Jul 18 2016

Common Table Expression, Just a Name

The Common Table Expression (CTE) is a great tool in T-SQL. The CTE provides a mechanism to define a query that can be easily reused over and over within another query. The CTE also provides a mechanism for recursion which, though a little dangerous and overused, is extremely handy for certain types of queries. However, the CTE has a very unfortunate name. Over and over I’ve had to walk people back from the “Table” in Common Table Expression. The CTE is just a query. It’s not a table. It’s not providing a temporary storage space like a table variable or a temporary table. It’s just a query. Think of it more like a temporary view, which is also just a query.

Every time I explain this, there are people who don’t believe me. They point to the “Table” in the name, “See. Says so right there. It’s a table.”

It’s not and I can prove it. Let’s create a relatively simple CTE and use it in a query:

WITH    MyCTE
          AS (SELECT    c.CustomerName,
                        cc.CustomerCategoryName
              FROM      Sales.Customers AS c
              JOIN      Sales.CustomerCategories AS cc
              ON        cc.CustomerCategoryID = c.CustomerCategoryID
              WHERE     c.CustomerCategoryID = 4)
    SELECT  *
    FROM    MyCTE;

Now, I’m going to run the query within the CTE and the CTE together as two statements in a batch and capture the execution plans:

ExecPlans

On the top, the CTE, on the bottom, the query. You’ll note that the execution plans are identical. They each have the exact same Query Plan Hash value in the properties, 0x88EFD2B7C165E667, even though they have different Query Hash values, 0x192FFC125A08CC35 and 0xFEB7F2BCAC853CD5, respectively. Further, if I capture the query metrics using extended events, I get identical reads and, on average, identical execution times:

duration

This is because, there is no table being created. The data is not treated differently. A CTE is just a query, not some type of temporary storage.

Heck, let’s do one more thing. Let’s use the latest SSMS plan comparison tool and highlight one of the operators to see what differences there are internally in the plan:

 

plancompare

I don’t see a lot of differences. In fact, I don’t see any. That’s because the optimizer recognizes these two queries as identical. If it was loading data into temporary storage, you would see differences in something. We don’t. This is because, despite the somewhat unfortunate emphasis that gets placed on the Table portion of the name, the emphasis of the name, Common Table Expression, should be on the word Expression.

I will point out an interesting difference, especially useful for those who plug in CTEs everywhere, whether it’s needed or not. Let’s look at the properties of the two plans:

peroperties

You can see the similarities and differences that I pointed out earlier in the Statement, Query Hash and Query Plan Hash, as well as the Estimated Subtree Cost and others. What’s truly interesting is that the CompileCPU, CompileMemory and CompileTime for the CTE is higher than the regular query. While the CTE is just a query, it’s a query that adds a non-zero overhead when used, and therefore, should only be used where appropriate (good gosh, I’ve seen people put it EVERWHERE, on every single query, don’t do that).

Hopefully, this is enough to establish, truly, completely, and thoroughly, that the Common Table Expression is an expression, not a table.

Yeah, I did this before, but it keeps coming up, so I tried a different approach. Let’s see if the word gets out. Your Common Table Expression is not a table.


I love talking about execution plans and query tuning. I’ll be doing this at an all day pre-con at SQLServer Geeks Annual Summit in Bangalore India.

Don’t  miss your chance to an all day training course on execution plans before SQL Saturday Oslo in September.

Jul 11 2016

Same Query, Different Servers, Different Performance. Now What?

Based on the number of times I see this question on forums, it must be occurring all the time. You have two different servers that, as far as you know, are identical in terms of their options and setup (although not necessarily in terms of power, think a test or pre-production system versus production). On these servers you have a database on each that, as far as you know, is the same as the other in terms of options, objects, maybe even data (although, this does mean that you have unmasked production information in your QA environment, which potentially means you’re going to jail, might want to address this, especially now that I’ve told you about it, mens rea, you’re welcome). On each database you run, as far as you know, the exact same query (whether a straight up ad hoc query, stored procedure, parameterized query, whatever). On your production server, let’s call it PROD, the query runs for 3 hours before it returns, but on the test server, let’s call it TEST, it returns in about 3 seconds.

What. The. Hell.

This is especially troubling because PROD has 8 processors that are much more powerful than TEST, which has only 2 (either can go parallel, so this is the same). TEST only has 16GB of RAM and PROD has 64GB. Further, you know that the databases, data, and statistics are the same because you restored the TEST database from PROD (although, see that jail thing above). However, you’re on top of things. You capture the query metrics so you know exactly the amount of time and the reads or writes from each query and you even do this using extended events so you’re minimizing observer effects. You also have monitoring in place, so you know that there is no blocking in PROD. The query is just a pig there, but not on TEST. You’ve even gone so far as to get an estimated execution plan from each server and they are absolutely different.

Now what?

Well obviously something somewhere is different. Start by comparing everything on both servers and both databases down to… hang on, here, we’ll write a PowerShell script and then….

Wait, wait, wait!

You have the execution plans? Before we start digging through all the properties everywhere and comparing everything to everything, updating statistics 14 times, and all the rest, what if we look at the execution plans. They’re different, so we should start looking at scans & indexes & statistics &….

Wait!

Let’s start simple and clear. Let’s take a look at the properties of the first operator:

AllProperties

This contains a ton of information about the settings of the systems where each plan was generated. Let’s assume that, probably, the databases are the same, as is the data and the statistics, but the problem is a system or database setting. These differences can result in different execution plans and therefore different execution times. If we are dealing with two different servers and we are fairly sure the data, structure and the statistics are the same, the properties of the first operator are a great starting point for understanding what went wrong.

Oh, and the first operator is this one with the red square around it:

first operator

Let’s use the new SSMS Compare Showplan to see the differences between our two execution plans that we captured:

properties compared

(clicking on that will make it bigger)

There are a bunch of differences highlighted, but one ought to jump out pretty quick. That’s right, these two databases have different compatibility levels which resulted in one of them using the new optimizer and one using the old optimizer as evidenced by the CardinalityEstimatorModelVersion. Fun point, both have identical query hash values. Not surprising, but additional, quick, validation that we really are talking about the same query on each server (and one of them isn’t using a trace flag to change the cardinality estimator). You now have enough information to go and make some changes to your system without lots of further fumbling and work.

