SQL Server Error Log Reader

Reading the SQL Server Error Log is miserable.  It contains very useful information you should address as soon as possible, or at least know that it’s happening.  However, it’s hidden between so many informational messages that it’s hard to find, then it’s spread out between multiple files for every server reboot or automated file rollover event you may have set up.

Many DBAs skim these files, but when there’s a single login failure mixed into log backups running every 5 minutes for 100 databases then they’re just happy to have found something.  That login failure tells you nothing, just that someone should have been more careful typing in their password, right?  When you’re just happy you were even able to find something then you’re almost certainly not going to see it clearly enough to notice a trend, such as that login failure happens every Sunday between 10:00 PM and 10:15 PM.  However, if you knew that then you could tell someone that there’s an automated job that’s failing, it’s obviously part of a bigger process because the time varies a little, but it’s consistent enough to say it’s definitely a process.

So, the trick is to get past the junk and to the useful information.  You can listen to Warner Chaves (b|t) in his Most Important Trace Flags post and turn on trace flag 3226 to stop backup information from going to the logs, but I’m always paranoid (it’s part of the job) that it just may come in useful some day.  I know it never has, but I leave it in there anyways.

Even if you do take out information from the logs like that, it’s still a flat file that’s difficult to analyze for any number of reasons.  We’re just a T-SQL kind of group, and flat files just fall flat.

As with everything in SQL Server, I cheat my way through it.  This time I pull it into a temp table, delete the stuff I’m ignoring (please be very, very careful about what you ignore because you’ll never know it happened), then look at the results.  If there’s a login failure then I’ll uncomment the section that deletes everything except a single error and trends will pop out at me.  If I wanted to do more advanced analysis I would run queries doing aggregates of any kind against the temp table that was created.  Everything’s in the format you’re used to analyzing, so you can do crazy things without going crazy.

DECLARE @dStart DateTime 
	, @dEnd DateTime
	, @MaxLogFiles Int 

SELECT @dStart = GetDate()-30
	, @dEnd = GetDate()-0
	, @MaxLogFiles = 5

--Pulls into #TempLog because an empty log file causes errors in the temp table
--If there are no records, don't pass the issues onto your #Log table and return the results



	LogDate DateTime
	, ProcessInfo NVarChar(50)
	, LogText NVarChar(1000)

	LogDate DateTime
	, ProcessInfo NVarChar(50)
	, LogText NVarChar(1000)

DECLARE @Num int
SELECT @Num = 0

WHILE @Num < @MaxLogFiles BEGIN

	exec xp_readerrorlog @Num, 1, null, null, @dStart, @dEnd

		FROM #TempLog
		SELECT @Num = @MaxLogFiles
	SELECT @Num = @Num + 1

--Uncomment to trend out a specific message and ignore the rest
WHERE LogText NOT LIKE 'Login failed for user ''WhatAreYouDoingToMe?!?!?''%'

--Ignore most of the log file rollover process
--Keep "Attempting to cycle" and "The error log has been reinitialized" if you want to confirm it happened and succeeded
WHERE LogText LIKE '%(c) Microsoft Corporation%'
	OR LogText LIKE 'Logging SQL Server messages in file %'
	OR LogText LIKE 'Authentication mode is MIXED.'
	OR LogText LIKE 'System Manufacturer: %'
	OR LogText LIKE 'Server process ID %'
	OR LogText LIKE 'All rights reserved.'
	OR LogText LIKE 'Default collation: %'
	OR LogText LIKE 'The service account is %'
	OR LogText LIKE 'UTC adjustment: %'
	OR LogText LIKE '(c) 2005 Microsoft Corporation.'--Should I be ignoring this or fixing it?
	OR LogText LIKE 'Microsoft SQL Server % on Windows NT %'
	OR LogText LIKE 'The error log has been reinitialized. See the previous log for older entries.'
	OR LogText LIKE 'Attempting to cycle error log.%'

--Ignore databases being backed up and integrity checks running, assuming you verify this some other way.
--I don't want to complain to try to have these removed because I may need that info someday; today isn't that day.
WHERE LogText LIKE 'Log was backed up%'
	OR LogText LIKE 'Database differential changes were backed up%'
	OR LogText LIKE 'Backup database with differential successfully %'
	OR LogText LIKE 'Backup database successfully %'
	OR LogText LIKE 'Database backed up%'
	OR LogText LIKE 'DBCC CHECK% found 0 errors %'
	OR LogText LIKE 'CHECKDB for database % finished without errors %'

--We all have vendor databases...
--Ignore the stuff where it keeps making sure the setting is where the setting was.
WHERE LogText LIKE 'Configuration option % changed from 30 to 30.%'
	OR LogText LIKE 'Configuration option % changed from 5 to 5.%'
	OR LogText LIKE 'Setting database option COMPATIBILITY_LEVEL to 100 for database ReportServer%'
	OR LogText LIKE 'Configuration option ''user options'' changed from 0 to 0. Run the RECONFIGURE statement to install.'

--Now your own custom ones
--Just be careful.  You'll rarely read logs without this script once you see how easy it is.
--If you put it on the ignore list, you won't see it again.
--I have starting and stopping traces on mine, because my monitoring software likes to start and stop them a lot
----I'm accepting the risk that I won't see other people starting and stopping traces.
WHERE LogText LIKE 'Know what risk you''re taking on by putting stuff in here'
	OR LogText LIKE 'You will rarely read logs without this, so you won''t see these ever again'
	OR LogText LIKE 'DBCC TRACEON 3004,%'
	OR LogText LIKE 'DBCC TRACEON 3014,%'
	OR LogText LIKE 'DBCC TRACEON 3604,%'
	OR LogText LIKE 'DBCC TRACEON 3605,%'
	OR LogText LIKE 'Error: %, Severity:%'--They give the english version next
	OR LogText LIKE 'SQL Trace ID % was started by %'
	OR LogText LIKE 'SQL Trace stopped.%'
	OR LogText LIKE 'Changing the status to % for full-text catalog %'
	OR LogText LIKE 'I/O was resumed on database %'
	OR LogText LIKE 'I/O is frozen on database %' 

--When mirroring gives me trouble it lets me know by flooding the logs
--I uncomment this to see if there were other issues in the middle of all that.
WHERE LogText LIKE 'Database mirroring is inactive for database%'
	OR LogText LIKE 'The mirroring connection to%has timed out%'
	OR LogText LIKE 'Database mirroring is active with database%'

--This is only useful if you're using the trace flag 1222
--Only show the line that says 'deadlock-list'.  Remove this if you need to see the deadlock details.
--Note, only use this when needed.  It will give you a 1 second blind spot for every deadlock found.
--Why aren't you using extended events anyways?
	INNER JOIN #Log L2 ON L.LogDate BETWEEN L2.LogDate AND DateAdd(second, 1, L2.LogDate) AND L.ProcessInfo = L2.ProcessInfo 
WHERE L2.LogText = 'deadlock-list'
	AND L.LogText <> 'deadlock-list'


Don’t Ignore Me

Anything you ignore you won’t see here again. It’s still in the logs, but not in what you’re reading on your screen when you mentally check the logs off as being read through.  If you’re ignoring anything make sure it either doesn’t matter or you’re watching for it another way.

