Sep 19 2016

Query Store and Plan Forcing: What Do You Use It For

If you’re working with Azure SQL Database or you’ve moved into SQL Server 2016, one of the biggest new tools is the Query Store. It provides a mechanism of capturing query performance over time and persisting it with the database. You also get the execution plans for those queries. Finally, you can choose to have the Query Store override execution plan selection by use of Plan Forcing. I’ve written about Query Store a few times:

Query Store, Force Plan and “Better” Plans
Query Store, Force Plan and Dropped Objects
Precedence Goes to Query Store or Plan Guide
Query Store, Forced Plans and New Plans
Query Store and Optimize For Ad Hoc
Query Store and Recompile
Finding Your Query in Query Store
Removing All Query Store Data
Monitor Query Performance

OK, maybe I’m a little obsessed with Query Store. I can’t help it. It’s one of the most exciting developments in query tuning within SQL Server in quite some time. If you’re working with Azure SQL Database or SQL Server 2016, you should be using it. If you’re not, you’re missing a trick. If you’re preparing to move into Azure SQL Database or SQL Server 2016, you should learn this now.

Why?

You’ll note that I’ve written about Force Plan or Plan Forcing multiple times. The single biggest reason for this is because of one of the most common problems with query performance in SQL Server is when parameter sniffing goes bad and Force Plan lets you fix bad parameter sniffing without changing your code. That’s a huge win.

But…

And you knew there was a but coming.

But, what else does Force Plan do for you? What if you never experience bad parameter sniffing (you do, but I’m not going to argue the point)? Is there something else that Force Plan can do for you? Heck yes! The whole point of creating the Query Store was in order to address Plan Regression. What the heck is plan regression? When Microsoft makes any change to the Query Optimizer, and those changes come all the time, it’s possible that you might see a change in your execution plans. Most of the time, it’s going to be a positive change. That’s why they’re changing the Optimizer after all, to improve it. However, sometimes, you’re benefiting from the old behavior of the Optimizer and that new plan doesn’t work as well as the old plan. This is plan regression. When Microsoft changed the Cardinality Estimation engine in SQL Server 2014, more than a few people experienced the new estimator giving row estimates that resulted in a different execution plan that didn’t perform as well as the old plan. This is plan regression. What to do?

QUERY STORE! Ta-da!

Testing For Regression

Here’s what you’re supposed to do. You’ve decided to finally retire that SQL Server 2000/2005/2008R2/2012 database and join the future in SQL Server 2016 (what I’m about to describe actually doesn’t help for migrations to Azure SQL Database). Good. Backup your database (always, backup your database), restore it to that shiny new 2016 instance, but leave the compatibility mode in whatever version you’re coming from. Don’t change it to the new compatibility mode yet. This means it will use the old Cardinality Estimator and not implement new Query Optimizer choices, yet. Enable Query Store because it’s not on by default. Run a bunch of tests against your database. Run all the queries you can with as many different parameter values that you can. Now, change the compatibility level to 2016. Run the tests again. Use the nifty GUI (actually, I dislike the GUI, but it’s kind of cool to see this) to run the Regressed Queries report. That will show you queries that have run slower over time as you can see below (click to embiggen):

RegressionReport

I have highlighted the fourth query in the report because the first three all have identical plans, even though the execution times varied for those queries (blocking, resource contention, who knows). That one has two different plans, which I can see by the plan listing:

planid

Now I know that my plan changed over time. The trick is, investigate the plans and determine which plans changed because of the change in the compatibility level. You can tell this by clicking on the properties of the first operator (the plans shown in the report can be explored just like any other plan within SSMS) and look for the CardinalityEstimationModelVersion property:

cardinalityestimationmodelversion

Any plans that you like, that run better, based on the old model, you can choose, after testing and due consideration, please be careful with this, to force the plan simply by clicking a button:

forceplan

I can’t emphasize enough, be sure that you have tested the plan choice adequately. You can easily unforce a plan, but that may only occur to you after you have performance issues in production. This is a great new toy, uh, I mean tool, but you have to be careful when using it. You can force a plan, but it can be the wrong plan. Here are the steps in simple order:

  1. Backup the database on the old system
  2. Restore it to the new system
  3. Enable Query Store
  4. Run lots of tests
  5. Update the compatibility level
  6. Run the lots of tests again
  7. Check the Regression report
  8. Where applicable, use plan forcing

Conclusion

Query Store and the ability to force a plan is going to change the way we do query tuning. In some instances, but not all or even a majority, we won’t have to modify our code or add indexes in order to arrive at superior performance. We’ll be able to pick an appropriate plan, force it, and get better performance.