When dealing with the same query from two servers that ought to result in similar behavior, but doesn’t, get the execution plans (estimated plans are fine here) and compare the the properties of the first operator. That’s the quickest way to identify the issues that could be leading to the differences between the servers.


Want to talk more about execution plans and query tuning? Let’s do it.

In August, I’ll be doing an all day pre-con at SQLServer Geeks Annual Summit in Bangalore India.

I’m also going to be in Oslo Norway for a pre-con before SQL Saturday Oslo in September.

Jun 28 2016

CASE Statement in GROUP BY

Set based operations means you should put everything into a single statement, right?

Well, not really. People seem to think that having two queries is really bad, so when faced with logical gaps, they just cram them into the query they have. This is partly because SQL Server and T-SQL supports letting you do this, and it’s partly because it looks like a logical extension of code reuse to arrive at a query structure that supports multiple logic chains. However, let’s explore what happens when you do this on particular situation, a CASE statement in a GROUP BY clause.

You see this a lot because a given set of data may be needed in slightly different context by different groups within the company. Like many of my example queries, this could be better written. Like many of my example queries, it mirrors what I see in the wild (and for those following along at home, I’m using the WideWorldImporters database for tests now):

CREATE PROCEDURE dbo.InvoiceGrouping (@x INT)
AS
SELECT  SUM(il.UnitPrice),
        COUNT(i.ContactPersonID),
        COUNT(i.AccountsPersonID),
        COUNT(i.SalespersonPersonID)
FROM    Sales.Invoices AS i
JOIN    Sales.InvoiceLines AS il
        ON il.InvoiceID = i.InvoiceID
GROUP BY CASE WHEN @x = 7 THEN i.ContactPersonID
              WHEN @x = 15 THEN i.AccountsPersonID
              ELSE i.SalespersonPersonID
         END;
GO

Running this for any given value above, 7, 15 or other, you’ll get the same execution plan, regardless of the column used in the GROUP BY. However, Parameter Sniffing is still something of a factor. When you group this data by SalesPersonID, you only get 10 rows back. This will be shown as the estimated number of rows returned if some value other than 7 or 15 is used as a parameter. However, this is always the plan:

coreplan

You can click on that to expand it into something readable. We can eliminate the Parameter Sniffing from the equation if we want to by modifying the query thus:

CREATE PROCEDURE dbo.InvoiceGrouping_NoSniff (@x INT)
AS
DECLARE @x2 INT;
SET @x2 = @x;

SELECT  SUM(il.UnitPrice),
        COUNT(i.ContactPersonID),
        COUNT(i.AccountsPersonID),
        COUNT(i.SalespersonPersonID)
FROM    Sales.Invoices AS i
JOIN    Sales.InvoiceLines AS il
        ON il.InvoiceID = i.InvoiceID
GROUP BY CASE WHEN @x2 = 7 THEN i.ContactPersonID
              WHEN @x2 = 15 THEN i.AccountsPersonID
              ELSE i.SalespersonPersonID
         END;
GO

However, except for some deviation on the estimated rows (since it’s averaging the rows returned), the execution plan is the same.

What’s the big deal right? Well, let’s break down the code into three different procedures:

CREATE PROCEDURE dbo.InvoiceGrouping_Contact
AS
SELECT  SUM(il.UnitPrice),
        COUNT(i.ContactPersonID),
        COUNT(i.AccountsPersonID),
        COUNT(i.SalespersonPersonID)
FROM    Sales.Invoices AS i
JOIN    Sales.InvoiceLines AS il
        ON il.InvoiceID = i.InvoiceID
GROUP BY i.ContactPersonID;
GO

CREATE PROCEDURE dbo.InvoiceGrouping_Sales
AS
SELECT  SUM(il.UnitPrice),
        COUNT(i.ContactPersonID),
        COUNT(i.AccountsPersonID),
        COUNT(i.SalespersonPersonID)
FROM    Sales.Invoices AS i
JOIN    Sales.InvoiceLines AS il
        ON il.InvoiceID = i.InvoiceID
GROUP BY i.SalespersonPersonID;
GO

CREATE PROCEDURE dbo.InvoiceGrouping_Account
AS
SELECT  SUM(il.UnitPrice),
        COUNT(i.ContactPersonID),
        COUNT(i.AccountsPersonID),
        COUNT(i.SalespersonPersonID)
FROM    Sales.Invoices AS i
JOIN    Sales.InvoiceLines AS il
        ON il.InvoiceID = i.InvoiceID
GROUP BY i.AccountsPersonID;
GO

Interestingly enough, these three queries produce a nearly identical execution plan. The one big difference is the Compute Scalar operator that is used to generate a value for the Hash Match Aggregate is no longer in the query:

specificplan

The same basic set of structures, scans against both tables, to arrive at the data. Cost estimates between the two plans are very different though, with the targeted queries having a much lower estimated cost.

Performance-wise, interestingly enough, the average execution time of the first query, only returning the 10 rows, is 157ms on average, while the query grouping directly on the SalesPersonID averages about 190ms. Now, the reads tell a slightly different story with 17428 on the generic query and 5721 on the specific query. So, maybe a server under load will see a significant performance increase. However, let’s deal with what we have in front of us and say that, at least for these tests, the catch-all GROUP BY query performs well.

Now let’s change the paradigm slightly. Let’s add an index:

CREATE INDEX TestingGroupBy ON Sales.Invoices (SalespersonPersonID);

Frankly, this isn’t a very useful index. However, after adding it, the execution plan for the InvoiceGrouping_Sales query changes. Instead of scanning the table, it’s now scanning the index. Despite recompiles and attempts to force it using hints, the original InvoiceGrouping query will not use this index. Duration of the InvoiceGrouping_Sales query drops to 140ms on average and the reads drop a little further to 5021. Getting an 11% increase on performance is a win.

This is a pretty simplified example, however, making the CASE statement more complex won’t improve performance or further assist the optimizer to make good choices. Instead of trying to cram multiple different logical groupings into a single query, a better approach would be to create the three new procedures that I did above, and make the original InvoiceGrouping procedure into a wrapping procedure that chooses which of the individual procedures to call. This way, if you do add indexes in support of each of the different possible groupings, you would realize a positive outcome in your performance.


Want to talk more about execution plans and query tuning?. In August, I’ll be doing an all day pre-con at SQLServer Geeks Annual Summit in Bangalore India.