Backups are the first thing to be ignored.  Yes, yes, they ran successfully, they do that a lot, don’t tell me about them.  That can be good advice gone horribly wrong.  Do you have another way of saying I absolutely know I have backups taken of everything?

DBCC CheckDB ran successfully is next on the list.  Same thing goes for it, except more DBAs miss verifying that this is running and also miss running it.  If you ignore it, how are you verifying that it ran?

I don’t care how you do it.  Do what works best for you, just do something.

Be Careful

I’ll just end by saying be careful again. This code is a life saver when it’s not shooting you in the foot.

Extended Events Intro

Extended Events is supposed to be taking over for most of Profiler and server-side tracing functionality, but there were people like me out there that took their time making the switch. For me the excuse was SQL Batch Completed isn’t in 2008 / 2008 R2, most databases are slow to move off of 2008 because it’s hard to say 2008 can’t do everything you want, and, well, I really like SQL Batch Completed!!!

Now I’m losing my excuses. It’s been three years since 2012 came out and all the new servers I’m installing are 2012 or 2014, so I lost that excuse. Then I came up with “I don’t know how to use them, but I know how to use something that does almost the same thing”; that’s great logic during an outage, but terrible logic for the long term. So, I’m learning it, still learning it, and I have something to help out those who made it to 2012 but are still stuck on the “I don’t know how to use them” phase of denial.

In several of my blog posts I referenced my favorite server-side trace. Capture everything on the server that takes over X seconds, with 5 being my favorite starting place. Yes, this adds about 1ms to anything that takes over 5 seconds, which adds up to…insignificant. Seriously, if this kills your server then it was dead already, but you will see arguments against creating this for long-term runs.  I don’t agree with those arguments, but they’re your servers and I want you to make an informed decision on what you run.

Anyways, here’s how I set it up with Extended Events, for which I used Jonathan Kehayias’s (b|t) Trace to XE Converter to get started.

IF EXISTS (SELECT 1 FROM sys.server_event_sessions WHERE name = 'DurationOver5Sec')
ADD EVENT sqlserver.rpc_completed(
		  sqlserver.client_app_name	-- ApplicationName from SQLTrace
		, sqlserver.client_hostname	-- HostName from SQLTrace
		, sqlserver.client_pid	-- ClientProcessID from SQLTrace
		, sqlserver.database_id	-- DatabaseID from SQLTrace
		, sqlserver.request_id	-- RequestID from SQLTrace
		, sqlserver.server_principal_name	-- LoginName from SQLTrace
		, sqlserver.session_id	-- SPID from SQLTrace
			duration >= 5000000
ADD EVENT sqlserver.sql_batch_completed(
		  sqlserver.client_app_name	-- ApplicationName from SQLTrace
		, sqlserver.client_hostname	-- HostName from SQLTrace
		, sqlserver.client_pid	-- ClientProcessID from SQLTrace
		, sqlserver.database_id	-- DatabaseID from SQLTrace
		, sqlserver.request_id	-- RequestID from SQLTrace
		, sqlserver.server_principal_name	-- LoginName from SQLTrace
		, sqlserver.session_id	-- SPID from SQLTrace
		duration >= 5000000
ADD EVENT sqlos.wait_info(
		  sqlserver.client_app_name	-- ApplicationName from SQLTrace
		, sqlserver.client_hostname	-- HostName from SQLTrace
		, sqlserver.client_pid	-- ClientProcessID from SQLTrace
		, sqlserver.database_id	-- DatabaseID from SQLTrace
		, sqlserver.request_id	-- RequestID from SQLTrace
		, sqlserver.server_principal_name	-- LoginName from SQLTrace
		, sqlserver.session_id	-- SPID from SQLTrace
		duration > 5000 --This one is in milliseconds, and I'm not happy about that
            AND ((wait_type > 0 AND wait_type < 22) -- LCK_ waits
                    OR (wait_type > 31 AND wait_type < 38) -- LATCH_ waits
                    OR (wait_type > 47 AND wait_type < 54) -- PAGELATCH_ waits
                    OR (wait_type > 63 AND wait_type < 70) -- PAGEIOLATCH_ waits
                    OR (wait_type > 96 AND wait_type < 100) -- IO (Disk/Network) waits
                    OR (wait_type = 107) -- RESOURCE_SEMAPHORE waits
                    OR (wait_type = 113) -- SOS_WORKER waits
                    OR (wait_type = 120) -- SOS_SCHEDULER_YIELD waits
                    OR (wait_type = 178) -- WRITELOG waits
                    OR (wait_type > 174 AND wait_type < 177) -- FCB_REPLICA_ waits
                    OR (wait_type = 186) -- CMEMTHREAD waits
                    OR (wait_type = 187) -- CXPACKET waits
                    OR (wait_type = 207) -- TRACEWRITE waits
                    OR (wait_type = 269) -- RESOURCE_SEMAPHORE_MUTEX waits
                    OR (wait_type = 283) -- RESOURCE_SEMAPHORE_QUERY_COMPILE waits
                    OR (wait_type = 284) -- RESOURCE_SEMAPHORE_SMALL_QUERY waits
	--OR (wait_type = 195) -- WAITFOR
ADD TARGET package0.event_file
	SET filename = 'DurationOver5Sec.xel',
		max_file_size = 10,
		max_rollover_files = 5


What’s it all mean?

This captures all SQL Batch Completed and RPC Completed events that took over 5 seconds along with any waits that took over 5 seconds. Seems kind simple and much easier to read than the script to create a trace, but there are some things to point out here.

  • Duration – Milliseconds or Microseconds
  • File Location
  • Restart on server reboot

First, duration, is it milliseconds or microseconds? Trick question, it’s both!!! Ola Hallengren (b|t) opened This Connect Item which resulted in Microsoft apologizing and saying they’ll fix it in SQL 2016. They can “fix” it one of two ways. Either they’ll document them as being different scales and your script will still work without any modifications, or they’ll make them all be the same scale and the script I have here will behave differently on different versions of SQL Server. Anyways, the script I have here is 5 seconds all around, which is 5,000,000 microseconds for the SQL and 5,000 milliseconds for the waits.

Continuing on the duration confusion, the wait info’s duration still isn’t what you might think it is.  This is the duration for a single instance of a wait, not cumulative waits for the duration of a specific query or batch.  If you made a loop that executed 10 times calling a wait for delay of 1 second each time then those are 10 separate 1 second waits.  More important, if your query is doing physical reads from disk then it’s a ton of small PageIOLatch waits, not one large one this session will see.  I understand why it has to be this way, but understanding and enjoying are two different things.

The rest of this isn’t nearly as confusing, I promise.

