Finding the Worst Performing T-SQL Statements on an Instance

One of the
key things to consider when building a successful application is to make sure
the application performance meets the customer’s expectation. As DBAs and Developers,
we need to do everything we can to ensure that the T-SQL code to support an
application is running as efficiently as it can. You should not wait for
customers to complain, but instead you should be proactively monitoring your
applications to make sure the code that is being run is as efficient as
possible. When code is found that uses excessive CPU, I/O or has a long
duration you need to analyze the code to determine if the performance can be
improved. In this article, I will introduce you to some Dynamic Management
Views (DMVs) and Dynamic Management Functions (DMFs) that will help you monitor
your application’s T-SQL performance. In addition, I will provide you with a
Stored Procedure (SP) that will utilize the DMVs and DMFs to produce a report
that will allow you to quickly identify poorly performing T-SQL statements, at
either the instance, or database level.

Using DMVs and DMFs to Identify Poorly Performing T-SQL

Microsoft introduced SQL Server 2005 they provided DBAs and Developer with
Dynamic Management Views (DMVs) and Dynamic Management Functions (DMFs). These
new views provide you with a wealth of information about how your database
instance is running. Using these new objects allows you can look at the
internal workings of SQL Server with simple SELECT statements. There are four
different DMVs and DMFs that when used together allow you to peek under the
covers of the database engine and return resources usage statistics for T-SQL
statements that have executed on your instance. Here is a list of those DMVs
and DMFs: sys.dm_exec_query_stats, sys.dm_exec_cached_plans, sys.dm_exec_sql_text,
and sys.dm_exec_plan_attributes. The sys.dm_exec_query_stats DMV provides
aggregated statistics like I/O, CPU and Elapsed Time for statements within a
cached plan. The sys.dm_exec_cached_plans allows you the ability to identify
how often a cached plan has been used. The T-SQL text of each statement in a
cached plan can be found by using the sys.dm_exec_sql_text DMF. Lastly, the sys.dm_exec_plan_attributes
DMF can be used to identify different attributes of a plan, like DBID.

These DMV’s
and DMFs get their information from within memory. SQL Server keeps statistics
in memory from the time SQL Server starts up until it shuts down. If SQL Server
has not been up very long the statistics these DMVs and DMFs return might not
be a very representative sample of normal types of T-SQL statement executed on
an instance, as it might if SQL Server has been up for a number of days, or
weeks. Also worth noting is that T-SQL execution statistics are only
available for the statements that still have their execution plans in memory.
If a statement is executed and then its execution plan is overwritten, then its
associated statistics are flushed when the execution plan is overwritten.

To further
understand how to use these DMVs and DMFs I will go through a few examples. Let’s
first review this code:

FROM sys.dm_exec_query_stats qs
JOIN sys.dm_exec_cached_plans cp ON qs.plan_handle = cp.plan_handle
CROSS APPLY sys.dm_exec_sql_text(qs.plan_handle) st

If you run
this code against your SQL Server instance, you will be able to see statistics
that sys.dm_exec_query_stats DMV is able to show out of the cached plans like:

  • How many times a
    give execution plan has been executed.
  • The total CPU
    that a given T-SQL statement has consumed for all executions.
  • The total number
    of physical read, logical writes and logical read a given T-SQL statement has used
    for all executions.
  • The total amount
    of elapsed time it has taken to execute a given T-SQL statement.

statistics can help you identify which T-SQL statements are performing poorly
based on CPU, I/O, or elapsed time.

By joining
the sys.dm_exec_query_stats DMV with the sys.dm_exec_cached_plans DMV in the
above query, you are able to identify the type of plan being used like: Proc, Adhoc,
and Prepared. This information will help you identify which T-SQL statements
are coming from Stored Procedures and those that are submitted dynamically or
directly from some applications.

By using the
CROSS APPLY operator in conjunction with the sys.dm_exec_sql_text DMF you can
return the T-SQL batch text associated with an execution plan. Since a batch
can contain multiple T-SQL statements, you can use the offset columns from the sys.dm_exec_query_stats
to identify the actual T-SQL statement within the batch with which the
statistics are associated.

