Query the Buffer Pool

DBAs are known for asking for more memory, but often can’t say what’s in memory.  While I agree that many database servers can use more memory, I’m a firm believer in knowing how you’re using your resources before asking for more.  The script below allows me to do just that.

What It Returns

This will return every index that is using at least 1 MB of memory for every database on your server.  It also returns space in memory that is associated with unallocated space in the tables which shows up as NULL for everything except the size of the space and the table name.

I’ll warn you now that the unallocated space can be surprisingly high for TempDB, and I talk about that in TempDB Memory Leak?.  Hopefully we can get a good comment thread going on that post to talk through what we’re seeing and how common the issue really is.

The Script

	DROP TABLE #BufferSummary

	DROP TABLE #BufferPool

	Cached_MB Int
	, Database_Name SysName
	, Schema_Name SysName NULL
	, Object_Name SysName NULL
	, Index_ID Int NULL
	, Index_Name SysName NULL
	, Used_MB Int NULL
	, Used_InRow_MB Int NULL
	, Row_Count BigInt NULL

	, allocation_unit_id
	, database_id
INTO #BufferSummary
FROM sys.dm_os_buffer_descriptors 
GROUP BY allocation_unit_id, database_id 
DECLARE @DateAdded SmallDateTime  
SELECT @DateAdded = GETDATE()  
DECLARE @SQL NVarChar(4000)  
SELECT @SQL = ' USE [?]  
INSERT INTO #BufferPool (
	, Database_Name 
	, Schema_Name 
	, Object_Name 
	, Index_ID 
	, Index_Name 
	, Used_MB 
	, Used_InRow_MB 
	, Row_Count 
SELECT sum(bd.Pages)/128 
	, DB_Name(bd.database_id)
	, Schema_Name(o.schema_id)
	, o.name
	, p.index_id 
	, ix.Name
	, i.Used_MB
	, i.Used_InRow_MB
	, i.Row_Count     
FROM #BufferSummary AS bd 
	LEFT JOIN sys.allocation_units au ON bd.allocation_unit_id = au.allocation_unit_id
	LEFT JOIN sys.partitions p ON (au.container_id = p.hobt_id AND au.type in (1,3)) OR (au.container_id = p.partition_id and au.type = 2)
		SELECT PS.object_id
			, PS.index_id 
			, Used_MB = SUM(PS.used_page_count) / 128 
			, Used_InRow_MB = SUM(PS.in_row_used_page_count) / 128
			, Used_LOB_MB = SUM(PS.lob_used_page_count) / 128
			, Reserved_MB = SUM(PS.reserved_page_count) / 128
			, Row_Count = SUM(row_count)
		FROM sys.dm_db_partition_stats PS
		GROUP BY PS.object_id
			, PS.index_id
	) i ON p.object_id = i.object_id AND p.index_id = i.index_id
	LEFT JOIN sys.indexes ix ON i.object_id = ix.object_id AND i.index_id = ix.index_id
	LEFT JOIN sys.objects o ON p.object_id = o.object_id
WHERE database_id = db_id()  
GROUP BY bd.database_id   
	, o.schema_id
	, o.name
	, p.index_id
	, ix.Name
	, i.Used_MB
	, i.Used_InRow_MB
	, i.Row_Count     
HAVING SUM(bd.pages) > 128  

EXEC sp_MSforeachdb @SQL

	, Pct_of_Cache = CAST(Cached_MB * 100.0 / SUM(Cached_MB) OVER () as Dec(20,3))
	, Pct_Index_in_Cache = CAST(Cached_MB * 100.0 / CASE Used_MB WHEN 0 THEN 0.001 ELSE Used_MB END as DEC(20,3))
	, Database_Name 
	, Schema_Name 
	, Object_Name 
	, Index_ID 
	, Index_Name 
	, Used_MB 
	, Used_InRow_MB 
	, Row_Count 
FROM #BufferPool 

Where’d the Script Come From

I’ve had a script similar to this one around for a while.  It’s originally based off of Jonathan Kehayias’s script on his post Finding What Queries in the Plan Cache Use a Specific Index, and I couldn’t have done this without having his script to start with.

Then I originally posted a version of this script on my post Cleaning Up the Buffer Pool to Increase PLE, which was great to see the index usage for a single database.  It runs slower than this, only returns a single database, and does not show unallocated space in memory.  Those changes warranted either an update to that post or a completely new post…I opted for the latter.

What It Means

Now you can see what’s in your memory. Hopefully you’ll see one or two things that stand out on here that don’t make sense; those are your easy tuning opportunities.

If an index is 100% in cache then you’re scanning on it, and that may be an issue.  Yes, you can find when you did scans on indexes using the scripts in my Indexes – Unused and Duplicates post, but it helps to have another view of what that means in your memory.

One thing the index monitoring scripts in the post I just mentioned can’t do is tell you when you’re doing large seeks as opposed to small seeks.  With the typical phone book example, you could ask for all the names in the phone book where the last names begins with anything from A to Y, giving you 98% of the phone book as results.  Index usage stats will show you did a seek, which sounds efficient.  The script on this post will show that you have 98% of your index in cache immediately after running the query, and that gives you the opportunity to find the issue.

When you see an index that looks out of place here, dive back into the scripts on Cleaning Up the Buffer Pool to Increase PLE to see what’s in cache using that index.  If the query isn’t in cache for any reason, you may be able to look at the last time the index had a scan or seek against it in sys.dm_db_index_usage_stats and compare that to results from an Extended Events session you had running to see what it could have been.

The main point is that you have something to get you started.  You have specific indexes that are in memory, and you can hunt down when and why those indexes are being used that way.  It’s not always going to be easy, but you have a start.

We’re All On a Budget

It’s not too uncommon for this process to end in asking for more memory, and I view memory like being on a budget.  The amount of memory you have right now is your current budget.  Asking for more memory should be viewed like asking for more money in a financial budget.  For a financial budget increase, here are the questions I’d be prepared to answer:

  1. What did you spend the money we already gave you on?
  2. Did you spend that money as efficiently as possible?
  3. What else do you want to spend money on?

Now you can answer these questions in database form:

  1. Here’s what I have in cache at multiple times, specifically right after PLE dropped.
  2. I went through the queries that pulled the data into cache and tuned what I could.
  3. When I checked what’s in cache multiple times, these indexes fluctuated a lot in how much was in there.  I believe adding more memory would allow them to stay in cache instead of kicking each other out to make room.

Be it Virtual or Physical environments, there’s only so much memory that can be made available to us.  We’re on budgets of how much memory the host has, how many memory slots a server has, and how large the memory chips are that those slots can handle.  Prove you’re doing it right and it’s a lot harder to say no to you.

I have an odd habit of getting the memory I ask for because I answer these questions up front in the initial request for memory.


Query Stats

DBAs rarely use the full potential of sys.dm_exec_query_stats.  It’s common to see the queries for looking at the most expensive queries according to any of the stats contained within the current cache, which is great to see.  However, if you grab snapshots of this information the proper way then you can get more detail that will really make a difference in your job.  Capturing this every 4 hours is probably enough, and here’s what it’s going to do for you.

First, using it the way most DBAs do and look at the most expensive queries according to a specific metric.  It works the same for them all, so we’ll just say we’re interested in physical reads in this case because our PLE isn’t what we’re hoping it could be.  You’ve probably seen the queries that directly query sys.dm_exec_query_stats to give you this similar to the one on MSDN for this DMV.  That will give you everything that’s in the cache right now, which could contain queries that recompile every hour in combination with other queries that have remained in the cache since the server was rebooted two weeks ago.  This means that when you’re querying to see what you can do to improve your PLE during business hours you’re getting anything that happens to be there now, not what ran in the time window you’re looking to improve.

Now look at it from the point of view that you capture this information every 4 hours.  You can get the total number of physical reads grouped by sql_handle where the interval_end time is in the last month, on weekdays, between 9:00 AM and 5:00 PM.  This is both a more complete and more accurate picture of what you care about, and you’re more likely to be focusing on the queries you should be tuning from the start.

Once you do start tuning, you want to know what effect you really had, and not just rely on the couple sets of test parameters you used in your NonProd environments.  A couple days after your changes made it to production, it’s a good idea to make sure you had the desired effect.  Query the data you have saved off for the last three days and for the same days last week.