If you want to get started on Query Store and you’re attending PASS Summit 2016, I’ll be doing a session there on this topic on Wednesday, Oct 26 at 3:15 PM.

Sep 12 2016

The Clustered Index Is Vital To Your Database Design

This is post #9 supporting  Tim Ford’s (b|t) initiative on #iwanttohelp, #entrylevel. Read about it here.

You get one clustered index per table.

That bears repeating, you get one clustered index per table.

Choosing a clustered index is an extremely important and fundamental aspect of all your SQL Server work. The one clustered index that you get determines how the data in your table is stored. Because the clustered index determines how your data is stored, it also determines how your data is retrieved. Much of SQL Server is engineered around the clustered index because it is such a foundational object for the rest of all behavior. Without a clustered index, the data in your table is stored in what is called a heap. It is essentially a pile, a heap, of data, stored without internal structure. The data in a heap can be found fairly quickly when only a single value is needed, but as soon as you start to retrieve ranges of data, more than one row, heaps become extremely problematic. Meanwhile, the clustered index stores all your data at the bottom of a B+Tree, structured storage, that is used to retrieve this data through the key, or keys, that you give your clustered index. Further, each non-clustered index also uses the key value from your clustered index as it’s means of retrieving data from the clustered index.

All this means that the vast majority of your tables should have a clustered index. Yes, there are exceptions, but, exceptions should be exceptional.

The standard advice, and it’s not wrong, is to make the clustered key as narrow as possible. Next, clustered indexes are designed to support monotonically increasing values extremely well. Clustered indexes must be unique. You either have to create them on a unique value, or, internally you’ll get a UNIQUIFIER applied to the key to ensure that each value is unique. All this taken together means that they work nicely on IDENTITY columns. Because of this advice, you’ll frequently see all the clustered indexes in a database on the IDENTITY columns. However, this is a bit of a trap. You need to remember that the clustered index is not just about storage, but about retrieval. If the most common access path to your data is through, for example, a foreign key, that value might be a better choice for the clustered key. If you always retrieve the data through a date range, that might be a better choice for the clustered key. Keep in mind the standard advice for clustered keys; narrow, monotonically increase, unique. However, also remember, they are where your data is stored. It is frequently better to sacrifice one or more of the standard practices in order to be able to use your clustered index to retrieve the data in most of your queries.

Think through where and how to apply your clustered indexes, and don’t get stuck into using them in a single manner, everywhere, within your system. You get one clustered index on a table. Be sure you put it somewhere that it will work well and somewhere that it will be well used, both.

Aug 29 2016

Azure SQL Data Warehouse Execution Plans

Azure SQL Data Warehouse can sometimes feel like it’s completely different from SQL Server, but under the covers, it is still (mostly) SQL Server and it does have execution plans. Let’s take a look at one.

I’ve created a new SQL Data Warehouse using the sample database available on the portal, AdventureWorksDW. Here’s a query against that database:

SELECT dd.FullDateAlternateKey AS OrderDate,
dc.LastName,
SUM(fis.SalesAmount) AS SumSalesAmount
FROM dbo.FactInternetSales AS fis
JOIN dbo.DimDate AS dd
ON fis.OrderDateKey = dd.DateKey
JOIN dbo.DimCustomer AS dc
ON dc.CustomerKey = fis.CustomerKey
GROUP BY dd.FullDateAlternateKey,
dc.LastName
HAVING SUM(fis.SalesAmount) > 5000.0
ORDER BY OrderDate DESC;

If I attempt to capture an execution plan using the SQL Server Management Studio GUI, nothing happens. If I try to use T-SQL commands, I get an error that those commands are not supported with this version of SQL Server. Same thing if I try to capture a plan using Visual Studio. So… now what? Enter our new command:

EXPLAIN
SELECT  dd.FullDateAlternateKey AS OrderDate,
        dc.LastName,
        SUM(fis.SalesAmount) AS SumSalesAmount
FROM    dbo.FactInternetSales AS fis
JOIN    dbo.DimDate AS dd
        ON fis.OrderDateKey = dd.DateKey
JOIN    dbo.DimCustomer AS dc
        ON dc.CustomerKey = fis.CustomerKey
GROUP BY dd.FullDateAlternateKey,
        dc.LastName
HAVING  SUM(fis.SalesAmount) > 5000.0
ORDER BY OrderDate DESC;

