Optimizing for Ad Hoc Workloads

Published on: 2019-01-22

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The execution plan cache is a great feature: after SQL Server goes through the effort of generating a query plan, SQL Servers saves that plan in the plan cache to be reused again at a later date.

One downside to SQL Server caching almost all plans by default is that some of those plans won’t ever get reused. Those single use plans will exist in the plan cache, inefficiently tying up a piece of the server’s memory.

Today I want to look at a feature that will keep these one-time use plans out of the plan cache.

Plan Stubs

Instead of filling the execution plan cache with plans that will never get reused, the optimize for ad hoc workloads option will cache a plan stub instead of the full plan. The plan stub is significantly smaller in size and is only replaced with the full execution plan when SQL Server recognizes that the same query has executed multiple times.

This reduces the amount of size one-time queries take up in t he cache, allowing more reusable plans to remain in the cache for longer periods of time.

Enabling this server-level feature is as easy as (a database scoped versions :

sp_configure 'show advanced options',1
GO
reconfigure
GO	
sp_configure 'optimize for ad hoc workloads',1
GO
reconfigure
go

Once enabled you can watch the plan stub take up less space in the cache:

-- Run each of these queries once
DECLARE @Username varchar = 'A'
SELECT UserName 
FROM IndexDemos.dbo.[User] 
WHERE UserName like @Username+'%';
GO DECLARE @Username varchar = 'B' SELECT UserName FROM IndexDemos.dbo.[User] WHERE UserName like @Username+'%';
GO SELECT cp.cacheobjtype, cp.objtype, cp.plan_handle, cp.size_in_bytes, qp.query_plan, st.text FROM sys.dm_exec_cached_plans cp CROSS APPLY sys.dm_exec_query_plan(cp.plan_handle) qp INNER JOIN sys.dm_exec_query_stats qs ON cp.plan_handle = qs.plan_handle CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) st WHERE st.text like 'DECLARE @Username varchar =%';
424 bytes each, these plan stubs are tiny!

Now if we run our second query filtering on UserName LIKE ‘B%’ again and then check the plan cache, we’ll notice the stub is replaced with an actual compiled plan:

This super simple compiled plan takes up significantly more space. Multiple by several thousand user queries and your plan cache will be quickly filling up.

The downside to plan stubs is that they add some cpu load  to our server: each query gets compiled twice before it gets reused from cache.  However, since plan stubs reduce the size of our plan cache, this allows more reusable queries to be cached for longer periods of time.

Great! All my cache problems will be solved

Not necessarily.

If your workload truly involves lots of ad hoc queries (like many analysts all working on different problems or dynamic SQL that’s generating completely different statements on every execution), enabling Optimize for Ad hoc Workloads may be your best option (Kimberly Tripp also has a great alternative: clearing single use plans automatically on a schedule).

However, often times single-use query plans have a more nefarious origin: unparameterized queries. In this case, enabling Optimize for Ad hoc Workloads may not negatively impact your server, but it certainly won’t help. Why? Because those original queries will still be getting generated.

Brent Ozar has a good overview of why this happens, but the short answer is to force parameterization on your queries. When you enable force parameterization, SQL Server will not automatically parameterize your queries if they aren’t already, reducing the number of one off query plans in your cache.

Whether you are dealing with too many single use queries on your server or some other problem, just remember to find the root cause of the problem instead of just treating the symptoms.

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Displaying Long Values in SSMS

Published on: 2018-08-07

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I write a lot of dynamic SQL and frequently encounter variables that contain many characters:

DECLARE @LongValue NVARCHAR(MAX) = CAST('' AS NVARCHAR(MAX)) + 
N'SELECT
    ''A'' AS AShortValue,
    '''+REPLICATE(N'A',4000)+''' as ALongValue
ORDER BY 1';

This variable is 4059 characters long, and when I execute it it runs great.

SELECT LEN(@LongValue); -- 4059 characters
EXEC(@LongValue);

A homage to one of my favorite Uncyclopedia entries.

If my programmatically built query had an error in it, the first thing I’d want to do when debugging it would be to see the the text of the entire @LongValue variable.

I could do this by just saying SELECT @LongValue, and while recent versions of SSMS will display the whole value for me, it completely loses my formatting which stinks (and is especially bad if there are any comments prefixed with   in the query):

Need a ultra HD wide display to fit this all on one screen.

I can say PRINT @LongValue, which will keep the formatting, but it will get trimmed at 4,000 characters (notice the missing ORDER BY):

Some Better Ways

Erik Darling posts one solution to this problem in his T-SQL Tuesday #104 entry (as well as some other problems/solutions for lengthy SQL variables). Specifically he links to a SQL string printing script that will loop through the lengthy variable and print everything while maintaining formatting:

Not perfectly formatted, but good enough.

