88bf必发娱乐 2

88bf必发娱乐MSSQL 数据库 buildindex 出错

SELECT OBJECT_NAME(i.object_id) as TableName ,

Address Windowing Extensions (AWE) and SQL Server

Address Windowing Extensions (AWE) is an API that allows a 32-bit
application to manipulate physical memory beyond the inherent 32-bit
address limit. AWE mechanism technically is not necessary on 64-bit
platform. It is, however, present there. Memory pages that are allocated
through the AWE mechanism are referred as locked pages on the 64-bit

On both 32- and 64-bit platforms, memory that is allocated through the
AWE mechanism cannot be paged out. This can be beneficial to the
application. (This is one of the reasons for using AWE mechanism on
64-bit platform.) This also affects the amount of RAM that is available
to the system and to other applications, which may have detrimental
effects. For this reason, in order to use AWE, the Lock Pages in
 privilege must be enabled for the account that runs SQL Server.

From a troubleshooting perspective, an important point is that the
SQL Server buffer pool uses AWE mapped memory; however, only database
(hashed) pages can take full advantage of memory allocated through AWE.
Memory allocated through the AWE mechanism is not reported by Task
Manager or in the Process: Private Bytes performance counter. You
need to use SQL Server specific counters or Dynamic Management Views to
obtain this information.

For more information about AWE mapped memory, see “Managing memory for
large databases” and “Memory Architecture” in SQL Server Books
Online  topics and Large Memory
on MSDN.

The following table summarizes the maximum memory support options for
different configurations of SQL Server 2005. (Note that a particular
edition of SQL Server or Windows may put more restrictive limits on the
amount of supported memory.)

Table 1



Max physical memory

AWE/locked pages support

Native 32-bit on 32-bit OS

with /3GB boot parameter1

2 GB

3 GB

64 GB

16 GB



32-bit on x64 OS (WOW)

4 GB

64 GB


32-bit on IA64 OS (WOW)

2 GB

2 GB


Native 64-bit on x64 OS

8 terabyte

1 terabyte


Native 64-bit on IA64 OS

7 terabyte

1 terabyte


Executing the query “ALTER INDEX
[IX_liveConfigState_Service_ServiceId_…” failed with the
following error: “The index
“IX_liveConfigState_Service_ServiceId_GroupRightsVersion” on table
“liveConfigState_Service” cannot be reorganized because page level
locking is disabled.”. Possible failure reasons: Problems with the
query, “ResultSet” property not set correctly, parameters not set
correctly, or connection not established correctly.

Writers: Sunil Agarwal, Boris Baryshnikov, Tom Davidson, Keith
Elmore, Denzil Ribeiro, Juergen Thomas


Slow-Running Queries

Slow or long running queries can contribute to excessive resource
consumption and be the consequence of blocked queries.

Excessive resource consumption is not restricted to CPU resources, but
can also include I/O storage bandwidth and memory bandwidth. Even if SQL
Server queries are designed to avoid full table scans by restricting the
result set with a reasonable WHERE clause, they might not perform as
expected if there is not an appropriate index supporting that particular
query. Also, WHERE clauses can be dynamically constructed by
applications, depending on the user input. Given this, existing indexes
cannot cover all possible cases of restrictions. Excessive CPU, I/O, and
memory consumption by Transact-SQL statements are covered earlier in
this white paper.

In addition to missing indexes, there may be indexes that are not used.
As all indexes have to be maintained, this does not impact the
performance of a query, but does impact the DML queries.

Queries can also run slowly because of wait states for logical locks and
for system resources that are blocking the query. The cause of the
blocking can be a poor application design, bad query plans, the lack of
useful indexes, and an SQL Server instance that is improperly configured
for the workload.

This section focuses on two causes of a slow running query—blocking and
index problems.



Blocking is primarily waits for logical locks, such as the wait to
acquire an X lock on a resource or the waits that results from lower
level synchronization primitives such as latches.

Logical lock waits occur when a request to acquire a non-compatible lock
on an already locked resource is made. While this is needed to provide
the data consistency based on the transaction isolation level at which a
particular Transact-SQL statement is running, it does give the end user
a perception that SQL Server is running slowly. When a query is blocked,
it is not consuming any system resources so you will find it is taking
longer but the resource consumption is low. For details on the
concurrency control and blocking, see SQL Server Books Online.

Waits on lower level synchronization primitives can result if your
system is not configured to handle the workload.

The common scenarios for blocking/waits are:

  • Identifying the blocker

  • Identifying long blocks

  • Blocking per object

  • Page latching issues

  • Overall performance effect of blocking using SQL Server waits

A SQL Server session is placed in a wait state if system resources (or
locks) are not currently available to service the request. In other
words, the resource has a queue of outstanding requests. DMVs can
provide information for any sessions that are waiting on resources.

SQL Server 2005 provides more detailed and consistent wait information,
reporting approximately 125 wait types compared with the 76 wait types
available in SQL Server 2000. The DMVs that provide this information
range from sys.dm_os_wait_statistics for overall and cumulative
waits for SQL Server to the
session-specific sys.dm_os_waiting_tasks that breaks down waits
by session. The following DMV provides details on the wait queue of the
tasks that are waiting on some resource. It is a simultaneous
representation of all wait queues in the system. For example, you can
find out the details about the blocked session 56 by running the
following query.

select * from sys.dm_os_waiting_tasks where session_id=56 

waiting_task_address session_id exec_context_id wait_duration_ms   
 resource_address   blocking_task_address blocking_session_id  
blocking_exec_context_id resource_description 
-------------------- ---------- --------------- -------------------- ----- 
------------------------------------------------------- ------------------  
--------------------- ------------------- ------------------------ ------- 
0x022A8898           56         0               1103500               
LCK_M_S                                                      0x03696820     
     0x022A8D48            53                  NULL                      
ridlock fileid=1 pageid=143 dbid=9 id=lock3667d00 mode=X  

This result shows that session 56 is blocked by session 53 and that
session 56 has been waiting for a lock for 1103500 milliseconds.

To find sessions that have been granted locks or waiting for locks, you
can use the sys.dm_tran_locksDMVEach row represents a
currently active request to the lock manager that has either been
granted or is waiting to be granted as the request is blocked by a
request that has already been granted. For regular locks, a granted
request indicates that a lock has been granted on a resource to the
requestor. A waiting request indicates that the request has not yet been
granted. For example, the following query shows that session 56 is
blocked on the resource 1:143:3 that is held in X mode by session 53.

    request_session_id as spid,  
    resource_type as rt,   
    resource_database_id as rdb,  
    (case resource_type 
      WHEN 'OBJECT' then object_name(resource_associated_entity_id) 
      WHEN 'DATABASE' then ' ' 
      ELSE (select object_name(object_id)  
            from sys.partitions  
            where hobt_id=resource_associated_entity_id) 
    END) as objname,  
    resource_description as rd,   
    request_mode as rm,  
    request_status as rs 
from sys.dm_tran_locks 

Here is the sample output 
spid     rt           rdb         objname       rd            rm           
                                 56    DATABASE   9                    
            S          GRANT 
53    DATABASE    9                                S         GRANT 
56    PAGE        9       t_lock      1:143       IS         GRANT 
53    PAGE        9       t_lock      1:143       IX         GRANT 
53    PAGE        9       t_lock      1:153       IX         GRANT 
56   OBJECT       9       t_lock                  IS         GRANT 
53   OBJECT       9        t_lock                 IX         GRANT 
53    KEY         9        t_lock      (a400c34cb X          GRANT 
53    RID         9        t_lock      1:143:3    X          GRANT 
56    RID         9        t_lock      1:143:3    S           WAIT

You can, in fact, join the above two DMVs as shown with stored
proc sp_block. The block report in Figure 1 lists sessions that are
blocked and the sessions that are blocking them. You can find the source
code in Appendix B. You can alter the stored procedure, if needed, to
add/remove the attributes in the Select List. The optional @spid
parameter provides details on the lock request and the session that is
blocking this particular spid.

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Figure 1: sp_block report

In SQL Server 2000, you can see what spids are blocked information by
using the following statement.

select * from master..sysprocesses where blocked <> 0.

The associated locks can be seen with the stored procedure sp_lock.

 reference page:

Analyzing operational index statistics

This set of stored procedures can be used to analyze index usage.


create proc dbo.get_indexstats  
    (@dbid smallint=-1 
    ,@top varchar(100)=NULL 
    ,@columns varchar(500)=NULL 
    ,@order varchar(100)='lock waits' 
    ,@threshold varchar(500)=NULL) 
-- This stored procedure is provided "AS IS" with no warranties, and  
-- confers no rights.  
-- Use of included script samples are subject to the terms specified at  
-- http://www.microsoft.com/info/cpyright.htm 
-- T. Davidson 
-- This proc analyzes index statistics including accesses, overhead,  
-- locks, blocks, and waits 
-- Instructions: Order of execution is as follows: 
--    (1) truncate indexstats with init_indexstats 
--    (2) take initial index snapshot using insert_indexstats 
--    (3) Run workload 
--    (4) take final index snapshot using insert_indexstats 
--    (5) analyze with get_indexstats     

-- @dbid limits analysis to a database 
-- @top allows you to specify TOP n 
-- @columns is used to specify what columns from  
--            sys.dm_db_index_operational_stats will be included in  
-- the report 
--            For example, @columns='scans,lookups,waits' will include  
-- columns 
--            containing these keywords 
-- @order used to order results 
-- @threshold used to add a threshold,  
--            example: @threshold='[block %] > 5' only include if  
-- blocking is over 5% 
------  definition of some computed columns returned 
-- [blk %] = percentage of locks that cause blocks e.g. blk% = 100 * lock  
-- waits / locks 
-- [index usage] = range_scan_count + singleton_lookup_count +  
-- leaf_insert_count 
-- [nonleaf index overhead]=nonleaf_insert_count + nonleaf_delete_count +  
-- nonleaf_update_count 
-- [avg row lock wait ms]=row_lock_wait_in_ms/row_lock_wait_count 
-- [avg page lock wait ms]=page_lock_wait_in_ms/page_lock_wait_count 
-- [avg page latch wait ms]=page_latch_wait_in_ms/page_latch_wait_count 
-- [avg pageio latch wait  
-- ms]=page_io_latch_wait_in_ms/page_io_latch_wait_count 
--- Case 1 - only one snapshot of sys.dm_db_operational_index_stats was  
-- stored in  
---            indexstats. This is an error - return errormsg to user 
--- Case 2 - beginning snapshot taken, however some objects were not  
-- referenced 
---            at the time of the beginning snapshot. Thus, they will  
-- not be in the initial 
---            snapshot of sys.dm_db_operational_index_stats, use 0 for  
-- starting values. 
---            Print INFO msg for informational purposes. 
--- Case 3 - beginning and ending snapshots, beginning values for all  
-- objects and indexes 
---            this should be the normal case, especially if SQL Server  
-- is up a long time 
set nocount on 
declare @orderby varchar(100), @where_dbid_is varchar(100), @temp  
varchar(500), @threshold_temptab varchar(500) 
declare @cmd varchar(max),@col_stmt varchar(500),@addcol varchar(500) 
declare @begintime datetime, @endtime datetime, @duration datetime,  
@mincount int, @maxcount int 

select @begintime = min(now), @endtime = max(now) from indexstats 

if @begintime = @endtime 
        print 'Error: indexstats contains only 1 snapshot of  
        print 'Order of execution is as follows: ' 
        print '    (1) truncate indexstats with init_indexstats' 
        print '    (2) take initial index snapshot using insert_indexstats' 
        print '    (3) Run workload' 
        print '    (4) take final index snapshot using insert_indexstats' 
        print '    (5) analyze with get_indexstats' 
        return -99 

select @mincount = count(*) from indexstats where now = @begintime 
select @maxcount = count(*) from indexstats where now = @endtime 

if @mincount < @maxcount 
        print 'InfoMsg1: sys.dm_db_index_operational_stats only contains  
entries for objects referenced since last SQL re-cycle' 
        print 'InfoMsg2: Any newly referenced objects and indexes captured  
in the ending snapshot will use 0 as a beginning value' 

select @top = case  
        when @top is NULL then '' 
        else lower(@top) 
        @where_dbid_is = case (@dbid) 
        when -1 then '' 
        else ' and i1.database_id = ' + cast(@dbid as varchar(10)) 
--- thresholding requires a temp table 
        @threshold_temptab = case  
        when @threshold is NULL then '' 
        else ' select * from #t where ' + @threshold 
--- thresholding requires temp table, add 'into #t' to select statement  
select @temp = case (@threshold_temptab) 
        when '' then '' 
        else ' into #t ' 
select @orderby=case(@order) 
when 'leaf inserts' then 'order by [' + @order + ']' 
when 'leaf deletes' then 'order by [' + @order + ']' 
when 'leaf updates' then 'order by [' + @order + ']' 
when 'nonleaf inserts' then 'order by [' + @order + ']' 
when 'nonleaf deletes' then 'order by [' + @order + ']' 
when 'nonleaf updates' then 'order by [' + @order + ']' 
when 'nonleaf index overhead' then 'order by [' + @order + ']'  
when 'leaf allocations' then 'order by [' + @order + ']' 
when 'nonleaf allocations' then 'order by [' + @order + ']' 
when 'allocations' then 'order by [' + @order + ']' 
when 'leaf page merges' then 'order by [' + @order + ']' 
when 'nonleaf page merges' then 'order by [' + @order + ']' 
when 'range scans' then 'order by [' + @order + ']' 
when 'singleton lookups' then 'order by [' + @order + ']' 
when 'index usage' then 'order by [' + @order + ']' 
when 'row locks' then 'order by [' + @order + ']' 
when 'row lock waits' then 'order by [' + @order + ']' 
when 'block %' then 'order by [' + @order + ']'  
when 'row lock wait ms' then 'order by [' + @order + ']' 
when 'avg row lock wait ms' then 'order by [' + @order + ']' 
when 'page locks' then 'order by [' + @order + ']' 
when 'page lock waits' then 'order by [' + @order + ']' 
when 'page lock wait ms' then 'order by [' + @order + ']' 
when 'avg page lock wait ms' then 'order by [' + @order + ']' 
when 'index lock promotion attempts' then 'order by [' + @order + ']' 
when 'index lock promotions' then 'order by [' + @order + ']' 
when 'page latch waits' then 'order by [' + @order + ']' 
when 'page latch wait ms' then 'order by [' + @order + ']' 
when 'pageio latch waits' then 'order by [' + @order + ']' 
when 'pageio latch wait ms' then 'order by [' + @order + ']' 
else '' 

if @orderby <> '' select @orderby = @orderby + ' desc' 
    'start time'=@begintime, 
    'end time'=@endtime, 
    'duration (hh:mm:ss:ms)'=convert(varchar(50), 
    'Report'=case (@dbid)  
               when -1 then 'all databases' 
               else db_name(@dbid) 
             end + 
                when @top = '' then '' 
                when @top is NULL then '' 
                when @top = 'none' then '' 
                else ', ' + @top 
            end + 
                when @columns = '' then '' 
                when @columns is NULL then '' 
                when @columns = 'none' then '' 
                else ', include only columns containing ' + @columns 
            end + 
                when '' then '' 
                when NULL then '' 
                when 'none' then '' 
                else ', ' + @orderby 
            end +  
                when @threshold = '' then '' 
                when @threshold is NULL then '' 
                when @threshold = 'none' then '' 
                else ', threshold on ' + @threshold 

select @cmd = ' select i2.database_id, i2.object_id, i2.index_id,  
i2.partition_number ' 
select @cmd = @cmd +' , begintime=case min(i1.now) when max(i2.now) then  
NULL else min(i1.now) end ' 
select @cmd = @cmd +'     , endtime=max(i2.now) ' 
select @cmd = @cmd +' into #i ' 
select @cmd = @cmd +' from indexstats i2 ' 
select @cmd = @cmd +' full outer join ' 
select @cmd = @cmd +'     indexstats i1 ' 
select @cmd = @cmd +' on i1.database_id = i2.database_id ' 
select @cmd = @cmd +' and i1.object_id = i2.object_id ' 
select @cmd = @cmd +' and i1.index_id = i2.index_id ' 
select @cmd = @cmd +' and i1.partition_number = i2.partition_number ' 
select @cmd = @cmd +' where i1.now >= ''' +   
convert(varchar(100),@begintime, 109) + '''' 
select @cmd = @cmd +' and i2.now = ''' + convert(varchar(100),@endtime,  
109) + '''' 
select @cmd = @cmd + ' ' + @where_dbid_is + ' ' 
select @cmd = @cmd + ' group by i2.database_id, i2.object_id, i2.index_id,  
i2.partition_number ' 
select @cmd = @cmd + ' select ' + @top + ' i.database_id,  
object=isnull(object_name(i.object_id),i.object_id), indid=i.index_id,  
part_no=i.partition_number ' 

exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[leaf inserts]=i2.leaf_insert_count -   

select @cmd = @cmd +@addcol 
exec dbo.add_column  
     @add_stmt=@addcol out, 
     @cols_containing=@columns,@col_stmt=' , 
       [leaf deletes]=i2.leaf_delete_count – 

select @cmd = @cmd + @addcol 

exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[leaf updates]=i2.leaf_update_count –  

select @cmd = @cmd + @addcol 

exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[nonleaf inserts]=i2.nonleaf_insert_count –  

select @cmd = @cmd + @addcol 

exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[nonleaf deletes]=i2.nonleaf_delete_count –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[nonleaf updates]=i2.nonleaf_update_count –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[nonleaf index overhead]=(i2.nonleaf_insert_count –  
isnull(i1.nonleaf_insert_count,0)) + (i2.nonleaf_delete_count –  
isnull(i1.nonleaf_delete_count,0)) + (i2.nonleaf_update_count –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[leaf allocations]=i2.leaf_allocation_count –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[nonleaf allocations]=i2.nonleaf_allocation_count –  

select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[allocations]=(i2.leaf_allocation_count –  
isnull(i1.leaf_allocation_count,0)) + (i2.nonleaf_allocation_count –  

select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[leaf page merges]=i2.leaf_page_merge_count –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[nonleaf page merges]=i2.nonleaf_page_merge_count –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[range scans]=i2.range_scan_count –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @cols_containing= @columns, 
    @col_stmt=' ,[singleton lookups]=i2.singleton_lookup_count –  

select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[index usage]=(i2.range_scan_count –  
isnull(i1.range_scan_count,0)) + (i2.singleton_lookup_count –  
isnull(i1.singleton_lookup_count,0)) + (i2.leaf_insert_count –  
select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[row locks]=i2.row_lock_count –  
select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[row lock waits]=i2.row_lock_wait_count –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[block %]=cast (100.0 * (i2.row_lock_wait_count –  
isnull(i1.row_lock_wait_count,0)) / (1 + i2.row_lock_count –  
isnull(i1.row_lock_count,0)) as numeric(5,2))' 