I’m also going to be in Oslo Norway for a pre-con before SQL Saturday Oslo in September.

Jun 13 2016

Choosing the Right SQL Server Edition

Post #6 of #entrylevel #iwanttohelp in support of Tim Ford’s (b|t) beginner’s initiative.

If you’re just getting started with SQL Server, the choices you have in front of you are legion. Which drive do you install your instance on? Which drives hold the databases? How many files do you need for a database? What do the tables look like? Which column or columns should be the primary key? Clustered index? Stored procedures? In-Memory? MAXDOP? Et, as they say, cetera. Ad, as they also say, nauseum.

Before any of that though, you need to pick the correct type of SQL Server to install. That’s right, just saying “SQL Server” is not enough. You must pick between:

SQL Server Developer’s Edition
SQL Server Express
Azure SQL Database
SQL Server Standard
SQL Server Enterprise

I could also add Microsoft Analytics Platform (APS) and Azure SQL Data Warehouse to that list, but that’s going to overly complicate things. We’ll just focus on the five core SQL Server types.

The list above is in the order in which you should be getting started with SQL Server. Let me explain why.

SQL Server Developer’s Edition

If you’re just getting started with SQL Server, SQL Server Developer Edition should be your first stop. With the release of SQL Server 2016 two weeks ago, this edition is now free. You should immediately go and download your own copy. You want to get this because it is everything that is available within SQL Server, all the way up to Enterprise Edition. It’s just licensed for development. If you want to learn about any aspect of SQL Server, start here. If you’re developing against any set of functionality within SQL Server, start here. If you need to support different parts of a SQL Server infrastructure, you start here. Prior to 2016, the Developer’s Edition was only $50-$60, and even then, I would have recommended it over free versions of the product. Now, with Developer Edition being completely free, there’s literally no excuse to not use this to get started learning SQL Server.

SQL Server Express

Assuming you’re just building out your first databases and your first server instances, you may be starting off very small, with only a few users and very little data. SQL Server Express allows you to grow your database up to 10gb, in a production environment (production and development are where licensing and costs differ wildly). This provides a mechanism for you to ensure that you really need SQL Server and all that it offers before you go into a full production installation and all that entails. If you’re only ever going to be very small, this offers a no-cost way to have a professional level database, despite the size. There are additional limitations in what Express supports and you can read about them at the link. It’s just a great way to help get you started.

Azure SQL Database

Here we begin to explore the paid versions of SQL Server. If you’re just getting started and you don’t have much experience administering SQL Server, then Azure SQL Database is a great option for you to start. It is a Platform as a Service offering. This means that it’s not a full instance of SQL Server like everything else listed here. Unlike everything else listed here, it’s not a full instance of SQL Server and that can be a good thing. Backups are managed for you as are a whole slew of other server-level settings, maintenance, etc. The cost model is completely based on what you need to store and how much activity you’re going to generate. You can start very small, and then grow as needed, only paying for what you use. This is extremely attractive if you’re just starting out. Further, the capabilities can grow with you as needed including setting up what would otherwise be extremely complicated things like geo-replication.

SQL Server Standard Edition

SQL Server Standard Edition is the workhorse for SQL Server. It does almost everything that most people need. It scales to the size that most people need to scale to. For your standard business and even standard enterprise needs, Standard Edition (note the name) works extremely well. There are limitations on what it can do. However, these limitations are primarily around extreme scale, or high-end behavior. If you’re just getting started, you don’t need to be messing with that kind of stuff.

SQL Server Enterprise Edition

SQL Server Enterprise Edition is the high end. Here is where you need to go to multi-terrabytes in size and you have massive transaction loads. You’re looking at very sophisticated availability and disaster recovery. Again, the name gives it away. You’re generally only going to this edition when you’re working at an enterprise level of scale and architecture. Since you’re just getting started, don’t worry about this.

Yet.

Conclusion

If you’re just starting to explore the SQL Server space, you should have a copy of Developer Edition. It costs nothing and does all you need. From there, expand into the other Editions as and where needed. Just remember to size your system according to how it’s being used, not how it might be used a year from now. Expanding to Standard from Express is easy. Adding a higher level of support in Azure is simple. Going from Standard to Enterprise is easy. Going backwards, that’s hard. Don’t pick the bigger more sophisticated Edition just in case. Make this choice carefully at the start.

Jun 01 2016

Independent Azure Data Platform Instructors

azureThe Azure Data Platform is taking off. I’m seeing more and more interest on the forums, at conferences and in my personal interactions. I’ve been teaching the data platform for six years. Almost as soon as it was available, I started working with it, putting up blog posts and setting up sessions. I’ve had stuff in production on the platform for almost that long too. I’m an advocate and, I hope, an independent voice on the topic. By independent in this case, I mean non-Microsoft. Don’t get me wrong, most of the people I learn from work for Microsoft. They are excellent instructors and more knowledgeable on the topic than I’ll ever be. I’m not questioning the ability of Microsoft people to deliver the very best Data Platform content. I just believe that people also want voices that are not directly beholden to the company.

That brings up my list. I have a list of Azure Data Platform Instructors. I’ve been maintaining this for about a eight weeks now. I’m adding to it regularly. At this point, the list is small and in alphabetical order. As it grows, I will begin the process of curating the list. I’ll probably start with categories only, but eventually, I’ll start to pick and choose who goes on the list based on my knowledge of the individuals involved. For now, these are the independent voices who are teaching Azure and the Azure Data Platform. If you’re getting started in Azure, this is the list you need to consult to find the very best independent voices.

May 31 2016

Precedence Goes to Query Store or Plan Guide?

While presenting at SQLDay in Wroclaw, Poland, on the Query Store, I was asked a pretty simple question, which takes precedence, the Query Store or a Plan Guide?

One of my favorite answers to questions is “I don’t know” because it gives me the opportunity to learn. Let’s figure this one out together.

I’ll post the code to recreate this experiment within AdventureWorks at the end of the article. I’m doing this because the code for forcing execution plans using Plan Guides can be pretty doggone long (you may need to generate your own XML from a plan on your own system, fair warning).

