Tip

Tracking query execution with SQL Server 2005 Profiler

 
A single transaction in SQL Server is the lowest common denominator for performance tuning. When it comes to capturing and analyzing data at this level, the best native tool is SQL Server Profiler. Analyzing the results from SQL Server Profiler enables

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you to discern a great deal of information about your SQL Server; the collected data reflects the actual processing on the server, not just theory or conjecture.

From a performance-tuning perspective, Profiler gives you the option to filter data and then store it in a table for additional analysis. In a previous tip, SQL Profiler: Features, functions and setup in SQL Server 2005, I outlined the steps in a sequential manner. Here I will focus on how to analyze Profiler results in order to find opportunities for query performance improvements in two common scenarios. The first is based on a single user's issues, and the second establishes key SQL Server query performance issues.

TABLE OF CONTENTS
   SQL Server Profiler results
   Scenario 1: Single user analysis
   Scenario 2: Overall data analysis
   Query optimization

 
SQL Server Profiler results

As SQL Server Profiler captures user transactions, the results are stored in a table with a maximum of 41 columns. Some of the columns have Profiler system-related data, but most have valuable raw data that can be queried and analyzed to determine areas for improvement. Before analyzing the two scenarios, the table below outlines a subset of the columns that are key to the data analysis.

ID Column Data Type Description
1 EventClass int Classification used by Profiler to identify a super class for the row and/or overall transaction
2 TextData ntext T-SQL statements issued
3 ApplicationName nvarchar(128) Registered application in SQL Server
4 NTUserName nvarchar(128) Windows user without the domain
5 LoginName nvarchar(128) The Windows or SQL Server login
6 cpu int CPU usage for the statement or transaction
7 Reads bigint Number of reads performed by the statement or transaction
8 Writes bigint Number of writes performed by the statement or transaction
9 Duration bigint Elapsed time by the statement or transaction
10 SPID int System process identifier in SQL Server
11 StartTime datetime Start time for the statement or transaction
12 EndTime datetime End time for the statement or transaction
13 DatabaseID int Database identifier where the transaction or statement is performed, which is the value from the sys.databases.database_id view
14 ObjectID int Database object identifier for the transaction or statement, which is the value from the sys.all_objects view
15 DatabaseName nvarchar(128) Database name where the transaction or statement is being performed, which is derived from the sys.databases view
16 HostName nvarchar(128) Host name where the transaction or statement is originating
17 NTDomainName nvarchar(128) Domain name where the transaction is originating
18 ServerName nvarchar(128) SQL Server name and instance name
19 IndexID int Index identifier used for the table
20 RowCounts bigint Number of rows returned by the statement or transaction

 
Scenario 1: Single user analysis

A common scenario is a user or department of users experiencing an unknown performance issue. Two options are available to capture the needed data for analysis. The first is to filter the data initially captured by Profiler to only capture a specific user's transactions for a finite period of time. The advantage is a small set of data that could be manually reviewed. The disadvantage is that this data would not show how another process impacts the user. The second option is not to limit the data captured via filtering in Profiler. This gives you the ability to capture and subsequently query the data in order to review all data associated with and potentially impacting a user's session.

CPU Duration
-- Top 10 statements by CPU usage
SELECT TOP 10 *
FROM [ProfilerTest].[dbo].[ProfilerResults]
WHERE LoginName = 'Domain\User'
ORDER BY CPU DESC
-- Top 10 statements by duration
SELECT TOP 10 *
FROM [ProfilerTest].[dbo].[ProfilerResults]
WHERE LoginName = 'Domain\User'
ORDER BY Duration DESC
Reads Writes
-- Top 10 statements by reads
SELECT TOP 10 *
FROM [ProfilerTest].[dbo].[ProfilerResults]
WHERE LoginName = 'Domain\User'
ORDER BY Reads DESC
-- Top 10 statements by writes
SELECT TOP 10 *
FROM [ProfilerTest].[dbo].[ProfilerResults]
WHERE LoginName = 'Domain\User'
ORDER BY Writes DESC
RowCounts ObjectID
-- Top 10 statements by rows returned to the user
SELECT TOP 10 *
FROM [ProfilerTest].[dbo].[ProfilerResults]
WHERE LoginName = 'Domain\User'
ORDER BY RowCounts DESC
-- Most used objects by the user
SELECT COUNT(ObjectID) AS 'ObjectCount',
OBJECT_NAME(ObjectID) AS 'ObjectName'
FROM [ProfilerTest].[dbo].[ProfilerResults]
WHERE LoginName = 'Domain\User'
GROUP BY ObjectID
ORDER BY ObjectCount DESC

Note: In the code snippets above, replace the FROM clause, i.e., [ProfilerTest].[dbo].[ProfilerResults] with the database, schema and table where you captured your results. In addition, replace the 'Domain\User' with the associated user's value.
 