And while I like using that stored procedure on my primary server, I’m too lazy to install it every where I need it.

Instead, I have a couple of go-to solutions that work on all SQL Server instances 2008 forward.

Solution 1: CAST to XML
SELECT CAST(@LongValue AS XML) AS LongValue

Casting the long variable to XML allows SSMS to generate a clickable, single-row result that preserves formatting:

IntelliSense complains but I’m OK with it

The only downside to this approach is that certain charaters, like “<” and “>”, can’t be converted to XML:

Solution 2: FOR XML PATH

A slight variation on solution 1, we can get similar results using FOR XML PATH:

SET @LongValue = '<' + @LongValue -- Let's add in an invalid character
SELECT @LongValue FOR XML PATH('')

FOR XML PATH is one of the most abused SQL Server functions.

In this solution, the “<” is escaped to “&lt;”, which isn’t perfect but at least my variable can be displayed with formatting intact.  A quick find and replace for any escaped characters and I’m good to go.

Good Enough

These techniques aren’t perfect, but for purposes of debugging dynamically generated code they are good enough.

Maybe one day SSMS will print longer strings or include a syntax formatter and I won’t care nearly as much.

And if not, I’ll happily continue to abuse FOR XML to do things other than generate XML documents.

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Building Dynamic Table-Driven Queries

Published on: 2018-07-10

This post is a response to this month’s T-SQL Tuesday #104 prompt by me! T-SQL Tuesday is a way for SQL Server bloggers to share ideas about different database and professional topics every month.

This month’s topic is asking what code would you hate to live without?


When given the choice between working on new projects versus maintaining old ones, I’m always more excited to work on something new.

That means that when I build something that is going to used for years to come, I try to build it so that it will require as little maintenance as possible in the future.

One technique I use for minimizing maintenance is making my queries dynamic.  Dynamic queries, while not right for every situation, do one thing really well: they allow you to modify functionality without needing a complete rewrite when your data changes.  The way I look it, it’s much easier to add rules and logic to rows in table than having to modify a table’s columns or structure.

To show you what I mean,let’s say I want to write a query selecting data from model.sys.database_permissions:

SELECT class
      ,class_desc
      ,major_id
      ,minor_id
      ,grantee_principal_id
      ,grantor_principal_id
      ,type
      ,permission_name
      ,state
      ,state_desc
  FROM model.sys.database_permissions

Writing the query as above is pretty simple, but it isn’t flexible in case the table structure changes in the future or if we want to programmatically write some conditions.

Instead of hardcoding the query as above, here is a general pattern I use for writing dynamic table-driven queries.  SQL Server has the handy views sys.all_views and sys.all_columns that show information about what columns are stored in each table/view:

Using these two views, I can use this dynamic SQL pattern to build the same exact query as above:

-- Declare some variables up front
DECLARE 
	@FullQuery nvarchar(max),
	@Columns nvarchar(max),
	@ObjectName nvarchar(128)

-- Build our SELECT statment and schema+table name
SELECT 
	@Columns = COALESCE(@Columns + ', ', '') + '[' + c.[name] + ']',
	@ObjectName = QUOTENAME(s.name) + '.' + QUOTENAME(o.name)
FROM 
	sys.all_views o  
	INNER JOIN sys.schemas s
		ON o.schema_id = s.schema_id
	INNER JOIN sys.all_columns c
		ON o.object_id = c.object_id
WHERE 
	o.[name] = 'database_permissions'
ORDER BY
	c.column_id	

-- Put all of the pieces together an execute
SET @FullQuery = 'SELECT ' + @Columns + ' FROM ' + @ObjectName

EXEC(@FullQuery)

The way building a dynamic statement like this works is that I build my SELECT statement as a string based on the values stored in my all_columns view.  If a column is ever added to this view, my dynamic code will handle it (I wouldn’t expect this view to change that much in future versions of SQL, but in other real-world tables I can regularly expect changing data).

Yes, writing certain queries dynamically like this means more up front work.  It also means some queries won’t run to their full potential (not necessarily reusing plans, not tuning every individual query, needing to be thoughtful about SQL injection attacks, etc…).  There are A LOT of downsides to building queries dynamically like this.

But dynamically built queries make my systems flexible and drastically reduce the amount of work I have to do down the road.  In the next few weeks I hope to go into this type of dynamically built, table-driven process in more detail (so you should see the pattern in the example above get reused soon!).

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