The file location I’m using is just a filename without a path, which will default to where your error log files are. It’s going to be a much more complex name once SQL Server is done with it, and not just a underscore with a rollover count like server-side traces.  However, it will start how it’s written here and you can use wildcards to say which files you want to read.

Now when you restarted the server, or just SQL services, with server-side traces you’d have to manually start that trace again if you wanted it to run all the time. This is exactly how the script I have here works, too. However, Extended Events also added “Startup State” which means it will start when the server starts. The only issue I have with this is that it will ONLY automatically start when the services start. That’s all fine and good if you’re a patient person….it’s not all fine and good for me, so I manually start it when it’s created.

The other minor details I can see having questions on are on the Max Dispatch Latency and Event Retention Mode. These are limits on how it writes to the file location I’m using. Max Dispatch means that SQL Server will write it to the output file within that many seconds after the event actually happens, so it’s obviously an asynchronous action. Event Retention Mode can give SQL Server permission to say it’s too overworked to write the entries and skip them if needed, and I chose to tell SQL Server to drop as many as it wants to if I’d be slowing it down.  It’s all documented in BOL, and it’d be good for you to read through that instead of just listening to me picking out select details.

So, how do you read this? It’s going to put it all in XML, isn’t it!?!?

If you can read this, Kendra Little (b|t) may suggest you’re a demon robot! (While her comment makes me laugh, that post is a great XE reference or it wouldn’t be on here.) Well, she’s probably right in calling this next script names because friendly robots don’t treat your CPU like this. In fact, if you’re on a stressed server or want to do a lot of analysis on this, it’s going to be best to copy the .xel files to a server you care a little less about and shred the XML there.

Unfortunately, I wrote my script to automatically find the files and add a wildcard character, so you’d have to modify my script to run it on another server. The point is getting you started with as little frustration as possible, and this is perfect for that reason. The biggest catch is that the session has to be running for this to work without changes because it’s grabbing the full file name with path from the DMVs for running sessions.

Anyways, here goes….

	@SessionName SysName 
	, @TopCount Int = 1000

--SELECT @SessionName = 'UserErrors'
SELECT @SessionName = 'DurationOver5Sec'
--SELECT @SessionName = 'system_health'
SELECT * FROM sys.traces

SELECT  Session_Name = s.name, s.blocked_event_fire_time, s.dropped_buffer_count, s.dropped_event_count, s.pending_buffers
FROM sys.dm_xe_session_targets t
	INNER JOIN sys.dm_xe_sessions s ON s.address = t.event_session_address
WHERE target_name = 'event_file'



IF OBJECT_ID('tempdb..#Queries') IS NOT NULL BEGIN
	DROP TABLE #Queries 

DECLARE @Target_File NVarChar(1000)
	, @Target_Dir NVarChar(1000)
	, @Target_File_WildCard NVarChar(1000)

SELECT @Target_File = CAST(t.target_data as XML).value('EventFileTarget[1]/File[1]/@name', 'NVARCHAR(256)')
FROM sys.dm_xe_session_targets t
	INNER JOIN sys.dm_xe_sessions s ON s.address = t.event_session_address
WHERE s.name = @SessionName
	AND t.target_name = 'event_file'

SELECT @Target_Dir = LEFT(@Target_File, Len(@Target_File) - CHARINDEX('\', REVERSE(@Target_File))) 

SELECT @Target_File_WildCard = @Target_Dir + '\'  + @SessionName + '_*.xel'

--SELECT @Target_File_WildCard

SELECT TOP (@TopCount) CAST(event_data AS XML) AS event_data_XML
INTO #Events
FROM sys.fn_xe_file_target_read_file(@Target_File_WildCard, null, null, null) AS F
	, file_offset DESC 

SELECT  EventType = event_data_XML.value('(event/@name)[1]', 'varchar(50)')
	, Duration_sec = CAST(event_data_XML.value ('(/event/data[@name=''duration'']/value)[1]', 'BIGINT')/CASE WHEN event_data_XML.value('(event/@name)[1]', 'varchar(50)') LIKE 'wait%' THEN 1000.0 ELSE 1000000.0 END as DEC(20,3)) 
	, CPU_sec = CAST(event_data_XML.value ('(/event/data[@name=''cpu_time'']/value)[1]', 'BIGINT')/1000000.0 as DEC(20,3))
	, physical_reads_k = CAST(event_data_XML.value ('(/event/data  [@name=''physical_reads'']/value)[1]', 'BIGINT')/1000.0 as DEC(20,3))
	, logical_reads_k = CAST(event_data_XML.value ('(/event/data  [@name=''logical_reads'']/value)[1]', 'BIGINT') /1000.0 as DEC(20,3))
	, writes_k = CAST(event_data_XML.value ('(/event/data  [@name=''writes'']/value)[1]', 'BIGINT')/1000.0 as DEC(20,3))
	, row_count = event_data_XML.value ('(/event/data  [@name=''row_count'']/value)[1]', 'BIGINT')
	, Statement_Text = ISNULL(event_data_XML.value ('(/event/data  [@name=''statement'']/value)[1]', 'NVARCHAR(4000)'), event_data_XML.value ('(/event/data  [@name=''batch_text''     ]/value)[1]', 'NVARCHAR(4000)')) 
	, TimeStamp = DateAdd(Hour, DateDiff(Hour, GetUTCDate(), GetDate()) , CAST(event_data_XML.value('(event/@timestamp)[1]', 'varchar(50)') as DateTime2))
	, SPID = event_data_XML.value ('(/event/action  [@name=''session_id'']/value)[1]', 'BIGINT')
	, Username = event_data_XML.value ('(/event/action  [@name=''server_principal_name'']/value)[1]', 'NVARCHAR(256)')
	, Database_Name = DB_Name(event_data_XML.value ('(/event/action  [@name=''database_id'']/value)[1]', 'BIGINT'))
	, client_app_name = event_data_XML.value ('(/event/action  [@name=''client_app_name'']/value)[1]', 'NVARCHAR(256)')
	, client_hostname = event_data_XML.value ('(/event/action  [@name=''client_hostname'']/value)[1]', 'NVARCHAR(256)')
	, result = ISNULL(event_data_XML.value('(/event/data  [@name=''result'']/text)[1]', 'NVARCHAR(256)'),event_data_XML.value('(/event/data  [@name=''message'']/value)[1]', 'NVARCHAR(256)'))
	, Error = event_data_XML.value ('(/event/data  [@name=''error_number'']/value)[1]', 'BIGINT')
	, Severity = event_data_XML.value ('(/event/data  [@name=''severity'']/value)[1]', 'BIGINT')
	, EventDetails = event_data_XML 
INTO #Queries
FROM #Events

