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一次性能优化实战经历

时间:2019-11-06 12:36来源:澳门新葡8522最新网站
老是经历数据库质量调优,都是对性能优化的双重认知、对和煦文化紧缺的无敌表明,只一时时处处总括、学习本事少走弯路。   内容摘要: 后生可畏、品质难题呈报 二、监测剖判

老是经历数据库质量调优,都是对性能优化的双重认知、对和煦文化紧缺的无敌表明,只一时时处处总括、学习本事少走弯路。

 

内容摘要:

后生可畏、品质难题呈报

二、监测剖判

三、等待类型解析

四、优化方案

五、优化职能

 

意气风发、质量难点陈述

应用端反应系统查询缓慢,长日子出不来结果。SQLServer数据库服务器吞吐量不足,CPU财富缺少,平日飙到百分百....... 

二、监测深入分析

征集质量数据利用三种办法:再而三生龙活虎段时间搜罗和高峰期实时搜集

 

连年一天搜集质量指标(以下简单的称呼“一而再监测”卡塔尔国

目的:通过此办法获得CPU/内部存款和储蓄器/磁盘/SQLServer总体处境,宏观上解析当前服务器的重要的属性瓶颈。

工具: 品质计数器Perfmon PAL日志解析器(工具使用方法请参谋其它黄金时代篇博文)

配置:

  1. Perfmon配置主要品质流速计内容具体如下表

  2. Perfmon收罗的年华间距:15秒 (不宜过短,否则会对服务器品质产生额外压力卡塔 尔(英语:State of Qatar)

  3. 征集时间:  8:00~20:00业务时间,收罗一天

 图片 1

 

浅析监测结果

采访实现后,通过PAL(意气风发款日志剖析工具,可以看到后生可畏篇博文介绍卡塔尔国工具自动剖判出结果来得首要质量难题:

工作高峰期CPU临近百分之百,并伴随超多的Latch(闩锁卡塔 尔(英语:State of Qatar)等待,查询时有一大波的扫表操作。那个只是宏观上赢得的“现象级“的品质难点表现,并不可能料定表明是CPU能源远远不足招致的,供给更加的找证据深入分析。

 PAL深入分析得出多少个优异品质难点

1. 专门的学业高峰期CPU临近瓶颈:CPU平均在三分之一左右,高峰在百分之八十之上,极端达到百分之百

 图片 2

 

2. Latch等候一贯一再存在,平均在>500。Non-Page Latch等待严重

  图片 3

图片 4

 

3. 业务高峰期有大量的表扫描

图片 5

  4. SQL编写翻译和反编写翻译参数高周振天常

 图片 6

 

 

5.PLE即页在内存中的生命周期,其数据从有些时间点现身断崖式下落

**其数据从早上某些时间点下降后直持续到上午4点,表明前段时间内部存款和储蓄器中页面切换相比较频仍,现身从磁盘读取大批量页数据到内部存款和储蓄器,很大概是大范围扫表招致。**

 图片 7

 

实时监测质量指标

 

**目的: 轶闻“接二连三监测“已知的事务高峰期**PeakTime首要爆发时段,接下去通超过实际时监测入眼关心方今各样指标,进一步确认难点。**

**工具:SQLCheck(工具使用请见别的后生可畏篇 博文**介绍****

配置: 顾客端连接到SQLCheck配置

小贴士:提议不用在时下服务器运营,可选用其它后生可畏台机器运营SQLCheck

深入分析监测结果

实时监测展现Non-Page Latch等待严重,这一点与地点“接二连三监测”得到结果直接

Session之间窒碍现象日常产生,经深入分析是大的结果集查询堵塞了别的查询、更新、删除操作招致

详细解析

数据库存存在多量表扫描操作,招致缓存中多少不可能满意查询,供给从磁盘中读取数据,发生IO等待招致短路。

 

 1. Non-Page Latch等待时间长

 图片 8

 

2. 当 Non-Page Latch等待发生时候,实时监测展现正在实施大的查询操作

图片 9

 

 

3. 伴有session之间梗塞现象,在大的查询时产生窒碍现象,CPU也随时飙到95%上述

图片 10

 

 

缓慢解决方案

找到问题讲话,创造基于条件的目录来裁减扫描,并改进总计新闻。

上面方法还不或然消释,思考将受影响的数额转移到越来越快的IO子系统,考虑扩张内存。

 

 

 

三、等待类型深入分析

经过等待类型,换个角度更是解析到底时怎么着财富现身瓶颈

 

工具:  DMV/DMO

操作:

1. 先息灭历史等待数据

分选上午8点左右实践上面语句

DBCC SQLPERF('sys.dm_os_wait_stats', CLEAR);

 