What I went over so far is just realizing how this takes the traditional use of this to a new level, but there’s more functionality here now that it’s being saved off.  Now this is extremely useful in incident response.  Before you could see if anything was hogging resources as seen in my Recently Recompiled Resource Hogs post, but now you take it a step further by being able to verify if what’s running long now has always run that way or if something is different.  Also, with having the plan_handles saved off you can even see if it’s getting a different execution plan, which will help you determine if this was a change in data or if it’s a bad execution plan.

Not to shoot myself in the foot, but this isn’t perfect.  If you capture this data every 4 hours starting at 12:00 and it recompiles at 3:59 then you’ll only capture one minute for that interval.  Also, if you’re dealing with a query that can’t be cached then it won’t be found here at all; this is why you’re also running traces to find the big queries and watching sys.dm_os_performance_counters to know when there are a lot of compiles and recompiles.

To start capturing this, here are the tables you’ll need to capture everything.




    DROP TABLE QueryStats

    DROP TABLE QueryStatsLast

    DROP TABLE QueryText

CREATE TABLE [dbo].[QueryStats](
    [interval_start] [smalldatetime] NOT NULL,
    [interval_end] [smalldatetime] NOT NULL,
    [sql_handle] [varbinary](64) NOT NULL,
    [plan_handle] [varbinary](64) NOT NULL,
    [statement_start_offset] [int] NOT NULL,
    [statement_end_offset] [int] NOT NULL,
    [execution_count] [int] NOT NULL,
    [total_elapsed_time_ms] [int] NOT NULL,
    [min_elapsed_time_ms] [int] NOT NULL,
    [max_elapsed_time_ms] [int] NOT NULL,
    [total_worker_time_ms] [int] NOT NULL,
    [min_worker_time_ms] [int] NOT NULL,
    [max_worker_time_ms] [int] NOT NULL,
    [total_logical_reads] [int] NOT NULL,
    [min_logical_reads] [int] NOT NULL,
    [max_logical_reads] [int] NOT NULL,
    [total_physical_reads] [int] NOT NULL,
    [min_physical_reads] [int] NOT NULL,
    [max_physical_reads] [int] NOT NULL,
    [total_logical_writes] [int] NOT NULL,
    [min_logical_writes] [int] NOT NULL,
    [max_logical_writes] [int] NOT NULL,
    [creation_time] [smalldatetime] NOT NULL,
    [last_execution_time] [smalldatetime] NOT NULL,

--Wide is bad, if you have a lot of nonclustered indexes.  
--This is a high-writes, low-reads table, so a single clustered index is probably all I want.
--With Interval_End being the first key field, they're inserted in order and eliminate mass fragmentation
  -- as well as bad write performance.
CREATE UNIQUE CLUSTERED INDEX IX_QueryStats_intervalend_sqlhandle_statementstartoffset_planhandle_U_C ON QueryStats
    , sql_handle
    , statement_start_offset
    , plan_handle

CREATE TABLE [dbo].[QueryStatsLast](
    [sql_handle] [varbinary](64) NOT NULL,
    [plan_handle] [varbinary](64) NOT NULL,
    [statement_start_offset] [int] NOT NULL,
    [statement_end_offset] [int] NOT NULL,
    [objtype] [nvarchar](20) NOT NULL,
    [execution_count] [bigint] NOT NULL,
    [total_elapsed_time_ms] [bigint] NOT NULL,
    [min_elapsed_time_ms] [bigint] NOT NULL,
    [max_elapsed_time_ms] [bigint] NOT NULL,
    [total_worker_time_ms] [bigint] NOT NULL,
    [min_worker_time_ms] [bigint] NOT NULL,
    [max_worker_time_ms] [bigint] NOT NULL,
    [total_logical_reads] [bigint] NOT NULL,
    [min_logical_reads] [bigint] NOT NULL,
    [max_logical_reads] [bigint] NOT NULL,
    [total_physical_reads] [bigint] NOT NULL,
    [min_physical_reads] [bigint] NOT NULL,
    [max_physical_reads] [bigint] NOT NULL,
    [total_logical_writes] [bigint] NOT NULL,
    [min_logical_writes] [bigint] NOT NULL,
    [max_logical_writes] [bigint] NOT NULL,
    [creation_time] [datetime] NOT NULL,
    [last_execution_time] [datetime] NOT NULL,
    [DateAdded] [datetime] NOT NULL

CREATE UNIQUE CLUSTERED INDEX [IX_QueryStatsLast_sqlhandle_planhandle_statementstartoffset_U_C] ON [dbo].[QueryStatsLast] 
    [sql_handle] ASC,
    [plan_handle] ASC,
    [statement_start_offset] ASC

CREATE TABLE [dbo].[QueryText](
    [sql_handle] [varbinary](64) NOT NULL,
    [QueryText] [nvarchar](max) NOT NULL,
    [DatabaseName] [nvarchar](128) NULL,
    [objtype] [nvarchar](20) NULL

CREATE UNIQUE CLUSTERED INDEX [IX_QueryText_sqlhandle_U_C] ON [dbo].[QueryText] 
    [sql_handle] ASC

Now that the tables are created, here’s how I populate them.  Set this up to run once every 4 hours to keep the data small during typical running, and kick it up to once an hour or more if needed to pinpoint an issue if you need it.

DECLARE @interval_start smalldatetime
    , @interval_end smalldatetime


    [sql_handle] [varbinary](64) NOT NULL,
    [plan_handle] [varbinary](64) NOT NULL,
    [statement_start_offset] [int] NOT NULL,
    [statement_end_offset] [int] NOT NULL,
    [objtype] [nvarchar](20) NOT NULL,
    [execution_count] [bigint] NOT NULL,
    [total_elapsed_time_ms] [bigint] NOT NULL,
    [min_elapsed_time_ms] [bigint] NOT NULL,
    [max_elapsed_time_ms] [bigint] NOT NULL,
    [total_worker_time_ms] [bigint] NOT NULL,
    [min_worker_time_ms] [bigint] NOT NULL,
    [max_worker_time_ms] [bigint] NOT NULL,
    [total_logical_reads] [bigint] NOT NULL,
    [min_logical_reads] [bigint] NOT NULL,
    [max_logical_reads] [bigint] NOT NULL,
    [total_physical_reads] [bigint] NOT NULL,
    [min_physical_reads] [bigint] NOT NULL,
    [max_physical_reads] [bigint] NOT NULL,
    [total_logical_writes] [bigint] NOT NULL,
    [min_logical_writes] [bigint] NOT NULL,
    [max_logical_writes] [bigint] NOT NULL,
    [creation_time] [datetime] NOT NULL,
    [last_execution_time] [datetime] NOT NULL,
    [DateAdded] [datetime] NOT NULL
--This sounded like a great idea, but it just slowed it down several seconds.
    , plan_handle
    , statement_start_offset

--The Cached Plans Object Type is in here in case you want to treat ad-hoc or prepared statements differently
SELECT qs.sql_handle
    , qs.plan_handle
    , qs.statement_start_offset
    , qs.statement_end_offset
    , cp.objtype
    , qs.execution_count 
    , total_elapsed_time_ms = qs.total_elapsed_time/1000
    , min_elapsed_time_ms = qs.min_elapsed_time/1000
    , max_elapsed_time_ms = qs.max_elapsed_time/1000 
    , total_worker_time_ms = qs.total_worker_time/1000 
    , min_worker_time_ms = qs.min_worker_time/1000
    , max_worker_time_ms = qs.max_worker_time/1000
    , qs.total_logical_reads
    , qs.min_logical_reads
    , qs.max_logical_reads
    , qs.total_physical_reads
    , qs.min_physical_reads 
    , qs.max_physical_reads
    , qs.total_logical_writes 
    , qs.min_logical_writes 
    , qs.max_logical_writes
    , qs.creation_time
    , qs.last_execution_time
    , DateAdded = getDate()
FROM sys.dm_exec_query_stats AS qs
    INNER JOIN sys.dm_exec_cached_plans cp on qs.plan_handle = cp.plan_handle
--WHERE cp.objtype NOT IN ('Adhoc')

INSERT INTO QueryText (sql_handle, QueryText, DatabaseName, objtype)
SELECT QS.sql_handle
    , QueryText = qt.text
    , DatabaseName = DB_NAME(max(qt.dbid))
    , max(QS.objtype)
FROM (SELECT #QS.sql_handle
        , #QS.objtype  
        FROM #QS
            LEFT JOIN QueryText QST ON #QS.sql_handle = QST.sql_handle
        WHERE QST.sql_handle IS NULL) QS
    CROSS APPLY sys.dm_exec_sql_text(QS.sql_handle) qt
GROUP BY QS.sql_handle
    , qt.text