SELECT q.EventType
	, q.Duration_sec
	, q.CPU_sec
	, q.physical_reads_k
	, q.logical_reads_k
	, q.writes_k
	, q.row_count
	, q.Statement_Text
	, q.TimeStamp
	, q.SPID
	, q.Username
	, q.Database_Name
	, client_app_name = CASE LEFT(q.client_app_name, 29)
					WHEN 'SQLAgent - TSQL JobStep (Job '
						THEN 'SQLAgent Job: ' + (SELECT name FROM msdb..sysjobs sj WHERE substring(q.client_app_name,32,32)=(substring(sys.fn_varbintohexstr(sj.job_id),3,100))) + ' - ' + SUBSTRING(q.client_app_name, 67, len(q.client_app_name)-67)
					ELSE q.client_app_name
	, q.client_hostname
	, q.result
	, q.Error
	, q.Severity
	, q.EventDetails
FROM #Queries q
--WHERE eventtype NOT IN /*rather typical filtering*/ ('security_error_ring_buffer_recorded', 'sp_server_diagnostics_component_result', 'scheduler_monitor_system_health_ring_buffer_record')
	--AND eventtype NOT IN /*specific troubleshooting filtering*/ ('connectivity_ring_buffer_recorded', 'wait_info')

The top is all I typically edit. The Session Name is ‘DurationOver5Sec’ for this purpose, but as you can see it can also read some of the information from the system_health session. The TopCount is just saying I’m only going to look at the most recent 1,000 records BEFORE I shred and filter it, mostly because I love my CPU and this changes how much XML I’m throwing at it.

No matter how I write this to filter before playing with XML or try to be 100% safe, my scripts will not be perfect for you.  In fact they may crash your server. Unfortunately, I’m only half kidding, because nothing is 100%. The space is limited to 50 MB, the overhead of writing to this file should be fine since we’re only working with events that took over 5 seconds asynchronously with data loss allowed, so I don’t foresee any issues in most cases unless you change these numbers. However, reading them gets expensive and you have to use the most caution here.  Keep in mind that you can put the .xel files on any server with SQL installed, even developer edition, and query it.

Why’d I write this and what else is going through my head?

The inspiration for this post actually came from a question a former coworker asked me quite a while ago, just seeing if I knew a lot about Extended Events.  At the time the technically correct answer was “I don’t know”, but instead I told him that although I didn’t have experience with Extended Events that he could look at Jonathan Kehayias’s (b|t) blog, specifically his XEvent A Day Series.  However, I also warned him that Jonathan’s work tends to go deeper than a beginner is prepared for and that he should start with his coworker, Erin Stellato’s (b|t), version that is easier to start with, specifically her Making the Leap From Profilers to Extended Events session she made available on YouTube. This actually inspired two blog posts, I just haven’t written the How to Say “I don’t know” Better post yet.

Have fun, experiment (with queries), and ask questions.

Monitoring and Baselines Presentation

Here’s my full presentation for SQL Saturday #250 in Pittsburgh this past Saturday along with some notes on what I got out of it.

My Lessons

I learned a lot doing this, and I hope people learned a lot from it. For the people on the other side of the podium from me, the main lessons were in what I put in the abstract; they learned mostly about SQL Server. On my side of the podium though, the lessons were far from the same. Public speaking, creating presentation, prepping for unknown audiences, and seeing the gratitude of the SQL Server community in person. Anything that was on the abstract I learned slightly better, but that didn’t compare to what wasn’t on it.

Public speaking was always a fear of mine, and I tend to be someone who avoids my fears. However, throwing myself out there at the public as a whole was never my thing either, and I’ve been doing half decent, at least in my own mind. That being said, I decided to go all out and push myself a little further. After all, what are the chances of them picking me to present with all those professional teachers, consultants, and MVPs out there throwing up their abstracts. Best case I could say I tried, worst case I was going to throw myself out of my comfort zone and hope for the best.

They picked me, which I didn’t let myself expect. Everyone that knew I put in my abstract was also told by me that I only had a 50/50 chance at best, which was more optimistic than I really was, but I don’t like people knowing when I’m being dismissive of myself. It turns I was wrong a lot, and in every case I was wrong I was glad I was wrong.

Being Forced to Get Better

I’m not one to learn things for the sake of learning them, which is why I sucked in school. To really learn things I need a real-life use case, somewhere I’m going to apply it. I didn’t practice speaking ever before because I didn’t speak in front of large groups. I didn’t know how to put together a presentation properly because that’s not my thing…well, wasn’t is probably a better word now. Just like a couple months ago I wasn’t quite sure how to blog, but I’ve made it past that point.

Like everything in my life, blogging went from unknown to addiction quick. I’m not sure that I can say the same thing about speaking, but I can see the possibility. SQL Saturday only comes to Pittsburgh once a year and the local user group, which I’ll be joining soon, only meets once a month and has a single speaker. However, I can’t say that I didn’t look up when the SQL Saturdays in Cleveland and Washington DC were. Also, I took my own thoughts on my presentation and the feedback I received in and immediately starting thinking about how I could make that presentation better.

What’s below is the original, not touched up at all in the last 4 days. In part to show where I went wrong and how I’m going to fix it, and in part because Monday was my first day at a new job. By the way, starting a new job when between jobs is the best and worst thing you could ever do to yourself. You don’t have time to be too nervous about anything because you’re too overworked, but you’re also too overworked. Eh, you can’t win them all.

Well, on to the presentation. What you have here is my script that I talked through and taught myself before the presentation. However, if you were there, you’ll notice this isn’t exactly what I said. I didn’t read it, I presented it. There were no demos (the biggest complaint from the crowd) to avoid me from stumping myself, and the PowerPoint was just a whimsical picture for the start of each paragraph to keep me on track because I knew I’d be nervous and lose my way without a paragraph-by-paragraph guide helping me along.

I must have done at least descent because my reviews came back with two 3’s and the rest 4’s and 5’s out of 5. You’re just asking me to get off topic and start talking about why I hate ratings that are odd numbers, but no, I’m not getting off topic, I’m getting on to my presentation!!!

The Presentation

Download slide deck here.

I’ve been working on databases for over a decade, and most of that without having good standards or monitoring in place. My job was rough and I was much less effective. Now I know what I’m doing and I want to share that knowledge. In this presentation I am going to tell you what I watch and why I watch it. A presentation is a poor format to go through how to do this; having it in writing is much more beneficial to you. That is why I’m pretty much skipping that portion here and diverting you to my blog at SimpleSQLServer.com. Not so much to promote a blog that I lose money on, but instead to give you the resources in the best format.

As a DBA you will hit problems with the performance of your databases. It doesn’t matter if it’s one process or across the board, or if it’s just today or it has always been that way. No matter what it is, the more you know about your servers the easier it’s going to be to fix them.

It’s not easy, there’s no single spot to watch, no single solution to all of your problems, and there’s no “normal” values for these counters you can apply across every server you manage (Note: Thank you, Mike John, you stressed this point a lot). I watch several things on all the important servers, and most of these on every server. The important part is that you watch them continuously, even when you aren’t expecting to use the data. Some parts are cumulative and you can’t tell what was during an incident or what was from overnight maintenance. Other parts are snapshots and there’s no looking back.