2. 晚间8点左右实践,施行下边语句搜集Top 10的等候类型新闻计算。

图片 11图片 12

WITH    [Waits]
          AS ( SELECT   [wait_type] ,
                        [wait_time_ms] / 1000.0 AS [WaitS] ,
                        ( [wait_time_ms] - [signal_wait_time_ms] ) / 1000.0 AS [ResourceS] ,
                        [signal_wait_time_ms] / 1000.0 AS [SignalS] ,
                        [waiting_tasks_count] AS [WaitCount] ,
                        100.0 * [wait_time_ms] / SUM([wait_time_ms]) OVER ( ) AS [Percentage] ,
                                                              ROW_NUMBER() OVER ( ORDER BY [wait_time_ms] DESC ) AS [RowNum]
               FROM                                           sys.dm_os_wait_stats
               WHERE                                          [wait_type] NOT IN (
                                                              N'CLR_SEMAPHORE',
                                                              N'LAZYWRITER_SLEEP',
                                                              N'RESOURCE_QUEUE',
                                                              N'SQLTRACE_BUFFER_FLUSH',
                                                              N'SLEEP_TASK',
                                                              N'SLEEP_SYSTEMTASK',
                                                              N'WAITFOR',
                                                              N'HADR_FILESTREAM_IOMGR_IOCOMPLETION',
                                                              N'CHECKPOINT_QUEUE',
                                                              N'REQUEST_FOR_DEADLOCK_SEARCH',
                                                              N'XE_TIMER_EVENT',
                                                              N'XE_DISPATCHER_JOIN',
                                                              N'LOGMGR_QUEUE',
                                                              N'FT_IFTS_SCHEDULER_IDLE_WAIT',
                                                              N'BROKER_TASK_STOP',
                                                              N'CLR_MANUAL_EVENT',
                                                              N'CLR_AUTO_EVENT',
                                                              N'DISPATCHER_QUEUE_SEMAPHORE',
                                                              N'TRACEWRITE',
                                                              N'XE_DISPATCHER_WAIT',
                                                              N'BROKER_TO_FLUSH',
                                                              N'BROKER_EVENTHANDLER',
                                                              N'FT_IFTSHC_MUTEX',
                                                              N'SQLTRACE_INCREMENTAL_FLUSH_SLEEP',
                                                              N'DIRTY_PAGE_POLL',
                                                              N'SP_SERVER_DIAGNOSTICS_SLEEP' )
             )
    SELECT  [W1].[wait_type] AS [WaitType] ,
            CAST ([W1].[WaitS] AS DECIMAL(14, 2)) AS [Wait_S] ,
            CAST ([W1].[ResourceS] AS DECIMAL(14, 2)) AS [Resource_S] ,
            CAST ([W1].[SignalS] AS DECIMAL(14, 2)) AS [Signal_S] ,
            [W1].[WaitCount] AS [WaitCount] ,
            CAST ([W1].[Percentage] AS DECIMAL(4, 2)) AS [Percentage] ,
            CAST (( [W1].[WaitS] / [W1].[WaitCount] ) AS DECIMAL(14, 4)) AS [AvgWait_S] ,
            CAST (( [W1].[ResourceS] / [W1].[WaitCount] ) AS DECIMAL(14, 4)) AS [AvgRes_S] ,
            CAST (( [W1].[SignalS] / [W1].[WaitCount] ) AS DECIMAL(14, 4)) AS [AvgSig_S]
    FROM    [Waits] AS [W1]
            INNER JOIN [Waits] AS [W2] ON [W2].[RowNum] <= [W1].[RowNum]
    GROUP BY [W1].[RowNum] ,
            [W1].[wait_type] ,
            [W1].[WaitS] ,
            [W1].[ResourceS] ,
            [W1].[SignalS] ,
            [W1].[WaitCount] ,
            [W1].[Percentage]
    HAVING  SUM([W2].[Percentage]) - [W1].[Percentage] <95; -- percentage threshold
GO

View Code

 

3.领取消息**

图片 13

**询问结果得出排行:**

1:CXPACKET

2:LATCH_X

3:IO_COMPITION

4:SOS_SCHEDULER_YIELD

5:   ASYNC_NETWORK_IO

6.   PAGELATCH_XX

7/8.PAGEIOLATCH_XX

跟首要能源相关的等候方阵如下:

CPU相关:CXPACKET 和SOS_SCHEDULER_YIELD

    IO相关: PAGEIOLATCH_XXIO_COMPLETION

Memory相关: PAGELATCH_XX、LATCH_X

 

越来越解析前几名等待类型

当下排前二个人:CXPACKET、LATCH_EX、IO_COMPLETION等待,开始三个个剖判其发生等待背后原因 

小贴士:关于等待类型的学识学习,可参照他事他说加以考察PaulRandal的多种文章。

CXPACKET等待分析

CXPACKET等待排第四位, SOS_SCHEDULER_YIELD排在4位,伴有第7、8位的PAGEIOLATCH_XX等待。产生了并行操作worker被卡住

说明:

1.    存在大范围的表Scan

2.    有些并行线程实行时间过长,那些要将PAGEIOLATCH_XX和非页闩锁Latch_XX的ACCESS_METHODS_DATASET_PARENT Latch结合起来看,后边会给到有关信息

3.    试行布置不客观的可能

分析:

1.     首先看一下花在实施等待和财富等待的时刻

2.     PAGEIOLATCH_XX是不是存在,PAGEIOLATCH_SH等待,那意味大规模SCAN

3.     是不是还要有ACCESS_METHODS_DATASET_PARENT Latch或ACCESS_METHODS_SCAN_RANGE_GENERATOR LATCH等待

4.     试行布署是还是不是创建

 

信提取息:

收获CPU的进行等待和财富等待的时光所占比重

实行上边语句:

--CPU Wait Queue (threshold:<=6)
select  scheduler_id,idle_switches_count,context_switches_count,current_tasks_count, active_workers_count from  sys.dm_os_schedulers
where scheduler_id<255

 

 