If I run this through the SSMS query window, I get a syntax error. So we’re now in Visual Studio. This is how we generate an execution plan from within Azure SQL Data Warehouse. What you get is XML output in the results like this:

xmlresults

According to the documentation on EXPLAIN, I should be able to click on the XML and it will open up to explore. In my version of Visual Studio (2015), I didn’t find that to be the case. Instead I had to copy and paste the XML into an XML file window that I created within Visual Studio. This is what I finished with:

<?xml version="1.0" encoding="utf-8"?>
<dsql_query number_nodes="1" number_distributions="60" number_distributions_per_node="60">
  <sql>SELECT  dd.FullDateAlternateKey AS OrderDate,          dc.LastName,          SUM(fis.SalesAmount) AS SumSalesAmount  FROM    dbo.FactInternetSales AS fis  JOIN    dbo.DimDate AS dd          ON fis.OrderDateKey = dd.DateKey  JOIN    dbo.DimCustomer AS dc          ON dc.CustomerKey = fis.CustomerKey  GROUP BY dd.FullDateAlternateKey,          dc.LastName  HAVING  SUM(fis.SalesAmount) &gt; 5000.0  ORDER BY OrderDate DESC</sql>
  <dsql_operations total_cost="5.98868068474576" total_number_operations="13">
    <dsql_operation operation_type="RND_ID">
      <identifier>TEMP_ID_14</identifier>
    </dsql_operation>
    <dsql_operation operation_type="ON">
      <location permanent="false" distribution="AllComputeNodes" />
      <sql_operations>
        <sql_operation type="statement">CREATE TABLE [tempdb].[dbo].[TEMP_ID_14] ([DateKey] INT NOT NULL, [FullDateAlternateKey] DATE NOT NULL ) WITH(DATA_COMPRESSION=PAGE);</sql_operation>
      </sql_operations>
    </dsql_operation>
    <dsql_operation operation_type="BROADCAST_MOVE">
      <operation_cost cost="1.99584" accumulative_cost="1.99584" average_rowsize="7" output_rows="1188" GroupNumber="12" />
      <source_statement>SELECT [T1_1].[DateKey] AS [DateKey],         [T1_1].[FullDateAlternateKey] AS [FullDateAlternateKey]  FROM   [DWTest].[dbo].[DimDate] AS T1_1</source_statement>
      <destination_table>[TEMP_ID_14]</destination_table>
    </dsql_operation>
    <dsql_operation operation_type="RND_ID">
      <identifier>TEMP_ID_15</identifier>
    </dsql_operation>
    <dsql_operation operation_type="ON">
      <location permanent="false" distribution="AllDistributions" />
      <sql_operations>
        <sql_operation type="statement">CREATE TABLE [tempdb].[dbo].[TEMP_ID_15] ([OrderDateKey] INT NOT NULL, [CustomerKey] INT NOT NULL, [SalesAmount] MONEY NOT NULL ) WITH(DATA_COMPRESSION=PAGE);</sql_operation>
      </sql_operations>
    </dsql_operation>
    <dsql_operation operation_type="SHUFFLE_MOVE">
      <operation_cost cost="3.93098847457627" accumulative_cost="5.92682847457627" average_rowsize="16" output_rows="60398" GroupNumber="10" />
      <source_statement>SELECT [T1_1].[OrderDateKey] AS [OrderDateKey],         [T1_1].[CustomerKey] AS [CustomerKey],         [T1_1].[SalesAmount] AS [SalesAmount]  FROM   [DWTest].[dbo].[FactInternetSales] AS T1_1</source_statement>
      <destination_table>[TEMP_ID_15]</destination_table>
      <shuffle_columns>CustomerKey;</shuffle_columns>
    </dsql_operation>
    <dsql_operation operation_type="RND_ID">
      <identifier>TEMP_ID_16</identifier>
    </dsql_operation>
    <dsql_operation operation_type="ON">
      <location permanent="false" distribution="AllDistributions" />
      <sql_operations>
        <sql_operation type="statement">CREATE TABLE [tempdb].[dbo].[TEMP_ID_16] ([FullDateAlternateKey] DATE NOT NULL, [LastName] NVARCHAR(50) COLLATE SQL_Latin1_General_CP1_CI_AS, [col] MONEY NOT NULL ) WITH(DATA_COMPRESSION=PAGE);</sql_operation>