(Note: Now I’m on to the stuff Brian Castle taught me. He’s the best you could hope for in a manager, and last I checked he was still hiring at EDMC in Robinson Township near Pittsburgh, PA)

To me, monitoring and baselining is the same thing. I know others will do a specific baselining process on a specific day and keep that. I feel you lose too much doing that and I watch enough to say that I have a continuous baseline going back for 13 months in most instances. I’m not all that worked up about lucky numbers or anything, it’s just nice to be able to say “that annual process we ran last week ran like this last year”. If you’re superstitious or have OCD, 400 days makes me just as happy.

Traces – Snapshot
Wait Stats – Cumulative – Resets on restart
Blocking – Snapshot
Query Stats – Cumulative – Resets on recompile
Index Stats – Cumulative – Resets on restart
OS Perf Counters – Varies
Database Sizes – Snapshot
Table Sizes – Snapshot

Traces – What long running processes have completed on the server?

You have to be careful, this one can kill your server. I have, on more than one occasion (sorry, Brian), caused a large server to reboot in the middle of the day because I filled the drives writing 40 GB of traces in 5 minutes. That being said, there is a safer way to trace, just don’t do an unfiltered trace catching everything.

I personally feel that absolutely every server should have a trace capturing RPC:Completed and SQL Batch:Completed that ran over X seconds duration. What is X? Well, that depends on the server. I’ve seen the best number for X be as low as 100 ms and as high as 10 seconds. Start high, and work your way down. You can add or do a separate trace for the Statement Completed for each of these if you want more detail.

You can get all of this except for SQL Batch:Completed with the text data from Extended Events, and that is a very good alternative. I haven’t made that jump for several reasons, although I would recommend it. First, this level of tracing hasn’t hurt me. Second, I still support several important servers that are still on SQL 2005 and I want to be as consistent as possible to provide as consistent as possible support.

What you’re trying to solve here is answering what ran long, have some hints as to why it ran long, and be able to say how often it has been running that way in recent history. A long duration doing very little work was waiting on something else, and other monitoring will help solve that problem. A lot of CPU, reads, or writes shows that you may need to look into tuning or statistics. Just keep in mind that reads can be reads of work tables, and writes can be writes to tempdb.

If you do it right you should have 4 or more days worth of history, and by doing it right I mean having 5 to 10 rollover files sized a reasonable size you can send off if needed (100 MB at most, they zip well) and capturing over the best duration threshold for your environment.

Wait Stats – What’s slowing you down?

The traces tell you what work was done, and the waist statistics tell you what went on when the query couldn’t actively do its work. This is cumulative over the server, so you can’t get details down to an individual process, however, you can see how much time is wasted and where it’s wasted at.

There are almost 500 distinct wait types in 2008 R2, but you don’t need to worry about that. The important thing is to know what are your biggest waits are, and you can look them up if you don’t recognize them. In the last slide I have links to a free eBook written by Jonathan Kehayias and Tom Kruger for the Accidental DBA that does an amazing job documenting what you can ignore and the meaning of the big ones you’re most likely to see.

If you’re looking for overall server health then you would want to look at waits over a long period of time, specifically the times of day you’d like to see better performance. For incidents, you want to look at what your wait stats are now and compare them to your running baseline. For me, the typical baseline I use is the same timeframe yesterday and 7 days ago. These are actual relevant days that have all but the changes you put in over the last couple of days, and it’s a true baseline for this specific server. If this server never waits on PageIOLatch, but it’s in your top 3 today then you instantly have a direction your heading in troubleshooting.

Blocking – Who’s in your way?

Locking occurs when one query is using data, and blocking occurs when another query needs to do something that is incompatible with that lock. If you don’t keep an eye on it then you’re looking back at a trace and seeing that a query took 1 second of CPU, did 1,000 reads, and no writes, yet it figured out a way to take 5 minutes in duration. The users complain the app is slow or unusable, and you’re giving them the answer that you’ll try to figure it out next time it happens and THEN try to find out the root cause.

That’s a bad idea. There are three types of users – those who don’t know you because things are running smooth, those that like you because you can say “I see exactly what happened and I can start working on avoiding reoccurrences”, and those users that you keep telling that you’re not sure what happened and you’ll try to get a better idea the next time it happens. I wish I could tell you that I could have more users not know your name, but watching blocking is a very easy way to get the users that like you to outnumber the users that hate you.

So, what can you do to tell them you know what just happened? My answer is to capture everything that’s blocking on the server every minute. Sure, there’s going to be a lot you capture that is actually only blocking for 10ms, and there are going to be things that were blocking for 59 seconds that were timed perfectly so you never saw it happen. The trick is to know that there is no perfect solution, and capture what you can. The DMVs are there to provide all the information, and being able to capture that on the fly along with in a proactive monitoring setup will make you look amazing to the users. If something blocked for 5 minutes, you have no excuse not to say “This query blocked this query, and this was the head blocker”. Sure you may have an excuse to say “It’s a vendor database and I passed it on to their support”, but at least you can tell the users something, which is always better than nothing.

Query Stats

The DMV dm_exec_query_stats is used by many DBAs, even if all we realized was that we were running one of the “Top 10 Most Expensive Query” scripts off the internet. The problem is that this is using it wrong. This DMV holds everything from when the query first went into cache and loses that information when the query goes out of the cache, even just for being recompiled. So the big process you ran overnight could still be in cache, leading you down a path that’s actually low priority for you. However, that relatively large process that runs every 5 minutes that just recompiled a couple minutes ago due to stats auto updating, it’s not even in there. This is too volatile to say that you’re getting good numbers out of it.

The answer isn’t to find somewhere else to get this information, it’s to capture this information more often and more intelligently. Capture it once and you have a snapshot of what it looked like, capture it again an hour later and you know what work has been done in that hour. Repeat the process over and over again, constantly adding another hour’s worth of data and saving off the latest snapshot in the process and you have some real information to go off of. This isn’t so much of what happened to be in cache at the time, it’s a pretty good summary of what ran and when it ran. Now you can query in detail, specifically saying “I want to know what queries are doing the most physical reads between 9:00 AM and 5:00 PM on weekdays so I can have the biggest impact when I tune this database.”

Index Stats

The DMV dm_exec_index_usage_stats isn’t too different from dm_exec_query_stats except that it’s cumulative since the time SQL services were started. Looking at a snapshot still leaves you half blind, unable to see what happened before the last time you rebooted the server and unable to tell when that index was used. Personally, seeing when it was used is less important to me than how much it was used long-term. Tracking this can do amazing things for you if you’re really fine-tuning a database.

This DMV is really under-used in my experience. As time goes on with a database the data changes and you add more indexes to make it run faster, but you don’t know if or when the index it used to use was being used by anything else so it stays there. Over time that means you have more and more unused indexes, and there’s no solid proof that they aren’t being used without you doing the work to collect that proof. This, in addition to duplicated indexes, adds to the workload for inserts and updates, adds to the workload of index maintenance, adds to the database size, and put additional strain on your cache and thus the PLE.