SELECT  sum(signal_wait_time_ms) as total_signal_wait_time_ms, 
sum(wait_time_ms-signal_wait_time_ms) as resource_wait_time_percent, 
sum(signal_wait_time_ms)*1.0/sum(wait_time_ms)*100 as signal_wait_percent,
sum(wait_time_ms-signal_wait_time_ms)*1.0/sum(wait_time_ms)*100 as resource_wait_percent  FROM  SYS.dm_os_wait_stats

 

图片 14

 

结论:从下表采撷到音讯CPU重要花在财富等待上,而试行时候等待占比率小,所以无法武断认为CPU财富相当不够。

 

招致原因:

缺点和失误聚焦索引、不正确的实行布置、并行线程实践时间过长、是否留存隐式转变、TempDB能源争用

建设方案:

首要从什么压缩CPU花在能源等待的时光

1.    设置查询的MAXDOP,依照CPU核数设置合适的值(消除多CPU并行管理现身水桶短板现象卡塔尔国

2.    检查”cost threshold parallelism”的值,设置为更合理的值

3.    减弱全表扫描:创设合适的聚焦索引、非聚焦索引,减弱全表扫描

4.    不确切的试行安顿:选拔更优化实施陈设

5.    总括音信:确认保证总结音信是风尚的

6.    提出加多三个Temp DB 数据文件,缩小Latch争用,最好实行:>8核数,建议增添4个或8个等大小的数据文件

 

LATCH_EX等待解析

LATCH_EX等待排首位。

说明:

有恢宏的非页闩锁等待,首先肯定是哪二个闩锁等待时间过长,是还是不是还要发生CXPACKET等待类型。

分析:

查询全数闩锁等待新闻,开采ACCESS_METHODS_DATASET_PARENT等待最长,查询相关资料呈现因从磁盘->IO读取大量的数目到缓存,结合与事先Perfmon结果做综合深入分析剖断,认清存在大气扫描。

运营脚本

SELECT * FROM sys.dm_os_latch_stats

 

信提取息:

图片 15

  

 

引致原因:

有雅量的并行管理等待、IO页面管理等待,那更是推定期存款在大面积的扫描表操作。

与开垦人士确认存款和储蓄进程中运用大批量的有时表,并监测到业务中管理用频繁利用不常表、标量值函数,不断开创客户对象等,TEMPDB 管理内部存储器相关PFSGAMSGAM时,有非常多内部能源申请征用的Latch等待现象。

 

一网打尽方案:

1.    优化TempDB

2.    创造非集中索引来收缩扫描

3.    更新总括音信

4.    在地点方法仍旧望尘莫及解决,可将受影响的数码转移到更加快的IO子系统,考虑增添内部存储器

 

IO_COMPLETION等待剖析

现象:

IO_COMPLETION等待排第三位

说明:

IO延迟难点,数据从磁盘到内部存款和储蓄器等待时间长

分析:

从数据库的文书读写效能解析哪个一点也不慢,再与“CXPACKET等待剖判”的结果合起来分析。

Temp IO读/写财富效能

1.    TempDB的数据文件的平均IO在80左右,那些超过日常值,TempDB存在严重的延期。

2.    TempDB所在磁盘的Read latency为65,也比日常值偏高。

 

运作脚本:

图片 16图片 17

 1 --数据库文件读写IO性能
 2 SELECT DB_NAME(fs.database_id) AS [Database Name], CAST(fs.io_stall_read_ms/(1.0   fs.num_of_reads) AS NUMERIC(10,1)) AS [avg_read_stall_ms],
 3 CAST(fs.io_stall_write_ms/(1.0   fs.num_of_writes) AS NUMERIC(10,1)) AS [avg_write_stall_ms],
 4 CAST((fs.io_stall_read_ms   fs.io_stall_write_ms)/(1.0   fs.num_of_reads   fs.num_of_writes) AS NUMERIC(10,1)) AS [avg_io_stall_ms],
 5 CONVERT(DECIMAL(18,2), mf.size/128.0) AS [File Size (MB)], mf.physical_name, mf.type_desc, fs.io_stall_read_ms, fs.num_of_reads,
 6 fs.io_stall_write_ms, fs.num_of_writes, fs.io_stall_read_ms   fs.io_stall_write_ms AS [io_stalls], fs.num_of_reads   fs.num_of_writes AS [total_io]
 7 FROM sys.dm_io_virtual_file_stats(null,null) AS fs
 8 INNER JOIN sys.master_files AS mf WITH (NOLOCK)
 9 ON fs.database_id = mf.database_id
10 AND fs.[file_id] = mf.[file_id]
11 ORDER BY avg_io_stall_ms DESC OPTION (RECOMPILE);
12 
13 --驱动磁盘-IO文件情况
14 SELECT [Drive],
15        CASE
16               WHEN num_of_reads = 0 THEN 0
17               ELSE (io_stall_read_ms/num_of_reads)
18        END AS [Read Latency],
19        CASE
20               WHEN io_stall_write_ms = 0 THEN 0
21               ELSE (io_stall_write_ms/num_of_writes)
22        END AS [Write Latency],
23        CASE
24               WHEN (num_of_reads = 0 AND num_of_writes = 0) THEN 0
25               ELSE (io_stall/(num_of_reads   num_of_writes))
26        END AS [Overall Latency],
27        CASE
28               WHEN num_of_reads = 0 THEN 0
29               ELSE (num_of_bytes_read/num_of_reads)
30        END AS [Avg Bytes/Read],
31        CASE
32               WHEN io_stall_write_ms = 0 THEN 0
33               ELSE (num_of_bytes_written/num_of_writes)
34        END AS [Avg Bytes/Write],
35        CASE
36               WHEN (num_of_reads = 0 AND num_of_writes = 0) THEN 0
37               ELSE ((num_of_bytes_read   num_of_bytes_written)/(num_of_reads   num_of_writes))
38        END AS [Avg Bytes/Transfer]
39 FROM (SELECT LEFT(mf.physical_name, 2) AS Drive, SUM(num_of_reads) AS num_of_reads,
40                 SUM(io_stall_read_ms) AS io_stall_read_ms, SUM(num_of_writes) AS num_of_writes,
41                 SUM(io_stall_write_ms) AS io_stall_write_ms, SUM(num_of_bytes_read) AS num_of_bytes_read,
42                 SUM(num_of_bytes_written) AS num_of_bytes_written, SUM(io_stall) AS io_stall
43       FROM sys.dm_io_virtual_file_stats(NULL, NULL) AS vfs
44       INNER JOIN sys.master_files AS mf WITH (NOLOCK)
45       ON vfs.database_id = mf.database_id AND vfs.file_id = mf.file_id
46       GROUP BY LEFT(mf.physical_name, 2)) AS tab
47 ORDER BY [Overall Latency] OPTION (RECOMPILE);