--All the values are the same in each table
SELECT TOP 1 @interval_start = dateadded FROM QueryStatsLast
SELECT TOP 1 @interval_end = dateadded FROM #QS

IF @interval_start IS NULL BEGIN
    SELECT @interval_start = create_date
    FROM sys.databases
    WHERE name = 'tempdb'

INSERT INTO QueryStats (interval_start
    , interval_end
    , sql_handle
    , plan_handle
    , statement_start_offset
    , statement_end_offset
    , execution_count 
    , total_elapsed_time_ms
    , min_elapsed_time_ms 
    , max_elapsed_time_ms
    , total_worker_time_ms  
    , min_worker_time_ms 
    , max_worker_time_ms 
    , total_logical_reads
    , min_logical_reads
    , max_logical_reads
    , total_physical_reads
    , min_physical_reads 
    , max_physical_reads
    , total_logical_writes 
    , min_logical_writes 
    , max_logical_writes
    , creation_time
    , last_execution_time)
SELECT @interval_start
    , @interval_end
    , qs.sql_handle
    , qs.plan_handle
    , qs.statement_start_offset
    , qs.statement_end_offset
    , qs.execution_count - ISNULL(qsl.execution_count, 0)
    , qs.total_elapsed_time_ms - ISNULL(qsl.total_elapsed_time_ms, 0)
    , qs.min_elapsed_time_ms 
    , qs.max_elapsed_time_ms 
    , qs.total_worker_time_ms - ISNULL(qsl.total_worker_time_ms, 0)
    , qs.min_worker_time_ms
    , qs.max_worker_time_ms 
    , qs.total_logical_reads - ISNULL(qsl.total_logical_reads, 0)
    , qs.min_logical_reads 
    , qs.max_logical_reads 
    , qs.total_physical_reads - ISNULL(qsl.total_physical_reads, 0)
    , qs.min_physical_reads 
    , qs.max_physical_reads 
    , qs.total_logical_writes - ISNULL(qsl.total_logical_writes, 0)
    , qs.min_logical_writes 
    , qs.max_logical_writes 
    , qs.creation_time
    , qs.last_execution_time
    LEFT OUTER JOIN QueryStatsLast qsl ON qs.sql_handle = qsl.sql_handle
        AND qs.plan_handle = qsl.plan_handle
        AND qs.statement_start_offset = qsl.statement_start_offset
        AND qs.creation_time = qsl.creation_time 
WHERE qs.execution_count - ISNULL(qsl.execution_count, 0) > 0 --Only keep records for when it was executed in that interval


INSERT INTO QueryStatsLast
SELECT sql_handle
    , plan_handle
    , statement_start_offset
    , statement_end_offset
    , objtype
    , execution_count 
    , total_elapsed_time_ms
    , min_elapsed_time_ms 
    , max_elapsed_time_ms
    , total_worker_time_ms  
    , min_worker_time_ms 
    , max_worker_time_ms 
    , total_logical_reads
    , min_logical_reads
    , max_logical_reads
    , total_physical_reads
    , min_physical_reads 
    , max_physical_reads
    , total_logical_writes 
    , min_logical_writes 
    , max_logical_writes
    , creation_time
    , last_execution_time
    , DateAdded

Fixing Page Life Expectancy (PLE)

What is Page Life Expectancy (PLE), what makes it drop, and how can I manage memory better? Abusing disks slows many database servers, and it’s often something you can fix with tuning and not spending extra money on better disks or more memory. It takes a very long post to get through all of that, but if you stick with me through this then you’ll be looking at your servers from new angles with an effort that will be noticed by the Sys Admins, SAN Admin, the users, and your boss.

Before we being, there are some ground rules we need to get out of the way defining PLE and understanding there are external memory factors. There’s no shame in skipping that and going straight to the focus of this post. Personally, I find the next two sections a little dry, but I’m also my harshest critic!

Define PLE

Before we get too deep into it, lets make sure we’re on the same page on a couple things.

Page Life Expectancy is the number of seconds the average page of data has been in the buffer pool.  Keeping the data in memory gives SQL Server quicker access to it instead of making the long, slow trip to disk.  While none of the counters in SQL Server means everything on its own, this one can open your eyes and lead you towards issues that can be resolved.

Keep in mind that SQL reads the data pages from the buffer pool, always from the buffer pool. If the data you need isn’t there then SQL Server does a physical read to put it there. After that’s done it will do a logical read to use the page that’s now in memory. If you want to dive into the detail you can do it here, specifically with the reading pages and writing pages links on that page.

That physical read is going to disk, the slowest part of your server, to read the page(s) from disk, be processed by the CPU, possibly decrypting it if you’re using TDE, then placing it into memory. This makes PLE critical because, even if you ignore the extra load you just placed on the I/O system and CPU, you’re waiting for an additional, slower action to take place.

The speed of your disk does matter, but it also doesn’t really matter.  This is an EXTRA step with SQL Server doing a physical read THEN a logical read, not instead of a logical read.  Also, your persisted storage is the slowest part of your server, be it spinning disks, SSD, flash, or anything else.  The expensive stuff just isn’t AS slow.

You can find your page life expectancy in sys.dm_os_performance_counters using my post on OS Perf Counters. That post will also help you realize how much load you take off of your disks by raising PLE, then you’re really going to start to understand the burning drive picture you find there.  While it’s great that I have the code out there to get this yourself, the tracking of this counter should be coming from your monitoring software.

The problem is that many people see the 300 value for Page Life Expectancy you can still find documented is wrong, very wrong. If you have a server with 64 GB of memory with 56 GB allocated to SQL Server, that means you’re reading about 56 GB of data from disk every 300 seconds. If you look at Page 36 of Troubleshooting SQL Server – A Guide for the Accidental DBA by Jonathan Kehayias and Ted Krueger you’ll see an actual scalable version of this; PLE should be 300 for every 4 GB of RAM on your server. That means for 64 GB of memory you should be looking at closer to 4,800 as what you should view as a critical point.

The reason you see 300 written everywhere is because we were limited by 32-bit servers for so long while at the same time SQL Server documentation was really being developed well.  Those servers could only have 4 GB of memory, thus making this number make sense as well as the Buffer Pool / 4 GB * 300 formula.  I’ll go so far as to say that you should be cautious of anything that doesn’t have a formula to it because our servers keep getting faster, our databases keep getting bigger, and static values…don’t.

Jonathan Kehayias also put this in his blog post Finding What Queries in the Plan Cache Use a Specific Index, and that had a great series of comments going back and forth between him and Brent Ozar. Brent is discussing not putting so much faith in PLE and watching wait stats instead as different speed I/O systems can greatly sway the impact of low PLE or even make it so a high PLE just isn’t high enough. They’re both right, and if you can understand the intent of each one then you’ll be in a much more comfortable place when tuning your servers.

If you force me to take a side on that post, Jonathan wins out over Brent here. This is because Jonathan is talking about having a higher PLE, which in turn reduces load on the disks and typically reduces the amount of work that needs to be done to execute a query. Brent is basically implying that money may have already be thrown at the issue, which negates the current symptoms on what could be a less scalable solution. However, you can’t discount Brent’s logic here as the things that are limiting your server’s performance at this time lie in the Wait Stats, not this counter.

Quick Rant: We’re Not Alone

So, you want higher PLE to show that you’re using your resources better, and the only way PLE goes up is by leaving data in the data cache once it’s there. The problem is, there’s a lot going on that wants to work the other way. Even if you have 64 GB of memory dedicated to SQL Server to host a single 64 GB database it won’t all fit in memory, that’s because other things want to play as well.

First, your memory isn’t completely dedicated to the buffer pool. Memory pressure from the OS or VMs changing size can drop the size of the buffer pool has unless you’re using “lock pages in memory”, which is not recommended in most situations especially on VMs. If you’re not on a completely dedicated box, which means a physical box with no apps, no GUI, no RDP sessions, no SQL Server, no network card driver, etc., then this can happen. Wait a minute…you can’t uninstall SQL Server to make sure SQL Server’s buffer pool remains untouched, yet SQL Server uses memory outside of this area for any purpose that isn’t specifically assigned to the buffer pool and that memory usage can get out of control. If there’s memory pressure then some pages are forced out of data cache to drop the size of the buffer pool, leaving you open to more contention and a lower PLE.