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[row lock wait ms]=i2.row_lock_wait_in_ms –  

select @cmd = @cmd + @addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[avg row lock wait ms]=cast ((1.0*(i2.row_lock_wait_in_ms 
- isnull(i1.row_lock_wait_in_ms,0)))/(1 + i2.row_lock_wait_count -  
isnull(i1.row_lock_wait_count,0)) as numeric(20,1))' 
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[page locks]=i2.page_lock_count –  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[page lock waits]=i2.page_lock_wait_count –  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[page lock wait ms]=i2.page_lock_wait_in_ms –  
select @cmd = @cmd +@addcol 
exec dbo.add_column     
    @add_stmt=@addcol out, 
    @col_stmt=' ,[avg page lock wait ms]=cast  
((1.0*(i2.page_lock_wait_in_ms - isnull(i1.page_lock_wait_in_ms,0)))/(1 +  
i2.page_lock_wait_count - isnull(i1.page_lock_wait_count,0)) as  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[index lock promotion  
attempts]=i2.index_lock_promotion_attempt_count –  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[index lock promotions]=i2.index_lock_promotion_count –  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[page latch waits]=i2.page_latch_wait_count –  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[page latch wait ms]=i2.page_latch_wait_in_ms –  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[avg page latch wait ms]=cast  
((1.0*(i2.page_latch_wait_in_ms - isnull(i1.page_latch_wait_in_ms,0)))/(1  
+ i2.page_latch_wait_count - isnull(i1.page_latch_wait_count,0)) as  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[pageio latch waits]=i2.page_io_latch_wait_count –  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[pageio latch wait ms]=i2.page_io_latch_wait_in_ms –  
select @cmd = @cmd +@addcol 
exec dbo.add_column  
    @add_stmt=@addcol out, 
    @col_stmt=' ,[avg pageio latch wait ms]=cast  
((1.0*(i2.page_io_latch_wait_in_ms –  
isnull(i1.page_io_latch_wait_in_ms,0)))/(1 + i2.page_io_latch_wait_count –  
isnull(i1.page_io_latch_wait_count,0)) as numeric(20,1))' 

select @cmd = @cmd +@addcol 
select @cmd = @cmd + @temp 
select @cmd = @cmd + ' from #i i ' 
select @cmd = @cmd + ' left join indexstats i1 on i.begintime = i1.now and  
i.database_id = i1.database_id and i.object_id = i1.object_id and  
i.index_id = i1.index_id and i.partition_number = i1.partition_number ' 

select @cmd = @cmd + ' left join indexstats i2 on i.endtime = i2.now and  
i.database_id = i2.database_id and i.object_id = i2.object_id and  
i.index_id = i2.index_id and i.partition_number = i2.partition_number ' 
select @cmd = @cmd + ' ' + @orderby + ' ' 
select @cmd = @cmd + @threshold_temptab 
exec ( @cmd ) 


create proc insert_indexstats (@dbid smallint=NULL, 
                               @objid int=NULL, 
                               @indid int=NULL, 
                               @partitionid int=NULL) 
-- This stored procedure is provided "AS IS" with no warranties, and  
confers no rights.  
-- Use of included script samples are subject to the terms specified at  
-- This stored procedure stores a snapshot of  
sys.dm_db_index_operational_stats into the table indexstas 
-- for later analysis by the stored procedure get_indexstats. Please note  
that the indexstats table has an additional  
-- column to store the timestamp when the snapshot is taken 
-- T. Davidson 
-- snapshot sys.dm_db_index_operational_stats 
declare @now datetime 
select @now = getdate() 
insert into indexstats  
select  database_id 
from sys.dm_db_index_operational_stats(@dbid,@objid,@indid,@partitionid) 


CREATE proc dbo.init_index_operational_stats 
-- This stored procedure is provided "AS IS" with no warranties, and 
-- confers no rights.  
-- Use of included script samples are subject to the terms specified at  
-- http://www.microsoft.com/info/cpyright.htm 
-- T. Davidson 
-- create indexstats table if it doesn't exist, otherwise truncate 
set nocount on 
if not exists (select 1 from dbo.sysobjects where  
id=object_id(N'[dbo].[indexstats]') and OBJECTPROPERTY(id, N'IsUserTable') 
= 1) 
    create table dbo.indexstats ( 
        database_id smallint NOT NULL 
        ,object_id int NOT NULL 
        ,index_id int NOT NULL 
        ,partition_number int NOT NULL 
        ,leaf_insert_count bigint NOT NULL 
        ,leaf_delete_count bigint NOT NULL 
        ,leaf_update_count bigint NOT NULL 
        ,leaf_ghost_count bigint NOT NULL 
        ,nonleaf_insert_count bigint NOT NULL 
        ,nonleaf_delete_count bigint NOT NULL 
        ,nonleaf_update_count bigint NOT NULL 
        ,leaf_allocation_count bigint NOT NULL 
        ,nonleaf_allocation_count bigint NOT NULL 
        ,leaf_page_merge_count bigint NOT NULL 
        ,nonleaf_page_merge_count bigint NOT NULL 
        ,range_scan_count bigint NOT NULL 
        ,singleton_lookup_count bigint NOT NULL 
        ,forwarded_fetch_count bigint NOT NULL 
        ,lob_fetch_in_pages bigint NOT NULL 
        ,lob_fetch_in_bytes bigint NOT NULL 
        ,lob_orphan_create_count bigint NOT NULL 
        ,lob_orphan_insert_count bigint NOT NULL 
        ,row_overflow_fetch_in_pages bigint NOT NULL 
        ,row_overflow_fetch_in_bytes bigint NOT NULL 
        ,column_value_push_off_row_count bigint NOT NULL 
        ,column_value_pull_in_row_count bigint NOT NULL 
        ,row_lock_count bigint NOT NULL 
        ,row_lock_wait_count bigint NOT NULL 
        ,row_lock_wait_in_ms bigint NOT NULL 
        ,page_lock_count bigint NOT NULL 
        ,page_lock_wait_count bigint NOT NULL 
        ,page_lock_wait_in_ms bigint NOT NULL 
        ,index_lock_promotion_attempt_count bigint NOT NULL 
        ,index_lock_promotion_count bigint NOT NULL 
        ,page_latch_wait_count bigint NOT NULL 
        ,page_latch_wait_in_ms bigint NOT NULL 
        ,page_io_latch_wait_count bigint NOT NULL 
        ,page_io_latch_wait_in_ms bigint NOT NULL 
        ,now datetime default getdate()) 
else     truncate table dbo.indexstats 


create proc dbo.add_column ( 
            @add_stmt varchar(500) output, 
            @find varchar(100)=NULL, 
            @cols_containing varchar(500)=NULL,     
            @col_stmt varchar(max)) 
-- This stored procedure is provided "AS IS" with no warranties, and  
-- confers no rights.  
-- Use of included script samples are subject to the terms specified at 
-- http://www.microsoft.com/info/cpyright.htm 
-- T. Davidson 
-- @add_stmt is the result passed back to the caller 
-- @find is a keyword from @cols_containing 
-- @cols_containing is the list of keywords to include in the report 
-- @col_stmt is the statement that will be compared with @find.  
--            If @col_stmt contains @find, include this statement.  
--            set @add_stmt = @col_stmt 
declare @length int, @strindex int, @EOS bit 
if @cols_containing is NULL  
        select @add_stmt=@col_stmt 
select @add_stmt = '', @EOS = 0 

while @add_stmt is not null and @EOS = 0  
    select @strindex = charindex(',',@cols_containing) 
    if @strindex = 0 
            select @find = @cols_containing, @EOS = 1 
        select @find = substring(@cols_containing,1,@strindex-1) 
        select @cols_containing =       
                      datalength(@cols_containing) - @strindex) 
    select @add_stmt=case 
--when @cols_containing is NULL then NULL 
    when charindex(@find,@col_stmt) > 0 then NULL 
    else '' 
--- NULL indicates this statement is to be passed back through out parm  
if @add_stmt is NULL select @add_stmt=@col_stmt 


Monitoring index usage

Another aspect of query performance is related to DML queries, queries
deleting, inserting and modifying data. The more indexes that are
defined on a specific table, the more resources are needed to modify
data. In combination with locks held over transactions, longer
modification operations can hurt concurrency. Therefore, it can be very
important to know which indexes are used by an application over time.
You can then figure out whether there is a lot of weight in the database
schema in the form of indices which never get used.

SQL Server 2005 provides the
new sys.dm_db_index_usage_stats dynamic management view that
shows which indexes are used, and whether they are in use by the user
query or only by a system operation. With every execution of a query,
the columns in this view are incremented according to the query plan
that is used for the execution of that query. The data is collected
while SQL Server is up and running. The data in this DMV is kept in
memory only and is not persisted. So when the SQL Server instance is
shut down, the data is lost. You can poll this table periodically and
save the data for later analysis.

The operation on indexes is categorized into user type and system type.
User type refers to SELECT and INSERT/DELETE/UPDATE operations. System
type operations are commands like DBCC statements or DDL commands or
update statistics. The columns for each category of statements
differentiate into:

  • Seek Operations against an index (user_seeks or system_seeks)

  • Lookup Operations against an index (user_lookups or

  • Scan Operations against an index (user_scans or system_scans)

  • Update Operations against an index (user_updates or

For each of these accesses of indexes, the timestamp of the last access
is noted as well.

An index itself is identified by three columns covering its
database_id, object_id and index_id. Whereas index_id=0 represents a
heap table, index_id=1 represents a clustered index whereas
index_id>1 represents nonclustered indexes

Over days of runtime of an application against a database, the list of
indexes getting accessed in sys.dm_db_index_usage_stats will

The rules and definitions for seek, scan, and lookup work as follows in
SQL Server 2005.

  • SEEK: Indicates the count the B-tree structure was used to access
    the data. It doesn’t matter whether the B-tree structure is used
    just to read a few pages of each level of the index in order to
    retrieve one data row or whether half of the index pages are read in
    order to read gigabytes of data or millions of rows out of the
    underlying table. So it should be expected that most of the hits
    against an index are accumulated in this category.

  • SCAN: Indicates the count the data layer of the table gets used for
    retrieval without using one of the index B-trees. In the case of
    tables that do not have any index defined, this would be the case.
    In the case of table with indexes defined on it, this can happen
    when the indexes defined on the table are of no use for the query
    executed against that statement.

  • LOOKUP: Indicates that a clustered index that is defined on a table
    did get used to look up data which was identified by ‘seeking’
    through a nonclustered index that is defined on that table as well.
    This describes the scenario known as bookmark lookup in SQL
    Server 2000. It represents a scenario where a nonclustered index is
    used to access a table and the nonclustered index does not cover the
    columns of the query select list AND the columns defined in the
    where clause, SQL Server would increment the value of the column


    for the nonclustered index used plus the column


    for the entry of the clustered index. This count can become very
    high if there are multiple nonclustered indexes defined on the
    table. If the number of


    against a clustered index of a table is pretty high, the number of


    is pretty high as well plus the number of


    of one particular nonclustered index is very high as well, one might
    be better off by making the nonclustered index with the high count
    to be the clustered index.

The following DMV query can be used to get useful information about the
index usage for all objects in all databases.

select object_id, index_id, user_seeks, user_scans, user_lookups  
from sys.dm_db_index_usage_stats  
order by object_id index_id

One can see the following results for a given table.

object_id       index_id    user_seeks    user_scans    user_lookups  
------------      ------------- -------------- --------------  ----------- 
521690298         1                  0                 251             
521690298         2                123                 0               

In this case, there were 251 executions of a query directly accessing
the data layer of the table without using one of the indexes. There were
123 executions of a query accessing the table by using the first
nonclustered index, which does not cover either the select list of the
query or the columns specified in the WHERE clause since we see
123 lookups on the clustered index.

The most interesting category to look at is the ‘user type statement’
category. Usage indication in the ‘system category’ can be seen as a
result of the existence of the index. If the index did not exist, it
would not have to be updated in statistics and it would not need to be
checked for consistency. Therefore, the analysis needs to focus on the
four columns that indicate usage by ad hoc statements or by the user

To get information about the indexes of a specific table that has not
been used since the last start of SQL Server, this query can be executed
in the context of the database that owns the object.

select i.name 
from sys.indexes i  
where i.object_id=object_id('<table_name>') and 
    i.index_id NOT IN  (select s.index_id  
                        from sys.dm_db_index_usage_stats s  
                        where s.object_id=i.object_id and      
                        i.index_id=s.index_id and 
                        database_id = <dbid> )

All indexes which haven’t been used yet can be retrieved with the
following statement:

select object_name(i.object_id), 
from sys.indexes i  
            left join sys.dm_db_index_usage_stats s 
on s.object_id = i.object_id and  
                  i.index_id = s.index_id and s.database_id = <dbid>>
where objectproperty(i.object_id, 'IsIndexable') = 1 and
-- index_usage_stats has no reference to this index (not being used)
s.index_id is null or
-- index is being updated, but not used by seeks/scans/lookups
(s.user_updates > 0 and s.user_seeks = 0 
and s.user_scans = 0 and s.user_lookups = 0)
order by object_name(i.object_id) asc

In this case, the table name and the index name are sorted according to
the table name.

The real purpose of this dynamic management view is to observe the usage
of indexes in the long run. It might make sense to take a snapshot of
that view or a snapshot of the result of the query and store it away
every day to compare the changes over time. If you can identify that
particular indexes did not get used for months or during periods such as
quarter-end reporting or fiscal-year reporting, you could eventually
delete those indexes from the database.



Intra-query parallelism

When generating an execution plan for a query, the SQL Server optimizer
attempts to choose the plan that provides the fastest response time for
that query. If the query’s cost exceeds the value specified in
the cost threshold for parallelism option and parallelism has not
been disabled, then the optimizer attempts to generate a plan that can
be run in parallel. A parallel query plan uses multiple threads to
process the query, with each thread distributed across the available
CPUs and concurrently utilizing CPU time from each processor. The
maximum degree of parallelism can be limited server wide using the max
degree of parallelism
 option or on a per-query level using the OPTION
(MAXDOP) hint.