View Code

 

信提取息:

 

 

图片 18

 

 

 

各数据文件IO/CPU/Buffer访谈处境,Temp DB的IO Rank达到60%以上

 

 图片 19

 

解决方案:

   增加三个Temp DB 数据文件,收缩Latch争用。最棒推行:>8核数,提议增多4个或8个等大大小小的数据文件。

 

 

 

别的等待

分析:

透过等待类型开采与IO相关 的PAGEIOLATCH_XX 值相当的高,数据仓库储存存在大方表扫描操作,引致缓存中数据无法满足查询,须要从磁盘中读取数据,产生IO等待。

减轻方案:

创造合理非聚焦索引来减弱扫描,更新总结消息

地点方法还无法消除,寻思将受影响的数量转移到更加快的IO子系统,酌量扩展内部存款和储蓄器。

 

 

四、优化方案

 依照上述监测和解析结果,从“优化顺序”和“实行标准”发轫实质性的优化。

 

优化顺序

 1.    从数据库配置优化

 理由:代价最小,依照监测剖析结果,通过退换配置可晋级空间相当的大。

 2.    目录优化

理由:索引不会动数据库表等与事务紧凑的布局,业务规模不会有危害。

步骤:考虑到库中打表(抢先100G卡塔 尔(阿拉伯语:قطر‎,在目录优化也要分步进行。 优化索引步骤:无用索引->重复索引->错过索引加多->聚焦索引->索引碎片收拾。

 3.    询问优化

 理由:语句优化内需结合职业,需求和开荒职员紧密关联,最后选项优化语句的方案

 步骤:DBA抓取施行时间、使用CPU、IO、内部存款和储蓄器最多的TOP SQL语句/存款和储蓄进程,交由开辟职员并拉扯寻找可优化的办法,如加索引、语句写法等。

 

实行规范

 整个确诊和优化方案首先在测量试验景况中张开测量试验,将在测量检验情形中测量试验通过并断定的逐步执行到专门的学问碰到。  

数据库配置优化

 1. 脚下数据库服务器有超过二十二个核数, 当前MAXDOP为0,配置不客观,招致调节并发管理时现身一点都不小交互作用等待现象(水桶短板原理卡塔 尔(阿拉伯语:قطر‎ 

优化建议:提议修改MAXDOP 值,最棒实行>8核的,先安装为4

 2. 当前COST THRESHOLD FOR PARALLELISM值默认5秒 

优化建议:建议校勘 COST THRESHOLD FORAV4 PARALLELISM值,超越15秒允许并行管理

 3. 监测到工作中拍卖用频仍使用一时表、标量值函数,不断创设顾客对象等,TEMPDB 管理内部存款和储蓄器相关PFSGAMSGAM时,有大多的Latch等待现象,给品质形成影响 

优化提出:建议增添多个Temp DB 数据文件,收缩Latch争用。最好奉行:>8核数,建议加多4个或8个等大小的数据文件。

 4. 启用optimize for ad hoc workloads

 5. Ad Hoc Distributed Queries开启即席查询优化  

 

 

目录优化

 1. 无用索引优化

眼下库中留存大批量无用索引,可因而脚本搜索无用的目录并删除,减少系统对索引维护资金财产,升高立异性能。此外,依照读比率低于1%的表的目录,可结合工作最后确定是或不是删除索引。

详细列表请参见:质量调优数据收罗_索引.xlsx-无用索引

无用索引,参考实行语句:

图片 20图片 21

SELECT  OBJECT_NAME(i.object_id) AS table_name ,
        COALESCE(i.name, SPACE(0)) AS index_name ,
        ps.partition_number ,
        ps.row_count ,
        CAST(( ps.reserved_page_count * 8 ) / 1024. AS DECIMAL(12, 2)) AS size_in_mb ,
        COALESCE(ius.user_seeks, 0) AS user_seeks ,
        COALESCE(ius.user_scans, 0) AS user_scans ,
        COALESCE(ius.user_lookups, 0) AS user_lookups ,
        i.type_desc
FROM    sys.all_objects t
        INNER JOIN sys.indexes i ON t.object_id = i.object_id
        INNER JOIN sys.dm_db_partition_stats ps ON i.object_id = ps.object_id
                                                   AND i.index_id = ps.index_id
        LEFT OUTER JOIN sys.dm_db_index_usage_stats ius ON ius.database_id = DB_ID()
                                                           AND i.object_id = ius.object_id
                                                           AND i.index_id = ius.index_id
WHERE   i.type_desc NOT IN ( 'HEAP', 'CLUSTERED' )
        AND i.is_unique = 0
        AND i.is_primary_key = 0
        AND i.is_unique_constraint = 0
        AND COALESCE(ius.user_seeks, 0) <= 0
        AND COALESCE(ius.user_scans, 0) <= 0
        AND COALESCE(ius.user_lookups, 0) <= 0
ORDER BY OBJECT_NAME(i.object_id) ,
        i.name


    --1. Finding unused non-clustered indexes.

    SELECT OBJECT_SCHEMA_NAME(i.object_id) AS SchemaName ,
    OBJECT_NAME(i.object_id) AS TableName ,
    i.name ,
    ius.user_seeks ,
    ius.user_scans ,
    ius.user_lookups ,
    ius.user_updates
    FROM sys.dm_db_index_usage_stats AS ius
    JOIN sys.indexes AS i ON i.index_id = ius.index_id
    AND i.object_id = ius.object_id
    WHERE ius.database_id = DB_ID()
    AND i.is_unique_constraint = 0 -- no unique indexes
    AND i.is_primary_key = 0
    AND i.is_disabled = 0
    AND i.type > 1 -- don't consider heaps/clustered index
    AND ( ( ius.user_seeks   ius.user_scans  
    ius.user_lookups ) < ius.user_updates
    OR ( ius.user_seeks = 0
    AND ius.user_scans = 0
    )
    )

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  表的读写比,仿效实施语句

图片 22图片 23

 1 DECLARE @dbid int
 2 SELECT @dbid = db_id()
 3 SELECT TableName = object_name(s.object_id),
 4        Reads = SUM(user_seeks   user_scans   user_lookups), Writes = SUM(user_updates),CONVERT(BIGINT,SUM(user_seeks   user_scans   user_lookups))*100/( SUM(user_updates) SUM(user_seeks   user_scans   user_lookups))
 5 FROM sys.dm_db_index_usage_stats AS s
 6 INNER JOIN sys.indexes AS i
 7 ON s.object_id = i.object_id
 8 AND i.index_id = s.index_id
 9 WHERE objectproperty(s.object_id,'IsUserTable') = 1
10 AND s.database_id = @dbid
11 GROUP BY object_name(s.object_id)
12 ORDER BY writes DESC

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2. 移除、合并重复索引

 这段日子系统中非常多索引重复,对该类索引实行统后生可畏,减弱索引的护卫花费,进而晋级立异质量。

 重复索引,参谋实行语句:

图片 24图片 25

 1 WITH MyDuplicate AS (SELECT  
 2  Sch.[name] AS SchemaName,
 3  Obj.[name] AS TableName,
 4  Idx.[name] AS IndexName,
 5  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 1) AS Col1,
 6  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 2) AS Col2,
 7  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 3) AS Col3,
 8  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 4) AS Col4,
 9  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 5) AS Col5,
10  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 6) AS Col6,
11  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 7) AS Col7,
12  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 8) AS Col8,
13  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 9) AS Col9,
14  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 10) AS Col10,
15  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 11) AS Col11,
16  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 12) AS Col12,
17  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 13) AS Col13,
18  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 14) AS Col14,
19  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 15) AS Col15,
20  INDEX_Col(Sch.[name]   '.'   Obj.[name], Idx.index_id, 16) AS Col16
21 FROM sys.indexes Idx
22 INNER JOIN sys.objects Obj ON Idx.[object_id] = Obj.[object_id]
23 INNER JOIN sys.schemas Sch ON Sch.[schema_id] = Obj.[schema_id]
24 WHERE index_id > 0 AND  Obj.[name]='DOC_INVPLU')
25 SELECT    MD1.SchemaName, MD1.TableName, MD1.IndexName,
26   MD2.IndexName AS OverLappingIndex,
27   MD1.Col1, MD1.Col2, MD1.Col3, MD1.Col4,
28   MD1.Col5, MD1.Col6, MD1.Col7, MD1.Col8,
29   MD1.Col9, MD1.Col10, MD1.Col11, MD1.Col12,
30   MD1.Col13, MD1.Col14, MD1.Col15, MD1.Col16
31 FROM MyDuplicate MD1
32 INNER JOIN MyDuplicate MD2 ON MD1.tablename = MD2.tablename
33  AND MD1.indexname <> MD2.indexname
34  AND MD1.Col1 = MD2.Col1
35  AND (MD1.Col2 IS NULL OR MD2.Col2 IS NULL OR MD1.Col2 = MD2.Col2)
36  AND (MD1.Col3 IS NULL OR MD2.Col3 IS NULL OR MD1.Col3 = MD2.Col3)
37  AND (MD1.Col4 IS NULL OR MD2.Col4 IS NULL OR MD1.Col4 = MD2.Col4)
38  AND (MD1.Col5 IS NULL OR MD2.Col5 IS NULL OR MD1.Col5 = MD2.Col5)
39  AND (MD1.Col6 IS NULL OR MD2.Col6 IS NULL OR MD1.Col6 = MD2.Col6)
40  AND (MD1.Col7 IS NULL OR MD2.Col7 IS NULL OR MD1.Col7 = MD2.Col7)
41  AND (MD1.Col8 IS NULL OR MD2.Col8 IS NULL OR MD1.Col8 = MD2.Col8)
42  AND (MD1.Col9 IS NULL OR MD2.Col9 IS NULL OR MD1.Col9 = MD2.Col9)
43  AND (MD1.Col10 IS NULL OR MD2.Col10 IS NULL OR MD1.Col10 = MD2.Col10)
44  AND (MD1.Col11 IS NULL OR MD2.Col11 IS NULL OR MD1.Col11 = MD2.Col11)
45  AND (MD1.Col12 IS NULL OR MD2.Col12 IS NULL OR MD1.Col12 = MD2.Col12)
46  AND (MD1.Col13 IS NULL OR MD2.Col13 IS NULL OR MD1.Col13 = MD2.Col13)
47  AND (MD1.Col14 IS NULL OR MD2.Col14 IS NULL OR MD1.Col14 = MD2.Col14)
48  AND (MD1.Col15 IS NULL OR MD2.Col15 IS NULL OR MD1.Col15 = MD2.Col15)
49  AND (MD1.Col16 IS NULL OR MD2.Col16 IS NULL OR MD1.Col16 = MD2.Col16)
50 ORDER BY
51  MD1.SchemaName,MD1.TableName,MD1.IndexName