I have a stored procedure that I use frequently to demonstrate parameter sniffing and bad parameter sniffing, AddressByCity (listed below). This query when passed the value ‘London’ will generate a plan with a Merge Join. When passed the value ‘Mentor’ it will generate a plan with a Loops Join. These different plans are caused by the statistics for the different values. 434 rows for ‘London’ and 1 row for ‘Mentor’. To start the test, I’ll generate a plan for each value, dropping the plan from cache between executions, so that I have both plans in the Query Store. Now, we’re ready for phase 1.

Phase 1

I’m going to remove the current plan from cache. I will then use sp_query_store_force_plan to select the plan with the Loops join. I’ll run the query using ‘London’ to validate that the plan generated is NOT the plan for ‘London’ but rather the plan for ‘Mentor’. Done, and I can see the Use Plan results in the Properties of the Select operator:

QSvPG_UsePlan

I’ve highlighted a couple of interesting points. First, even though we are using a plan that is compiled for the value of ‘Mentor’, because this plan was compiled using the value ‘London’, that’s what gets stored, despite the plan used. You can then see down at the bottom, that the ‘Use plan’ property is set to true. Now, let’s force the plan using a plan guide (in my script below). To be very sure what happens, I’ll again clear the plan from cache.

If the Query Store wins, I should see a plan with a Loops Join. If the Plan Guide wins, I should see a plan with a Merge Join. The moment of truth:

QSvPG_Plan

Query Store wins! Let’s see how it shows in the Properties:

QSvPG_PlanGuideName

You’ll see that the Parameter Compile & Runtime values are the same and that the ‘Use plan’ property is the same. Note however, the PlanGuideName property is in play. In fact, the indication is that the optimizer thinks that a Plan Guide has been successfully applied. In fact, if I watch the Extended Events for the plan_guide_successful event, the best way to check how Plan Guides are being used, apart from looking at the execution plans, it shows success for the Plan Guide we created.

Phase 2

Precedence could matter. First, I remove the Plan Guide using sp_control_plan_guide. Then, I remove the Query Store force using sp_query_store_unforce_plan. I’ll also clear the plan out of cache again, just to be sure. Next, I create the Plan Guide. I’ll run the procedure using the value ‘Mentor’ to ensure that I see the Merge/’London’ plan, the PlanGuideName property in that plan, and a successful application of a Plan Guide from the plan_guide_successful ExEvent. All good.

Now, I once again force the plan from Query Store. Rerun the plan…

The winner is Query Store.

Phase 3

OK, so, if we try to force plans using Plan Guide, Query Store wins. What about if I’m just trying to apply a hint? Let’s clear everything up again and then create an OPTIMIZE FOR query hint in a Plan Guide. Simple right?

Query Store wins again… Except things get a little odd. Take a look at the Properties now:

QSvPG_Parameter

Normally, with a Plan Guide that includes an OPTIMIZE FOR hint, I’d see the Compiled Value as the defined value within the hint (in this case ‘London’). However, this time I’m seeing just the value of ‘Mentor’.

Conclusion

I don’t know what’s occurring in the internals. I just know the evidence I have in front of me. First, Query Store vs. Plan Guides? Query Store wins. However, internally, something is still going on. The Plan Guide is still successfully applied, at least according to what I can see. The processes that determine if a Plan Guide should be applied or not are not interfered with by the Query Store plan forcing. The fact that I’m seeing a Query Store plan and evidence of a successful Plan Guide though, that’s a problem that could cause issues when troubleshooting. Now you know, and knowing, as they say, is half the battle.


Let’s get together and talk about execution plans. In August, I’ll be doing an all day pre-con at SQLServer Geeks Annual Summit in Bangalore India.

I’m extremely exited to be in Oslo Norway for a pre-con before SQL Saturday Oslo in September.


/*NOTE: These scripts are not in the exact order in which
they must be run to re-create all my tests. These are
however, all the scripts you'll need.
*/
--the procedure
CREATE PROC dbo.AddressByCity @City NVARCHAR(30)
AS
SELECT  a.AddressID,
        a.AddressLine1,
        a.AddressLine2,
        a.City,
        sp.Name AS StateProvinceName,
        a.PostalCode
FROM    Person.Address AS a
JOIN    Person.StateProvince AS sp
        ON a.StateProvinceID = sp.StateProvinceID
WHERE   a.City = @City;
GO

--generating the first plan
EXEC dbo.AddressByCity
    @City = N'London';



--remove this from cache
DECLARE @PlanHandle VARBINARY(64);

SELECT  @PlanHandle = deps.plan_handle
FROM    sys.dm_exec_procedure_stats AS deps
WHERE   deps.object_id = OBJECT_ID('dbo.AddressByCity');

IF @PlanHandle IS NOT NULL
    BEGIN
        DBCC FREEPROCCACHE(@PlanHandle);
    END
GO



--generate the second plan
EXEC dbo.AddressByCity
    @City = N'Mentor';




--get the query & plan id
SELECT  qsq.query_id,
        qsp.plan_id
FROM    sys.query_store_query AS qsq
JOIN    sys.query_store_plan AS qsp
        ON qsp.query_id = qsq.query_id
WHERE   qsq.object_id = OBJECT_ID('dbo.AddressByCity');


--force the plan
EXEC sys.sp_query_store_force_plan 414,460;

--unforce the plan
EXEC sys.sp_query_store_unforce_plan 414,460;




--Get the plan_handle and offset 
SELECT  deqs.plan_handle,
        deqs.statement_start_offset
FROM    sys.dm_exec_query_stats AS deqs
CROSS APPLY sys.dm_exec_sql_text(deqs.sql_handle) AS dest
WHERE   dest.objectid = OBJECT_ID('dbo.AddressByCity');

--create a plan guide
EXEC sys.sp_create_plan_guide_from_handle
    @name = N'PlanGuidePrecedence',
    @plan_handle = 0x05000500C26A7610A03A4F53A600000001000000000000000000000000000000000000000000000000000000,
    @statement_start_offset = 108;

--remove plan guide
EXEC sys.sp_control_plan_guide
    @operation = N'DROP',
    @name = N'PlanGuidePrecedence';


--pull a plan out of cache. You'll need to make sure the right plan is there first
DECLARE @xmlplan NVARCHAR(MAX)

SELECT  @xmlplan = deqp.query_plan
FROM    sys.dm_exec_query_stats AS deqs
CROSS APPLY sys.dm_exec_text_query_plan(deqs.plan_handle,DEFAULT,DEFAULT) AS deqp
WHERE   deqp.objectid = OBJECT_ID('dbo.AddressByCity');