OS Performance Counters

This is one of the most misunderstood DMVs while also being one of the most useful. It’s giving you critical counters that give you an idea of how SQL Server is interacting with the hardware, but it’s doing it with several different types of counters that have to be measured differently. There are a couple great posts out on the internet to understanding how each one needs to be measured. As for right now, we’re focusing on why you want to watch it and what it will do for you. And wouldn’t you know that I saved the hardest one to describe for last, because each value is different and there will be controversy on which ones you should be watching and which ones are just a waste of your time monitoring.

PLE is one that this is no controversy about if you should watch it. This is the average age of the pages in cache, and a good measurement of when you’re doing too many physical reads. Peeking in on this from time to time is good, you can make sure it’s high enough. However, watching this will let you know when it’s dropping and help you dive into why it’s dropping. If you’re also running a trace you can see what did a lot of reads in that timeframe, and if you’re capturing Query Stats then you can find out which one of your large read queries is doing all the physical reads that destroy your PLE.

Server Target and Total memory are also great to watch even though they rarely change after the server is back up to speed following a restart of SQL services. The target memory is how much SQL Server would like to have, and Total memory is how much it actually does have. If target memory drops then there’s OS pressure you need to worry about. Also, as Total memory is increasing then it means the server is still filling the cache and PLE can pretty much be ignored. Of course it’s low, everything in there is new.

Page Lookups, Page Reads, Page Writes and Lazy Writes give you a better idea of how SQL Server is interacting with your cache and disks. If you want a real picture of what SQL Server is doing behind the scenes, this is much more useful than glancing at cache hit ratio.

Deadlocks and Memory Grants Pending are two things you’d like to always see at zero. That may be out of the question with deadlocks, but if you’re getting above zero on memory grants than you need to find out when that happened and everything that was running at that time.

SQL Compilations and Recompilations are hidden CPU hogs. They’re never going to be at zero because very few servers have stuff stay in cache forever, ad-hoc code runs, and several other reasons. However, when one of these counters jump up, you may have a hidden cause. For instance, if a piece of code that runs every minute or more was written in a way that it can’t be stored in cache, these numbers will be noticeably higher. When this happens, your CPU will be noticeably more stressed with no other indicators as to why, and this query won’t even show up in your Query Stats as it relies on showing the stats of everything that’s currently in cache.

Database Sizes

This seems simple, and it is. Most people know how to find the sizes of their databases, but do you track it? If not, you should. Some questions you should be able to answer readily are how fast your databases are growing, when will you run out of space, and is the steady decline in PLE justified by the growth of the database. Getting into more details, you can touch on when does the database grow such as an accounting app jumping in size every April, or answering if the data is growing steadily or exponentially. These are important things to know when it comes time to budget for new servers and disk space.

On the other side of things, you also have information to push back on application teams on how much space they’re using and if that’s really necessary. A lot of times you’ll find that they’re surprised by their growth to the point that you have to show them the numbers for them to believe it. It’s not uncommon for the response from this to be a cleanup project that helps keep the databases smaller and running faster, which is a big goal being accomplished.

Table Sizes

This is just building off of watching your database sizes in more detail. When a database starts filling up, and it’s filling up quick, it’s good to tell an application team what table is doing it. In my current environment it makes sense to watch every table that is over .5% of the database size AND over 100 MB. This means that some databases we don’t watch anything and other databases we watch about the 20 biggest tables. The biggest point is that we’re not trying to watch everything while watching anything big enough to make a difference to us.

If a database is filling a lot faster than normal then a lot of the time there’s a process that isn’t running like it should. The biggest table in the database may be a rather static value, but the third biggest table in the database wasn’t even on your radar two weeks ago. The app teams love it when you can tell them that not only is the database growing out of control, but we also see the growth in table X which has been growing at 200 MB per day starting on the first Saturday of last month. By the way, wasn’t last Saturday the day you changed a couple procs around or implemented an upgrade?

Now you just graduated from “Hey, there’s a problem” to also include “and here’s a huge lead to finding the root cause.” It’s rare to use this information, but it’s lightweight to capture for something that gives you a heavyweight appearance when you reference this knowledge to other teams.

My Critiques

I’d love to hear what you think about this presentation. Before we get to that, here are the complaints from the most harsh person in the room when I was presenting…

I didn’t do enough to say what each thing was. Although I marked the presentation as intermediate, you don’t know who’s going to be there. In this case, I knew a couple people in the crowd, and they ranged from data analyst to database manager. It should have been presented in more of “If you’re not ready for intermediate then you have everything you need, but it will take some effort to keep up”.

My slides were lacking. I stick with the idea that there should be few words on the screen, but I took it too far. I admitted above that the slides were there more to keep me on track then it was to help the audience, which I probably needed for my first public speaking venue. However, next time I’ll be less nervous and more prepared to make it geared for the audience better. Instead of random pictures, I should have more of graphs and data that I can talk through to give the audience visuals.

Nothing was given to the audience, and there should have been something. To be fair, none of the presentations that I know of handed anything to the audience. However, I set my own bar, and I feel that people would like a sheet of paper with the outline on it with links to online sources. My blog, of course, because it shows how to grab everything. Also, an outside link for each item. This gives a physical reminder to turn this knowledge into action.

There was no demo, and that was the audience’s biggest complaint. While I’m still not sure that I would do a live demo on my second go around, I’m going to hold myself on the remark above about my slides having graphs and data. That would also drag me away from the podium to talk through the slides, making it a more dynamic and interesting presentation; that would really help the audience take things in.

Your Turn

Now I need you to do two things. Put this information to work in your environment is what will help you the most. What will help me the most is you commenting on this post both before and after you implement any of this so you can help me become a better presenter.

Thank you for taking the time to read this!

White papers, DMVs and Monitoring Concepts

According to Jason Strate (SQL Server MVP) there are 5 white papers ever SQL Server DBA should read. Considering my blogging is focused on how to monitor and tune previously installed servers, we’ll focus on the first two of these white papers: Troubleshooting Performance Problems in SQL Server 2008 and SQL Server 2005 Waits and Queues. Ignore the age of these papers, after all you’re running mostly the same code to do mostly the same tasks in SQL Server 6.5 and SQL Server 2012…well, except for the lack of DMVs in 6.5.

I don’t agree with them 100%, but not to the point that I don’t agree 100% that everyone should read them. The sources of the information, the uses of the DMVs, all of it is what you’ll need to know to progress as a DBA. Ask any DBA III or higher and they’ll be able to tell you most of what’s said in these papers, even if they went the hard route and made it to where they are without reading them. Where they fall short is by giving static values that aren’t worded as just starting points, and the trending they do is the trending you can do as a Microsoft Support Engineer where nothing is saved or long-term.