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 3. 增多遗失索引

 依据对语句的频次,表中读写比,结合专门的职业对缺点和失误的目录进行确立。

 错失索引,参谋实践语句:

图片 26图片 27

 1 -- Missing Indexes in current database by Index Advantage 
 2 SELECT  user_seeks * avg_total_user_cost * ( avg_user_impact * 0.01 ) AS [index_advantage] ,
 3         migs.last_user_seek ,
 4         mid.[statement] AS [Database.Schema.Table] ,
 5         mid.equality_columns ,
 6         mid.inequality_columns ,
 7         mid.included_columns ,
 8         migs.unique_compiles ,
 9         migs.user_seeks ,
10         migs.avg_total_user_cost ,
11         migs.avg_user_impact ,
12         N'CREATE NONCLUSTERED INDEX [IX_'   SUBSTRING(mid.statement,
13                                                       CHARINDEX('.',
14                                                               mid.statement,
15                                                               CHARINDEX('.',
16                                                               mid.statement)
17                                                                 1)   2,
18                                                       LEN(mid.statement) - 3
19                                                       - CHARINDEX('.',
20                                                               mid.statement,
21                                                               CHARINDEX('.',
22                                                               mid.statement)
23                                                                 1)   1)   '_'
24           REPLACE(REPLACE(REPLACE(CASE WHEN mid.equality_columns IS NOT NULL
25                                             AND mid.inequality_columns IS NOT NULL
26                                             AND mid.included_columns IS NOT NULL
27                                        THEN mid.equality_columns   '_'
28                                               mid.inequality_columns
29                                               '_Includes'
30                                        WHEN mid.equality_columns IS NOT NULL
31                                             AND mid.inequality_columns IS NOT NULL
32                                             AND mid.included_columns IS NULL
33                                        THEN mid.equality_columns   '_'
34                                               mid.inequality_columns
35                                        WHEN mid.equality_columns IS NOT NULL
36                                             AND mid.inequality_columns IS NULL
37                                             AND mid.included_columns IS NOT NULL
38                                        THEN mid.equality_columns   '_Includes'
39                                        WHEN mid.equality_columns IS NOT NULL
40                                             AND mid.inequality_columns IS NULL
41                                             AND mid.included_columns IS NULL
42                                        THEN mid.equality_columns
43                                        WHEN mid.equality_columns IS NULL
44                                             AND mid.inequality_columns IS NOT NULL
45                                             AND mid.included_columns IS NOT NULL
46                                        THEN mid.inequality_columns
47                                               '_Includes'
48                                        WHEN mid.equality_columns IS NULL
49                                             AND mid.inequality_columns IS NOT NULL
50                                             AND mid.included_columns IS NULL
51                                        THEN mid.inequality_columns
52                                   END, ', ', '_'), ']', ''), '[', '')   '] '
53           N'ON '   mid.[statement]   N' ('   ISNULL(mid.equality_columns, N'')
54           CASE WHEN mid.equality_columns IS NULL
55                THEN ISNULL(mid.inequality_columns, N'')
56                ELSE ISNULL(', '   mid.inequality_columns, N'')
57           END   N') '   ISNULL(N'INCLUDE ('   mid.included_columns   N');',
58                                ';') AS CreateStatement
59 FROM    sys.dm_db_missing_index_group_stats AS migs WITH ( NOLOCK )
60         INNER JOIN sys.dm_db_missing_index_groups AS mig WITH ( NOLOCK ) ON migs.group_handle = mig.index_group_handle
61         INNER JOIN sys.dm_db_missing_index_details AS mid WITH ( NOLOCK ) ON mig.index_handle = mid.index_handle
62 WHERE   mid.database_id = DB_ID()
63 ORDER BY index_advantage DESC;

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4. 目录碎片收拾

 须要通过DBCC check实现目录碎片清理,进步查询实功效。

 备注:当前据库超多表比相当大(>50G卡塔 尔(阿拉伯语:قطر‎,做表上索引或许开销很短日子,平日1个T的库要8小时以上,建议制订贰个详尽布置,以表为单位稳步碎片清理。