The decision on the actual degree of parallelism (DOP) used for
execution—a measure of how many threads will do a given operation in
parallel—is deferred until execution time. Before executing the query,
SQL Server  2005 determines how many schedulers are under-utilized and
chooses a DOP for the query that fully utilizes the remaining
schedulers. Once a DOP is chosen, the query runs with the chosen degree
of parallelism until completion. A parallel query typically uses a
similar but slightly higher amount of CPU time as compared to the
corresponding serial execution plan, but it does so in a shorter
duration of elapsed time. As long as there are no other bottlenecks,
such as waits for physical I/O, parallel plans generally should use 100%
of the CPU across all of the processors.

One key factor (how idle the system is) that led to running a parallel
plan can change after the query starts executing. This can change,
however, after the query starts executing. For example, if a query comes
in during an idle time, the server may choose to run with a parallel
plan and use a DOP of four and spawn up threads on four different
processors. Once those threads start executing, existing connections may
submit other queries that also require a lot of CPU. At that point, all
the different threads will share short time slices of the available CPU,
resulting in higher query duration.

Running with a parallel plan is not inherently bad and should provide
the fastest response time for that query. However, the response time for
a given query must be weighed against the overall throughput and
responsiveness of the rest of the queries on the system. Parallel
queries are generally best suited to batch processing and decision
support workloads and might not be desirable in a transaction processing


CPU Bottlenecks

A CPU bottleneck that happens suddenly and unexpectedly, without
additional load on the server, is commonly caused by a nonoptimal query
plan, a poor configuration, or design factors, and not insufficient
hardware resources. Before rushing out to buy faster and/or more
processors, you should first identify the largest consumers of CPU
bandwidth and see if they can be tuned.

System Monitor is generally the best means to determine if the server is
CPU bound. You should look to see if the Processor:% Processor
 counter is high; values in excess of 80% processor time per CPU
are generally deemed to be a bottleneck. You can also monitor the
SQL Server schedulers using the sys.dm_os_schedulers view to see if
the number of runnable tasks is typically nonzero. A nonzero value
indicates that tasks have to wait for their time slice to run; high
values for this counter are a symptom of a CPU bottleneck. You can use
the following query to list all the schedulers and look at the number of
runnable tasks.

    scheduler_id < 255

The following query gives you a high-level view of which currently
cached batches or procedures are using the most CPU. The query
aggregates the CPU consumed by all statements with the same
plan__handle (meaning that they are part of the same batch or
procedure). If a given plan_handle has more than one statement, you may
have to drill in further to find the specific query that is the largest
contributor to the overall CPU usage.

select top 50  
    sum(qs.total_worker_time) as total_cpu_time,  
    sum(qs.execution_count) as total_execution_count, 
    count(*) as  number_of_statements,  
    sys.dm_exec_query_stats qs 
group by qs.plan_handle 
order by sum(qs.total_worker_time) desc

The remainder of this section discusses some common CPU-intensive
operations that can occur with SQL Server, as well as efficient methods
to detect and resolve these problems.

Query the indexes and tables list with follow query, then enable
their(index) page lock setting from property setting dialog.


You can use the following methods to troubleshoot poor cursor usage.

System Monitor (Perfmon)

By looking at the SQL Server:Cursor Manager By Type – Cursor
 counter, you can get a general feel for how many cursors
are being used on the system by looking at this performance counter.
Systems that have high CPU utilization because of small fetch sizes
typically have hundreds of cursor requests per second. There are no
specific counters to tell you about the fetch buffer size.


The following query can be used to determine the connections with API
cursors (as opposed to TSQL cursors) that are using a fetch buffer size
of one row. It is much more efficient to use a larger fetch buffer, such
as 100 rows.

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

    sys.dm_exec_connections con 
    cross apply sys.dm_exec_cursors(con.session_id) as cur 
    cur.fetch_buffer_size = 1  
    and cur.properties LIKE 'API%'    -- API  
cursor (TSQL cursors always have fetch buffer of 1)

SQL Trace

Use a trace that includes the RPC:Completed event class search
for sp_cursorfetch statements. The value of the fourth parameter is
the number of rows returned by the fetch. The maximum number of rows
that are requested to be returned is specified as an input parameter in
the corresponding RPC:Starting event class.


External physical memory pressure

Open Task Manager in Performance view and check the Physical
 section, Available value. If the available memory amount is
low, external memory pressure may be present. The exact value depends on
many factors, however you can start looking into this when the value
drops below 50-100 MB. External memory pressure is clearly present when
this amount is less than 10 MB.

The equivalent information can also be obtained using the Memory:
Available Bytes
 counter in System Monitor.

If external memory pressure exists and you are seeing memory-related
errors, you will need to identify major consumers of the physical memory
on the system. To do this, look at  Process: Working Set performance
counters or the Mem Usage column on the Processes tab of Task
Manager and identify the largest consumers.

The total use of physical memory on the system can be roughly accounted
for by summing the following counters.

  • Process object, Working Set counter for each process

  • Memory object

    • Cache Bytes counter for system working set

    • Pool Nonpaged Bytes counter for size of unpaged pool

    • Available Bytes (equivalent of the Available value in
      Task Manager)

If there’s no external pressure, the Process: Private Bytes counter
or the VM Size in Task Manager should be close to the size of the
working set (Process: Working Set or Task Manager Mem Usage),
which means that we have no memory paged out.

Note that the Mem Usage column in Task Manager and corresponding
performance counters do not count memory that is allocated through AWE.
Thus the information is insufficient if AWE is enabled. In this case,
you need to look at the memory distribution inside SQL Server to get a
full picture.

You can use the sys.dm_os_memory_clerks DMV as follows to find
out how much memory SQL Server has allocated through AWE mechanism.

    sum(awe_allocated_kb) / 1024 as [AWE allocated, Mb]  

Note that in SQL Server, currently only buffer pool clerks (type =
‘MEMORYCLERK_SQLBUFFERPOOL’) use this mechanism and only when AWE is

Relieving external memory pressure by identifying and eliminating major
physical memory consumers (if possible) and/or by adding more memory
should generally resolve the problems related to memory.

88bf必发娱乐 2


Intra-query parallelism problems can be detected using the following

System Monitor (Perfmon)

Look at the SQL Server:SQL Statistics – Batch Requests/sec counter
and see “SQL Statistics Object” in SQL Server Books Online for more

Because a query must have an estimated cost that exceeds the cost
threshold for the parallelism configuration setting (which defaults to
5) before it is considered for a parallel plan, the more batches a
server is processing per second the less likely it is that the batches
are running with parallel plans. Servers that are running many parallel
queries normally have small batch requests per second (for example,
values less than 100).


From a running server, you can determine whether any active requests are
running in parallel for a given session by using the following query.

    max(isnull(exec_context_id, 0)) as number_of_workers, 
    sys.dm_exec_requests r 
    join sys.dm_os_tasks t on r.session_id = t.session_id 
    join sys.dm_exec_sessions s on r.session_id = s.session_id 
    s.is_user_process = 0x1 
group by  
    r.session_id, r.request_id,  
    r.sql_handle, r.plan_handle,  
    r.statement_start_offset, r.statement_end_offset 
having max(isnull(exec_context_id, 0)) > 0

With this information, the text of the query can easily be retrieved by
using sys.dm_exec_sql_text, while the plan can be retrieved
using sys.dm_exec_cached_plan.

You may also search for plans that are eligible to run in parallel. This
can be done by searching the cached plans to see if a relational
operator has its Parallel attribute as a nonzero value. These plans
may not run in parallel, but they are eligible to do so if the system is
not too busy.

-- Find query plans that may run in parallel 
    sys.dm_exec_cached_plans cp 
    cross apply sys.dm_exec_query_plan(cp.plan_handle) p 
    cross apply sys.dm_exec_sql_text(cp.plan_handle) as q 
    cp.cacheobjtype = 'Compiled Plan' and 
    p.query_plan.value('declare namespace  
        max(//p:RelOp/@Parallel)', 'float') > 0

In general, the duration of a query is longer than the amount of CPU
time, because some of the time was spent waiting on resources such as a
lock or physical I/O. The only scenario where a query can use more CPU
time than the elapsed duration is when the query runs with a parallel
plan such that multiple threads are concurrently using CPU. Note that
not all parallel queries will demonstrate this behavior (CPU time
greater than the duration).

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

    sys.dm_exec_query_stats qs 
    cross apply sys.dm_exec_sql_text(qs.plan_handle) as q 
    qs.total_worker_time > qs.total_elapsed_time 
SQL Trace 
Look for the following signs of parallel queries,  
which could be either statements or batches that have 
CPU time greater than the duration. 
    EventClass in (10, 12)    -- RPC:Completed,  
    and CPU > Duration/1000    -- CPU is in  
milliseconds, Duration in microseconds Or can be  
Showplans (un-encoded) that have Parallelism operators] 
in them 
    TextData LIKE '%Parallelism%'



For more information:




Internal virtual memory pressure

VAS consumption can be tracked by using
the sys.dm_os_virtual_address_dump DMV. VAS summary can be
queries using the following view.

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

-- virtual address space summary view 
-- generates a list of SQL Server regions 
-- showing number of reserved and free regions 
of a given size  
    Size = VaDump.Size, 
    Reserved =  SUM(CASE(CONVERT(INT, VaDump.Base)^0) 
    WHEN 0 THEN 0 ELSE 1 END), 
    Free = SUM(CASE(CONVERT(INT, VaDump.Base)^0) 
    WHEN 0 THEN 1 ELSE 0 END) 
    --- combine all allocation according with allocation 
base, don't take into 
    --- account allocations with zero allocation_base 
        CONVERT(VARBINARY, SUM(region_size_in_bytes)) 
        AS Size,  
        region_allocation_base_address AS Base 
    FROM sys.dm_os_virtual_address_dump  
    WHERE region_allocation_base_address <> 0x0 
    GROUP BY region_allocation_base_address  
       --- we shouldn't be grouping allocations with 
       zero allocation base 
       --- just get them as is 
    SELECT CONVERT(VARBINARY, region_size_in_bytes), 
    FROM sys.dm_os_virtual_address_dump 
    WHERE region_allocation_base_address  = 0x0 
AS VaDump 

The following queries can be used to assess VAS state.

-- available memory in all free regions 
SELECT SUM(Size*Free)/1024 AS [Total avail mem, KB]  
FROM VASummary  
WHERE Free <> 0 

-- get size of largest availble region 
SELECT CAST(MAX(Size) AS INT)/1024 AS [Max free size, KB]  
FROM VASummary  
WHERE Free <> 0

If the largest available region is smaller than 4 MB, we are likely to
be experiencing VAS pressure. SQL Server 2005 monitors and responds to
VAS pressure. SQL Server 2000 does not actively monitor for VAS
pressure, but reacts by clearing caches when an out-of-virtual-memory
error occurs.

name as IndexName ,

Blocking per object with sys.dm_db_index_operational_stats

The new SQL Server 2005
DMV Sys.dm_db_index_operational_stats provides comprehensive
index usage statistics, including blocks. In terms of blocking, it
provides a detailed accounting of locking statistics per table, index,
and partition. Examples of this includes a history of accesses, locks
(row_lock_count), blocks (row_lock_wait_count), and waits
(row_lock_wait_in_ms) for a given index or table.

The type of information available through this DMV includes:

  • Count of locks held, for example, row or page.

  • Count of blocks or waits, for example, row, page.

  • Duration of blocks or waits, for example, row, page.

  • Count of page latches waits.

  • Duration of page_latch_wait: This involves contention for a
    particular page for say, ascending key inserts. In such cases, the
    hot spot is the last page so multiple writers to the same last page
    each try to get an exclusive page latch at same time. This will show
    up as Pagelatch waits.

  • Duration of page_io_latch_wait: I/O latch occurs when a user
    requests a page that is not in the buffer pool. A slow I/O
    subsystem, or overworked I/O subsystem will sometimes experience
    high PageIOlatch waits that are actually I/O issues. These issues
    can be compounded by cache flushes or missing indexes.

  • Duration of page latch waits.

Besides blocking related information, there is additional information
kept for access to index.

  • Types of accesses, for example,  range, singleton lookups.

  • Inserts, updates, deletes at the leaf level.

  • Inserts, updates, deletes above the leaf level. Activity above the
    leaf is index maintenance. The first row on each leaf page has an
    entry in the level above. If a new page is allocated at the leaf,
    the level above will have a new entry for the first row on the new
    leaf page.

  • Pages merged at the leaf level represent empty pages that are
    de-allocated because rows were deleted.

  • Index maintenance. Pages merged above the leaf level are empty pages
    de-allocated, due to rows deleted at leaf, thereby leaving
    intermediate level pages empty. The first row on each leaf page has
    an entry in the level above. If enough rows are deleted at the leaf
    level, intermediate level index pages that originally contained
    entries for the first row on leaf pages will be empty. This causes
    merges to occur above the leaf.

This information is cumulative from instance startup. The information is
not retained across instance restarts, and there is no way to reset it.
The data returned by this DMV exists only as long as the metadata cache
object representing the heap or index is available. The values for each
column are set to zero whenever the metadata for the heap or index is
brought into the metadata cache. Statistics are accumulated until the
cache object is removed from the metadata cache. However, you can
periodically poll this table and collect this information in table that
can be queried further.

.aspx#_Appendix_B:_Blocking_Scripts) defines
one such set of stored procedures that can be used to collect index
operational data. You can then analyze the data for the time period of
interest. Here are the steps to use the stored procedures defined in
Appendix B.

  1. Initialize the indexstats table by using

  2. Capture a baseline with insert_indexstats.

  3. Run the workload.

  4. Capture the final snapshot of index statistics by
    using insert_indexstats.

  5. To analyze the collected index statistics, run the stored
    procedure get_indexstats to generatethe average number of locks
    (Row_lock_count for an index and partition), blocks, and waits per
    index. A high blocking % and/or high average waits can indicate poor
    index or query formulation.

Here are some examples that show the kind of information you can get
using the above set of stored procedures.

  • Get the top five indexes for all databases, order by index usage

    exec get_indexstats  
                @top='top 5',  
                @columns='index, usage',  
                @order='index usage'
  • Get the top five (all columns) index lock promotions where a lock
    promotion was attempted.

    exec get_indexstats  
                @top='top 5', 
                @order='index lock promotions', 
                @threshold='[index lock promotion attempts] > 0'
  • Get the top five singleton lookups with avg row lock waits>2ms,
    return columns containing wait, scan, singleton.

    exec get_indexstats  
            @top='top 5', 
            @order='singleton lookups', 
            @threshold='[avg row lock wait ms] > 2'
  • Get the top ten for all databases, columns containing ‘avg, wait’,
    order by wait ms, where row lock waits > 1.

    exec get_indexstats  
                @top='top 10 ', 
                @order='row lock wait ms',  
                 @threshold='[row lock waits] > 1'
  • Get the top five index stats, order by avg row lock waits desc.

    exec get_indexstats  
            @top='top 5', 
            @order='avg row lock wait ms'
  • Get the top five index stats, order by avg page latch lock waits

    exec get_indexstats  
            @top='top 5', 
            @order='avg page latch wait ms'
  • Get the top 5 percent index stats, order by avg pageio latch waits.

    exec get_indexstats  
            @top='top 3 percent', 
            @order='avg pageio latch wait ms', 
            @threshold='[pageio latch waits] > 0'
  • Get all index stats for the top ten in db=5, ordered by block% where
    block% > .1.

    exec get_indexstats  
            @top='top 10', 
            @order='block %', 
            @threshold='[block %] > 0.1'

See the sample Blocking Analysis Report in Figure 4.

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Figure 4:  Blocking Analysis Report

SQL Server 2000 does not provide any statistics for the utilization of
objects or indexes.

FROM sys.indexes as i


You can use System Monitor (PerfMon) or SQL Trace (SQL Server Profiler)
to detect excessive compiles and recompiles.

System Monitor (Perfmon)

The SQL Statistics object provides counters to monitor compilation
and the type of requests that are sent to an instance of SQL Server. You
must monitor the number of query compilations and recompilations in
conjunction with the number of batches received to find out if the
compiles are contributing to high CPU use. Ideally, the ratio of SQL
 to Batch Requests/sec should be very low unless
users are submitting ad hoc queries.

The key data counters to look are as follows.

  • SQL Server: SQL Statistics: Batch Requests/sec

  • SQL Server: SQL Statistics: SQL Compilations/sec

  • SQL Server: SQL Statistics: SQL Recompilations/sec

For more information, see “SQL Statistics Object” in SQL Server Books

SQL Trace

If the PerfMon counters indicate a high number of recompiles, the
recompiles could be contributing to the high CPU consumed by SQL Server.
We would then need to look at the profiler trace to find the stored
procedures that were being recompiled. The SQL Server Profiler trace
gives us that information along with the reason for the recompilation.
You can use the following events to get this information.