My differences range from minor complaints such as me feeling that a blanket statement saying Page Life Expectancy under 300 seconds is when you should start to worry. First, it’s a blanket statement, and I’m a firm believer in that the word always means you’re almost always wrong. Second, servers have more memory now and my SAN team would kill me if I told them I would have to read 500 GB of data every 300 seconds on some of my bigger servers. I do agree that there are times you should worry about PLE, but you have to keep in mind two things; how much memory are you cycling through in that time and what’s normal for this specific server.  I just hope that regular drops down to 3,000 are closer to the point that grabs your attention.

Lets build off of that last big there…What’s normal for this specific server? The queries they’re giving you are snapshots of what info the server has for you right now. Some of that is what’s happening now, some is what has happened since the last restart of SQL services, and other pieces fall somewhere in the middle. You can’t tell me that I’ve done X amount of work since the server was rebooted two weeks ago and expect me to tell you if that’s an issue. You also can’t tell me that “This proc has used more CPU than any other by far since that reboot two weeks ago” and expect me to tell you if that’s a critical issue or an intense, yet normal and expected, off-hours maintenance task. You can, however, tell me that on a typical Monday between 2:00 PM and 5:00 PM we do X, but this Monday we’re doing 100 times that while the business has no reason to say this Monday should be any different. Now we’re on to something…we know there’s an issue, and we just found our starting point to solving it. Chances are if you’re reading this then either you or your immediate manager would be able to create a database to keep this trending information if it doesn’t exist already, and you’re making it harder on yourself if you don’t.

These two white papers are almost 200 pages long together, so I’m not going to pretend to cover them in one post. Actually, I’m hoping my next 50 posts might cover most of them. The points I’m going for are that these white papers are telling you the perfect stats to watch, and these stats are going to be more meaningful when you monitor them. I’ve started this in some of my posts such as looking at Wait Stats and Blocking, but there’s a long way to go. More than these two DMVs need to be watched like this, but they are good examples of wait stats capturing cumulative data and blocking capturing point-in-time data from DMVs.

If you see a DMV in these papers, you’ll see it in my posts. If it’s not there yet, it will be. Some like dm_db_exec_sessions will never have a dedicated post unless you count Running Processes which links it to several DMVs. Others like dm_os_performance_counters might not fit comfortably in a single post. The point here is that all the DMVs, especially the ones mentioned here, are worth looking into more.

Read the papers. Always go beyond the question of “what should I do”, get to the “why should I do it”, and strive for “how can I make this mean more and be more useful”. Never take my scripts or anyone else’s as-is. Tinker with them, really learn the DMVs, and it will all start to come together.

Blocking – Capturing and Monitoring

If a query is taking longer to run than normal, there’s a good chance it’s being blocked by something else. This is especially true when you’re doing something rediculously simple and SQL Server just sits there thinking. Symptoms of blocking problems include a trace of SQL:BatchCompleted and RPC:Completed with durations over 10 seconds comes back with results using less than a second of CPU time and very few reads and writes. Also, if you’re watching your wait stats, then you’ll start to see more waits that start with LCK_ than normal.  Not the mention the overly obvious blocking found in your Running Processes

This query will show you the blocking currently occurring on your server.

SELECT tl.resource_type
    , database_name = DB_NAME(tl.resource_database_id)
    , assoc_entity_id = tl.resource_associated_entity_id
    , lock_req = tl.request_mode
    , waiter_sid = tl.request_session_id
    , wait_duration = wt.wait_duration_ms
    , wt.wait_type
    , waiter_batch = wait_st.text
    , waiter_stmt = substring(wait_st.text,er.statement_start_offset/2 + 1,
                abs(case when er.statement_end_offset = -1
                then len(convert(nvarchar(max), wait_st.text)) * 2
                else er.statement_end_offset end - er.statement_start_offset)/2 + 1)
    , waiter_host = es.host_name
    , waiter_user = es.login_name
    , blocker_sid = wt.blocking_session_id
    , blocker_stmt = block_st.text 
    , blocker_host = block_es.host_name
    , blocker_user = block_es.login_name
FROM sys.dm_tran_locks tl (nolock)
    INNER JOIN sys.dm_os_waiting_tasks wt (nolock) ON tl.lock_owner_address = wt.resource_address
    INNER JOIN sys.dm_os_tasks ot (nolock) ON tl.request_session_id = ot.session_id AND tl.request_request_id = ot.request_id AND tl.request_exec_context_id = ot.exec_context_id
    INNER JOIN sys.dm_exec_requests er (nolock) ON tl.request_session_id = er.session_id AND tl.request_request_id = er.request_id
    INNER JOIN sys.dm_exec_sessions es (nolock) ON tl.request_session_id = es.session_id
    LEFT JOIN sys.dm_exec_requests block_er (nolock) ON wt.blocking_session_id = block_er.session_id
    LEFT JOIN sys.dm_exec_sessions block_es (nolock) ON wt.blocking_session_id = block_es.session_id 
    CROSS APPLY sys.dm_exec_sql_text(er.sql_handle) wait_st
    OUTER APPLY sys.dm_exec_sql_text(block_er.sql_handle) block_st

However, if you have time to watch your servers all the time and keep running that query then you’re overstaffed in a world that leans towards being understaffed. For way too many reasons to list here, throw the results of this into a table every minute. If someone says a query ran long and you see the server was waiting on locks, look here. If you want to be proactive and look for ways you can improve server performance, look here.

    BlockingID BigInt Identity(1,1) NOT NULL
    , resource_type NVarChar(60)
    , database_name SysName
    , assoc_entity_id BigInt
    , lock_req NVarChar(60)
    , wait_spid Int
    , wait_duration_ms Int
    , wait_type NVarChar(60)
    , wait_batch NVarChar(max)
    , wait_stmt NVarChar(max)
    , wait_host SysName
    , wait_user SysName
    , block_spid Int
    , block_stmt NVarChar(max)
    , block_host SysName
    , block_user SysName
    , DateAdded datetime NOT NULL DEFAULT (GetDate())

CREATE UNIQUE CLUSTERED INDEX IX_Blocking_DateAdded_BlockingID_U_C ON Blocking
    , BlockingID
) WITH (Fillfactor = 95)

So, now you know what your blocking problems are. So, how do you fix them? That’s a deeper dive than I’ll be doing now, but here’s they key words to look into:

  • NOLOCK hint
  • Query tuning
  • Process timing
  • Eliminate cursors

Every situation is going to be unique, and I can’t pretend like I can give you all the answers. Finding that there is an issue can be difficult enough, and those are the answers I can help with.

Wait Stats – Monitoring and Using

sys.dm_os_wait_stats is one of the most important DMVs out there, and one that you should know the historical values of for every server you care about. This DMV will tell you the cumulative amount of time you waited for each wait type since SQL Server services started, which is nice to know, but limited in its usefulness as-is.

If, however, you knew what the values were 30 minutes ago and what they are now then you could know why recent queries were waiting. Take it a step further and know what you were waiting for in the same 30 minute period yesterday and you have a bit of a baseline to go off of. Now you can compare what you’re waiting for now to your baseline so you can say if what you’re seeing now is just normal for the server or if you’re seeing a true incident. Remember, the only way to know if numbers on a server are a problem are not is to know what’s normal for that specific server.

