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

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

1	`DBCC Inputbuffer(sid)`

2. 查询SQL连接分布

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

3. 查询最消耗CPU的SQL Top10

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

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：Configure the max degree of parallelism Server Configuration Option - SQL Server | Microsoft Docs

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

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）

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

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

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

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

原文链接：https://blog.csdn.net/huang714/article/details/108822085