      </sql_operations>
    </dsql_operation>
    <dsql_operation operation_type="SHUFFLE_MOVE">
      <operation_cost cost="0.0618522101694915" accumulative_cost="5.98868068474576" average_rowsize="111" output_rows="136.985" GroupNumber="25" />
      <source_statement>SELECT [T1_1].[FullDateAlternateKey] AS [FullDateAlternateKey],         [T1_1].[LastName] AS [LastName],         [T1_1].[col] AS [col]  FROM   (SELECT   SUM([T2_2].[SalesAmount]) AS [col],                   [T2_1].[FullDateAlternateKey] AS [FullDateAlternateKey],                   [T2_2].[LastName] AS [LastName]          FROM     [tempdb].[dbo].[TEMP_ID_14] AS T2_1                   INNER JOIN                   (SELECT [T3_2].[OrderDateKey] AS [OrderDateKey],                           [T3_2].[SalesAmount] AS [SalesAmount],                           [T3_1].[LastName] AS [LastName]                    FROM   [DWTest].[dbo].[DimCustomer] AS T3_1                           INNER JOIN                           [tempdb].[dbo].[TEMP_ID_15] AS T3_2                           ON ([T3_2].[CustomerKey] = [T3_1].[CustomerKey])) AS T2_2                   ON ([T2_1].[DateKey] = [T2_2].[OrderDateKey])          GROUP BY [T2_1].[FullDateAlternateKey], [T2_2].[LastName]) AS T1_1</source_statement>
      <destination_table>[TEMP_ID_16]</destination_table>
      <shuffle_columns>FullDateAlternateKey;</shuffle_columns>
    </dsql_operation>
    <dsql_operation operation_type="ON">
      <location permanent="false" distribution="AllDistributions" />
      <sql_operations>
        <sql_operation type="statement">DROP TABLE [tempdb].[dbo].[TEMP_ID_15]</sql_operation>
      </sql_operations>
    </dsql_operation>
    <dsql_operation operation_type="ON">
      <location permanent="false" distribution="AllComputeNodes" />
      <sql_operations>
        <sql_operation type="statement">DROP TABLE [tempdb].[dbo].[TEMP_ID_14]</sql_operation>
      </sql_operations>
    </dsql_operation>
    <dsql_operation operation_type="RETURN">
      <location distribution="AllDistributions" />
      <select>SELECT   [T1_1].[FullDateAlternateKey] AS [FullDateAlternateKey],           [T1_1].[LastName] AS [LastName],           [T1_1].[col] AS [col]  FROM     (SELECT [T2_1].[FullDateAlternateKey] AS [FullDateAlternateKey],                   [T2_1].[LastName] AS [LastName],                   [T2_1].[col] AS [col]            FROM   (SELECT   SUM([T3_1].[col]) AS [col],                             [T3_1].[FullDateAlternateKey] AS [FullDateAlternateKey],                             [T3_1].[LastName] AS [LastName]                    FROM     [tempdb].[dbo].[TEMP_ID_16] AS T3_1                    GROUP BY [T3_1].[FullDateAlternateKey], [T3_1].[LastName]) AS T2_1            WHERE  ([T2_1].[col] &gt; CAST ((5000.0) AS DECIMAL (5, 1)))) AS T1_1  ORDER BY [T1_1].[FullDateAlternateKey] DESC</select>
    </dsql_operation>
    <dsql_operation operation_type="ON">
      <location permanent="false" distribution="AllDistributions" />
      <sql_operations>
        <sql_operation type="statement">DROP TABLE [tempdb].[dbo].[TEMP_ID_16]</sql_operation>
      </sql_operations>
    </dsql_operation>
  </dsql_operations>
</dsql_query>

So now we just save this as a .sqlplan file and open it in SSMS, right?

Nope!

See, that’s not a regular execution plan, at all. Instead, it’s a D-SQL plan. It’s not the same as our old execution plans. You can’t open it as a graphical plan (and no, not even in that very popular 3rd party tool, I tried). You will have to learn how to read these plans differently because, well, they are different.