Second, your buffer pool isn’t dedicated to your data cache as it also houses your proc cache, which are execution plans stored to be run again. These plans take a lot of CPU to create and reusing them saves you a lot of resources, so you can’t complain that you’re sharing space here. SQL Server determines the amount of the buffer pool to assign to each, and it’s possible for either side to have unnecessary data in it. I’m going to focus on the data cache here, so I’ll defer the abuse of proc cache conversation to Kimberly Tripp. There’s more to it than just her post, but this is a great place to start understanding what’s there and not in use.

Focus, Focus, Focus

Ok, so we know what PLE is and that external factors can affect it, but that’s not really why you’re here, is it? Are you here because you have your data cache and you want to manage it to keep your PLE going up…maybe a little. My guess is that you’re here because you heard that PLE is critical to your server’s performance and you want to increase performance by raising this counter the right way. You do that in two ways.

First, make sure you have as much room as possible. If your server can handle more memory it’s often cheaper to buy that much memory than it is to intervene on the database side. Tuning and purging scales well, will help a lot more than just memory management, and is the best thing you can do given the time budgeted to it, but it just isn’t as quick and easy as a memory upgrade in many cases. Even if you buy memory now and tune later, it will continue to help. 64 GB of memory (it’s 128 GB max on Standard Edition now), as Brent Ozar pointed out here, is only a couple hundred dollars here. FYI, I absolutely hate throwing money at issues, and I still listed this first.  Partially because it’s an easy fix to an often neglected issue, and partially because every fix is throwing money at the problem because your time isn’t worthless.

Second, keep as little in memory as possible to make efficient use of the space you have. Yeah, it’s that easy. Well, at least it’s that easy to say, but doing it takes a little more work. There are a list of ways you can trim down on the space you need.

  • Drop Unused Indexes
  • Merge Duplicate Indexes
  • Use Your Indexes – SARGability
  • Watch for Big Queries
  • Look in Your Proc Cache for Opportunities
  • Know What’s in Your Buffer Pool
  • Index Maintenance – Defrag
  • Index Maintenance – Statistics
  • Purge Your Data
  • Other

Through all of this, please remember that your initial thought may be to raise a counter, but is that your real goal? I’m betting you want your server to run faster, and PLE is merely one counter that helps put a value on performance. Servers with high PLE can run horrible, they just don’t need to keep running back to disk. Servers with low PLE can run great, they just need to go back to disk to get what they need.

A little bit ago I mentioned buying more memory, this will raise your PLE without making any queries really run more efficiently. Sure, they’re more likely to have their data in cache which is great, but they’re doing just as many logical reads and using just as much CPU to do their joins. The rest of this post isn’t like that, the rest is making queries more efficient in a way that just happens to make your PLE go up.

Drop Unused Indexes

This part’s tricky. SQL 2005-2008 R2 told you how much indexes were used since the last time SQL Server was started in the DMV dm_db_index_usage_stats. It’s still there in SQL 2012 and beyond, but these statistics get reset when you rebuild an index now. That means that best case scenario on a server that’s patched monthly, you have a month’s worth of data to go off of and you can’t even rely on that being the whole picture anymore. I get into more about how to keep track of this over time in my post Indexes – Unused and Duplicates.

This has a minor implication on PLE because as the pages of your unused indexes are updated SQL Server has to read part of the B+tree into memory to find the page, then it has to read the page to be updated into memory, it updates it in memory and marks it as a dirty page, then eventually writes it back to disk. The key here being that pieces are read into memory, AKA the buffer pool, the part of SQL Server you’re trying to clean up.

Merge Duplicated Indexes

This one’s not as tricky, but the performance issues are much worse. The problem is that you have two indexes, we’ll call them ix_A and ix_B, that are very similar. They have the same first two key fields, and stray a little after that. The differences mean that ix_A will be better for one query and ix_B will be better for another, and that means SQL Server will be dragging them both into cache. Sure, if you take the columns that are in ix_B that aren’t in ix_A and include them in ix_A then drop ix_B then the queries that used to use ix_B will probably need to do a little more work, but you’ll end up with a more efficient use of the cache. Your typical testing will show you the worst case scenario here, because you’ll run it each way multiple times and see what it’s like when the indexes are completely in cache. However, the real world is more friendly than that (that’s a first), and the query that used to use ix_B may actually run faster because now it’s using an index that’s more likely to be in cache, cutting down on physical reads. Going to the same blog post, Indexes – Unused and Duplicates, you can see details on how to help relieve this problem.

There’s a bit of an art to this at first, but you’ll get it down to a science with practice.

Use Your Indexes – SARGability

SARGability (Search ARGument capable) is it’s own topic, and it deserves at least one post of its own. You need to understand how indexes work, as stated in my post Indexing Fundamentals, and make sure that your queries can take advantage of indexes. If you look in a phone book for people with the last name like ‘%ood’ it’s no good because you need the first letter of the last name to take advantage of that index. If you look up people with the first name ‘Steve’ then that’s useless because the first key field was last name. Doing functions, implicit conversions, and many other techniques can cause you to need to scan an entire index.

In terms of PLE, that means you read the entire index into memory instead of just the pieces you need. That can add up to a lot of data being kicked out of cache to make room for a lot of physical reads from disk.

This isn’t an easy topic, and I’m not sure of any resources that say everything that needs to be said on the topic. While I have plans to write a post on it and will update this one when I do, it’s still an unwritten post. Until that time comes, Google “SQL Server SARGable” and see what pops up.

If you know how to read execution plans, there are two sections on a seek, scan, or lookup that you need to know here. Predicate and Seek Predicate. Predicate is what it had to scan for, while a seek predicate is what it could find efficiently using the fact that the key fields are sorted.

Watch for Big Queries

It doesn’t matter if you use Extended Events or Profiler Traces, you need to know when large queries are being run on your servers. If you want to watch this at a statement level then you’ll want to make the jump to Extended Events, but I’ve never seen it hurt to watch SQL Batch Completed and RPC Completed filtered only by duration over 10 seconds.

The point of this when it comes to PLE is that the queries that aren’t SARGable or don’t have an index to take advantage of will be doing a lot of scans. The bigger the scan the longer the query will take to run, and if you care about PLE then you’ll know what queries those are. Sure, some are legitimately doing enough work where they’ll always take that long to run, but most of the queries that take a while can be tuned. Be it changes to the query itself (should be your first instinct) or indexing changes, there’s often a way to have a query require less data in cache.

This is an extremely useful technique to use in correlation with watching for drops in PLE. If it drops then check to see what was running at the time. This shows you what queries were running, which would be most of your issues, but it could be outside of the query itself and actually be something like a large auto update statistics task being kicked off that you won’t see here.

Know What’s in Your Buffer Pool

Very few people do this, yet it’s so obvious once you start. You want to raise PLE, right? PLE is a measurement of how long stuff stays in your buffer pool, and drops because something else needed to be put in there. So, what’s in there that’s taking up all your space and how’d it get there? I felt like a fool that it took me years to come up with that questions, but then I realized that most people never ask it at all.

It was actually one of my biggest tuning revelations since I read Grant Fritchey’s Execution Plans book. And you always know it’s a great revelation when you find yourself yelling at yourself for not realizing it earlier…years earlier.

So, how do you do this? Query your cache to find the indexes that take up the most space there, pick one that is an excessive amount of space or doesn’t look like it belongs there, query the proc cache to find out where that index is used, and tune that query either through code changes or indexing changes. The scripts to do this and a more detailed description of the process can be found in my post Cleaning up the Buffer Pool to Increase PLE, and a newer post of mine, Query the Buffer Pool, has an even better script to see what’s in cache across all databases on the instance at once in a more efficient query.

Note that if your PLE is low then what’s in your buffer pool will be changing quickly.  That does NOT mean the scripts in the posts I just mentioned are useless, it means you’ll come up with new opportunities each time you run them.

Look In Your Proc Cache for Opportunities

Lets start by saying this isn’t perfect. These numbers get reset throughout the day and some queries never make it in here at all. That’s saying this isn’t an all-inclusive, one-stop shop. It is in no way saying that you can’t make amazing improvements on the queries you find here.

There are two way you can use the cache. Most people know that you can get your most expensive queries such as on the MSDN sys.dm_exec_query_stats page, and you can use your imagination to sort this by any of the counters available here. That will give you an idea of what needs tuning, if you needed somewhere to start. I love taking this a step further and monitoring that exact information so I have a historical view instead of just what happens to be in cache now. You can read all about that in my post Query Stats.