 索引碎片参谋施行语句:

图片 28图片 29

 1 SELECT '['   DB_NAME()   '].['   OBJECT_SCHEMA_NAME(ddips.[object_id],
 2 DB_ID())   '].['
 3   OBJECT_NAME(ddips.[object_id], DB_ID())   ']' AS [statement] ,
 4 i.[name] AS [index_name] ,
 5 ddips.[index_type_desc] ,
 6 ddips.[partition_number] ,
 7 ddips.[alloc_unit_type_desc] ,
 8 ddips.[index_depth] ,
 9 ddips.[index_level] ,
10 CAST(ddips.[avg_fragmentation_in_percent] AS SMALLINT)
11 AS [avg_frag_%] ,
12 CAST(ddips.[avg_fragment_size_in_pages] AS SMALLINT)
13 AS [avg_frag_size_in_pages] ,
14 ddips.[fragment_count] ,
15 ddips.[page_count]
16 FROM sys.dm_db_index_physical_stats(DB_ID(), NULL,
17 NULL, NULL, 'limited') ddips
18 INNER JOIN sys.[indexes] i ON ddips.[object_id] = i.[object_id]
19 AND ddips.[index_id] = i.[index_id]
20 WHERE ddips.[avg_fragmentation_in_percent] > 15
21 AND ddips.[page_count] > 500
22 ORDER BY ddips.[avg_fragmentation_in_percent] ,
23 OBJECT_NAME(ddips.[object_id], DB_ID()) ,
24 i.[name]

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 5. 审查未有聚焦、主键索引的表

 当前库相当多表没有集中索引,要求细查原因是否专门的学业须要,若无非凡原因能够加上。

 

 

  查询语句优化 

1.  从数据库历史保存音讯中,通过DMV获取 

  • 收获Top100耗费时间最多询问SQL
  • 得到Top100开支时间最多囤积进度
  • 收获Top100成本I/O时间最多

仿照效法获取Top100实施语句

图片 30图片 31

  1 --执行时间最长的语句
  2 SELECT TOP 100
  3     execution_count, 
  4     total_worker_time / 1000 AS total_worker_time,  
  5     total_logical_reads,
  6     total_logical_writes,max_elapsed_time,
  7     [text]
  8 FROM 
  9     sys.dm_exec_query_stats qs
 10 CROSS APPLY 
 11     sys.dm_exec_sql_text(qs.sql_handle) AS st
 12 ORDER BY 
 13     max_elapsed_time DESC
 14 
 15 
 16 --消耗CPU最多的语句
 17 SELECT TOP 100
 18     execution_count, 
 19     total_worker_time / 1000 AS total_worker_time,  
 20     total_logical_reads,
 21     total_logical_writes,
 22     [text]
 23 FROM 
 24     sys.dm_exec_query_stats qs
 25 CROSS APPLY 
 26     sys.dm_exec_sql_text(qs.sql_handle) AS st
 27 ORDER BY 
 28     total_worker_time DESC
 29 
 30 --消耗IO读最多的语句
 31 SELECT TOP 100
 32     execution_count, 
 33     total_worker_time / 1000 AS total_worker_time,  
 34     total_logical_reads,
 35     total_logical_writes,
 36     [text]
 37 FROM 
 38     sys.dm_exec_query_stats qs
 39 CROSS APPLY 
 40     sys.dm_exec_sql_text(qs.sql_handle) AS st
 41 ORDER BY 
 42     total_logical_reads DESC
 43 
 44 --消耗IO写最多的语句
 45 SELECT TOP 100
 46     execution_count, 
 47     total_worker_time / 1000 AS total_worker_time,  
 48     total_logical_reads,
 49     total_logical_writes,
 50     [text]
 51 FROM 
 52     sys.dm_exec_query_stats qs
 53 CROSS APPLY 
 54     sys.dm_exec_sql_text(qs.sql_handle) AS st
 55 ORDER BY 
 56     total_logical_writes DESC
 57 
 58 
 59 --单个语句查询平均IO时间
 60 SELECT TOP 100
 61 [Total IO] = (qs.total_logical_writes qs.total_logical_reads)
 62 , [Average IO] = (qs.total_logical_writes qs.total_logical_reads) /
 63 qs.execution_count
 64 , qs.execution_count
 65 , SUBSTRING (qt.text,(qs.statement_start_offset/2)   1,
 66 ((CASE WHEN qs.statement_end_offset = -1
 67 THEN LEN(CONVERT(NVARCHAR(MAX), qt.text)) * 2
 68 ELSE qs.statement_end_offset
 69 END - qs.statement_start_offset)/2)   1) AS [Individual Query]
 70 , qt.text AS [Parent Query]
 71 , DB_NAME(qt.dbid) AS DatabaseName
 72 , qp.query_plan
 73 FROM sys.dm_exec_query_stats qs
 74 CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) as qt
 75 CROSS APPLY sys.dm_exec_query_plan(qs.plan_handle) qp
 76 WHERE DB_NAME(qt.dbid)='tyyl_sqlserver' and execution_count>3 AND qs.total_logical_writes qs.total_logical_reads>10000
 77 --and qt.text like '%POSCREDIT%'
 78 ORDER BY [Average IO] DESC
 79 
 80 --单个语句查询平均‘逻辑读’时间
 81 SELECT TOP 100         
 82 deqs.execution_count, 
 83 deqs.total_logical_reads/deqs.execution_count as "Avg Logical Reads",
 84 deqs.total_elapsed_time/deqs.execution_count as "Avg Elapsed Time",
 85 deqs.total_worker_time/deqs.execution_count as "Avg Worker Time",SUBSTRING(dest.text, (deqs.statement_start_offset/2) 1, 
 86         ((CASE deqs.statement_end_offset
 87           WHEN -1 THEN DATALENGTH(dest.text)
 88          ELSE deqs.statement_end_offset
 89          END - deqs.statement_start_offset)/2) 1) as query,dest.text AS [Parent Query],
 90 , qp.query_plan
 91 FROM sys.dm_exec_query_stats deqs
 92 CROSS APPLY sys.dm_exec_sql_text(deqs.sql_handle) dest
 93 CROSS APPLY sys.dm_exec_query_plan(deqs.sql_handle) qp
 94 WHERE dest.encrypted=0 
 95 --AND dest.text LIKE'%INCOMINGTRANS%' 
 96 order by  "Avg Logical Reads"  DESC
 97 
 98 --单个语句查询平均‘逻辑写’时间
 99 SELECT TOP 100
100 [Total WRITES] = (qs.total_logical_writes)
101 , [Average WRITES] = (qs.total_logical_writes) /
102 qs.execution_count
103 , qs.execution_count
104 , SUBSTRING (qt.text,(qs.statement_start_offset/2)   1,
105 ((CASE WHEN qs.statement_end_offset = -1
106 THEN LEN(CONVERT(NVARCHAR(MAX), qt.text)) * 2
107 ELSE qs.statement_end_offset
108 END - qs.statement_start_offset)/2)   1) AS [Individual Query]
109 , qt.text AS [Parent Query]
110 , DB_NAME(qt.dbid) AS DatabaseName
111 , qp.query_plan
112 FROM sys.dm_exec_query_stats qs
113 CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) as qt
114 CROSS APPLY sys.dm_exec_query_plan(qs.plan_handle) qp
115 WHERE DB_NAME(qt.dbid)='DRSDataCN'
116 and qt.text like '%POSCREDIT%'
117 ORDER BY [Average WRITES] DESC
118 
119 
120 
121 --单个语句查询平均CPU执行时间
122 SELECT SUBSTRING(dest.text, (deqs.statement_start_offset/2) 1, 
123         ((CASE deqs.statement_end_offset
124           WHEN -1 THEN DATALENGTH(dest.text)
125          ELSE deqs.statement_end_offset
126          END - deqs.statement_start_offset)/2) 1) as query, 
127 deqs.execution_count, 
128 deqs.total_logical_reads/deqs.execution_count as "Avg Logical Reads",
129 deqs.total_elapsed_time/deqs.execution_count as "Avg Elapsed Time",
130 deqs.total_worker_time/deqs.execution_count as "Avg Worker Time"
131 ,deqs.last_execution_time,deqs.creation_time
132  FROM sys.dm_exec_query_stats deqs
133 CROSS APPLY sys.dm_exec_sql_text(deqs.sql_handle) dest
134 WHERE dest.encrypted=0
135 AND deqs.total_logical_reads/deqs.execution_count>50
136 ORDER BY  QUERY,[Avg Worker Time] DESC