Scenario 2: Overall data analysis

The second analysis is much broader and requires that you determine the queries where potential issues could occur.

Averages Max Values
-- Average usage for key metrics
SELECT AVG(CPU) AS 'AvgCPU',
AVG(Duration) AS 'AvgDuration',
AVG(Reads) AS 'AvgReads',
AVG(Writes) AS 'AvgWrites',
AVG(RowCounts) AS 'AvgRows'
FROM [ProfilerTest].[dbo].[ProfilerResults]
-- Highest usage statements
SELECT MAX(CPU) AS 'MAXCPU',
MAX(Duration) AS 'MAXDuration',
MAX(Reads) AS 'MAXReads',
MAX(Writes) AS 'MAXWrites',
MAX(RowCounts) AS 'MAXRows'
FROM [ProfilerTest].[dbo].[ProfilerResults]
Specific Host DISTINCT Applications
-- Transactions count by machine
SELECT COUNT(HostName) AS 'HostCount', HostName
FROM [ProfilerTest].[dbo].[ProfilerResults]
GROUP BY HostName
ORDER BY HostName DESC
-- Determine all applications on the SQL Server
SELECT DISTINCT(ApplicationName)
FROM [ProfilerTest].[dbo].[ProfilerResults]
ORDER BY ApplicationName
Transactions Per Database Most Used Objects
-- Transaction per database in descending order
SELECT COUNT(DatabaseID), DB_NAME(DatabaseID)
FROM [ProfilerTest].[dbo].[ProfilerResults]
GROUP BY DatabaseID
ORDER BY DatabaseID DESC
-- Most used objects in descending order
SELECT COUNT(ObjectID) AS 'ObjectCount', OBJECT_NAME(ObjectID) AS 'ObjectName'
FROM [ProfilerTest].[dbo].[ProfilerResults]
GROUP BY ObjectID
ORDER BY ObjectCount DESC

Note: In the code snippets above, replace the FROM clause, i.e., [ProfilerTest].[dbo].[ProfilerResults] with the database, schema and table where you captured your results.
 
SQL Query optimization

Based on your analysis, apply the following rules to improve the query performance:

  • Review the query plan for possible improvements in queries that consistently appear as offenders in Profiler analyses.
  • Leverage the Database Engine Tuning Advisor for index and partitioning recommendations.
  • Review the query join order to minimize the number of rows that are being processed.
  • Validate that indexes meet individual query needs for the JOIN, WHERE, GROUP BY and ORDER BY clauses.
  • Ensure that data types are the most efficient for the data (i.e., use integers for keys as opposed to character data types).
  • Remove any unneeded logic in the statements such as GROUP BY or ORDER BY when they are not needed by users.
  • Return only the needed amount of data and nothing more.
  • Ensure statements do not explicitly hold any transactions longer than necessary.

Starting off, you may need to chip away at the statements one at a time, but over time you will begin to find recognizable patterns with similar solutions yielding a significant performance improvement.


Top 5 SQL Server query tips

 Home: Introduction
 Tip 1: SQL Server query design: 10 mistakes to avoid
 Tip 2: Troubleshoot SQL Server queries and improve I/O
 Tip 3: Tracking query execution with SQL Server 2005 Profiler
 Tip 4: Find and fix resource-intensive SQL Server queries
 Tip 5: Running analytical queries with Analysis Services

For more help, check out our FAQ: SQL Server query errors and explanations.

ABOUT THE AUTHOR 
Jeremy Kadlec is the principal database engineer at Edgewood Solutions, a technology services company delivering professional services and product solutions for Microsoft SQL Server. He has authored numerous articles and delivers frequent presentations at regional SQL Server users groups and nationally at SQL PASS. Kadlec is the SearchSQLServer.com Performance Tuning expert. Ask him a question here.


More information from SearchSQLServer.com


 

This was first published in March 2006

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