The other way is parsing through the XML in the plans to find specific items. I heard that grunt when I mentioned XML, and you’re not alone. Not many of us are good at XML; Jonathan Kehayias is an extreme exception here. He gives you his queries so you can run with it, do what he does, and alter it to go even further. This is exactly what I did in my post Cleaning up the Buffer Pool to Increase PLE that I just mentioned in the last section.

Index Maintenance – Defrag

Many people only thing about defragmenting their indexes to help get contiguous reads on their disk, but that’s only half the story. In fact, the better you do everything in this post the less you’ll have to worry about your disks.

However, you also have to keep in mind how full each page of your indexes is. If you have an index page that’s 100% full then you have a full 8kb of data there. Add another row and you now have 8.1kb of data that’s split between two 8kb pages, so you’re wasting almost 50%. 50% isn’t even your worst case scenario because pages are never automatically removed or merged if records are deleted unless you delete every row stored in that page. Paul Randal’s post Performance Issues From Wasted Buffer Pool Memory takes a deeper look at this problem, and Ola Hallengren’s scripts can help you with a solution with trusted scripts to clean up fragmentation.

Index Maintenance – Statistics

It may seem obvious to do your index defrag job off hours. While I’ve seen servers without the job, I’ve never seen one scheduled for mid-day. That’s a good thing, because a lot of data needs to be pulled into cache to play around with indexes like that.

However, do you have auto update statistics turned on? Do NOT turn it off because of this, but understand that it updates the statistics by reading about 1% of the index into cache. If that’s a 100 GB index then you’re reading 1 GB of data which is a descent portion of your cache. To help avoid this, you should be updating your stats off hours, and Ola Hallengren’s scripts can help with this, too.

You need to update your stats because as more and more updates are made to a table the less accurate your stats are, making your execution plans less accurate. Once you update about 20% of the rows of a table since the last time your statistics were updated then they’ll automatically be updated again if you leave the default option turned on. Again, don’t go turning it off because of this post alone. Instead, schedule your stats to be updated off-hours and only the tables that have 20% of their rows updated throughout the day will get their stats updated automatically, and that will stop most auto-updates on the larger tables that would cause issues.

Purge Your Data

We talked a lot about what to do with the data you have, but do you need all of your data? Step away from SQL Server once and go talk to an accountant. Ask them where they have paper work from last month and they’ll point to a filing cabinet. Now, knowing they’re legally obligated to keep a lot of stuff for 7 years, ask them where a 5-year-old document is. It’s not right in front of them, but they know where to look for it. It’s not taking up valuable space in their office (read: not on the main production server), often not in the same building they’re in (read: not online), and it will take a bit to get to it. They no longer have an active business use for it, and they can get to it in the rare case that it’s needed.

Ask them where something is that’s 8 years old, and they’ll find a friendly way to tell you that they bought an OCD pyromaniac a pair of scissors and a book of matches. We’re more eloquent with deleting things in batches, but their way sounds like a lot more fun.

If they kept everything in one room would you call that room a cluttered, unmanageable mess? If they kept historical records forever, would you think they were being obsessive and wasteful with purchasing storage?

So, can you pull up a detailed sales report for February 29th, 2000 from Prod for me?

Shrink TempDB

This one’s an eye opener, and one that I believe is a bug enough where I opened a connect item on the issue.  The larger your TempDB data files the more space you’ll find in your buffer pool used by unallocated pages in TempDB.

I go into details in my post TempDB Memory Leak?, but here are the basics.  TempDB can use memory up to the size of the data files, not just the size of the used space in the data files.  The only workaround I know of right now is to make the size of the data files smaller.

I’m not talking about making TempDB tiny and letting it grow, there’s no excuse for that.  Look at your monitoring software to see how large TempDB has to be to avoid hitting autogrowth outside of accidental issues, then size TempDB about 20% larger than that.  I specifically avoid the best practice of presizing TempDB to fill a dedicated drive for this reason alone.

Other…Am I Giving Up?

There are too many things to list in one blog post. You could write a book on this subject. I listed what I feel will help you the most, but I also wanted to take time to let you know that my list isn’t some magical, all-inclusive, everything you can do to make your servers run better. It’s a start. Hopefully a good one you found to be productive, but a mere start no matter how you look at it.

Speaking of “you could write a book on the subject”, well, they did. A lot of them did. Some even made them free PDFs. Not bootleg bit-torrent copies, but actual real, legal, free PDFs made possible by Red Gate in their book selection on SQL Server Central

Some books you have to buy, if that’s your thing:

Also, I’m not the only person blogging about this topic:


If you did all of this and there’s something running on your servers to trend performance, you’ll notice:

  • Page Life Expectancy: Raised significantly, but you saw that coming
  • Page Reads/sec: (Physical Reads) Dropped because we’re not cycling data in and out of cache as much
  • % Processor Time: Dropped due to lower I/O, more efficient queries, less pressure on procedure cache, etc.
  • Critical query execution time: Typically less due to less chance of waiting for physical reads and lower CPU stress.
  • PageIOLatch wait types: Dropped due to fewer physical reads leading to fewer waits on physical reads.
  • DBA Pay Rate: If this isn’t on the list, try using a chart of the above counters

Keep Reading

The largest part of memory management in SQL Server is indexing.  This can be changing the indexes themselves or how your queries interact with them.  Doing either requires a great understanding of what indexes are and how they work.  I’ve written several posts on the topic and recently added them to my Indexing page to help you browse them easier.

Recently Recompiled Resource Hogs

It’s not too uncommon for a query to get a new execution plan that performs a lot worse than it could, and sometimes it’s bad enough to drag the whole server down to a halt.  When it’s something obvious such as a query going from 2 seconds duration to 30 seconds you’ll pick it up on a standard monitoring trace.  That’s not always the case, and the query that hits your server twice a second going from 100ms to 900ms can fly under the radar except for users saying the server isn’t as responsive as it was yesterday.

Before you go all out and throw up an intrusive trace that can slow down your server capturing an unfiltered sample of RPC:Completed and SQL:BatchCompleted to run aggregates on, see what the DMVs can tell you.  If you look in sys.dm_exec_query_stats you can get the total amount of resources used since the last recompilation, which is going to give you pretty much everything you need as long as you’re looking for a query that can stay in the cache.  The only trick is to look at everything in the same context.  Something that did 10,000,000 reads since it was compiled yesterday is nothing compared to something that did 1,000,000 reads since it was compiled 10 minutes ago.  Here’s my answer to this:

SELECT top 10 --WorkerTime_Sec = cast(round(total_worker_time / 1000 / 1000.0, 0) as Int)
    WorkerTimeSec_PerSec =  cast(cast(round(total_worker_time / 1000 / 1000.0, 0) as Int) / (Cast(DateDiff(second, Creation_Time, GetDate()) as DEC(20,2)) + .00000001) as DEC(20,2))
    --, LogicalReads_K = total_logical_reads / 1000
    , LogicalReads_K_PerSec = cast(total_logical_reads / 1000 / (Cast(DateDiff(second, Creation_Time, GetDate())as DEC(20,2)) + .00000001) as DEC(20,2))
    --, ElapsedTime_Sec = cast(round(total_elapsed_time / 1000 / 1000.0, 0) as Int)
    , ElapsedTimeSec_PerSec = cast(cast(round(total_elapsed_time / 1000 / 1000.0, 0) as Int)/ (Cast(DateDiff(second, Creation_Time, GetDate())as DEC(20,2)) + .00000001) as DEC(20,2))
    , Seconds_Since_Recompile = DateDiff(second, Creation_Time, GetDate())
    , Creation_Time
    , qs.execution_count
    , avg_reads = total_logical_reads / qs.execution_count
    , avg_elapsed_ms = total_elapsed_time / 1000 / qs.execution_count
    , avg_worker_ms = total_worker_time / 1000 / qs.execution_count
    , Offset = statement_start_offset
    , ObjectName = OBJECT_NAME(qt.objectid, qt.dbid)
    , qt.text
    , StatementText = SUBSTRING (qt.text, qs.statement_start_offset/2 + 1,
        abs(CASE WHEN qs.statement_end_offset = -1
               THEN LEN(CONVERT(NVARCHAR(MAX), qt.text)) * 2 
            ELSE qs.statement_end_offset END - qs.statement_start_offset)/2 + 1)
    --, qt.*
    --, qs.*
FROM sys.dm_exec_query_stats AS qs
    CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) AS qt
WHERE Creation_Time > DateAdd(Hour, -24, GetDate())
    AND (creation_time < DATEADD(SECOND, -60, GETDATE()) or execution_count > 5) --Has to be a minute old or executed 6 times before I consider the data stable
    --AND (total_worker_time > 20000 or execution_count > 15000)

I comment out what I don’t typically use, same as always with me.  Those pieces don’t usually change.  The only thing that does typically change is the date filter on the creation time; if someone says there weren’t any problems this time yesterday then it stays at the default value, if they say it worked fine before lunch then it drops down to 4 hours.