SP:Recompile / SQL:StmtRecompile. The SP:Recompile and
the SQL:StmtRecompile event classes indicate which stored procedures
and statements have been recompiled. When you compile a stored
procedure, one event is generated for the stored procedure and one for
each statement that is compiled. However, when a stored procedure
recompiles, only the statement that caused the recompilation is
recompiled (not the entire stored procedure as in SQL Server 2000). Some
of the more important data columns for the SP:Recompile event class
are listed below. The EventSubClass data column in particular is
important for determining the reason for the
recompile. SP:Recompile is triggered once for the procedure or
trigger that is recompiled and is not fired for an ad hoc batch that
could likely be recompiled. In SQL Server 2005, it is more useful to
monitor SQL:StmtRecompiles as this event class is fired when any
type of batch, ad hoc, stored procedure, or trigger is recompiled.

The key data columns we look at in these events are as follows.

  • EventClass

  • EventSubClass

  • ObjectID (represents stored procedure that contains this statement)

  • SPID

  • StartTime

  • SqlHandle

  • TextData

For more information, see “SQL:StmtRecompile Event Class” in SQL Server
Books Online.

If you have a trace file saved, you can use the following query to see
all the recompile events that were captured in the trace.

    fn_trace_gettable ( 'e:\recompiletrace.trc' , 1) 
    EventClass in(37,75,166)

EventClass  37 = Sp:Recompile, 75 = CursorRecompile,

You could further group the results from this query by the SqlHandle and
ObjectID columns, or by various other columns, in order to see if most
of the recompiles are attributed by one stored procedure or are due to
some other reason (such as a SET option that has changed).

Showplan XML For Query Compile. The Showplan XML For Query
 event class occurs when Microsoft SQL Server compiles or
recompiles a SQL statement. This event has information about the
statement that is being compiled or recompiled. This information
includes the query plan and the object ID of the procedure in question.
Capturing this event has significant performance overhead, as it is
captured for each compilation or recompilation. If you see a high value
for the SQL Compilations/sec counter in System Monitor, you should
monitor this event. With this information, you can see which statements
are frequently recompiled. You can use this information to change the
parameters of those statements. This should reduce the number of

DMVs. When you use the sys.dm_exec_query_optimizer_info DMV,
you can get a good idea of the time SQL Server spends optimizing. If you
take two snapshots of this DMV, you can get a good feel for the time
that is spent optimizing in the given time period.

select *  
from sys.dm_exec_query_optimizer_info 

counter          occurrence           value                 
---------------- -------------------- ---------------------  
optimizations    81                   1.0 
elapsed time     81                   6.4547820702944486E-2

In particular, look at the elapsed time, which is the time elapsed due
to optimizations. Since the elapsed time during optimization is
generally close to the CPU time that is used for the optimization (since
the optimization process is very CPU bound), you can get a good measure
of the extent to which the compile time is contributing to the high CPU

Another DMV that is useful for capturing this information
is sys.dm_exec_query_stats.

The data columns that you want to look at are as follows. :

  • Sql_handle

  • Total worker time

  • Plan generation number

  • Statement Start Offset

For more information, see the SQL Server Books Online topic
on sys.dm_exec_query_stats.

In particular, plan_generation_num indicates the number of times the
query has recompiled. The following sample query gives you the top
25 stored procedures that have been recompiled.

select *  
from sys.dm_exec_query_optimizer_info 

select top 25 
    sys.dm_exec_query_stats a 
    cross apply sys.dm_exec_sql_text(sql_handle) as sql_text 
    plan_generation_num >1 
order by plan_generation_num desc

For additional information, see Batch Compilation, Recompilation, and
Plan Caching Issues in SQL Server
on Microsoft TechNet.

Caches and memory pressure

An alternative way to look at external and internal memory pressure is
to look at the behavior of memory caches.

One of the differences of internal implementation of SQL Server 
2005  compared to SQL Server 2000 is uniform caching framework. In order
to remove the least recently used entries from caches, the framework
implements a clock algorithm. Currently it uses two clock hands—an
internal clock hand and an external clock hand.

The internal clock hand controls the size of a cache relative to other
caches. It starts moving when the framework predicts that the cache is
about to reach its cap.

the external clock hand starts to move when SQL Server as a whole gets
into memory pressure. Movement of the external clock hand can be due
external as well as internal memory pressure. Do not confuse movement of
the internal and external clock hands with internal and external memory

Information about clock hands movements is exposed through
the sys.dm_os_memory_cache_clock_hands DMV as shown in the
following code. Each cache entry has a separate row for the internal and
the external clock hand. If you see
increasing rounds_count and removed_all_rounds_count, then the
server is under the internal/external memory pressure.

select * 
    rounds_count > 0 
    and removed_all_rounds_count > 0

You can get additional information about the caches such as their size
by joining with sys.dm_os_cache_counters DMV as follows.

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

    distinct cc.cache_address,  
    cc.single_pages_kb + cc.multi_pages_kb as total_kb,  
    cc.single_pages_in_use_kb + cc.multi_pages_in_use_kb 
    as total_in_use_kb,  
    sys.dm_os_memory_cache_counters cc  
    join sys.dm_os_memory_cache_clock_hands ch on 
    (cc.cache_address =ch.cache_address) 
--uncomment this block to have the information only 
for moving hands caches 
    ch.rounds_count > 0 
    and ch.removed_all_rounds_count > 0 
order by total_kb desc

Note that for USERSTORE entries, the amount of pages in use is not
reported and thus will be NULL.

Published: October 1, 2005

Troubleshooting Performance Problems in SQL Server 2005


If the contention in tempdb is due to excessive DDL operation, you
will need to look at your application and see if you can minimize the
DDL operation. You can try the following suggestions.

  • If you use stored procedure scoped temporary tables, consider if
    these tables can be moved outside of the stored procedure.
    Otherwise, each execution of the stored procedure will cause a
    create/drop of the temporary table.

  • Look at query plans to see if some plans create lot of temporary
    objects, spools, sorts, or worktables. You may need to eliminate
    some temporary objects. For example, creating an index on a column
    that is used in ORDER BY may eliminate the sort.

If the contention is due to the contention in SGAM and PFS pages, you
can mitigate it by trying the following:

  • Increase the tempdb data files by an equal amount to distribute
    the workload across all of the disks and files. Ideally, you want to
    have as many files as there are CPUs (taking into account the

  • Use TF-1118 to eliminate mixed extent allocations.


Monitoring tempdb space

It is better to prevent a problem then work to solve it later. You can
use the following performance counters to monitor the amount of space
tempdb is using.

  • Free Space in tempdb (KB). This counter tracks free space
    in tempdb in kilobytes. Administrators can use this counter to
    determine if tempdb is running low on free space.

However, identifying how the different categories, as defined above, are
using the disk space in tempdb is a more interesting, and
productive, question.

The following query returns the tempdb space used by user and by
internal objects. Currently, it provides information
for tempdb only.

    SUM (user_object_reserved_page_count)*8 as user_objects_kb, 
    SUM (internal_object_reserved_page_count)*8 as internal_objects_kb, 
    SUM (version_store_reserved_page_count)*8  as version_store_kb, 
    SUM (unallocated_extent_page_count)*8 as freespace_kb 
From sys.dm_db_file_space_usage 
Where database_id = 2

Here is one sample output (with space in KBs).

user_objets_kb   internal_objects_kb   version_store_kb   freespace_kb 
---------------- -------------------- ------------------ ------------ 
8736               128                    64                    448

Note that these calculations don’t account for pages in mixed extents.
The pages in mixed extents can be allocated to user and internal

Excessive compilation and recompilation

When a batch or remote procedure call (RPC) is submitted to SQL Server,
before it begins executing the server checks for the validity and
correctness of the query plan. If one of these checks fails, the batch
may have to be compiled again to produce a different query plan. Such
compilations are known as recompilations. These recompilations are
generally necessary to ensure correctness and are often performed when
the server determines that there could be a more optimal query plan due
to changes in underlying data. Compilations by nature are CPU intensive
and hence excessive recompilations could result in a CPU-bound
performance problem on the system.

In SQL Server 2000, when SQL Server recompiles a stored procedure, the
entire stored procedure is recompiled, not just the statement that
triggered the recompile. SQL Server 2005 introduces statement-level
recompilation of stored procedures. When SQL Server 2005 recompiles
stored procedures, only the statement that caused the recompilation is
compiled—not the entire procedure. This uses less CPU bandwidth and
results in less contention on lock resources such as COMPILE locks.
Recompilation can happen due to various reasons, such as:

  • Schema changed

  • Statistics changed

  • Deferred compile

  • SET option changed

  • Temporary table changed

  • Stored procedure created with the RECOMPILE query hint or which uses

Wait states

This set of stored procedures can be used to analyze the blocking in SQL


CREATE proc [dbo].[track_waitstats_2005] ( 
                            @num_samples int=10, 
                            @delay_interval int=1, 
                            @delay_type nvarchar(10)='minutes', 
                            @truncate_history nvarchar(1)='N', 
                            @clear_waitstats nvarchar(1)='Y') 
-- This stored procedure is provided "AS IS" with no warranties, and  
-- confers no rights.  
-- Use of included script samples are subject to the terms specified at  
-- http://www.microsoft.com/info/cpyright.htm 
-- T. Davidson 
-- @num_samples is the number of times to capture waitstats, default is 10 
-- times 
-- default delay interval is 1 minute 
-- delaynum is the delay interval - can be minutes or seconds 
-- delaytype specifies whether the delay interval is minutes or seconds 
-- create waitstats table if it doesn't exist, otherwise truncate 
-- Revision: 4/19/05  
--- (1) added object owner qualifier 
--- (2) optional parameters to truncate history and clear waitstats 
set nocount on 
if not exists (select 1  
               from sys.objects  
               where object_id = object_id ( N'[dbo].[waitstats]') and  
               OBJECTPROPERTY(object_id, N'IsUserTable') = 1) 
    create table [dbo].[waitstats]  
        ([wait_type] nvarchar(60) not null,  
        [waiting_tasks_count] bigint not null, 
        [wait_time_ms] bigint not null, 
        [max_wait_time_ms] bigint not null, 
        [signal_wait_time_ms] bigint not null, 
        now datetime not null default getdate()) 

If lower(@truncate_history) not in (N'y',N'n') 
        raiserror ('valid @truncate_history values are ''y'' or  
''n''',16,1) with nowait     
If lower(@clear_waitstats) not in (N'y',N'n') 
        raiserror ('valid @clear_waitstats values are ''y'' or  
''n''',16,1) with nowait     
If lower(@truncate_history) = N'y'  
    truncate table dbo.waitstats 

If lower (@clear_waitstats) = N'y'  
    -- clear out waitstats 
    dbcc sqlperf ([sys.dm_os_wait_stats],clear) with no_infomsgs  

declare @i int, 
        @delay varchar(8), 
        @dt varchar(3),  
        @now datetime,  
        @totalwait numeric(20,1), 
        @endtime datetime, 
        @begintime datetime, 
        @hr int,  
        @min int,  
        @sec int 

select @i = 1 
select @dt = case lower(@delay_type) 
    when N'minutes' then 'm' 
    when N'minute' then 'm' 
    when N'min' then 'm' 
    when N'mi' then 'm' 
    when N'n' then 'm' 
    when N'm' then 'm' 
    when N'seconds' then 's' 
    when N'second' then 's' 
    when N'sec' then 's' 
    when N'ss' then 's' 
    when N's' then 's' 
    else @delay_type 

if @dt not in ('s','m') 
    raiserror ('delay type must be either ''seconds'' or  
''minutes''',16,1) with nowait 
if @dt = 's' 
    select @sec = @delay_interval % 60, @min = cast((@delay_interval / 60)  
as int), @hr = cast((@min / 60) as int) 
if @dt = 'm' 
    select @sec = 0, @min = @delay_interval % 60, @hr =  
cast((@delay_interval / 60) as int) 
select @delay= right('0'+ convert(varchar(2),@hr),2) + ':' +  
    + right('0'+convert(varchar(2),@min),2) + ':' +  
    + right('0'+convert(varchar(2),@sec),2) 

if @hr > 23 or @min > 59 or @sec > 59 
    select 'delay interval and type: ' + convert  
(varchar(10),@delay_interval) + ',' + @delay_type + ' converts to ' +  
    raiserror ('hh:mm:ss delay time cannot > 23:59:59',16,1) with nowait 
while (@i <= @num_samples) 
    select @now = getdate() 
    insert into [dbo].[waitstats] ( 
            from sys.dm_os_wait_stats 

    insert into [dbo].[waitstats] ( 
            from [dbo].[waitstats] 
            where now = @now 

    select @i = @i + 1 
    waitfor delay @delay 
--- create waitstats report 
execute dbo.get_waitstats_2005 
exec dbo.track_waitstats @num_samples=6 


CREATE proc [dbo].[get_waitstats_2005] ( 
                @report_format varchar(20)='all',  
                @report_order varchar(20)='resource') 
-- This stored procedure is provided "AS IS" with no warranties, and  
-- confers no rights.  
-- Use of included script samples are subject to the terms specified at  
-- http://www.microsoft.com/info/cpyright.htm 
-- this proc will create waitstats report listing wait types by  
-- percentage.  
--     (1) total wait time is the sum of resource & signal waits,  
--            @report_format='all' reports resource & signal 
--    (2) Basics of execution model (simplified) 
--        a. spid is running then needs unavailable resource, moves to  
--         resource wait list at time T0 
--        b. a signal indicates resource available, spid moves to  
--         runnable queue at time T1 
--        c. spid awaits running status until T2 as cpu works its way 
--         through runnable queue in order of arrival 
--    (3) resource wait time is the actual time waiting for the 
--        resource to be available, T1-T0 
--    (4) signal wait time is the time it takes from the point the  
--        resource is available (T1) 
--        to the point in which the process is running again at T2.  
--        Thus, signal waits are T2-T1 
--    (5) Key questions: Are Resource and Signal time significant? 
--        a. Highest waits indicate the bottleneck you need to solve  
--          for scalability 
--        b. Generally if you have LOW% SIGNAL WAITS, the CPU is  
--         handling the workload e.g. spids spend move through  
--         runnable queue quickly 
--        c. HIGH % SIGNAL WAITS indicates CPU can't keep up,  
--         significant time for spids to move up the runnable queue  
--         to reach running status 
--     (6) This proc can be run when track_waitstats is executing 
-- Revision 4/19/2005 
-- (1) add computation for CPU Resource Waits = Sum(signal waits /  
--                                    total waits) 
-- (2) add @report_order parm to allow sorting by resource, signal  
--     or total waits 
set nocount on 

declare @now datetime,  
        @totalwait numeric(20,1),  
        @totalsignalwait numeric(20,1),  
        @totalresourcewait numeric(20,1), 
        @endtime datetime,@begintime datetime, 
        @hr int,  
        @min int,  
        @sec int 

if not exists (select 1  
                from sysobjects  
                where id = object_id ( N'[dbo].[waitstats]') and  
                      OBJECTPROPERTY(id, N'IsUserTable') = 1) 
        raiserror('Error [dbo].[waitstats] table does not exist', 
                 16, 1) with nowait 

if lower(@report_format) not in ('all','detail','simple') 
        raiserror ('@report_format must be either ''all'', 
                    ''detail'', or ''simple''',16,1) with nowait 
if lower(@report_order) not in ('resource','signal','total') 
        raiserror ('@report_order must be either ''resource'',  
            ''signal'', or ''total''',16,1) with nowait 
if lower(@report_format) = 'simple' and lower(@report_order) <> 'total' 
        raiserror ('@report_format is simple so order defaults to  
                        16,1) with nowait 
        select @report_order = 'total' 

from [dbo].[waitstats]  
where [wait_type] = 'Total' 