SELECT @xmlplan


--copied and pasted the value from @xmlplan for easy reuse
DECLARE @xmlplan NVARCHAR(MAX) =
'<ShowPlanXML xmlns="http://schemas.microsoft.com/sqlserver/2004/07/showplan" Version="1.5" Build="13.0.1300.275"><BatchSequence><Batch><Statements><StmtSimple StatementText="CREATE PROC dbo.AddressByCity @City NVARCHAR(30)
AS
SELECT  a.AddressID,
        a.AddressLine1,
        a.AddressLine2,
        a.City,
        sp.Name AS StateProvinceName,
        a.PostalCode
FROM    Person.Address AS a
JOIN    Person.StateProvince AS sp
        ON a.StateProvinceID = sp.StateProvinceID
WHERE   a.City = @City" StatementId="1" StatementCompId="3" StatementType="SELECT" StatementSqlHandle="0x0900D56A5A43E8B56B541DFF8DC13F149C6A0000000000000000000000000000000000000000000000000000" DatabaseContextSettingsId="1" ParentObjectId="276196034" StatementParameterizationType="0" RetrievedFromCache="true" StatementSubTreeCost="0.316799" StatementEstRows="434" SecurityPolicyApplied="false" StatementOptmLevel="FULL" QueryHash="0xDD75E124763781F2" QueryPlanHash="0x6C408B64C5E8A83F" StatementOptmEarlyAbortReason="GoodEnoughPlanFound" CardinalityEstimationModelVersion="120"><StatementSetOptions QUOTED_IDENTIFIER="true" ARITHABORT="true" CONCAT_NULL_YIELDS_NULL="true" ANSI_NULLS="true" ANSI_PADDING="true" ANSI_WARNINGS="true" NUMERIC_ROUNDABORT="false"></StatementSetOptions><QueryPlan CachedPlanSize="32" CompileTime="3" CompileCPU="3" CompileMemory="360"><MissingIndexes><MissingIndexGroup Impact="89.7983"><MissingIndex Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]"><ColumnGroup Usage="EQUALITY"><Column Name="[City]" ColumnId="4"></Column></ColumnGroup></MissingIndex></MissingIndexGroup></MissingIndexes><MemoryGrantInfo SerialRequiredMemory="512" SerialDesiredMemory="720"></MemoryGrantInfo><OptimizerHardwareDependentProperties EstimatedAvailableMemoryGrant="260900" EstimatedPagesCached="65225" EstimatedAvailableDegreeOfParallelism="2"></OptimizerHardwareDependentProperties><RelOp NodeId="0" PhysicalOp="Merge Join" LogicalOp="Inner Join" EstimateRows="434" EstimateIO="0" EstimateCPU="0.0069349" AvgRowSize="225" EstimatedTotalSubtreeCost="0.316799" Parallel="0" EstimateRebinds="0" EstimateRewinds="0" EstimatedExecutionMode="Row"><OutputList><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressID"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressLine1"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressLine2"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="City"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="PostalCode"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[StateProvince]" Alias="[sp]" Column="Name"></ColumnReference></OutputList><Merge ManyToMany="0"><InnerSideJoinColumns><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="StateProvinceID"></ColumnReference></InnerSideJoinColumns><OuterSideJoinColumns><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[StateProvince]" Alias="[sp]" Column="StateProvinceID"></ColumnReference></OuterSideJoinColumns><Residual><ScalarOperator ScalarString="[AdventureWorks2014].[Person].[StateProvince].[StateProvinceID] as [sp].[StateProvinceID]=[AdventureWorks2014].[Person].[Address].[StateProvinceID] as [a].[StateProvinceID]"><Compare CompareOp="EQ"><ScalarOperator><Identifier><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[StateProvince]" Alias="[sp]" Column="StateProvinceID"></ColumnReference></Identifier></ScalarOperator><ScalarOperator><Identifier><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="StateProvinceID"></ColumnReference></Identifier></ScalarOperator></Compare></ScalarOperator></Residual><RelOp NodeId="1" PhysicalOp="Clustered Index Scan" LogicalOp="Clustered Index Scan" EstimateRows="181" EstimateIO="0.00386574" EstimateCPU="0.0003561" AvgRowSize="65" EstimatedTotalSubtreeCost="0.00422184" TableCardinality="181" Parallel="0" EstimateRebinds="0" EstimateRewinds="0" EstimatedExecutionMode="Row"><OutputList><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[StateProvince]" Alias="[sp]" Column="StateProvinceID"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[StateProvince]" Alias="[sp]" Column="Name"></ColumnReference></OutputList><IndexScan Ordered="1" ScanDirection="FORWARD" ForcedIndex="0" ForceSeek="0" ForceScan="0" NoExpandHint="0" Storage="RowStore"><DefinedValues><DefinedValue><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[StateProvince]" Alias="[sp]" Column="StateProvinceID"></ColumnReference></DefinedValue><DefinedValue><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[StateProvince]" Alias="[sp]" Column="Name"></ColumnReference></DefinedValue></DefinedValues><Object Database="[AdventureWorks2014]" Schema="[Person]" Table="[StateProvince]" Index="[PK_StateProvince_StateProvinceID]" Alias="[sp]" IndexKind="Clustered" Storage="RowStore"></Object></IndexScan></RelOp><RelOp NodeId="2" PhysicalOp="Sort" LogicalOp="Sort" EstimateRows="434" EstimateIO="0.0112613" EstimateCPU="0.0060321" AvgRowSize="177" EstimatedTotalSubtreeCost="0.30564" Parallel="0" EstimateRebinds="0" EstimateRewinds="0" EstimatedExecutionMode="Row"><OutputList><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressID"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressLine1"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressLine2"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="City"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="StateProvinceID"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="PostalCode"></ColumnReference></OutputList><MemoryFractions Input="1" Output="1"></MemoryFractions><Sort Distinct="0"><OrderBy><OrderByColumn Ascending="1"><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="StateProvinceID"></ColumnReference></OrderByColumn></OrderBy><RelOp NodeId="3" PhysicalOp="Clustered Index Scan" LogicalOp="Clustered Index Scan" EstimateRows="434" EstimateIO="0.257199" EstimateCPU="0.0217324" AvgRowSize="177" EstimatedTotalSubtreeCost="0.278931" TableCardinality="19614" Parallel="0" EstimateRebinds="0" EstimateRewinds="0" EstimatedExecutionMode="Row"><OutputList><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressID"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressLine1"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressLine2"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="City"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="StateProvinceID"></ColumnReference><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="PostalCode"></ColumnReference></OutputList><IndexScan Ordered="0" ForcedIndex="0" ForceScan="0" NoExpandHint="0" Storage="RowStore"><DefinedValues><DefinedValue><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressID"></ColumnReference></DefinedValue><DefinedValue><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressLine1"></ColumnReference></DefinedValue><DefinedValue><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="AddressLine2"></ColumnReference></DefinedValue><DefinedValue><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="City"></ColumnReference></DefinedValue><DefinedValue><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="StateProvinceID"></ColumnReference></DefinedValue><DefinedValue><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="PostalCode"></ColumnReference></DefinedValue></DefinedValues><Object Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Index="[PK_Address_AddressID]" Alias="[a]" IndexKind="Clustered" Storage="RowStore"></Object><Predicate><ScalarOperator ScalarString="[AdventureWorks2014].[Person].[Address].[City] as [a].[City]=[@City]"><Compare CompareOp="EQ"><ScalarOperator><Identifier><ColumnReference Database="[AdventureWorks2014]" Schema="[Person]" Table="[Address]" Alias="[a]" Column="City"></ColumnReference></Identifier></ScalarOperator><ScalarOperator><Identifier><ColumnReference Column="@City"></ColumnReference></Identifier></ScalarOperator></Compare></ScalarOperator></Predicate></IndexScan></RelOp></Sort></RelOp></Merge></RelOp><ParameterList><ColumnReference Column="@City" ParameterCompiledValue="N'London'"></ColumnReference></ParameterList></QueryPlan></StmtSimple></Statements></Batch></BatchSequence></ShowPlanXML>'