There are four things to keep in mind with this DMV.  Everything is in microseconds which I don’t use, so I “fixed” the problem in my query.  It will only hold data while the query is in cache, which means it’s never tracked here if it’s never in cache.  Each row is for a single plan for a single statement, although  you could easily aggregate it if you wanted to.  Also, it’s possible for the worker time or duration per second to be over 1 due to multiple simultaneous runs or, with worker time, parallelism can cause this.  I typically try not to restate what you should be reading on MSDN or BOL, but these aren’t things everyone expects.

For me to run this, I typically have to already have a complaint that a server is running slower than expected. Check out my Server Running Slow post to see everything else that runs along with this.

Reading Traces

Capturing information is useless unless you know how to use it. Here’s what I use to get the most out of my basic trace of anything taking over so many seconds. This is also my template for more invasive traces, and it gets tweaked on a case-by-case basis.

It’s my typical style with half of it commented out where less run portions can be highlighted and run and portions of my WHERE clause don’t have to be rewritten to be added in. The first line gets highlighted as soon as I open this script every time so I can run it, copy the path of the trace I’m looking for, and paste it into my main script. As for the WHERE clause, call me lazy or whatever it is you call people who can’t remember the right word, but I don’t always remember the exact wording of each column name that I want to filter by. I simply uncomment them as I need them.

The only thing I can say is really impressive here is finding the job names, which is a piece of this script I will never forget figuring out. Other than that, just little things to help readability when both running this script and when playing with the WHERE clause. I can’t tell you why duration is microseconds and CPU is milliseconds, but I can say the SELECT and WHERE clauses are set up to use the same units I would use when I talk to someone.

--SELECT * FROM sys.traces

--sp_trace_setstatus id, status
exec sp_trace_setstatus 3,0 --stop
exec sp_trace_setstatus 3,2 --close
--0 stop
--1 start
--2 close

--query running trace
SELECT TOP 10000 Database_Name = DB_Name(DatabaseID)
    , DatabaseName
    , starttime
    , endtime
    , textdata = cast(textdata as varchar(4000)) 
    , Duration_Sec = cast(duration/1000/1000.0 as Dec(10,1)) 
    , CPU_Sec = cast(cpu/1000.0 as Dec(10,1)) 
    , Reads_K = cast(reads/1000.0 as Dec(10,0)) 
    , Writes_K = Cast(writes/1000.0 as Dec(10,1)) 
    , hostname  
    , LoginName 
    , NTUserName 
    , ApplicationName = CASE LEFT(ApplicationName, 29)
                    WHEN 'SQLAgent - TSQL JobStep (Job '
                        THEN 'SQLAgent Job: ' + (SELECT name FROM msdb..sysjobs sj WHERE substring(ApplicationName,32,32)=(substring(sys.fn_varbintohexstr(sj.job_id),3,100))) + ' - ' + SUBSTRING(ApplicationName, 67, len(ApplicationName)-67)
                    ELSE ApplicationName
    , SPID
    , TE.*
FROM fn_trace_gettable('D:\Traces\DurationOver10sec_110.trc',default) T
    LEFT JOIN sys.trace_events TE ON  T.EventClass = TE.trace_event_id
WHERE endtime > DATEADD(Hour, -3, GetDate()) 
    --AND Duration > 1000000 * 15 --in seconds
    --AND CPU > 1000 * 10 --in seconds
    --AND Writes > 10000
    --AND Reads > 1000000
    --AND te.category_id = 2 --Auto Grow/Shrink found in default trace
    --AND t.databaseid = DB_ID('master')
    --AND t.DatabaseName = 'tempdb'
    --AND t.LoginName <> 'shood'
    --AND t.textdata like '%%'
ORDER BY endTime DESC, starttime DESC

So, for those of you not too familiar with tracing, what does all of this mean and what am I looking for? It depends on what’s wrong. The day after an app’s upgrade I’m looking for procs that snuck through testing and are killing the server. When a user complains about performance you might see a new proc showing up because it’s getting a bad execution plan. If tempdb or the logs fill up I can tell you what had 100,000 writes and just happened to end at that exact moment. Sometimes I’ll glance to see if anything is hitting or approaching the app’s 30 second timeout.

A general trace such as the RPC:Completed and SQL:BatchCompleted that take over X seconds won’t answer all of your questions, but it will be a good start. The most important things are to always have something running (could be extended events instead of a trace) to tell you the big things that are running and to have your monitoring limited enough where you aren’t the one slowing down the server. Make sure it’s customized to each server since a 1 second duration in a web app is a big deal, but other servers you would get flooded with results unless you bump it up to 10 seconds. You can run more invasive traces when you need them.

There are two hits against extended events where I’m even using traces for this. First, I still have too many SQL 2005 servers around and like to do things as consistent as reasonably possible across all servers to make it simple. Second, I’m still not too happy about extended events skipping SQL:BatchCompleted in SQL 2008 as that misses out on finding large loops of small statements. Extended events are much more efficient, can do a lot that traces can’t do, but aren’t a 100% replacement for traces.

Comment on my posts, let me know what you think. Every post I have at Simple SQL Server is written to myself 5 years ago, and is exactly what I wish I had back then in my own mind. The key words being “in my own mind”, and I’d like to hear what’s on your mind. Be it constructive criticism on this post or a completely separate SQL Server subject you wish you knew more about either now or 5 years ago that you’d like to see thrown up for everyone to use, I’ll never know until you tell me.

Table Sizes

There’s no simple way in SQL Server to see the sizes of all the tables and their indexes.  Even seeing a single table’s size through SSMS can take a while.  The solution below is grabbed from statistics, so there’s no guarantee you’re getting exact numbers.  However, exact numbers don’t tend to matter, as you care more that the table is about 1 GB, not that it’s exactly 998 MB.

There are variations of this code floating all over the place, and I tweaked the results to be in the easiest to read format possible.

The only issue I have with this script is that you have to scroll to the next-to-the-last line to filter which tables it’s pulling. Yes, I’m picky, and I don’t like to scroll to see things that change.

SELECT Database_ID = DB_ID()
    , Database_Name = DB_NAME()
    , Schema_Name = a3.name
    , TableName = a2.name
    , TableSize_MB = (a1.reserved + ISNULL(a4.reserved,0)) / 128
    , RowCounts = a1.rows
    , DataSize_MB = a1.data / 128
    , IndexSize_MB = (CASE WHEN (a1.used + ISNULL(a4.used,0)) > a1.data 
                        THEN (a1.used + ISNULL(a4.used,0)) - a1.data 
                        ELSE 0 
                    END) /128
    , Free_MB = (CASE WHEN (a1.reserved + ISNULL(a4.reserved,0)) > a1.used 
                        THEN (a1.reserved + ISNULL(a4.reserved,0)) - a1.used 
                        ELSE 0 
                    END) / 128
FROM (SELECT ps.object_id
            , [rows] = SUM(CASE
                                WHEN (ps.index_id < 2) THEN row_count
                                ELSE 0
            , reserved = SUM(ps.reserved_page_count)
            , data = SUM(CASE
                            WHEN (ps.index_id < 2) 
                                THEN (ps.in_row_data_page_count + ps.lob_used_page_count + ps.row_overflow_used_page_count)
                            ELSE (ps.lob_used_page_count + ps.row_overflow_used_page_count)
            , used = SUM (ps.used_page_count) 
        FROM sys.dm_db_partition_stats ps
        GROUP BY ps.object_id) AS a1
    INNER JOIN sys.all_objects a2  ON a1.object_id = a2.object_id
    INNER JOIN sys.schemas a3 ON a2.schema_id = a3.schema_id
    LEFT JOIN (SELECT it.parent_id
            , reserved = SUM(ps.reserved_page_count)
            , used = SUM(ps.used_page_count)
        FROM sys.dm_db_partition_stats ps
            INNER JOIN sys.internal_tables it ON it.object_id = ps.object_id
        WHERE it.internal_type IN (202,204)
        GROUP BY it.parent_id) AS a4 ON a4.parent_id = a1.object_id
WHERE a2.type <> 'S' and a2.type <> 'IT'
    --AND a2.name IN ('spt_values')
ORDER BY a1.reserved desc

Running Processes

Sp_who2 is not used by this DBA.  That info is amazingly good to have, but I want more.  Don’t get me wrong, sp_who2 is kinda hidden in this one as it’s really just a proc that does little more than “SELECT * FROM SysProcesses“, and I use SysProcesses where I couldn’t find the same info in the DMVs that were supposed to replace it.  Don’t go thinking that SysProcesses is just a depreciated dinosaur, even MVPs still use it: http://sqlblog.com/blogs/kalen_delaney/archive/2008/06/29/why-i-still-need-sysprocesses.aspx

Threads and RunningThreads are new for me here.  It’s good to see when a query is split into 100 threads and only 1 is doing anything.  It’s also easier on the eyes to see the number 100 instead of seeing 100 separate rows for one SPID.

