SQLServer常用运维SQL整理

今天线上SQLServer数据库的CPU被打爆了，紧急情况下，分析了数据库阻塞、连接分布、最耗CPU的TOP10 SQL、查询SQL并行度配置、查询SQL 重编译的原因等等

整理了一些常用的SQL

1. 查询数据库阻塞

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1	SELECT * FROM  sys.sysprocesses WHERE blocked<>0

查询结果中，重点看Blocked这一列，先找出最多的SID，然后循环找出Root的阻塞根源SID

查询阻塞根源Session的SQL

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1	DBCC Inputbuffer(sid)

2. 查询SQL连接分布

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1	SELECT Hostname FROM  sys.sysprocesses WHERE hostname<>''

3. 查询最消耗CPU的SQL Top10

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select top(10) st.text as Query, qs.total_worker_time, qs.execution_count from sys.dm_exec_query_stats as qs CROSS Apply sys.dm_exec_sql_text(qs.sql_handle) AS st order by qs.total_worker_time desc

4. 查看SQLServer并行度

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1	SELECT value_in_use  FROM sys.configurations WHERE name = 'max degree of parallelism'

并行度如果设置为1，To suppress parallel plan generation, set max degree of parallelism to 1

将阻止并行编译生成SQL执行计划，最大并行度设置为1

设置策略和具体设置方法，请参考：https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/configure-the-max-degree-of-parallelism-server-configuration-option?view=sql-server-2017

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USE DatabaseName ;  GO   EXEC sp_configure 'show advanced options', 1;  GO  RECONFIGURE WITH OVERRIDE;  GO  EXEC sp_configure 'max degree of parallelism', 16;  GO  RECONFIGURE WITH OVERRIDE;  GO

　　

5. 查询SQL Server Recompilation Reasons

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1 2	select dxmv.name, dxmv.map_key,dxmv.map_value from sys.dm_xe_map_values as dxmv where dxmv.name='statement_recompile_cause' order by dxmv.map_key

6. 将SQL Trace文件存入一张表，做聚合分析（CPU、IO、执行时间等）

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SELECT * INTO TabSQL FROM fn_trace_gettable('C:\Users\***\Desktop\Trace\sql05trace20180606-业务.trc', default); GO

对上述表数据进行聚合分析最耗时的SQL

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select  top 100             replace(replace(replace(  substring(Textdata,1,6600) ,char(10),' '),char(13),' ') ,char(9),' ')  as '名称',         --substring(Textdata,1,6600)  as old,        count(*) as '数量',        sum(duration/1000) as '总执行时间ms',        avg(duration/1000) as '平均执行时间ms',        avg(cpu) as '平均CPU时间ms',        avg(reads) as '平均读次数',        avg(writes) as '平均写次数', LoginName from TabSQL   t group by   replace(replace(replace(  substring(Textdata,1,6600) ,char(10),' '),char(13),' ') ,char(9),' ') , LoginName order by sum(duration) desc

最耗IO的SQL

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select  TOP 100 replace(replace(replace(  substring(Textdata,1,6600) ,char(10),' '),char(13),' ') ,char(9),' ') as '名称' ,LoginName,        count(*) as '数量',        sum(duration/1000) as '总执行时间ms',        avg(duration/1000) as '平均执行时间ms',        sum(cpu) as '总CPU时间ms',        avg(cpu) as '平均CPU时间ms',        sum(reads) as '总读次数',        avg(reads) as '平均读次数',        avg(writes) as '平均写次数' from TabSQL group by replace(replace(replace(  substring(Textdata,1,6600) ,char(10),' '),char(13),' ') ,char(9),' ')  ,LoginName order by  sum(reads) desc

最耗CPU的SQL

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SELECT TOP 100 replace(replace(replace(  substring(Textdata,1,6600) ,char(10),' '),char(13),' ') ,char(9),' ')  as '名称',LoginName,        count(*) as '数量',        sum(duration/1000) as '总执行时间ms',        avg(duration/1000) as '平均执行时间ms',        sum(cpu) as '总CPU时间',        avg(cpu) as '平均CPU时间',        avg(reads) as '平均读次数',        avg(writes) as '平均写次数' from TabSQL group by replace(replace(replace(  substring(Textdata,1,6600) ,char(10),' '),char(13),' ') ,char(9),' ')   ,LoginName order by avg(cpu) desc

 

　　

 

周国庆

2019/7/8

 

2024-05-28 10:41:56【出处】：https://www.cnblogs.com/tianqing/p/11152799.html

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