EXEC sys.sp_create_plan_guide
    @name = N'PlanGuidePrecedence',
    @stmt = N'SELECT  a.AddressID,
        a.AddressLine1,
        a.AddressLine2,
        a.City,
        sp.Name AS StateProvinceName,
        a.PostalCode
FROM    Person.Address AS a
JOIN    Person.StateProvince AS sp
        ON a.StateProvinceID = sp.StateProvinceID
WHERE   a.City = @City;',
    @type = N'OBJECT',
    @module_or_batch = N'dbo.AddressByCity',
    @params = NULL,
    @hints = @xmlplan;


--create a plan guide with just a hint in it
EXEC sys.sp_create_plan_guide @name = 'PlanGuidePrecedence', -- sysname
    @stmt = N'SELECT  a.AddressID,
        a.AddressLine1,
        a.AddressLine2,
        a.City,
        sp.Name AS StateProvinceName,
        a.PostalCode
FROM    Person.Address AS a
JOIN    Person.StateProvince AS sp
        ON a.StateProvinceID = sp.StateProvinceID
WHERE   a.City = @City;', -- nvarchar(max)
    @type = N'Object', -- nvarchar(60)
    @module_or_batch = N'dbo.AddressByCity', -- nvarchar(max)
    @params = NULL, -- nvarchar(max)
    @hints = N'OPTION(OPTIMIZE FOR(@City = ''London''))'; -- nvarchar(max)
May 16 2016

Query Store, Forced Plans, and New Plans

I love questions. I recently received one about new plans in the Query Store (available in Azure SQL Database now and in SQL Server 2016 after June 1).

Let’s say you have selected a plan that you want to force. You set it up. Now, let’s say the plan ages out of cache or even goes through a recompile. During the recompile, due to out of date statistics or skew in the statistics, you would, under normal circumstances, get a new plan. However, with Query Store and plan forcing, the plan that’s going to be used is the plan that is being forced. But, does that other plan, the one not used, get stored in Query Store?

I have no idea. Let’s find out.

The Setup

To start with, a small stored procedure that I use all the time for bad parameter sniffing demos that reliably gets different plans with different values due to statistics skew:

CREATE PROC dbo.spAddressByCity @City NVARCHAR(30)
AS
SELECT  a.AddressID,
        a.AddressLine1,
        a.AddressLine2,
        a.City,
        sp.Name AS StateProvinceName,
        a.PostalCode
FROM    Person.Address AS a
JOIN    Person.StateProvince AS sp
        ON a.StateProvinceID = sp.StateProvinceID
WHERE   a.City = @City;

If this procedure is called for a value of ‘London’ it gets a plan with a Merge Join. For most other value it gets a plan with a Loops Join. Here’s an example of the ‘London’ plan:

2016-02-22_10-38-43

I’ve run both queries on my test system. I can check their existence in Query Store this way:

SELECT  CAST(qsp.query_plan AS XML),
		qsq.query_id,
		qsp.plan_id,
		qsp.is_forced_plan
FROM    sys.query_store_query AS qsq
JOIN    sys.query_store_plan AS qsp
        ON qsp.query_id = qsq.query_id
WHERE   qsq.object_id = OBJECT_ID('dbo.spAddressByCity');

That’s going to return two (2) rows:

newplanresults

Now, I’m going to remove the second plan, the ‘Mentor’ plan, and I’m going to force the ‘London’ plan with two statements:

EXEC sys.sp_query_store_remove_plan @plan_id =219;

EXEC sys.sp_query_store_force_plan 2,2;

Now, when I execute the query, no matter what values I pass, I’m going to arrive at the ‘London’ plan. However, let’s pull the plan from cache in order to force a recompile:

DECLARE @PlanHandle varbinary(64);

SELECT  @PlanHandle = deps.plan_handle
FROM    sys.dm_exec_procedure_stats AS deps
WHERE   deps.object_id = OBJECT_ID('dbo.spAddressByCity');

DBCC FREEPROCCACHE(@PlanHandle);

The Reveal

When I execute the query using the value of ‘Mentor’ do I get that plan stored in the Query Store? I know which plan will be in the cache. The ‘London’ plan because of I have chosen to force that plan. But, when I query the Query Store again to see what’s there for my stored procedure:

newplannegativeresults

The short answer is, nope. Any possible new plans that could have been stored in Query Store are not there. Yes, we have gone into the compile process, but, due to plan forcing, that process is getting a small bypass and the plan is simply applied from the Query Store. This, even though, you’re going to see a compile or recompile event if you capture them using Extended Events. For more fun on recompiles and the Query Store, see this earlier post of mine.