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2.  由此工具实时抓取业务高峰期近些日子奉行语句

收罗工具:

  推荐应用SQLTrace或Extend Event,不引入应用Profiler

搜聚内容:

  • SQL语句
  • 储存进度
  • Statment语句

浅析工具:

  推荐ClearTrace,无偿。具体选拔情势请见笔者的其它一篇博文介绍。 

3.  急需各样分析以上二点收罗到讲话,通过相通施行安顿深入分析搜索更优化的方案语句 

  单条语句的施行布置解析工具Plan Explorer,请见自个儿的此外黄金年代篇博文介绍 

4.  此次优化针对当前库,特别关爱下边多少个个性杀手难题

  • 隐式转变(请仿效宋硬汉的博文SQL Server中提前找到隐式调换提高品质的方式)
  • 参数嗅探(参谋桦仔博文名称为SQLSEEvoqueVE君越参数嗅探)
  • 连天情势
  • 缺失聚集索引

  

五、优化功能

 1.  平均CPU使用时间在30000飞秒以上语句由贰13个降到3个

 2.  进行语句在CPU使用当先10000飞秒的,从1500滑坡到500个

 3.  CPU保持在 十分四左右,高峰期在伍分一~五分一,极端超过30%之上,极少七成

 4.  Batch Request从原来的1500增进到4000

末段方一张优化前后的机能相比较,有较料定的属性进步,只是解决如今的瓶颈难题。

图片 32

 

 小结

   数据库的优化只是一个层面,只怕消除日前的能源瓶颈难点,相当多发觉数据库架构划虚构计难点,受专门的工作的界定,无法出手去做其它优化,只好到此文停止,那就像是也是生龙活虎种常态。从此以后次阅历中,到想到别的二个标题,当独有发生质量瓶颈时候,公司的做法是不久找人来灭火,救完火后,然后就....好像就未有然后...停止。换生机勃勃种思维,借使能从常见爱惜中抓好监督、提前预先警报,做好正规,也许这种救火的表现会少许。

感谢2016!

 

 如要转发,请加本文链接并注解出处,谢谢。

 

 

 

编辑:澳门新葡8522最新网站 本文来源:一次性能优化实战经历

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