--- subtract waitfor, sleep, and resource_queue from Total 
select @totalwait = sum([wait_time_ms]) + 1, @totalsignalwait =  
sum([signal_wait_time_ms]) + 1  
from waitstats  
where [wait_type] not in ( 
        'Total' ,'WAITFOR',  
        '***total***') and  
    now = @now 

select @totalresourcewait = 1 + @totalwait - @totalsignalwait 

-- insert adjusted totals, rank by percentage descending 
delete waitstats  
where [wait_type] = '***total***' and  
now = @now 

insert into waitstats  

select 'start time'=@begintime,'end time'=@endtime, 
       'duration (hh:mm:ss:ms)'=convert(varchar(50),@endtime- 
       'report format'=@report_format, 'report order'=@report_order 

if lower(@report_format) in ('all','detail')  
----- format=detail, column order is resource, signal, total. order by  
resource desc 
    if lower(@report_order) = 'resource' 
        select [wait_type],[waiting_tasks_count], 
            'Resource wt (T1-T0)'=[wait_time_ms]-[signal_wait_time_ms], 
            'res_wt_%'=cast (100*([wait_time_ms] -  
                    [signal_wait_time_ms]) /@totalresourcewait as  
            'Signal wt (T2-T1)'=[signal_wait_time_ms], 
            'sig_wt_%'=cast (100*[signal_wait_time_ms]/@totalsignalwait as  
            'Total wt (T2-T0)'=[wait_time_ms], 
            'wt_%'=cast (100*[wait_time_ms]/@totalwait as numeric(20,1)) 
        from waitstats  
        where [wait_type] not in ( 
                'WAITFOR') and 
                now = @now 
        order by 'res_wt_%' desc 

----- format=detail, column order signal, resource, total. order by signal 
    if lower(@report_order) = 'signal' 
        select    [wait_type], 
                'Signal wt (T2-T1)'=[signal_wait_time_ms], 
                'sig_wt_%'=cast (100*[signal_wait_time_ms]/@totalsignalwait  
                as numeric(20,1)), 
                'Resource wt (T1-T0)'=[wait_time_ms]-[signal_wait_time_ms], 
                'res_wt_%'=cast (100*([wait_time_ms] -  
                        [signal_wait_time_ms]) /@totalresourcewait as  
                'Total wt (T2-T0)'=[wait_time_ms], 
                'wt_%'=cast (100*[wait_time_ms]/@totalwait as  
        from waitstats  
        where [wait_type] not in ( 
                    'WAITFOR') and 
                    now = @now 
        order by 'sig_wt_%' desc 

----- format=detail, column order total, resource, signal. order by total 
    if lower(@report_order) = 'total' 
            'Total wt (T2-T0)'=[wait_time_ms], 
            'wt_%'=cast (100*[wait_time_ms]/@totalwait as numeric(20,1)), 
            'Resource wt (T1-T0)'=[wait_time_ms]-[signal_wait_time_ms], 
            'res_wt_%'=cast (100*([wait_time_ms] -  
                [signal_wait_time_ms]) /@totalresourcewait as numeric(20,1)), 
            'Signal wt (T2-T1)'=[signal_wait_time_ms], 
            'sig_wt_%'=cast (100*[signal_wait_time_ms]/@totalsignalwait as 
        from waitstats  
        where [wait_type] not in ( 
                'WAITFOR') and 
                now = @now 
        order by 'wt_%' desc 
---- simple format, total waits only 
        percentage=cast (100*[wait_time_ms]/@totalwait as numeric(20,1)) 
    from waitstats  
    where [wait_type] not in ( 
                    'WAITFOR') and 
                now = @now 
    order by percentage desc 

---- compute cpu resource waits 
    'total waits'=[wait_time_ms], 
    'total signal=CPU waits'=[signal_wait_time_ms], 
    'CPU resource waits % = signal waits / total waits'= 
            cast (100*[signal_wait_time_ms]/[wait_time_ms] as 
from [dbo].[waitstats] 
where [wait_type] = '***total***' 
order by now 

declare @now datetime  
select @now = getdate() 
select getdate()

Poor cursor usage

Versions of SQL Server prior to SQL Server 2005 only supported a single
active common per connection. A query that was executing or had results
pending to send to the client was considered active. In some situations,
the client application might need to read through the results and submit
other queries to SQL Server based on the row just read from the result
set. This could not be done with a default result set, since it could
have other pending results. A common solution was to change the
connection properties to use a server-side cursor.

When using a server-side cursor, the database client software (the OLE
DB provider or ODBC driver) transparently encapsulates client requests
inside of special extended stored procedures, such
as sp_cursoropensp_cursorfetch, and so forth. This is
referred to as an API cursor (as opposed to a TSQL cursor). When the
user executes the query, the query text is sent to the server
via sp_cursoropen, requests to read from the result set would
result in an sp_cursorfetch instructing the server to only send
back a certain number of rows. By controlling the number of rows that
are fetched, it is possible for the ODBC driver or OLE DB provider to
cache the row(s). This prevents a situation where the server is waiting
for the client to read all the rows it has sent. Thus, the server is
ready to accept a new request on that connection.

Applications that open cursors and fetch one row (or a small number of
rows) at a time can easily become bottlenecked by the network latency,
especially on a wide area network (WAN). On a fast network with many
different user connections, the overhead required to process many cursor
requests may become significant. Because of the overhead associated with
repositioning the cursor to the appropriate location in the result set,
per-request processing overhead, and similar processing, it is more
efficient for the server to process a single request that returns
100 rows than to process 100 separate requests which return the same
100 rows but one row at a time.

Detecting memory pressures

Memory pressure by itself does not indicate a problem. Memory pressure
is a necessary but not a sufficient condition for the server to
encounter memory errors later on. Working under memory pressure could be
a normal operating condition of the server. However, signs of memory
pressure may indicate that the server runs close to its capacity and the
potential for out-of-memory errors exists. In the case of normally
operating server, this information could serve as a baseline for
determining reasons for out-of-memory conditions later.


Any query that runs with a parallel plan is one that the optimizer
believes is expensive enough that it would exceed the cost threshold of
parallelism, which defaults to five (roughly 5-second execution time on
a reference machine). Any queries identified through the methods above
are candidates for further tuning.

  • Use the Database Engine Tuning Advisor to see if any indexing
    changes, changes to indexed views, or partitioning changes could
    reduce the cost of the query.

  • Check for significant differences in the actual versus the estimated
    cardinality since the cardinality estimates are the primary factor
    in estimating the cost of the query. If any significant differences
    are found:

    If the auto create statistics database option is disabled, make
    sure that there are no MISSING STATS entries in
    the Warnings column of the Showplan output.

    Try running UPDATE STATISTICS on the tables where the cardinality
    estimates are off.

    Verify that the query doesn’t use a query construct that the
    optimizer can’t accurately estimate, such as multi-statement
    table-valued functions or CLR functions, table variables, or
    comparisons with a Transact-SQL variable (comparisons with a
    parameter are OK).

  • Evaluate whether the query could be written in a more efficient
    fashion using different Transact-SQL statements or expressions.

General troubleshooting steps in case of memory errors

The following list outlines general steps that will help you
troubleshoot memory errors.

  1. Verify if the server is operating under external memory pressure. If
    external pressure is present, try resolving it first, and then see
    if the problem/errors still exist.

  2. Start collecting performance monitor counters for SQL Server: Buffer
    Manager, SQL Server: Memory Manager.

  3. Verify the memory configuration parameters
    (sp_configure), min memory per querymin/max server
    awe enabled, and the Lock Pages in
     privilege. Watch for unusual settings. Correct them as
    necessary. Account for increased memory requirements for
    SQL Server 2005.

  4. Check for any nondefault sp_configure parameters that might
    indirectly affect the server.

  5. Check for internal memory pressures.

  6. Observe DBCC MEMORYSTATUS output and the way it changes when you see
    memory error messages.

  7. Check the workload (number of concurrent sessions, currently
    executing queries).


It is quite common to refer to different memory resources by using the
single generic term memory. As there are several types of memory
resources, it is important to understand and differentiate which
particular memory resource is referred to.

Memory errors

701 – There is insufficient system memory to run this query.


This is very generic out-of-memory error for the server. It indicates a
failed memory allocation. It can be due to a variety of reasons,
including hitting memory limits on the current workload. With increased
memory requirements for SQL Server 2005 and certain configuration
settings (such as the max server memory option) users are more
likely to see this error as compared to SQL Server 2000. Usually the
transaction that failed is not the cause of this error.


Regardless of whether the error is consistent and repeatable (same
state) or random (appears at random times with different states), you
will need to investigate server memory distribution during the time you
see this error. When this error is present, it is possible that the
diagnostic queries will fail. Start investigation from external
assessment. Follow the steps outlined in General troubleshooting steps
in case of memory

Possible solutions include: Remove external memory pressure. Increase
the max server memory setting. Free caches by using one of the
FREEPROCCACHE. If the problem reappears, reduce workload.

802 – There is insufficient memory available in the buffer pool.


This error does not necessarily indicate an out-of-memory condition. It
might indicate that the buffer pool memory is used by someone else. In
SQL Server 2005, this error should be relatively rare.


Use the general troubleshooting steps and recommendations outlined for
the 701 error.

8628 – A time out occurred while waiting to optimize the query. Rerun
the query.


This error indicates that a query compilation process failed because it
was unable to obtain the amount of memory required to complete the
process. As a query undergoes through the compilation process, which
includes parsing, algebraization, and optimization, its memory
requirements may increase. Thus the query will compete for memory
resources with other queries. If the query exceeds a predefined timeout
(which increases as the memory consumption for the query increases)
while waiting for resources, this error is returned. The most likely
reason for this is the presence of a number of large query compilations
on the server.


  1. Follow general troubleshooting steps to see if the server memory
    consumption is affected in general.

  2. Check the workload. Verify the amounts of memory consumed by
    different components. (See Internal Physical Memory
    .aspx#_Internal_physical_memory_pressure) earlier
    in this paper.)

  3. Check the output of DBCC MEMORYSTATUS for the number of waiters at
    each gateway (this information will tell you if there are other
    queries running that consume significant amounts of memory).

    Small Gateway                  Value 
    ------------------------------ -------------------- 
    Configured Units               8 
    Available Units                8 
    Acquires                       0 
    Waiters                        0 
    Threshold Factor               250000 
    Threshold                      250000 
    (6 row(s) affected) 
    Medium Gateway                 Value 
    ------------------------------ -------------------- 
    Configured Units               2 
    Available Units                2 
    Acquires                       0 
    Waiters                        0 
    Threshold Factor               12 
    (5 row(s) affected) 
    Big Gateway                    Value 
    ------------------------------ -------------------- 
    Configured Units               1 
    Available Units                1 
    Acquires                       0 
    Waiters                        0 
    Threshold Factor               8
  4. Reduce workload if possible.

8645 – A time out occurred while waiting for memory resources to
execute the query. Rerun the query.


This error indicates that many concurrent memory intensive queries are
being executed on the server. Queries that use sorts (ORDER BY) and
joins may consume significant amount of memory during execution. Query
memory requirements are significantly increased if there is a high
degree of parallelism enabled or if a query operates on a partitioned
table with non-aligned indexes. A query that cannot get the memory
resources it requires within the predefined timeout (by default, the
timeout is 25 times the estimated query cost or
thesp_configure ‘query wait’ amount if set) receives this error.
Usually, the query that receives the error is not the one that is
consuming the memory.


  1. Follow general steps to assess server memory condition.

  2. Identify problematic queries: verify if there is a significant
    number of queries that operate on partitioned tables, check if they
    use non-aligned indexes, check if there are many queries involving
    joins and/or sorts.

  3. Check the sp_configure parameters degree of
     and min memory per query. Try reducing the degree
    of parallelism and verify if min memory per query is not set to
    a high value. If it is set to a high value, even small queries will
    acquire the specified amount of memory.

  4. To find out if queries are waiting on RESOURCE_SEMAPHORE,
    see Blocking .aspx#_Blocking)later
    in this paper.

8651 – Could not perform the requested operation because the minimum
query memory is not available. Decrease the configured value for the
‘min memory per query’ server configuration option.


Causes in part are similar to the 8645 error; it may also be an
indication of general memory low condition on the server. A min memory
per query
 option setting that is too high may also generate this


  1. Follow general memory error troubleshooting steps.

  2. Verify the sp_configure min memory per query option setting.



Consider the following options if you have detected inefficient query

  • Tune the query with the Database Engine Tuning Advisor to see if it
    produces any index recommendations.

  • Check for issues with bad cardinality estimates.

    Are the queries written so that they use the most restrictive WHERE
    clause that is applicable? Unrestricted queries are resource
    intensive by their very nature.

    Run UPDATE STATISTICS on the tables involved in the query and check
    to see if the problem persists.

    Does the query use constructs for which the optimizer is unable to
    accurately estimate cardinality? Consider whether the query can be
    modified in a way so that the issue can be avoided.

  • If it is not possible to modify the schema or the query, SQL
    Server 2005 has a new plan guide feature that allows you to specify
    query hints to add to queries that match certain text. This can be
    done for ad hoc queries as well as inside a stored procedure. Hints
    such as OPTION (OPTIMIZE FOR) allow you to impact the cardinality
    estimates while leaving the optimizer its full array of potential
    plans. Other hints such as OPTION (FORCE ORDER) or OPTION (USE PLAN)
    allow you varying degrees of control over the query plan.

  • Determine if cursors are the most appropriate means to accomplish
    the processing or whether a set-based operation, which is generally
    more efficient, is possible.

  • Consider enabling multiple active results (MARS) when connecting to
    SQL Server 2005.

  • Consult the appropriate documentation for your specific API to
    determine how to specify a larger fetch buffer size for the cursor:


    OLE DB – IRowset::GetNextRows or IRowsetLocate::GetRowsAt


Ring buffers

Significant amount of diagnostic memory information can be obtained from
the sys.dm_os_ring_buffers ring buffers DMV. Each ring buffer
keeps a record of the last number of notifications of a certain kind.
Detailed information on specific ring buffers is provided next.


You can use information from resource monitor notifications to identify
memory state changes. Internally, SQL Server has a framework that
monitors different memory pressures. When the memory state changes, the
resource monitor task generates a notification. This notification is
used internally by the components to adjust their memory usage according
to the memory state and it is exposed to the user
through sys.dm_os_ring_buffers DMV as in the following code.

select record  
from sys.dm_os_ring_buffers  
where ring_buffer_type = 'RING_BUFFER_RESOURCE_MONITOR'

A record may look like this:

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

<Record id="1701" type="RING_BUFFER_RESOURCE_MONITOR"  
    <MemoryNode id="0"> 

From this record, you can deduce that the server received a low physical
memory notification. You can also see the amounts of memory in
kilobytes. You can query this information by using the XML capabilities
of SQL Server, for example in the following code.

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

    x.value('(//Notification)[1]', 'varchar(max)') as [Type], 
    x.value('(//Record/@time)[1]', 'bigint') as [Time Stamp], 
    x.value('(//AvailablePhysicalMemory)[1]', 'int')  
    as [Avail Phys Mem, Kb], 
    x.value('(//AvailableVirtualAddressSpace)[1]', 'int') 
    as [Avail VAS, Kb] 
    (select cast(record as xml) 
     from sys.dm_os_ring_buffers  
     where ring_buffer_type = 'RING_BUFFER_RESOURCE_MONITOR') 
     as R(x) order by 
    [Time Stamp] desc

Upon receiving a memory low notification, the buffer pool recalculates
its target. Note that the target count stays within the limits specified
by the min server memory and max server memory options. If the
new committed target for the buffer pool is lower than the currently
committed buffers, the buffer pool starts shrinking until external
physical memory pressure is removed. Note that SQL Server 2000 did not
react to physical memory pressure when running with AWE enabled.


This ring buffer will contain records indicating server out-of-memory
conditions as in the following code example.

select record  
from sys.dm_os_ring_buffers  
where ring_buffer_type = 'RING_BUFFER_OOM'

A record may look like this:

<Record id="7301" type="RING_BUFFER_OOM" time="345640123"> 

This record tells which operation has failed (commit, reserve, or page
allocation) and the amount of memory requested.

RING_BUFFER_MEMORY_BROKER and Internal Memory Pressure

As internal memory pressure is detected, low memory notification is
turned on for components that use the buffer pool as the source of
memory allocations. Turning on low memory notification allows reclaiming
the pages from caches and other components using them.

Internal memory pressure can also be triggered by adjusting the max
server memory
 option or when the percentage of the stolen pages from
the buffer pool exceeds 80%.

Internal memory pressure notifications (‘Shrink’) can be observed by
querying memory broker ring buffer as in the following code example.

    x.value('(//Record/@time)[1]', 'bigint') as [Time Stamp], 
    x.value('(//Notification)[1]', 'varchar(100)')  
    as [Last Notification] 
    (select cast(record as xml) 
     from sys.dm_os_ring_buffers  
     where ring_buffer_type = 'RING_BUFFER_MEMORY_BROKER') 
     as R(x) 
order by 
    [Time Stamp] desc


This ring buffer will contain records indicating severe buffer pool
failures, including buffer pool out of memory conditions.

select record  
from sys.dm_os_ring_buffers  
where ring_buffer_type = 'RING_BUFFER_BUFFER_POOL'

A record may look like this:

<Record id="1234" type="RING_BUFFER_BUFFER_POOL" 
    < BufferPoolFailure id="FAIL_OOM"> 
        <CommittedCount>84344 </CommittedCount>  
        <CommittedTarget>84350 </CommittedTarget >  
        <StolenCount>64001 </StolenCount>  
    <ReservedCount>64001 </ReservedCount>  
    </ BufferPoolFailure >

This record will tell what failure (FAIL_OOM, FAIL_MAP,
pool status at the time.