Keep those questions coming.


I love talking about query tuning. If you want to spend the day with me talking query tuning, you sure can. In August, I’ll be doing an all day pre-con at SQLServer Geeks Annual Summit in Bangalore India.

I’ll be doing a pre-conference seminar in Oslo Norway before SQL Saturday Oslo in September.

May 12 2016

Use The Correct Data Type

Blog post #5 in support of Tim Ford’s (b|t) #iwanttohelp, #entrylevel. Read about it here.

Saying that you should use the correct data type seems like something that should be very straight forward. Unfortunately it’s very easy for things to get confusing. Let’s take a simple example from AdventureWorks. If I run this query:

SELECT  a.ModifiedDate
FROM    Person.Address AS a
WHERE   a.AddressID = 42;

The output looks like this:

2009-01-20 00:00:00.000

Normal right? You see the year, the month and the day followed by the time in hours, minutes, and seconds as a decimal. Ah, but there is an issue. This query is supposed to be for the reporting system, and the business only cares about the date that the values in the Person.Address table have been modified, so they don’t want to see the time. Also, the person in charge is kind of picky. They really don’t like seeing the date formatted that way. They prefer to see “January 20, 2009.”

Far too often then, the easy answer, just change the column to varchar. You can trim the time and output in exactly the format needed by the business. Problem solved and it was easy…

Well, until someone inputs “Janry 20, 2009” slightly mangling the spelling and suddenly your report looks all messed up. Or, they ask you to start filtering just the last two weeks, regardless of when the report was run and you now can’t easily do date math on the column. Even after you get over that problem with a little formatting using CAST (along with ISDATE to try to catch all those other typos that are in the system now)  you notice that the performance is really slow so you go to put an index on the column and now you have an index key that is 50 bytes wide instead of the 3 bytes that the DATE data type would have been, making the index less efficient (not to mention, sorting the data is going to put February ahead of January, more formatting).

The list goes on and on. You’re going to hit issue after issue and all you tried to do was a little formatting, make the data look pretty by using the VARCHAR data type in place of DATETIME or DATE. This kind of thing happens all the time, especially to people just getting started. The rules should be very simple, use the correct data type for the data you’re collecting. However, it’s easy to get distracted. It’s easy to get pushed. The business says “put it in this format” and the only way you can do that is by changing the data type so…

Don’t do it. Take the time to understand the implications of mangling the data types. It’s not just date and time that cause issues either. Lots and lots of stuff can be easily stuffed into a VARCHAR field that more properly should be an INT or a DECIMAL or some other data type. Be able to communicate with the business so that they understand that you’re not simply being difficult, you’re trying to protect the functionality and integrity of the data. Show how formatting can be done using client tools. Explain all the downstream issues that this decision will have. Then, use the correct data type for the information being stored.

May 03 2016

Implicit Conversion and Performance

Letting SQL Server change data types automatically can seriously impact performance in a negative way. Because a calculation has to be run on each column, you can’t get an index seek. Instead, you’re forced to use a scan. I can demonstrate this pretty simply. Here’s a script that sets up a test table with three columns and three indexes and tosses a couple of rows in:

CREATE TABLE dbo.ConvertTest (
     BigIntColumn BIGINT NOT NULL,
     IntColumn INT NOT NULL,
     DateColumn VARCHAR(30)
    );

CREATE INDEX BigIntIndex 
ON dbo.ConvertTest 
(BigIntColumn);
CREATE INDEX IntIndex 
ON dbo.ConvertTest 
(IntColumn);
CREATE INDEX DateIndex 
ON dbo.ConvertTest
(DateColumn);

WITH    Nums
          AS (SELECT TOP (1000000)
                        ROW_NUMBER() OVER (ORDER BY (SELECT 1
                                                    )) AS n
              FROM      master.sys.all_columns ac1
              CROSS JOIN master.sys.all_columns ac2
             )
    INSERT  INTO dbo.ConvertTest
            (BigIntColumn,
             IntColumn,
             DateColumn
            )
    SELECT  Nums.n,
            Nums.n,
            DATEADD(HOUR, Nums.n, '1/1/1900')
    FROM    Nums;

If I run a very simple query like this:

DECLARE @param DATETIME;

SET @param = '3/15/1963 8:00:00';

SELECT  ct.DateColumn
FROM    dbo.ConvertTest AS ct
WHERE   ct.DateColumn = @param;

I get an execution plan that looks like this:

convert_scan

The issue is, while I put dates into the DateColumn, the data type there is VARCHAR, not DATETIME. Passing a DATETIME value results in an implicit conversion that we can see in the Index Scan Predicate property:

ConverImplicitHurtsPerformance

This is a function against a column and that is what results in the scan. If I modify the code as follows:

DECLARE @param VARCHAR(30);

SET @param = '3/15/1963 8:00:00';

SELECT  ct.DateColumn
FROM    dbo.ConvertTest AS ct
WHERE   ct.DateColumn = @param;

I get an execution plan that looks like this:

convert_seek

No change in data type is needed, so the index can be used. It’s a very simple demonstration of how implicit conversions hurt performance. Now, what about the integer columns? What happens when I do this with the code:

DECLARE @param INT;

SET @param = 650323;

SELECT  ct.BigIntColumn
FROM    dbo.ConvertTest AS ct
WHERE   ct.BigIntColumn = @param;

Here’s the execution plan:

convert_intseek

And here is the predicate:

convert_predicate

In this case, we’re still getting an implicit conversion of the data type, but SQL Server is smart enough to realize that, hey, it’s still just an integer, so it can convert the @param instead of the column, resulting in a seek. If I change the test so that it runs a query against the INT column passing a BIGINT value, it doesn’t even do a conversion of any kind.

In short, not all implicit conversions are a problem. However, it’s still a best practice to use the right data type for columns. You should also use the same data type for your parameters and local variables as the column data type. Taking these steps avoids issues with implicit conversion.


For lots more information on query tuning, in August, I’ll be doing an all day pre-con at SQLServer Geeks Annual Summit in Bangalore India.

I’m also going to be doing an all day training course before SQL Saturday Oslo in September.