The percent complete and estimated completion time on this script are typically blank, with the major exceptions being backups and restores where these columns are the best thing ever! 

BlockedBy is something overlooked by many people.  If your query seems to be running forever when it normally runs quick, there’s about a 75% chance you’re just waiting for another process to complete. It’s also why you’ll see the notorious (nolock) in almost everything I write for myself, but that’s another conversation.  The HeadBlocker attempts find the start of a blocking chain, although I can say that I know my logic here isn’t bulletproof.

My wait types used to be perfect where if there wait time was 0 then it was lowercase, otherwise it was all caps.  It seems I broke it slightly, but it’s almost perfect.  My OCD will eventually get the best of me here, but it has to contend with time with the rest of my life.

The rest is pretty self-explanitory with the exception of query plans.  They are the last line of the SELECT and FROM clauses that are commented out.  It’s somewhat rare to need the plan, and it’s not too uncommon to have this script run noticably slower when looking for plans.  Because of that, I uncomment that column and the the cross apply that makes it possible only when needed.

SELECT SPID = er.session_id
    , ot.Threads
    , RunningThreads = coalesce(rsp.RunningThreads,0)
    , Pct_Comp = er.percent_complete
    , Est_Comp_Time = CASE er.estimated_completion_time WHEN 0 THEN NULL ELSE dateadd(ms, er.estimated_completion_time, getdate()) END 
    , er.status
    , er.command
    , database_name = sd.name
    , BlockedBy = wt.blocking_session_id
    , HeadBlocker = coalesce(hb5.session_id, hb4.session_id, hb3.session_id, hb2.session_id, hb1.session_id)
    , wait_type = coalesce(CASE er.wait_type WHEN 'CXPACKET' THEN 'CXPACKET - ' + sp.lastwaittype1 ELSE sp.lastwaittype1 END, lower(er.last_wait_type)) --Lowercase denotes it's not currently waiting, also noted by a wait time of 0.
    , Wait_Time_Sec = Cast(er.wait_time/1000.0 as DEC(20,3))
    , er.wait_resource
    , Duration_Sec = Cast(DATEDIFF(s, er.start_time, GETDATE()) as DEC(20,0))
    , CPU_Sec = Cast(er.cpu_time/1000.0 as DEC(20,3))
    , Reads_K = Cast(er.reads/1000.0 as DEC(20,3))
    , Writes_K = Cast(er.writes/1000.0 as DEC(20,3))
    , [Statement] = SUBSTRING (st.text, er.statement_start_offset/2,
        abs(CASE WHEN er.statement_end_offset = -1
               THEN LEN(CONVERT(NVARCHAR(MAX), st.text)) * 2 
            ELSE er.statement_end_offset END - er.statement_start_offset)/2)
    , st.text as Query
    , es.login_time
    , es.host_name
    , program_name = CASE LEFT(es.program_name, 29)
                    WHEN 'SQLAgent - TSQL JobStep (Job '
                        THEN 'SQLAgent Job: ' + (SELECT name FROM msdb..sysjobs sj WHERE substring(es.program_name,32,32)=(substring(sys.fn_varbintohexstr(sj.job_id),3,100))) + ' - ' + SUBSTRING(es.program_name, 67, len(es.program_name)-67)
                    ELSE es.program_name
    , es.client_interface_name
    , es.login_name
    , es.status
    , es.total_scheduled_time
    , es.total_elapsed_time
    , er.start_time
    , es.last_request_start_time
    , es.last_request_end_time
    , er.database_id  
    --, qp.query_plan 
FROM sys.dm_exec_requests er
    INNER JOIN sys.dm_exec_Sessions es on er.session_id=es.session_id
    LEFT JOIN sys.databases sd on er.database_id=sd.database_id
    INNER JOIN (SELECT session_id, count(1) Threads FROM sys.dm_os_tasks GROUP BY session_id) ot on er.session_id=ot.session_id
    LEFT JOIN (SELECT spid, LastWaitType1 = MIN(lastwaittype), LastWaitType2 = MAX(lastwaittype) FROM sysprocesses sp WHERE waittime > 0 AND lastwaittype <> 'cxpacket' GROUP BY spid) sp ON er.session_id = sp.spid
    LEFT JOIN (SELECT spid, RunningThreads = COUNT(1) FROM sysprocesses sp WHERE waittime = 0 GROUP BY spid) rsp ON er.session_id = rsp.spid
    LEFT JOIN (SELECT session_id, max(blocking_session_id) blocking_session_id FROM sys.dm_os_waiting_tasks wt WHERE wt.blocking_session_id <> wt.session_id GROUP BY session_id) wt ON er.session_id = wt.session_id 
    LEFT JOIN (SELECT session_id, max(blocking_session_id) blocking_session_id FROM sys.dm_os_waiting_tasks wt GROUP BY session_id) hb1 ON wt.blocking_session_id = hb1.session_id 
    LEFT JOIN (SELECT session_id, max(blocking_session_id) blocking_session_id FROM sys.dm_os_waiting_tasks wt GROUP BY session_id) hb2 ON hb1.blocking_session_id = hb2.session_id 
    LEFT JOIN (SELECT session_id, max(blocking_session_id) blocking_session_id FROM sys.dm_os_waiting_tasks wt GROUP BY session_id) hb3 ON hb2.blocking_session_id = hb3.session_id 
    LEFT JOIN (SELECT session_id, max(blocking_session_id) blocking_session_id FROM sys.dm_os_waiting_tasks wt GROUP BY session_id) hb4 ON hb3.blocking_session_id = hb4.session_id 
    LEFT JOIN (SELECT session_id, max(blocking_session_id) blocking_session_id FROM sys.dm_os_waiting_tasks wt GROUP BY session_id) hb5 ON hb4.blocking_session_id = hb5.session_id 
    CROSS APPLY sys.dm_exec_sql_text(sql_handle) AS st  
    --CROSS APPLY sys.dm_exec_query_plan(er.plan_handle) qp
WHERE er.session_id <> @@SPID
    --AND es.host_name like '%%'
    --AND er.session_id = 2702
ORDER BY er.percent_complete DESC, er.cpu_time DESC, er.session_id

--Use the below command to get the last input of an open session id
--dbcc inputbuffer(61)

Cluster Info

This script is so simple you’ll start off by asking why you’d ever use it.  Then you’ll use it and wonder why no one gave this to you as part of your new-hire orientation at your first DBA job.  The truth is that in larger environments there will always be times when you’re asked what the active node is, are two instances running on the same node, what nodes are in that cluster, what drives belong to what instance, etc…  Here’s your answer.

SELECT VirtualServerName = SERVERPROPERTY('ServerName')
    , ActiveNode = SERVERPROPERTY('ComputerNamePhysicalNetBIOS') 
    , PassiveNode = CASE (SELECT COUNT(*) FROM sys.dm_os_cluster_nodes WHERE NodeName <> SERVERPROPERTY('ComputerNamePhysicalNetBIOS'))
        WHEN 0 THEN 'Stand-Alone'
        WHEN 1 THEN (SELECT NodeName FROM sys.dm_os_cluster_nodes WHERE NodeName <> SERVERPROPERTY('ComputerNamePhysicalNetBIOS'))
        ELSE Cast((SELECT COUNT(*) FROM sys.dm_os_cluster_nodes WHERE NodeName <> SERVERPROPERTY('ComputerNamePhysicalNetBIOS')) as VarChar(2)) + ' Passive Nodes'

SELECT PassiveNodes = NodeName 
FROM sys.dm_os_cluster_nodes 
WHERE NodeName <> SERVERPROPERTY('ComputerNamePhysicalNetBIOS') 

SELECT ClusterDrives = DriveName 
FROM sys.dm_io_cluster_shared_drives
ORDER BY DriveName

Database Assessment

This script looks much more intimidating than the results it produces, so I recommend running it before reading through it.  I’ll run this when I first get on a unfamiliar server as a basic assessment of what’s there and find some hard to find issues with no effort.