A half-hour increment is a bit much for me, and I would prefer to know it every 5 minutes to give a little more detail. Also, just looking at yesterday’s stats for comparison isn’t good enough for me, especially on when looking into an incident on a Monday where Sunday isn’t a very good baseline. Because of that, I want to look at the baseline data from the same time period yesterday and exactly a week ago.

To capture the data you need to capture a snapshot of sys.dm_os_wait_stats along with the time you grabbed it. Then grab it again, do a diff between the two values, and save that off with the starting and ending times. Keep this running every so many minutes in a SQL job and you have a running baseline to compare what you’re seeing now to what the server is typically doing.

After that, keep the data trimmed down to what you’d actually use. Personally, I’m a huge fan of 13 months. This running every 5 minutes for 13 months will accumulate about 750 MB of data, and you can adjust these numbers as you see fit.

Here are the tables; I comment out stuff if I would cry if I accidently ran it at the wrong time.

    DROP TABLE WaitStats

    DROP TABLE WaitStatsLast

    DateStart datetime
    , DateEnd datetime
    , wait_type nvarchar(60)
    , waiting_tasks_count bigint
    , wait_time_ms bigint
    , max_wait_time_ms bigint
    , signal_wait_time_ms bigint

CREATE CLUSTERED INDEX IX_WaitStats_DateStart_waittype_U_C ON WaitStats
    , wait_type
) WITH (Fillfactor = 95)

CREATE TABLE WaitStatsLast (
    DateAdded datetime
    , wait_type nvarchar(60)
    , waiting_tasks_count bigint
    , wait_time_ms bigint
    , max_wait_time_ms bigint
    , signal_wait_time_ms bigint

CREATE CLUSTERED INDEX IX_WaitStatsLast_waittype_U_C ON WaitStatsLast
) WITH (Fillfactor = 95)

Notice the lack of creativity. It is very intentional. The code is more stable as I’m copying the field sizes directly from the DMV, and the data is more legible to an outsider (new employee, Microsoft engineer, consultant, vendor) because the fields are exactly the same as the DMV they’re used to seeing. I’m not a fan of non-creative solutions, so there’s a reason to go with it when I decide to be boring.

Now to populate the tables. This script will need to be ran on a regular schedule. Preferably, throw it in a proc and run that proc every 5 minutes, but that’s just my opinion in a discussion about how to watch your servers.

IF OBJECT_ID('tempdb..#WaitStatsNew') IS NOT NULL BEGIN
    DROP TABLE #WaitStatsNew 

    , wait_type
    , waiting_tasks_count 
    , wait_time_ms
    , max_wait_time_ms
    , signal_wait_time_ms 
INTO #WaitStatsNew
FROM sys.dm_os_wait_stats

INSERT INTO WaitStats (DateStart, DateEnd, wait_type, waiting_tasks_count, wait_time_ms, max_wait_time_ms, signal_wait_time_ms)
SELECT DateStart = ISNULL(l.DateAdded, (SELECT create_date FROM sys.databases WHERE name = 'tempdb'))
    , DateEnd = n.DateAdded
    , wait_type = n.wait_type
    , waiting_tasks_count = n.waiting_tasks_count - ISNULL(l.waiting_tasks_count, 0)
    , wait_time_ms = n.wait_time_ms - ISNULL(l.wait_time_ms, 0)
    , max_wait_time_ms = n.max_wait_time_ms --It's a max, not cumulative
    , signal_wait_time_ms = n.signal_wait_time_ms - ISNULL(l.signal_wait_time_ms, 0)
FROM #WaitStatsNew n
    LEFT OUTER JOIN WaitStatsLast l ON n.wait_type = l.wait_type AND l.DateAdded > (SELECT create_date FROM sys.databases WHERE name = 'tempdb')


INSERT INTO WaitStatsLast (DateAdded, wait_type, waiting_tasks_count, wait_time_ms, max_wait_time_ms, signal_wait_time_ms)
SELECT DateAdded
    , wait_type
    , waiting_tasks_count
    , wait_time_ms
    , max_wait_time_ms
    , signal_wait_time_ms
FROM #WaitStatsNew 

DROP TABLE #WaitStatsNew

Finally, querying it. Give it a descent time frame, I typically start at 2 hours to get an overall picture without having a single event skew the numbers too much. Then look at your most relevant baseline data – yesterday and 7 days ago during the same timeframe.

DECLARE @dStart datetime
    , @dEnd datetime

SELECT @dStart = DATEADD(Hour, -2, GETDATE())
    , @dEnd = GETDATE()

IF OBJECT_ID('tempdb..#IgnoredWaits') IS NOT NULL BEGIN
    DROP TABLE #IgnoredWaits

CREATE TABLE #IgnoredWaits (Wait_Type NVarChar(60) PRIMARY KEY) 
--Values taken from p25 of "Troubleshooting SQL Server - A Guide for the Accidental DBA" by Jonathan Kehayias and Ted Krueger  
INSERT INTO #IgnoredWaits 

SELECT TOP 7 TimeFrame = 'Right Now'
    , DateStart = MIN(DateStart)
    , DateEnd = MAX(DateEnd)
    , wait_type
    , wait_minutes = SUM(wait_time_ms)/1000/60
FROM WaitStats
WHERE DateStart >= @dStart 
    AND DateEnd <= @dEnd 
    AND Wait_Type NOT IN (SELECT Wait_Type FROM #IgnoredWaits)
GROUP BY wait_type

SELECT TOP 7 TimeFrame = 'Yesterday'
    , DateStart = MIN(DateStart)
    , DateEnd = MAX(DateEnd)
    , wait_type
    , wait_minutes = SUM(wait_time_ms)/1000/60
FROM WaitStats
WHERE DateStart >= @dStart - 1
    AND DateEnd <= @dEnd - 1
    AND Wait_Type NOT IN (SELECT Wait_Type FROM #IgnoredWaits)
GROUP BY wait_type

SELECT TOP 7 TimeFrame = 'Last Week'
    , DateStart = MIN(DateStart)
    , DateEnd = MAX(DateEnd)
    , wait_type
    , wait_minutes = SUM(wait_time_ms)/1000/60
FROM WaitStats
WHERE DateStart >= @dStart - 7
    AND DateEnd <= @dEnd - 7
    AND Wait_Type NOT IN (SELECT Wait_Type FROM #IgnoredWaits)
GROUP BY wait_type

The only thing left to do is wait and interpret the data. All the wait types mean something, with some easily pointing to an issue and others just being normal operations. Doing a dive into what wait types are out there, what they mean to you, and what they mean when they’re not what they were yesterday isn’t something I’m going to get into with this post.  However, there is an Accidental DBA book out there that explains this amazingly well.  Here are links to the FREE PDF Version and the Physical Book.