Apr 12 2016

A View Is Not A Table

Blog post #4 in support of Tim Ford’s (b|t) #iwanttohelp, #entrylevel

In SQL Server, in the T-SQL you use to query it, a view looks just like a table (I’m using the AdventureWorks2014 database for all these examples):

SELECT  *
FROM    Production.vProductAndDescription AS vpad;

 

SELECT  vpad.Name,
        vpad.Description,
        vpmi.Instructions
FROM    Production.vProductAndDescription AS vpad
JOIN    Production.Product AS p
        ON p.ProductID = vpad.ProductID
JOIN    Production.vProductModelInstructions AS vpmi
        ON vpmi.ProductModelID = p.ProductModelID
WHERE   vpad.ProductID = 891
        AND vpad.CultureID = 'fr';

The above query actually combines two views and a table. This is what is commonly referred to as a “code smell”. A code smell is a coding practice that works, but that can lead to problems. In this case, we’re talking about performance problems. The performance problems when using views to join to tables and other views as if they were real tables comes about because a standard view is not a table. Its a query. For example, the second view introduced, vPorductModelInstructions looks like this:

ALTER VIEW [Production].[vProductModelInstructions] 
AS 
SELECT 
    [ProductModelID] 
    ,[Name] 
    ,[Instructions].value(N'declare default element namespace "http://schemas.microsoft.com/sqlserver/2004/07/adventure-works/ProductModelManuInstructions"; 
        (/root/text())[1]', 'nvarchar(max)') AS [Instructions] 
    ,[MfgInstructions].ref.value('@LocationID[1]', 'int') AS [LocationID] 
    ,[MfgInstructions].ref.value('@SetupHours[1]', 'decimal(9, 4)') AS [SetupHours] 
    ,[MfgInstructions].ref.value('@MachineHours[1]', 'decimal(9, 4)') AS [MachineHours] 
    ,[MfgInstructions].ref.value('@LaborHours[1]', 'decimal(9, 4)') AS [LaborHours] 
    ,[MfgInstructions].ref.value('@LotSize[1]', 'int') AS [LotSize] 
    ,[Steps].ref.value('string(.)[1]', 'nvarchar(1024)') AS [Step] 
    ,[rowguid] 
    ,[ModifiedDate]
FROM [Production].[ProductModel] 
CROSS APPLY [Instructions].nodes(N'declare default element namespace "http://schemas.microsoft.com/sqlserver/2004/07/adventure-works/ProductModelManuInstructions"; 
    /root/Location') MfgInstructions(ref)
CROSS APPLY [MfgInstructions].ref.nodes('declare default element namespace "http://schemas.microsoft.com/sqlserver/2004/07/adventure-works/ProductModelManuInstructions"; 
    step') Steps(ref);

GO

That’s a query against the XML stored in the ProductModel table. The view was created to mask the complexity of the necessary XPath code, while providing a mechanism for retrieving the data from the XML. This is a common use of views. However, when we then treat the view as a table, and join it to other tables and views, we present a problem for the optimizer. Because a view is not a table, but is instead a query, the optimizer has to resolve this query in combination with any other views or tables to arrive at an execution plan for the whole combined mess. While the optimizer is very good at what it does, because of the complexity caused by the additional unnecessary processing to figure out which parts of the view is not needed to satisfy the query, it can make poor choices. That can result in poor performance.

If I were to rewrite the query, it would look something like this:

SELECT  p.Name,
        pd.Description,
        pm.Instructions.value(N'declare default element namespace "http://schemas.microsoft.com/sqlserver/2004/07/adventure-works/ProductModelManuInstructions"; 
        (/root/text())[1]', 'nvarchar(max)') AS Instructions
FROM    Production.Product AS p
JOIN    Production.ProductModelProductDescriptionCulture AS pmpdc
        ON pmpdc.ProductModelID = p.ProductModelID
JOIN    Production.ProductDescription AS pd
        ON pd.ProductDescriptionID = pmpdc.ProductDescriptionID
JOIN    Production.ProductModel AS pm
        ON pm.ProductModelID = p.ProductModelID
CROSS APPLY Instructions.nodes(N'declare default element namespace "http://schemas.microsoft.com/sqlserver/2004/07/adventure-works/ProductModelManuInstructions"; 
    /root/Location') MfgInstructions (ref)
CROSS APPLY MfgInstructions.ref.nodes('declare default element namespace "http://schemas.microsoft.com/sqlserver/2004/07/adventure-works/ProductModelManuInstructions"; 
    step') Steps (ref)
WHERE   p.ProductID = 891
        AND pmpdc.CultureID = 'fr';

That’s a lot more complex than the query we had above that only referenced three objects and had only two JOIN operations. However, if you capture the I/O and the execution time for these queries, you’ll see a different story.

I used two methods for measuring performance. I used SET STATISTICS IO and SET STATISTICS TIME to ON for the queries for one set of tests. For another set I used Extended Events. Consistently the execution time for the query with the view was around 110ms. The query that didn’t reference any views was around 37ms. The reads were 155 for the query with views, but only 109 for the query without. If you look at the individual table I/O, you can start to see where the differences come from. These are the results from the query with the views:

Table ‘ProductDescription’. Scan count 0, logical reads 56
Table ‘ProductModelProductDescriptionCulture’. Scan count 28, logical reads 56
Table ‘xml_index_nodes_418100530_256001’. Scan count 13, logical reads 37
Table ‘ProductModel’. Scan count 0, logical reads 2
Table ‘Product’. Scan count 0, logical reads 4

These are the results for the query without the view:

Table ‘ProductDescription’. Scan count 0, logical reads 56
Table ‘xml_index_nodes_418100530_256001’. Scan count 13, logical reads 37
Table ‘ProductModelProductDescriptionCulture’. Scan count 6, logical reads 12
Table ‘ProductModel’. Scan count 0, logical reads 2
Table ‘Product’. Scan count 0, logical reads 2

You can see the differences in both ProductModelProductDescriptionCulture and Product. This is because of the differences in the execution plans caused by differences in the choices made by the optimizer.

A standard view is not a table. There is such a thing as a materialized view or indexed view, which is a table. That’s not what we’re talking about here. While you can use a view as if it was a table, don’t mistake it for a table. A view is just a mask in front of a query. It can’t be used like an object so that you avoid rewriting the same JOIN. That will lead to issues for the optimizer as this simple set of examples showed. Don’t shy away from using views, just understand what their real behavior is. A view is a query, not a table.