The issues you’ll find include:

  • DBCC CheckDB – When is the last time this was successfully run.  You need to know this, but it’s not as easy to find as it should be.  Personally, I feel it should be in sys.databases, but I’ll settle for having this script saved to find it for me.
  • Backups – Where are they, when did they last occur, how big, etc.
    • I always look for databases (excluding model) that are in full or bulk logged recovery with no log backups.  This is a common issue, especially with vendor databases, that will fill your drives with data you didn’t care to have.
      • To know how critical it is when you find this, and you probably will, I include the log size and percent full.
    • There are variables in this script for a cutoff date which will ignore old backups. If you set @use_cutoffdate to 1 then any database not backed up since the value of @cutoffdate will show up as never being backed up. I love this feature because there’s no chance that you’ll misread a year-old backup as being taken last night.
    • Warning, the size of the backup is the size of the data, not the backup file.  To keep this compatible with SQL 2005, I’m not grabbing the compressed_backup_size field.
    • Differential backup results are commented out for a reason…most people don’t use them.  There is absolutely no reason to comment this out other than limiting the number of columns in my results.
  • Database Size – Both allocated and used space.  It’s best that you store this and trend the data somewhere, such as on my post Monitoring Database and Table Sizes, but having a static value is a start.
  • Compatibility Level – If you upgrade a server it’s easy to leave a database behind.  I’ve seen SQL 2000 compatibility on SQL 2008 R2 for no other reason than “oops”.
  • Collation name – If this isn’t consistent you could run into issues that are next to impossible to debug.  The exceptions, ironically, are Microsoft databases.  Report Server and SharePoint databases specifically will be a different collation than anything else, but they were written with that in mind and handle it gracefully and shouldn’t be changed.
  • Auto Close – Ok, I admit it, I actually have this one commented out in my personal copy of this code.  If this is turned on it will typically blow up your error logs and make them half-way unreadable, and I’ll see this when I attempt to read them.  However, it’s good to know if this is ever turned on as it will kill performance.  I should uncomment this in my version with the mentality that too much information will slightly annoy you, too little will kill you.
Especially with most of us running more vendor databases than home-grown, don’t trust that this stuff was done right.
DECLARE @cutoffdate datetime
DECLARE @use_cutoffdate bit
SET @cutoffdate = DateAdd(Month, -3, GetDate())
SET @use_cutoffdate = 0

IF Object_ID('TempDB..#FileGroupSize') IS NOT NULL BEGIN
	DROP TABLE #FileGroupSize



(	  DB VarChar(128)
	, FileGroup VarChar(128)
	, Used_MB Int
	, Free_MB Int
	, Size_MB Int

	ParentObject VARCHAR(255),
	[Object] VARCHAR(255),
	Field VARCHAR(255),
	[Value] VARCHAR(255)

	DBName SysName 
	, DBCC_LastGood SmallDateTime

exec sp_MSforeachdb N'use [?]; 
INSERT INTO #FileGroupSize
SELECT DB = db_name()
	, FileGroup = ISNULL(g.name, f.Type_Desc) 
	, Used_MB = SUM(FileProperty(f.name, ''SpaceUsed'')) / 128
	, Free_MB = SUM(f.size - FileProperty(f.name, ''SpaceUsed'')) / 128
	, Size_MB = SUM(f.size) / 128 
FROM sys.database_files f
	LEFT JOIN sys.filegroups g on f.data_space_id = g.data_space_id
--WHERE f.Type_Desc = ''LOG''
GROUP BY f.Type_Desc, g.name



SELECT ''?'', NULLIF([Value],''1900-01-01 00:00:00.000'')
WHERE Field = ''dbi_dbccLastKnownGood''

SELECT database_name = d.name 
	, d.create_date
	, d.compatibility_level
	, #DBCC.DBCC_LastGood
	, LastFullBU = BUFull.last_backup  
	, LastDiffBU = BUDiff.last_backup  
	, LastLogBU = BULog.last_backup  
	, Recovery_Model = d.recovery_model_desc
	, Log_Size_MB = LogSize.Size_MB
	, Log_Pct_Used = Cast(100 * LogSize.Used_MB / (Cast(LogSize.Size_MB as Dec(20,2)) + .01) as Dec(20,2))
	, Data_Size_MB = DataSize.Size_MB
	, Data_Used_MB = DataSize.Used_MB
	, d.collation_name
	, LastFullLoc = BUFullLoc.physical_device_name
	, LastFullSize_MB = Cast(BUFullLoc.backup_size / 1024 / 1024 as bigint)
	--, LastDiffLoc = BUDiffLoc.physical_device_name
	--, LastDiffSize_MB = Cast(BUDiffLog.backup_size / 1024 / 1024 as bigint)
	, LastLogLoc = BULogLoc.physical_device_name
	, LastLogSize_MB = Cast(BULogLoc.backup_size / 1024 / 1024 as bigint)
	, d.is_auto_close_on
FROM master.sys.databases d with (nolock)
	LEFT JOIN (SELECT b.database_name, max(b.backup_finish_date) last_backup
		FROM msdb..backupset b with (nolock)
		WHERE b.type = 'D' 
			AND (b.backup_finish_date > @cutoffdate or @use_cutoffdate = 0)
		GROUP BY b.database_name
		) BUFull ON d.name = BUFull.database_name 
	LEFT JOIN (SELECT b.database_name, max(b.backup_finish_date) last_backup
		FROM msdb..backupset b with (nolock) 
		WHERE b.type = 'I' 
			AND (b.backup_finish_date > @cutoffdate or @use_cutoffdate = 0)
		GROUP BY b.database_name
		) BUDiff ON d.name = BUDiff.database_name 
	LEFT JOIN (SELECT b.database_name, max(b.backup_finish_date) last_backup
		FROM msdb..backupset b with (nolock) 
		WHERE b.type = 'L' 
			AND (b.backup_finish_date > @cutoffdate or @use_cutoffdate = 0)
		GROUP BY b.database_name
		) BULog ON d.name = BULog.database_name 
	LEFT JOIN (SELECT f.physical_device_name, b.database_name, b.backup_finish_date, b.backup_size
		FROM msdb..backupset b with (nolock)
			INNER JOIN msdb..backupmediafamily f ON b.media_set_id = f.media_set_id
		WHERE b.type = 'D') BUFullLoc ON BUFull.database_name = BUFullLoc.database_name AND BUFull.last_backup = BUFullLoc.backup_finish_date
	/*LEFT JOIN (SELECT f.physical_device_name, b.database_name, b.backup_finish_date, b.backup_size
		FROM msdb..backupset b with (nolock)
			INNER JOIN msdb..backupmediafamily f ON b.media_set_id = f.media_set_id
		WHERE b.type = 'I') BUDiffLoc ON BUDiff.database_name = BUDiffLoc.database_name AND BUDiff.last_backup = BUDiffLoc.backup_finish_date
	LEFT JOIN (SELECT f.physical_device_name, b.database_name, b.backup_finish_date, b.backup_size
		FROM msdb..backupset b with (nolock)
			INNER JOIN msdb..backupmediafamily f ON b.media_set_id = f.media_set_id 
		WHERE b.type = 'L') BULogLoc ON BULog.database_name = BULogLoc.database_name AND BULog.last_backup = BULogLoc.backup_finish_date
				FROM #FileGroupSize 
				WHERE FileGroup = 'Log') LogSize ON d.name = LogSize.DB
					, Used_MB = SUM(Used_MB)
					, DB 
				FROM #FileGroupSize 
				WHERE FileGroup <> 'Log' 
				GROUP BY DB) DataSize ON d.name = DataSize.DB
	LEFT JOIN #DBCC ON d.name = #DBCC.DBName 
WHERE d.name <> 'tempdb'
	AND d.state = 0 --Online

DROP TABLE #FileGroupSize