The above
code shows you all the statistics that a SQL Server instance is compiling
across all databases. When you are tuning a specific application, you might
want to narrow down the statistics being displayed to a specific database. You
can do this by adding the sys.dm_exec_plan_attributes DMF to the above T-SQL
code like so:

FROM sys.dm_exec_query_stats qs
JOIN sys.dm_exec_cached_plans cp ON qs.plan_handle = cp.plan_handle
CROSS APPLY sys.dm_exec_sql_text(qs.plan_handle) st
OUTER APPLY sys.dm_exec_plan_attributes(qs.plan_handle) pa
WHERE attribute = ‘dbid’
AND DB_NAME(CAST(pa.value AS int)) = ‘AdventureWorks’

Here I have
used the OUTER APPLY operator along with a WHERE clause to narrow down the
statistics to only those that are associated with statements from within the
“AdventureWorks” database. In reality, the “dbid” attribute from the sys.dm_exec_plan_attribute
DMF contains the DBID in which the session was connected when the T-SQL
statement was executed. If you are using three part naming conventions for
your database objects then the plan attribute dbid value might not be the
actual dbid against which the statements associated with the cached plan was

Now that you
have an understanding of the DMF’s and DMVs that can be used to obtain T-SQL
execution statistics let me show you how I used the information from these
management views to develop a parameter driven SP.


My SP is call “usp_Worst_TSQL”. This SP produces a report of worst T-SQL
statements, by instance or database. Note that this SP uses a new function
“OBJECT_SCHEMA_NAME” that was introduced with SQL Server 2005 SP2. If you
are on an earlier version of SQL Server 2005 you will need to modify the
code in the SP below. By passing optional parameters to this SP, you can focus
in on different types of performance problems, like CPU, I/O or duration. In addition,
you can identify the database and the number of poorly performing T-SQL
statements you want returned. This SP allows DBAs a quick way to return query
statistics using the above mentioned DMV’s and DMF’s without having to remember
how to join these views and functions. Here is the code for my SP:

CREATE PROC [dbo].[usp_Worst_TSQL]
Written by: Gregory A. Larsen
Copyright © 2008 Gregory A. Larsen. All rights reserved.

Name: usp_Worst_TSQL
Description: This stored procedure displays the top worst performing queries based on CPU, Execution Count,
I/O and Elapsed_Time as identified using DMV information. This can be display the worst
performing queries from an instance, or database perspective. The number of records shown,
the database, and the sort order are identified by passing pararmeters.

Parameters: There are three different parameters that can be passed to this procedures: @DBNAME, @COUNT
and @ORDERBY. The @DBNAME is used to constraint the output to a specific database. If
when calling this SP this parameter is set to a specific database name then only statements
that are associated with that database will be displayed. If the @DBNAME parameter is not set
then this SP will return rows associated with any database. The @COUNT parameter allows you
to control the number of rows returned by this SP. If this parameter is used then only the
TOP x rows, where x is equal to @COUNT will be returned, based on the @ORDERBY parameter.
The @ORDERBY parameter identifies the sort order of the rows returned in descending order.
This @ORDERBY parameters supports the following type: CPU, AE, TE, EC or AIO, TIO, ALR, TLR, ALW, TLW, APR, and TPR
where “ACPU” represents Average CPU Usage
“TCPU” represents Total CPU usage
“AE” represents Average Elapsed Time
“TE” represents Total Elapsed Time
“EC” represents Execution Count
“AIO” represents Average IOs
“TIO” represents Total IOs
“ALR” represents Average Logical Reads
“TLR” represents Total Logical Reads
“ALW” represents Average Logical Writes
“TLW” represents Total Logical Writes
“APR” represents Average Physical Reads
“TPR” represents Total Physical Read

Typical execution calls

Top 6 statements in the AdventureWorks database base on Average CPU Usage:
EXEC usp_Worst_TSQL @DBNAME=’AdventureWorks’,@COUNT=6,@ORDERBY=’ACPU’;

Top 100 statements order by Average IO

Show top all statements by Average IO
EXEC usp_Worst_TSQL;

