZHRaJSA4Lnz3zFrWdk5eTdj44WSXqx0YXnlClBbW/uqDk1XIwcPdp0JzGazUqmiV1bzBKBcY8Cay9uZs8HJyclTGBo306irq2uNj8/97Lw5P+ILsinVGtTM0yvgZhqpVGomq7Fb3DfnR3xBtuMnTt25c2cKQ+NmGkkkklOnv6lg1M75EV+QrYBc9NL74yZlhjQahSApl1rPaRC90hSqY8xmMxyxK2vYkx6Fvn556yN8amLs9ZjY/xDzqE8NoHLux8aNWnZO7jyJ2CMvDArpU0ZD/e2wLaGRQHHT9NRB85KIrZALOVW3jr/vu+336zeu277v4Jn4jmZQK53rsXGjNn8itkZYmxL14WtvvL5s6ZLdh1ytkaOIzWO1EYPW7LwWV/q0S9P65EHiiV+dS2vvbZ/rsXGjNn8i9uiwUSPvbmznplzcHvaTqzVyFLEVSqmIx2wRdvaq+zQCFoV4actfrzPEjX1zPzzu0uZbxH4BQRW3/S7HuVojZyN2bwOdEHPgf47imkCOYu6Hx13afIvYM6URCILfhoZV2YnYcBO10MmpF78LOvV5QDatRyGY67Fxo5ZNmi8RG2amNFIqVWVPGF0OZ7E7qbcignb9318PnMNXVz7TiORzPzzu0r4JDpknERtmpjRyehZb0lCScOWTXx0h8J50zf3wuEubPxEbZqY0cnoWW9tdX1xwcevu6BoqRzHnw+MuDYvYBpDfys796QS+soLbJ9f0t1Wm3/76f7+8zSlrfz7nw+MuDYvYBrC7jZVz9ctTp777EbgWffXf4SGnAk8llIEcydwPj7u0+RaxhyGo6WFYSnZqjdP32b2cl0bsXlBUFvPpwb2bNn6w6eMvTp65z+f3aed8bNyozbeIPSPAEdsHu1BkxppSNb8i9oyAXSgy022+RewZAbtQZKbbfIvYM4KTs9hYm3KbbxF7RnByFhtrU26LK2Kn38/uU2plKl2vXCNX66WK51jpknJuIrbBYBiYRQwGg1qtbmhoCDhw4FZsLIVK27pte3d3z/ETJ7HSJeWJkydnNWJTaGV6g2F4jigrK2tqahIKhTk5OTqdDitdWLa2ts6eRnNy8zXGvAXTCMMFYBphuABMIwwXgGmE4QIwjTBcAKYRhgvANMJwAZhGGC4A0wjDBfw/CiS8VFxcMUMAAAAASUVORK5CYII=” alt=”” />

小结:

1)TRUNCATE太快了,清除10W数据一点没压力,批量删除次之,最后的DELTE太慢了

2)TRUNCATE快是因为它属于DDL语句,只会产生极少的日志,普通的DELETE不仅会产生日志,而且会锁记录

PS:

参考学习网址:http://www.cnblogs.com/panchunting/archive/2013/04/27/SQL_Tech_001.html

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