Inefficient query plan

When generating an execution plan for a query, the SQL Server optimizer
attempts to choose a plan that provides the fastest response time for
that query. Note that the fastest response time doesn’t necessarily mean
minimizing the amount of I/O that is used, nor does it necessarily mean
using the least amount of CPU—it is a balance of the various resources.

Certain types of operators are more CPU intensive than others. By their
nature, the Hash operator and Sort operator scan through their
respective input data. With read ahead (prefetch) being used during such
a scan, the pages are almost always available in the buffer cache before
the page is needed by the operator. Thus, waits for physical I/O are
minimized or eliminated. When these types of operations are no longer
constrained by physical I/O, they tend to manifest themselves by high
CPU consumption. By contrast, nested loop joins have many index lookups
and can quickly become I/O bound if the index lookups are traversing to
many different parts of the table so that the pages can’t fit into the
buffer cache.

The most significant input the optimizer uses in evaluating the cost of
various alternative query plans is the cardinality estimates for each
operator, which you can see in the Showplan
(EstimateRows and EstimateExecutions attributes). Without
accurate cardinality estimates, the primary input used in optimization
is flawed, and many times so is the final plan.

For an excellent white paper that describes in detail how the SQL Server
optimizer uses statistics, see Statistics Used by the Query Optimizer
in Microsoft
SQL Server 2005
The white paper discusses how the optimizer uses statistics, best
practices for maintaining up-to-date statistics, and some common query
design issues that can prevent accurate estimate cardinality and thus
cause inefficient query plans.

Internal Objects

Internal objects are created and destroyed for each statement, with
exceptions as outlined in the
previous table.aspx#_Tempdb).
If you notice that a huge amount of tempdb space is allocated, you
will need to know which session or tasks are consuming the space and
then possibly take the corrective action.

SQL Server 2005 provides two additional
DMVs:  sys.dm_db_session_space_usage and sys.dm_db_task_space_usage to
track tempdb space that is allocated to sessions and tasks
respectively. Though tasks are run in the context of sessions, the space
used by tasks is accounted for under sessions only after the task

You can use the following query to find the top sessions that are
allocating internal objects. Note that this query includes only the
tasks that have been completed in the sessions.

from sys.dm_db_session_space_usage 
order by internal_objects_alloc_page_count DESC

You can use the following query to find the top user sessions that are
allocating internal objects, including currently active tasks.

    (t1.internal_objects_alloc_page_count + task_alloc) as allocated, 
    (t1.internal_objects_dealloc_page_count + task_dealloc) as     
from sys.dm_db_session_space_usage as t1,  
    (select session_id,  
            as task_alloc, 
    sum (internal_objects_dealloc_page_count) as  
      from sys.dm_db_task_space_usage group by session_id) as t2 
where t1.session_id = t2.session_id and t1.session_id >50 
order by allocated DESC

Here is a sample output.

session_id allocated            deallocated 
---------- -------------------- -------------------- 
52         5120                 5136 
51         16                   0

Once you have isolated the task or tasks that are generating a lot of
internal object allocations, you can find out which Transact-SQL
statement it is and its query plan for a more detailed analysis.

from (Select session_id,  
             sum(internal_objects_alloc_page_count) as task_alloc, 
             sum (internal_objects_dealloc_page_count) as task_dealloc  
      from sys.dm_db_task_space_usage  
      group by session_id, request_id) as t1,  
      sys.dm_exec_requests as t2 
where t1.session_id = t2.session_id and  
     (t1.request_id = t2.request_id) 
order by t1.task_alloc DESC

Here is a sample output.

session_id request_id  task_alloc           task_dealloc   
52         0           1024                 1024                  
sql_handle                          statement_start_offset  
0x02000000D490961BDD2A8BE3B0FB81ED67655EFEEB360172   356   

statement_end_offset  plan_handle       
-1                    0x06000500D490961BA8C19503000000000000000000000000

You can use the sql_handle and plan_handle to get the SQL statement
and the query plan as follows:

select text from sys.dm_exec_sql_text(@sql_handle) 
select * from sys.dm_exec_query_plan(@plan_handle)

Note that it is possible that a query plan may not be in the cache when
you want to access it. To guarantee the availability of the query plans,
you will need to poll the plan cache frequently and save the results,
preferably in a table, so that it can be queried later.

When SQL Server is restarted, the tempdb size goes back to the
initially configured size and it grows based on the requirements. This
can lead to fragmentation of the tempdb and can incur an overhead,
including the blocking of the allocation of new extents during the
database auto-grow, and expanding the size of the tempdb. This can
impact the performance of your workload. It is recommended that you
pre-allocate tempdb to the appropriate size.

Identifying long blocks

As mentioned earlier, blocking in SQL Server is common and is a
manifestation of the logical locks that are needed to maintain the
transactional consistency. However, when the wait for locks exceed a
threshold, it may impact the response time. To identify long running
blocking, you can use BlockedProcessThreshold configuration parameter to
establish a user configured server-wide block threshold. The threshold
defines a duration in seconds. Any block that exceeds this threshold
will fire an event that can be captured by SQL Trace.

For example, a 200-second blocked process threshold can be configured in
SQL Server Management Studio as follows:

  1. Execute Sp_configure ‘blocked process threshold’, 200  

  2. Reconfigure with override

    Once the blocked process threshold is established, the next step is
    to capture the trace event. The trace events of blocking events that
    exceed the user configured threshold can be captured with SQL Trace
    or Profiler.

  3. If using SQL Trace, use sp_trace_setevent and event_id=137.

  4. If using SQL Server Profiler, select the Blocked Process Report
    event class (under the Errors and Warnings object). See Figure 2.

    88bf必发娱乐 4.gif).gif)

    Figure 2: Tracing long blocks and deadlocks

    Note   This is a light weight trace, as events are only captured
    when (1) a block exceeds the threshold, or (2) a deadlock occurs.
    For each 200-second interval that a lock is blocked, a trace event
    fires. This means that a single lock that is blocked for 600 seconds
    results in 3 trace events. See Figure 3.

    88bf必发娱乐 5.gif).gif)

    Figure 3: Reporting Blocking > block threshold

The traced event includes the entire SQL statements of both the blocker
and the one blocked. In this case the “Update Customers” statement is
blocking the “Select from Customers” statement.

By comparison, checking for long blocking scenarios in SQL Server 2000
requires custom code to poll Sysprocesses and post-processing the
results. Knowledge Base article 271509 contains a sample script that can
be used to monitor blocking.

Memory Bottlenecks

This section specifically addresses low memory conditions and ways to
diagnose them as well as different memory errors, possible reasons for
them, and ways to troubleshoot.

Memory pressures

Memory pressure denotes a condition when limited amount of memory is
available. Identifying when SQL Server runs under a memory pressure will
help you troubleshoot memory-related issues. SQL Server responds
differently depending on the type of memory pressure that is present.
The following table summarizes the types of memory pressures, and their
general underlying causes. In all cases, you are more likely to see
timeout or explicit out-of-memory error messages.

Table 2





Physical memory (RAM) running low. This causes the system to trim working sets of currently running processes, which may result in overall slowdown.

SQL Server detects this condition and, depending on the configuration, may reduce the commit target of the buffer pool and start clearing internal caches.

SQL Server detects high memory consumption internally, causing redistribution of memory between internal components.

Internal memory pressure may be a result of:

  • Responding to the external memory pressure (SQL Server sets lower memory usage caps).

  • Changed memory settings (e.g. ‘max server memory’).

  • Changes in memory distribution of internal components (due to high percentage of reserved and stolen pages from the buffer pool).


Running low on space in the system page file(s). This may cause the system to fail memory allocations, as it is unable to page out currently allocated memory. This condition may result in the whole system responding very slowly or even bring it to a halt.

Running low on VAS due to fragmentation (a lot of VAS is available but in small blocks) and/or consumption (direct allocations, DLLs loaded in SQL Server VAS, high number of threads).

SQL Server detects this condition and may release reserved regions of VAS, reduce buffer pool commit target, and start shrinking caches.

Windows has a notification mechanism2 if physical memory is running
high or low. SQL Server uses this mechanism in its memory management

General troubleshooting steps in each case are explained in Table 3.

Table 3





  • Find major system memory consumers.

  • Attempt to eliminate (if possible).

  • Check for adequate system RAM and consider adding more RAM (usually requires more careful investigation beyond the scope of this paper).

  • Identify major memory consumers inside SQL Server.

  • Verify server configuration.

  • Further actions depend on the investigation: check for workload; possible design issues; other resource bottlenecks.


  • Increase swap file size.

  • Check for major physical memory consumers and follow steps of external physical memory pressure.

  • Follow steps of internal physical memory pressure.


The following tools and sources of information could be used for

  • Memory related DMVs


  • Performance counters: performance monitor or DMV for SQL Server
    specific object

  • Task Manager

  • Event viewer: application log, system log

Troubleshooting space issues

User objects, internal objects, and version storage can all cause space
issues in tempdb. In this section, we consider how you can
troubleshoot each of these categories.

External virtual memory pressure

You need to determine if page file(s) have enough space to accommodate
current memory allocations. To check this, open Task Manager in
Performance view and check the Commit Charge section.
If Total is close to the Limit, then there exists the potential
that page file space may be running low. Limit indicates the maximum
amount of memory that can be committed without extending page file
space. Note that the Commit Charge Total in Task Manager indicates
the potential for page file use, not the actual use. Actual use of the
page file will increase under physical memory pressure.

Equivalent information can be obtained from the following
counters: Memory: Commit LimitPaging File: %UsagePaging
File: %Usage Peak

You can roughly estimate the amount of memory that is paged out per
process by subtracting the value of Process: Working Set from
the Process Private Bytes counters.

If Paging File: %Usage Peak (or Peak Commit Charge) is high, check
the System Event Log for events indicating page file growth or
notifications of “running low on virtual memory”. You may need to
increase the size of your page file(s). High Paging File:
 indicates a physical memory over commitment and should be
considered together with external physical memory pressure (large
consumers, adequate amount of RAM installed).

Appendix A: DBCC MEMORYSTATUS Description

There is some information that is primarily available by using the DBCC
MEMORYSTATUS command. However, some of this information is also
available using the dynamic management views (DMVs).

SQL Server 2000 DBCC MEMORYSTATUS is described at


SQL Server 2005 DBCC MEMORYSTATUS command is described at



User objects

Since user objects are not owned by any specific sessions, you need to
understand the specifications of the application that created them and
adjust the tempdb size requirements accordingly. You can find the
space used by individual user objects by executing
exec sp_spaceused @objname='<user-object>'. For example, you can run
the following script to enumerate all the tempdb objects.

DECLARE userobj_cursor CURSOR FOR  
     sys.schemas.name + '.' + sys.objects.name  
from sys.objects, sys.schemas 
where object_id > 100 and  
      type_desc = 'USER_TABLE'and  
      sys.objects.schema_id = sys.schemas.schema_id 

open userobj_cursor 

declare @name varchar(256) 
fetch userobj_cursor into @name 
while (@@FETCH_STATUS = 0)  
    exec sp_spaceused @objname = @name 
        fetch userobj_cursor into @name     
close userobj_cursor

I/O Bottlenecks

SQL Server performance depends heavily on the I/O subsystem. Unless your
database fits into physical memory, SQL Server constantly brings
database pages in and out of the buffer pool. This generates substantial
I/O traffic. Similarly, the log records need to be flushed to the disk
before a transaction can be declared committed. And finally, SQL Server
uses tempdbfor various purposes such as to store intermediate
results, to sort, to keep row versions and so on. So a good I/O
subsystem is critical to the performance of SQL Server.

Access to log files is sequential except when a transaction needs to be
rolled back while access to data files, including tempdb, is
randomly accessed. So as a general rule, you should have log files on a
separate physical disk than data files for better performance. The focus
of this paper is not how to configure your I/O devices but to describe
ways to identify if you have I/O bottleneck. Once an I/O bottleneck is
identified, you may need to reconfigure your I/O subsystem.

If you have a slow I/O subsystem, your users may experience performance
problems such as slow response times, and tasks that abort due to

You can use the following performance counters to identify I/O
bottlenecks. Note, these AVG values tend to be skewed (to the low side)
if you have an infrequent collection interval. For example, it is hard
to tell the nature of an I/O spike with 60-second snapshots. Also, you
should not rely on one counter to determine a bottleneck; look for
multiple counters to cross check the validity of your findings.

  • PhysicalDisk Object: Avg. Disk Queue Length represents the
    average number of physical read and write requests that were queued
    on the selected physical disk during the sampling period. If your
    I/O system is overloaded, more read/write operations will be
    waiting. If your disk queue length frequently exceeds a value of 2
    during peak usage of SQL Server, then you might have an I/O

  • Avg. Disk Sec/Read is the average time, in seconds, of a read of
    data from the disk. Any number

    Less than 10 ms - very good 
    Between 10 - 20 ms - okay 
    Between 20 - 50 ms - slow, needs attention 
    Greater than 50 ms – Serious I/O bottleneck
  • Avg. Disk Sec/Write is the average time, in seconds, of a write
    of data to the disk. Please refer to the guideline in the previous

  • Physical Disk: %Disk Time is the percentage of elapsed time that
    the selected disk drive was busy servicing read or write requests. A
    general guideline is that if this value is greater than 50 percent,
    it represents an I/O bottleneck.

  • Avg. Disk Reads/Sec is the rate of read operations on the disk.
    You need to make sure that this number is less than 85 percent of
    the disk capacity. The disk access time increases exponentially
    beyond 85 percent capacity.

  • Avg. Disk Writes/Sec is the rate of write operations on the
    disk. Make sure that this number is less than 85 percent of the disk
    capacity. The disk access time increases exponentially beyond 85
    percent capacity.

When using above counters, you may need to adjust the values for RAID
configurations using the following formulas.

Raid 0 -- I/Os per disk = (reads + writes) / number of disks 
Raid 1 -- I/Os per disk = [reads + (2 * writes)] / 2 
Raid 5 -- I/Os per disk = [reads + (4 * writes)] / number of disks 
Raid 10 -- I/Os per disk = [reads + (2 * writes)] / number of disks

For example, you have a RAID-1 system with two physical disks with the
following values of the counters.

Disk Reads/sec            80 
Disk Writes/sec           70 
Avg. Disk Queue Length    5

In that case, you are encountering (80 + (2 * 70))/2 = 110 I/Os per
disk and your disk queue length = 5/2 = 2.5 which indicates a border
line I/O bottleneck.

You can also identify I/O bottlenecks by examining the latch waits.
These latch waits account for the physical I/O waits when a page is
accessed for reading or writing and the page is not available in the
buffer pool. When the page is not found in the buffer pool, an
asynchronous I/O is posted and then the status of the I/O is checked. If
I/O has already completed, the worker proceeds normally. Otherwise, it
waits on PAGEIOLATCH_EX or PAGEIOLATCH_SH, depending upon the type of
request. The following DMV query can be used to find I/O latch wait

Select  wait_type,  
from    sys.dm_os_wait_stats   
where    wait_type like 'PAGEIOLATCH%'   
order by wait_type 

wait_type       waiting_tasks_count  wait_time_ms   signal_wait_time_ms 
PAGEIOLATCH_DT  0                    0                    0 
PAGEIOLATCH_EX  1230                 791                  11 
PAGEIOLATCH_KP  0                    0                    0 
PAGEIOLATCH_NL  0                    0                    0 
PAGEIOLATCH_SH  13756                7241                 180 
PAGEIOLATCH_UP  80                   66                   0

Here the latch waits of interest are the underlined ones. When the I/O
completes, the worker is placed in the runnable queue. The time between
I/O completions until the time the worker is actually scheduled is
accounted under the signal_wait_time_ms column. You can identify an
I/O problem if your waiting_task_counts and wait_time_ms deviate
significantly from what you see normally. For this, it is important to
get a baseline of performance counters and key DMV query outputs when
SQL Server is running smoothly. These wait_types can indicate whether
your I/O subsystem is experiencing a bottleneck, but they do not provide
any visibility on the physical disk(s) that are experiencing the

You can use the following DMV query to find currently pending I/O
requests. You can execute this query periodically to check the health of
I/O subsystem and to isolate physical disk(s) that are involved in the
I/O bottlenecks.

from    sys.dm_io_virtual_file_stats(NULL, NULL)t1, 
        sys.dm_io_pending_io_requests as t2 
where    t1.file_handle = t2.io_handle

A sample output is as follows. It shows that on a given database, there
are three pending I/Os at this moment. You can use
the database_id and file_id to find the physical disk the files are
mapped to. The io_pending_ms_ticks represent the total time individual
I/Os are waiting in the pending queue.