(@DBNAME VARCHAR(128) = ‘<not supplied>’
,@COUNT INT = 999999999
— Check for valid @ORDERBY parameter
@ORDERBY in (‘ACPU’,’TCPU’,’AE’,’TE’,’EC’,’AIO’,’TIO’,’ALR’,’TLR’,’ALW’,’TLW’,’APR’,’TPR’)
THEN 1 ELSE 0 END) = 0)
— abort if invalid @ORDERBY parameter entered
DB_NAME(CAST(pa.value AS INT))+’*’,
‘Resource’) AS [Database Name]
— find the offset of the actual statement being executed
CASE WHEN statement_start_offset = 0
OR statement_start_offset IS NULL
ELSE statement_start_offset/2 + 1 END,
CASE WHEN statement_end_offset = 0
OR statement_end_offset = -1
OR statement_end_offset IS NULL
THEN LEN(text)
ELSE statement_end_offset/2 END –
CASE WHEN statement_start_offset = 0
OR statement_start_offset IS NULL
ELSE statement_start_offset/2 END + 1
) AS [Statement]
,OBJECT_SCHEMA_NAME(st.objectid,dbid) [Schema Name]
,OBJECT_NAME(st.objectid,dbid) [Object Name]
,objtype [Cached Plan objtype]
,execution_count [Execution Count]
,(total_logical_reads + total_logical_writes + total_physical_reads )/execution_count [Average IOs]
,total_logical_reads + total_logical_writes + total_physical_reads [Total IOs]
,total_logical_reads/execution_count [Avg Logical Reads]
,total_logical_reads [Total Logical Reads]
,total_logical_writes/execution_count [Avg Logical Writes]
,total_logical_writes [Total Logical Writes]
,total_physical_reads/execution_count [Avg Physical Reads]
,total_physical_reads [Total Physical Reads]
,total_worker_time / execution_count [Avg CPU]
,total_worker_time [Total CPU]
,total_elapsed_time / execution_count [Avg Elapsed Time]
,total_elapsed_time [Total Elasped Time]
,last_execution_time [Last Execution Time]
FROM sys.dm_exec_query_stats qs
JOIN sys.dm_exec_cached_plans cp ON qs.plan_handle = cp.plan_handle
CROSS APPLY sys.dm_exec_sql_text(qs.plan_handle) st
OUTER APPLY sys.dm_exec_plan_attributes(qs.plan_handle) pa
WHERE attribute = ‘dbid’ AND
CASE when @DBNAME = ‘<not supplied>’ THEN ‘<not supplied>’
DB_NAME(CAST(pa.value AS INT)) + ‘*’,
‘Resource’) END

WHEN @ORDERBY = ‘ACPU’ THEN total_worker_time / execution_count
WHEN @ORDERBY = ‘TCPU’ THEN total_worker_time
WHEN @ORDERBY = ‘AE’ THEN total_elapsed_time / execution_count
WHEN @ORDERBY = ‘TE’ THEN total_elapsed_time
WHEN @ORDERBY = ‘EC’ THEN execution_count
WHEN @ORDERBY = ‘AIO’ THEN (total_logical_reads + total_logical_writes + total_physical_reads) / execution_count
WHEN @ORDERBY = ‘TIO’ THEN total_logical_reads + total_logical_writes + total_physical_reads
WHEN @ORDERBY = ‘ALR’ THEN total_logical_reads / execution_count
WHEN @ORDERBY = ‘TLR’ THEN total_logical_reads
WHEN @ORDERBY = ‘ALW’ THEN total_logical_writes / execution_count
WHEN @ORDERBY = ‘TLW’ THEN total_logical_writes
WHEN @ORDERBY = ‘APR’ THEN total_physical_reads / execution_count
WHEN @ORDERBY = ‘TPR’ THEN total_physical_reads

As you can
see this SP supports three different parameters. The first parameter @DBNAME allows
you to identify the database you want to look at for the worst performing T-SQL
statements. If this parameter is not populated, the SP returns information for
all databases. The second parameter, @COUNT, identifies the number of worst
performing T-SQL statements you want to return. The default number is 999999999, which essentially
identifies all the statistics SQL Sever is retaining in memory. The last
parameter @ORDERBY allows you to return the statistics sorted based on one of
the DMV statistics columns. Look at the comments in the code above to
determine what are acceptable values for the @ORDERBY clause.

Here is a
typical call to this SP:

EXEC usp_Worst_TSQL

particular call will return the top five worst performing T-SQL statements in
the AdventureWorks database based on Average I/O used. You could use this call
to narrow in on statements within the AdventureWorks database that were using
an excessive number of I/O’s on average.

If you
wanted to return the top 100 worse performing commands based on average elapsed
time across all your databases you would call this SP with the following code:

EXEC usp_Worst_TSQL

When you are
doing a performance review of an application or an instance this SP becomes a
good tool to use. By using this SP, you can quickly determine those queries
that are consuming the most resources. Using this kind of information allows
you to quickly narrow in on the T-SQL statements or stored procedures that you
should focus your attention on if you want to improve performance.

Low Impact Performance Monitoring Tool

There is real
value in knowing which queries to optimize when you are doing a performance
tune-up of your application. If you want to identify the poorly performing
T-SQL statements or SPs that are using the most resources then the DMVs and DMFs
identified here is one option for providing this information. Having a simple
tool, like the SP example above will allow you a quick method of determining
which commands are the worst performing based on CPU, I/O or elapsed time. Next
time you have a need to look into the query execution performance consider
using the DMV’s and DMF’s I mentioned for a low impact performance monitoring
tool solution.


See All Articles by Columnist
Gregory A. Larsen

Gregory Larsen
Gregory Larsen
Gregory A. Larsen is a DBA at Washington State Department of Health (DOH). Greg is responsible for maintaining SQL Server and other database management software. Greg works with customers and developers to design and implement database changes, and solve database/application related problems. Greg builds homegrown solutions to simplify and streamline common database management tasks, such as capacity management.
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