Database_id File_Id io_stall io_pending_ms_ticks scheduler_address 
6           1        10804        78            0x0227A040 
6           1        10804        78            0x0227A040 
6           2        101451       31            0x02720040
Virtual address space and physical memory

In Microsoft Windows®, each process has its own virtual address space
(VAS). The set of all virtual addresses available for process use
constitutes the size of the VAS. The size of the VAS depends on the
architecture (32- or 64-bit) and the operating system. In the context of
troubleshooting, it is important to understand that virtual address
space is a consumable memory resource and an application can run out of
it even on a 64-bit platform while physical memory may still be

For more information about virtual address space, see “Process Address
Space” in SQL Server Books Online and the article called Virtual
on MSDN.

Overall performance effect of blocking using SQL waits

SQL Server 2000 provides 76 wait types for reporting waits.
SQL Server 2005 provides over 100 additional wait types for tracking
application performance. Any time a user connection is waiting,
SQL Server accumulates wait time. For example, the application requests
resources such as I/O, locks, or memory and can wait for the resource to
be available. This wait information is summarized and categorized across
all connections so that a performance profile can be obtained for a
given workload. Thus, SQL wait types identify and categorize user (or
thread) waits from an application workload or user perspective.

This query lists the top 10 waits in SQL Server. These waits are
cumulative but you can reset them using DBCC SQLPERF
([sys.dm_os_wait_stats], clear).

select top 10 * 
from sys.dm_os_wait_stats  
order by wait_time_ms desc

Following is the output. A few key points to notice are:

  • Some waits are normal such as the waits encountered by background
    threads such as lazy writer.

  • Some sessions waited a long time to get a SH lock.

  • The signal wait is the time between when a worker has been granted
    access to the resource and the time it gets scheduled on the CPU. A
    long signal wait may imply high CPU contention.

    wait_type     waiting_tasks_count  wait_time_ms     max_wait_time_ms  
    ------------------ -------------------- -------------------- ------------- 
    ------- ------- 
    LAZYWRITER_SLEEP      415088               415048437            1812       
    SQLTRACE_BUFFER_FLUSH 103762               415044000            4000       
    LCK_M_S               6                    25016812             23240921   
    WRITELOG              7413                 86843                187        
    LOGMGR_RESERVE_APPEND 82                   82000                1000       
    SLEEP_BPOOL_FLUSH     4948                 28687                31         
    LCK_M_X               1                    20000                20000      
    PAGEIOLATCH_SH        871                  11718                140        
    PAGEIOLATCH_UP        755                  9484                 187        
    IO_COMPLETION         636                  7031                 203        

To analyze the wait states, you need to poll the data periodically and
then analyze it later. Appendix B provides the following two sample
stored procedures.

  • Track_waitstats. Collects the data for the desired number of
    samples and interval between the samples. Here is a sample

    exec dbo.track_waitstats @num_samples=6 
  • Get_waitstats. Analyzes the data collected in the previous
    steps. Here is a sample invocation.

    exec [dbo].[get_waitstats_2005]
    • Spid is running. It then needs an resource that is currently
      unavailable. Since the resource is not available, it moves to
      the resource wait list at time T0.

    • A signal indicates that the resource is available, so spid moves
      to runnable queue at time T1.

    • Spid awaits running status until T2 as cpu works its way through
      runnable queue in order of arrival.

You can use these stored procedures to analyze the resource waits and
signal waits and use this information to isolate the resource

Figure 5 shows a sample report.

88bf必发娱乐 6.gif).gif)

Figure 5:  Wait Statistics Analysis Report

The sample Waitstats Analysis Report in Figure 5 indicates a performance
problem due to blocking (LCK_M_S) and memory allocation
(RESOURCE_SEMAPHORE). Specifically 55% of all waits are for shared
locks while 43% are due to memory requests. An analysis of blocking per
object will identify the principal points of contention.

Internal physical memory pressure

As internal memory pressure is set by SQL Server itself, a logical step
is to look at the memory distribution inside SQL Server by checking for
any anomalies in buffer distribution. Normally, the buffer pool accounts
for the most of the memory committed by SQL Server. To determine the
amount of memory that belongs to the buffer pool, we can take a look at
the DBCC MEMORYSTATUS output. In the Buffer Counts section, look for
the Target value. The following shows part of DBCC MEMORYSTATUS output
after the server has reached its normal load.

Buffer Counts                  Buffers 
------------------------------ -------------------- 
Committed                      201120 
Target                         201120 
Hashed                         166517 
Reserved Potential             143388 
Stolen Potential               173556 
External Reservation           0 
Min Free                       256 
Visible                        201120 
Available Paging File          460640

Target is computed by SQL Server as the number of 8-KB pages it can
commit without causing paging. Target is recomputed periodically and
in response to memory low/high notifications from Windows. A decrease in
the number of target pages on a normally loaded server may indicate
response to an external physical memory pressure.

If SQL Server consumed a lot of memory (as determined by Process:
Private Bytes 
or the Mem Usage column in Task Manager), see if
the Target count amounts for a significant portion of the memory. Note
that if AWE is enabled, you have to account for AWE allocated memory
either from sys.dm_os_memory_clerks or DBCC MEMORYSTATUS output.

Consider the example shown above (AWE not enabled), Target * 8 KB =
1.53 GB, while the Process: Private Bytes for the server is
approximately 1.62 GB or the Buffer Pool target accounts for 94% of the
memory consumed by SQL Server. Note that if the server is not
loaded, Target is likely to exceed the amount reported by Process:
Private Bytes
 performance counter, which is normal.

If Target is low, but the server Process: Private Bytes or
the Mem Usage in Task Manager is high, we might be facing internal
memory pressure from components that use memory from outside the buffer
pool. Components that are loaded into the SQL Server process, such as
COM objects, linked servers, extended stored procedures, SQLCLR and
others, contribute to memory consumption outside of the buffer pool.
There is no easy way to track memory consumed by these components
especially if they do not use SQL Server memory interfaces.

Components that are aware of the SQL Server memory management mechanisms
use the buffer pool for small memory allocations. If the allocation is
bigger than 8 KB, these components use memory outside of the buffer pool
through the multi-page allocator interface.

Following is a quick way to check the amount of memory that is consumed
through the multi-page allocator.

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

-- amount of mem allocated though multipage  
allocator interface select sum(multi_pages_kb) 
from sys.dm_os_memory_clerks

You can get a more detailed distribution of memory allocated through the
multi-page allocator as:

    type, sum(multi_pages_kb)  
    multi_pages_kb != 0  
group by type 
------------------------------------------ --------- 
MEMORYCLERK_SQLSTORENG                     56 
MEMORYCLERK_SQLOPTIMIZER                   48 
MEMORYCLERK_SQLGENERAL                     2176 
MEMORYCLERK_SOSNODE                        16288 
CACHESTORE_STACKFRAMES                     16 
MEMORYCLERK_SNI                            32

If a significant amount of memory is allocated through the multi-page
allocator (100-200 MB or more), further investigation is warranted.

If you are seeing large amounts of memory allocated through the
multi-page allocator, check the server configuration and try to
determine the components that consume most of the memory by using the
previous or the following query.

If Target is low but percentage-wise it accounts for most of the
memory consumed by SQL Server, look for sources of the external memory
pressure as described in the previous subsection (External Physical
or check the server memory configuration parameters.

If you have the max server memory and/or min server
 options set, you should compare your target against these
values. The max server memory option limits the maximum amount of
memory consumed by the buffer pool, while the server as a whole can
still consume more. The min server memory option tells the server
not to release buffer pool memory below the setting. If Target is less
than the min server memory setting and the server is under load,
this may indicate that the server operates under the external memory
pressure and was never able to acquire the amount specified by this
option. It may also indicate the pressure from internal components, as
described above. Target count cannot exceed the max server
 option setting.

First, check for stolen pages count from DBCC MEMORYSTATUS output.

Buffer Distribution            Buffers 
------------------------------ ----------- 
Stolen                         32871 
Free                           17845 
Cached                         1513 
Database (clean)               148864 
Database (dirty)               259 
I/O                            0 
Latched                        0

A high percentage (>75-80%) of stolen pages relative to target (see
the previous fragments of the output) is an indicator of the internal
memory pressure.

More detailed information about memory allocation by the server
components can be assessed by using
the sys.dm_os_memory_clerks DMV.

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

-- amount of memory consumed by components 
outside the Buffer pool  
-- note that we exclude single_pages_kb as 
they come from BPool 
-- BPool is accounted for by the next query 
        + virtual_memory_committed_kb 
        + shared_memory_committed_kb) as 
[Overall used w/o BPool, Kb] 

-- amount of memory consumed by BPool 
-- note that currenlty only BPool uses AWE 
        + virtual_memory_committed_kb 
        + shared_memory_committed_kb 
        + awe_allocated_kb) as [Used by BPool with AWE, Kb] 

Detailed information per component can be obtained as follows. (This
includes memory allocated from buffer pool as well as outside the buffer

Note: Some parts of the code snippet presented in the following
table have been displayed in multiple lines only for better readability.
These should be entered in a single line.

declare @total_alloc bigint  
declare @tab table ( 
    type nvarchar(128) collate database_default  
    ,allocated bigint 
    ,virtual_res bigint 
    ,virtual_com bigint 
    ,awe bigint 
    ,shared_res bigint 
    ,shared_com bigint 
    ,topFive nvarchar(128) 
    ,grand_total bigint 
-- note that this total excludes buffer pool  
committed memory as it represents the largest 
consumer which is normal 
    @total_alloc =  
            + multi_pages_kb  
                THEN virtual_memory_committed_kb  
                ELSE 0 END)  
            + shared_memory_committed_kb) 
    'Total allocated (including from Buffer Pool): '  
    + CAST(@total_alloc as varchar(10)) + ' Kb' 
insert into @tab 
    ,sum(single_pages_kb + multi_pages_kb) as allocated 
    ,sum(virtual_memory_reserved_kb) as vertual_res 
    ,sum(virtual_memory_committed_kb) as virtual_com 
    ,sum(awe_allocated_kb) as awe 
    ,sum(shared_memory_reserved_kb) as shared_res  
    ,sum(shared_memory_committed_kb) as shared_com 
    ,case  when  ( 
            + multi_pages_kb  
                THEN virtual_memory_committed_kb  
                ELSE 0 END)  
            + shared_memory_committed_kb))/ 
            (@total_alloc + 0.0)) >= 0.05  
          then type  
          else 'Other'  
    end as topFive 
        + multi_pages_kb  
            THEN virtual_memory_committed_kb  
            ELSE 0 END)  
        + shared_memory_committed_kb)) as grand_total  
group by type 
order by (sum(single_pages_kb + multi_pages_kb 
+ (CASE WHEN type <>  
virtual_memory_committed_kb ELSE 0 END) +  
shared_memory_committed_kb)) desc 
select  * from @tab

Note that the previous query treats Buffer Pool differently as it
provides memory to other components via a single-page allocator. To
determine the top ten consumers of the buffer pool pages (via a
single-page allocator) you can use the following query.

-- top 10 consumers of memory from BPool 
    top 10 type,  
    sum(single_pages_kb) as [SPA Mem, Kb] 
group by type  
order by sum(single_pages_kb) desc

You do not usually have control over memory consumption by internal
components. However, determining which components consume most of the
memory will help narrow down the investigation of the problem.

System Monitor (Perfmon)

You can also check the following counters for signs of memory pressure
(see SQL Server Books Online for detailed description):

SQL Server: Buffer Manager object

  • Low Buffer cache hit ratio

  • Low Page life expectancy

  • High number of Checkpoint pages/sec

  • High number Lazy writes/sec

Insufficient memory and I/O overhead are usually related bottlenecks.
See I/O
.aspx#_I/O_Bottlenecks) in
this paper.


Inefficient query plans are usually detected comparatively. An
inefficient query plan may cause increased CPU consumption.

The query against sys.dm_exec_query_stats is an efficient way to
determine which query is using the most cumulative CPU.

    (select top 50  
        sys.dm_exec_query_stats qs 
    order by qs.total_worker_time desc) as highest_cpu_queries 
    cross apply sys.dm_exec_sql_text(plan_handle) as q 
order by highest_cpu_queries.total_worker_time desc

Alternatively, query against sys.dm_exec_cached_plans by using
filters for various operators that may be CPU intensive, such as ‘%Hash
Match%’, ‘%Sort%’ to look for suspects.


When you have identified an I/O bottleneck, you can address it by doing
one or more of the following:

  • Check the memory configuration of SQL Server. If SQL Server has been
    configured with insufficient memory, it will incur more I/O
    overhead. You can examine following counters to identify memory

    • Buffer Cache hit ratio

    • Page Life Expectancy

    • Checkpoint pages/sec

    • Lazywrites/sec

    For more information on the memory pressure, see Memory

  • Increase I/O bandwidth.

    • Add more physical drives to the current disk arrays and/or
      replace your current disks with faster drives. This helps to
      boost both read and write access times. But don’t add more
      drives to the array than your I/O controller can support.

    • Add faster or additional I/O controllers. Consider adding more
      cache (if possible) to your current controllers.

  • Examine execution plans and see which plans lead to more I/O being
    consume. It is possible that a better plan (for example, index) can
    minimize I/O. If there are missing indexes, you may want to run
    Database Engine Tuning Advisor to find missing indexes

    The following DMV query can be used to find which batches/requests
    are generating the most I/O. You will notice that we are not
    accounting for physical writes. This is ok if you consider how
    databases work. The DML/DDL statements within a request do not
    directly write data pages to disk. Instead, the physical writes of
    pages to disks is triggered by statements only by committing
    transactions. Usually physical writes are done by either by
    Checkpoint or by the SQL Server lazy writer. A DMV query like the
    following can be used to find the top five requests that generate
    the most I/Os. Tuning those queries so that they perform fewer
    logical reads can relieve pressure on the buffer pool. This allows
    other requests to find the necessary data in the buffer pool in
    repeated executions (instead of performing physical I/O). Hence,
    overall system performance is improved.

    select top 5  
        (total_logical_reads/execution_count) as avg_logical_reads, 
        (total_logical_writes/execution_count) as avg_logical_writes, 
        (total_physical_reads/execution_count) as avg_phys_reads, 
        statement_start_offset as stmt_start_offset,  
    from sys.dm_exec_query_stats   
    order by  
     (total_logical_reads + total_logical_writes) Desc

    You can, of course, change this query to get different views on the
    data. For example, to generate the top five requests that generate
    most I/Os in single execution, you can order by:

        (total_logical_reads +

    Alternatively, you may want to order by physical I/Os and so on.
    However, logical read/write numbers are very helpful in determining
    whether or not the plan chosen by the query is optimal. For example,
    it may be doing a table scan instead of using an index. Some
    queries, such as those that use nested loop joins may have high
    logical counters but be more cache-friendly since they revisit the
    same pages.

    Example: Let us take the following two batches consisting of two
    SQL queries where each table has 1000 rows and rowsize > 8000
    (one row per page).


    from t1 INNER HASH JOIN t2 ON t1.c1 = t2.c4 
    order by c2 
    select * from t1

    For the purpose of this example, before running the DMV query, we
    clear the buffer pool and the procedure cache by running the
    following commands.

    dbcc freeproccache 
    dbcc dropcleanbuffers

    Here is the output of the DMV query. You will notice two rows
    representing the two batches.

    Avg_logical_reads Avg_logical_writes Avg_phys_reads Execution_count  
    2794                1                385                1                  
    1005                0                0                  1                  
    sql_handle                                         plan_handle 

    You will notice that the second batch only incurs logical reads but
    no physical I/O. This is because the data it needs was already
    cached by the first query (assuming there was sufficient memory).

    You can get the text of the query by running the following query.

    select text  
    from sys.dm_exec_sql_text( 
    Here is the output. 
    from t1 INNER HASH JOIN t2 ON t1.c1 = t2.c4 
    order by c2

    You can also find out the string for the individual statement by
    executing the following:

                  (<statement_end_offset> -<statement_start_offset>)/2)   
    from sys.dm_exec_sql_text                 

    The value
    of statement_start_offest and statement_end_offset need to be
    divided by two in order to compensate for the fact that SQL Server
    stores this kind of data in Unicode. Astatement_end_offset value
    of -1, indicates that the statement does go up to the end of the
    batch. However the substring() function does not accommodate -1
    as a valid value. Instead of using -1
    as (<statement_end_offset> -<statement_start_offset>)/2, one
    should enter the value 64000, which should make sure that the
    statement is covered in all cases. With this method, a long-running
    or resource-consuming statement can be filtered out of a large
    stored procedure or batch.

    Similarly, you can run the following query to find to the query plan
    to identify if the large number of I/Os is a result of a poor plan

    select *  
    from sys.dm_exec_query_plan  



Many customers can experience an occasional slow down of their
SQL Server database. The reasons can range from a poorly designed
database to a system that is improperly configured for the workload. As
an administrator, you want to proactively prevent or minimize problems
and, when they occur, diagnose the cause and, when possible, take
corrective actions to fix the problem. This white paper limits its scope
to the problems commonly seen by Customer Support Services (CSS or PSS)
at Microsoft® Corporation since an exhaustive analysis of all possible
problems is not feasible. We provide step-by-step guidelines for
diagnosing and troubleshooting common performance problems by using
publicly available tools such as SQL Server Profiler, System Monitor
(Perfmon), and the new Dynamic Management Views in Microsoft
SQL Server™ 2005.



There can be many reasons for a slowdown in SQL Server. We use the
following three key symptoms to start diagnosing problems.

  • Resource bottlenecks: CPU, memory, and I/O bottlenecks are
    covered in this paper. We do not consider network issues. For each
    resource bottleneck, we describe how to identify the problem and
    then iterate through the possible causes. For example, a memory
    bottleneck can lead to excessive paging that ultimately impacts

  • Tempdb bottlenecks: Since there is only one tempdb for each
    SQL Server instance, this can be a performance and a disk space
    bottleneck. A misbehaving application can overload tempdb both
    in terms of excessive DDL/DML operations and in space. This can
    cause unrelated applications running on the server to slow down or

  • A slow running user query: The performance of an existing query
    may regress or a new query may appear to be taking longer than
    expected. There can be many reasons for this. For example:

    • Changes in statistical information can lead to a poor query plan
      for an existing query.

    • Missing indexes can force table scans and slow down the query.

    • An application can slow down due to blocking even if resource
      utilization is normal.

    Excessive blocking, for example, can be due to poor application or
    schema design or choosing an improper isolation level for the

The causes of these symptoms are not necessarily independent of each
other. The poor choice of a query plan can tax system resources and
cause an overall slowdown of the workload. So, if a large table is
missing a useful index, or the query optimizer decides not to use it,
this not only causes the query to slow down but it also puts heavy
pressure on the I/O subsystem to read the unnecessary data pages and on
the memory (buffer pool) to store these pages in the cache. Similarly,
excessive recompilation of a frequently running query can put pressure
on the CPU.


Tools for resolving resource bottlenecks

One or more of the following tools are used to resolve a particular
resource bottleneck.

  • System Monitor (PerfMon): This tool is available as part of
    Windows. For more information, please see the System Monitor

  • SQL Server Profiler: See SQL Server Profiler in
    the Performance Tools group in the SQL Server 2005 program

  • DBCC commands: See SQL Server Books Online and Appendix A for

  • DMVs: See SQL Server Books Online for details.


Version store

SQL Server 2005 provides a row versioning framework that is used to
implement new and existing features. Currently, the following features
use row versioning framework. For more information about the following
features, see SQL Server Books Online.

  • Triggers

  • MARS

  • Online index

  • Row versioning-based isolation levels: requires setting an option at
    database level

Row versions are shared across sessions. The creator of the row version
has no control over when the row version can be reclaimed. You will need
to find and then possibly kill the longest running transaction that is
preventing the row version cleanup.

The following query returns the top two longest running transactions
that depend on the versions in the version store.

select top 2  
from sys.dm_tran_active_snapshot_database_transactions 
order by elapsed_time_seconds DESC

Here is a sample output that shows that a transaction with XSN 3 and
Transaction ID 8609 has been active for 6523 seconds.

transaction_id       transaction_sequence_num elapsed_time_seconds 
-------------------- ------------------------ -------------------- 
8609                 3                        6523 
20156                25                       783

Since the second transaction has been active for a relatively short
period, you can possibly free up a significant amount of version store
by killing the first transaction. However, there is no way to estimate
how much version space will be freed up by killing this transaction. You
may need to kill few a more transactions to free up significant space.

You can mitigate this problem by either sizing your tempdb properly
to account for the version store or by eliminating, where possible, long
running transactions under snapshot isolation or long running queries
under read-committed-snapshot. You can roughly estimate the size of the
version store that is needed by using the following formula. (A factor
of two is needed to account for the worst-case scenario, which occurs
when the two longest running transactions overlap.)

[Size of version store] = 2 * [version store data generated per minute] *  
[longest running time (minutes) of the transaction]

In all databases that are enabled for row versioning based isolation
levels, the version store data generated per minute for a transaction is
about the same as log generated per minute. However. there are some
exceptions: only differences are logged for updates; and a newly
inserted data row is not versioned but may be logged depending if it is
a bulk-logged operation and the recovery mode is not set to full

You can also use the Version Generation Rate and Version Cleanup
 performance counters to fine tune your computation. If
your Version Cleanup Rate is 0, this implies that there is a long
running transaction that is preventing the version store cleanup.

Incidentally, before generating an out of tempdb space error, SQL
Server 2005 makes a last ditch attempt by forcing the version stores to
shrink. During the shrink process, the longest running transactions that
have not yet generated any row versions are marked as victims. This
frees up the version space used by them. Message 3967 is generated in
the error log for each such victim transaction. If a transaction is
marked as a victim, it can no longer read the row versions in the
version store or create new ones. Message 3966 is generated and the
transaction is rolled back when the victim transaction attempts to read
row versions. If the shrink of the version store succeeds, then more
space is available in tempdb. Otherwise,tempdb runs out of

Summary: It is not uncommon to experience the occasional slow down
of a SQL Server database. A poorly designed database or a system that is
improperly configured for the workload are but several of many possible
causes of this type of performance problem. Administrators need to
proactively prevent or minimize problems and, when they occur, diagnose
the cause and take corrective actions to fix the problem. This paper
provides step-by-step guidelines for diagnosing and troubleshooting
common performance problems by using publicly available tools such as
SQL Server Profiler, System Monitor, and the new Dynamic Management
Views in SQL Server 2005.

On This Page

B: Blocking

Applies To: SQL Server 2005


Tempdb globally stores both internal and user objects and the
temporary tables, objects, and stored procedures that are created during
SQL Server operation.

There is a single tempdb for each SQL Server instance. It can be a
performance and disk space bottleneck. The tempdb can become
overloaded in terms of space available and excessive DDL/DML operations.
This can cause unrelated applications running on the server to slow down
or fail.

Some of the common issues with tempdb are as follows:

  • Running out of storage space in tempdb.

  • Queries that run slowly due to the I/O bottleneck in tempdb.
    This is covered under I/O

  • Excessive DDL operations leading to a bottleneck in the system

  • Allocation contention.

Before we start diagnosing problems with tempdb, let us first look
at how the space in tempdb is used. It can be grouped into four main

User objects

These are explicitly created by user sessions and are tracked in system catalog. They include the following:

  • Table and index.

  • Global temporary table (##t1) and index.

  • Local temporary table (#t1) and index.

    • Session scoped.

    • Stored procedure scoped in which it was created.

  • Table variable (@t1).

    • Session scoped.

    • Stored procedure scoped in which it was created.

Internal objects

These are statement scoped objects that are created and destroyed by SQL Server to process queries. These are not tracked in the system catalog. They include the following:

  • Work file (hash join)

  • Sort run

  • Work table (cursor, spool and temporary large object data type (LOB) storage)

As an optimization, when a work table is dropped, one IAM page and an extent is saved to be used with a new work table.

There are two exceptions; the temporary LOB storage is batch scoped and cursor worktable is session scoped.

Version Store

This is used for storing row versions. MARS, online index, triggers and snapshot-based isolation levels are based on row versioning. This is new in SQL Server 2005.

Free Space

This represents the disk space that is available in tempdb.

The total space used by tempdb equal to the User Objects plus the
Internal Objects plus the Version Store plus the Free Space.

This free space is same as the performance counter free space
in tempdb.

Appendix B: Blocking Scripts

This appendix provides the source listing of the stored procedures
referred to in this white paper. You can use these stored procedures as
they are or tailor them to suit your needs.


create proc dbo.sp_block (@spid bigint=NULL) 
-- This stored procedure is provided "AS IS" with no warranties, and  
-- confers no rights.  
-- Use of included script samples are subject to the terms specified at  
-- http://www.microsoft.com/info/cpyright.htm 
-- T. Davidson 
-- This proc reports blocks 
--    1. optional parameter @spid  

    'blk object' = t1.resource_associated_entity_id, 
    sys.dm_tran_locks as t1,  
    sys.dm_os_waiting_tasks as t2 
    t1.lock_owner_address = t2.resource_address and 
    t1.request_session_id = isnull(@spid,t1.request_session_id)


The primary goal of this paper is to provide a general methodology for
diagnosing and troubleshooting SQL Server performance problems in common
customer scenarios by using publicly available tools.

SQL Server 2005 has made great strides in supportability. The kernel
layer (SQL-OS) has been re-architected and internal structures and
statistical data are exposed as relational rowsets through dynamic
management views (DMVs). SQL Server 2000 exposes some of this
information though system tables such as sysprocesses, but sometimes
you need to generate a physical dump of the SQL Server process memory to
extract relevant information from internal structures. There are two
main issues with this. First, customers cannot always provide the
physical dump due to the size of the dump and the time it takes to
create it. Second, it can take longer to diagnose the problem because
the files must generally be transmitted to Microsoft Corporation for

This brings us to the secondary goal of this paper, which is to showcase
DMVs. DMVs can expedite the diagnosis process by eliminating the need to
generate and analyze physical dumps in most cases. This paper provides,
when possible, a side-by-side comparison of troubleshooting the same
problem in SQL Server 2000 and in SQL Server 2005. DMVs provide a
simplified and familiar relational interface for getting critical system
information. This information can be used for monitoring purposes to
alert administrators to any potential problems. Or, the information can
be polled and collected periodically for detailed analysis later.


Resource Bottlenecks

The next sections of this paper discuss the CPU, memory, and I/O
subsystem resources and how these can become bottlenecks. (Network
issues are outside of the scope of this paper.) For each resource
bottleneck, we describe how to identify the problem and then iterate
through the possible causes. For example, a memory bottleneck can lead
to excessive paging, which can ultimately impact performance.

Before you can determine if you have a resource bottleneck, you need to
know how resources are used under normal circumstances. You can use the
methods outlined in this paper to collect baseline information about the
use of the resource (when you are not having performance problems).

You might find that the problem is a resource that is running near
capacity and that SQL Server cannot support the workload in its current
configuration. To address this issue, you may need to add more
processing power, memory, or increase the bandwidth of your I/O or
network channel. But, before you take that step, it is useful to
understand some common causes of resource bottlenecks. There are
solutions that do not require adding additional resources as, for
example, reconfiguration.

Excessive DDL and allocation operations

Two sources of contention in tempdb can result in the following

  • Creating and dropping large number of temporary tables and table
    variables can cause contention on metadata. In SQL Server 2005,
    local temporary tables and table variables are cached to minimize
    metadata contention. However, the following conditions must be
    satisfied, otherwise the table is not cached.

    • No named constraints on the table.

    • No DDL on the table after the creating statement (for example,

  • Typically, most temporary/work tables are heaps; therefore, an
    insert, delete,  or drop operation can cause heavy contention on
    Page Free Space (PFS) pages. If most of these tables are under 64 KB
    and use mixed extent for allocation or deal location, this can put
    heavy contention on Shared Global Allocation Map (SGAM) pages.
    SQL Server 2005 caches one data page and one IAM page for local
    temporary tables to minimize allocation contention. This caching was
    already done for work tables starting with SQL Server 2000.

Since SGAM and PFS pages occur at fixed intervals in data files, it is
easy to find their resource description. So, for example, 2:1:1
represents the first PFS page in the tempdb(database-id = 2,
file-id =1, page-id = 1) and 2:1:3 represents the first SGAM page. SGAM
pages occur after every 511232 pages and each PFS page occurs after
every 8088 pages. You can use this to find all other PFS and SGAM pages
across all files in tempdb. Any time a task is waiting to acquire
latch on these pages, it will show up in sys.dm_os_waiting_tasks.
Since latch waits are transient, you will need to query this table
frequently (about once every 10 seconds) and collect this data for
analysis later. For example, you can use the following query to load all
tasks waiting on tempdb pages into a waiting_tasks table in the
analysis database.

-- get the current timestamp 
declare @now datetime  
select @now = getdate() 

-- insert data into a table for later analysis 
insert into analysis..waiting_tasks 
      from sys.dm_os_waiting_tasks 
      where wait_type like ‘PAGE%LATCH_%’ and 
            resource_description like ‘2:%’

Any time you see tasks waiting to acquire latches on tempdb pages,
you can analyze to see if it is due to PFS or SGAM pages. If it is, this
implies allocation contention in tempdb. If you see contention on
other pages in tempdb, and if you can identify that a page belongs
to the system table, this implies contention due to excessive DDL

You can also monitor the following Perfmon counters for any unusual
increase in the temporary objects allocation/deal location activity.

  • SQL Server:Access Methods\Workfiles Created /Sec

  • SQL Server:Access Methods\Worktables Created /Sec

  • SQL Server:Access Methods\Mixed Page Allocations /Sec

  • SQL Server:General Statistics\Temp Tables Created /Sec

  • SQL Server:General Statistics\Temp Tables for destruction


If you have detected excessive compilation/recompilation, consider the
following options.

  • If the recompile occurred because a SET option changed, use SQL
    Server Profiler to determine which SET option changed. Avoid
    changing SET options within stored procedures. It is better to set
    them at the connection level. Ensure that SET options are not
    changed during the lifetime of the connection.

  • Recompilation thresholds for temporary tables are lower than for
    normal tables. If the recompiles on a temporary table are due to
    statistics changes, you can change the temporary tables to table
    variables. A change in the cardinality of a table variable does not
    cause a recompilation. The drawback of this approach is that the
    query optimizer does not keep track of a table variable’s
    cardinality because statistics are not created or maintained on
    table variables. This can result in nonoptimal query plans. You can
    test the different options and choose the best one.

    Another option is to use the KEEP PLAN query hint. This sets the
    threshold of temporary tables to be the same as that of permanent
    tables. The EventSubclass column indicates that “Statistics
    Changed” for an operation on a temporary table.

  • To avoid recompilations that are due to changes in statistics (for
    example, when the plan becomes suboptimal due to change in the data
    statistics), specify the KEEPFIXED PLAN query hint. With this option
    in effect, recompilations can only happen because of
    correctness-related reasons (for example, when the underlying table
    structure has changed and the plan no longer applies) and not due to
    statistics. An example might be when a recompilation occurs if the
    schema of a table that is referenced by a statement changes, or if a
    table is marked with the sp_recompile stored procedure.

  • Turning off the automatic updates of statistics for indexes and
    statistics that are defined on a table or indexed view prevents
    recompiles that are due to statistics changes on that object. Note,
    however, that turning off the “auto-stats” feature by using this
    method is usually not a good idea. This is because the query
    optimizer is no longer sensitive to data changes in those objects
    and suboptimal query plans might result. Use this method only as a
    last resort after exhausting all other alternatives.

  • Batches should have qualified object names (for example, dbo.Table1)
    to avoid recompilation and to avoid ambiguity between objects.

  • To avoid recompiles that are due to deferred compiles, do not
    interleave DML and DDL or create the DDL from conditional constructs
    such as IF statements.

  • Run Database Engine Tuning Advisor (DTA) to see if any indexing
    changes improve the compile time and the execution time of the

  • Check to see if the stored procedure was created with the WITH
    RECOMPILE option or if the RECOMPILE query hint was used. If a
    procedure was created with the WITH RECOMPILE option, in
    SQL Server 2005, we may be able to take advantage of the statement
    level RECOMPILE hint if a particular statement within that procedure
    needs to be recompiled. This would avoid the necessity of
    recompiling the whole procedure each time it executes, while at the
    same time allowing the individual statement to be compiled. For more
    information on the RECOMPILE hint, see SQL Server Books Online.