Aug 15 2016

Query Store, Force Plan and Dropped Objects

I love the Query Store. Seriously. It’s a huge leap forward in the capabilities of Azure SQL Database and SQL Server in support of performance monitoring and query optimization. One of my favorite aspects of the Query Store is the ability to force plans. Frankly though, it’s also the scariest part of the Query Store. I do believe that plan forcing will be one of the most ill-used functions in SQL Server since the multi-statement table-valued user-defined function (don’t get me started). However, unlike the UDF, this ill-use will be because of poor understanding on the part of the user, not a fundamental design issue. No, plan forcing and the Query Store are very well constructed. Let me give you an example of just how well constructed they are.

Let’s imagine that have a situation such as bad parameter sniffing where you’ve determined that from the more than one possible execution plans against a table, there is a preferred plan. Enabling plan forcing to ensure that plan gets used is a no-brainer. Let’s further imagine that you have a junior DBA who is… let’s just say overly aggressive in their duties such that they do silly things occasionally. What happens when your pretty plan, which uses a particular index meets your junior DBA who just dropped that index?

Here’s the setup. We’re using the WideWorldImporters database and we have this query:

SELECT  *
FROM    Warehouse.StockItemTransactions AS sit
WHERE   sit.TransactionOccurredWhen BETWEEN '9/9/2015'
                                    AND     '9/11/2015';

This query, with the default configuration, will scan the existing table, so I’ll add an index:

CREATE INDEX TransactionOccurredWhenNCI
ON Warehouse.StockItemTransactions
(TransactionOccurredWhen);

For a limited range such as the one I’m passing above, I’ll get a plan with a key lookup operation which runs faster than the scan, so I’m happy. For a broader range, I’m likely to see a scan again, but since most of my queries have a very narrow range, I’d sure like to be able to force the plan to always compile to the seek and key lookup. To do this I need to find the query_id and plan_id from the Query Store (assuming I’m not using the GUI):

SELECT  qsp.plan_id,
        qsp.query_id,
		qsqt.query_sql_text,
		qsp.count_compiles
FROM    sys.query_store_plan AS qsp
JOIN    sys.query_store_query AS qsq
        ON qsq.query_id = qsp.query_id
JOIN    sys.query_store_query_text AS qsqt
        ON qsqt.query_text_id = qsq.query_text_id
WHERE   qsqt.query_sql_text LIKE 'SELECT  *
FROM    Warehouse.StockItemTransactions AS sit%';

With those values, I can force the execution plan so that it will always use the plan I want:

EXEC sys.sp_query_store_force_plan 42460,463;

That’s it. I’m happy because I’m going to see the execution plan used over and over, despite any values passed during a recompile.

Then…

Along comes our aggressive junior DBA who decides that there are “too many” indexes on the server. No, I don’t know what that means either, but they evidently read it on the internet or something so they drop the index we created before:

DROP INDEX TransactionOccurredWhenNCI ON Warehouse.StockItemTransactions;

What now happens to our lovely execution plan and the plan forcing? We’ll take a look at two events in Extended Events, sql_statement_recompile and query_store_plan_forcing_failed. Nothing happens immediately on dropping the index. The plans associated with that object, if any, are marked as invalid in the cache. The next time we call the query it’s going to recompile and we can see the event:

recompile_event

The most important part of the event is the recompile_cause which is “Schema changed”. However, I would also note the attach_activity_id.guid. I’ve chosen to enable causality tracking in this Extended Event session. This will cause all events associated with a common activity to get a GUID and then a sequence. This is interested because, after the recompile event, we get the query_store_plan_forcing_failed event:

plan_forcing_failed

The guid value is the same as the event above and the *.seq number is now 2, showing that, for these events, the recompile event occurred and then this event occurred. That makes perfect sense. The plan is marked for recompile, so, it’s going to be recompiled. I have enabled plan forcing though, so I have a particular plan that I want the optimizer to use. However, thanks to my “helpful” junior DBA, the plan is now invalid. You even get the description of what happened in the message field for the event:

Index ‘WideWorldImporters.Warehouse.StockItemTransactions.TransactionOccurredWhenNCI’, specified in the USE PLAN hint, does not exist. Specify an existing index, or create an index with the specified name.

The first question now is, what happens with this query and the execution plan? Does the new plan generated now that the index is missing get stored in cache? Yes, it does. We can validate that by querying the cache, or, when capturing the actual execution plan, checking the “Retrieved from cache” property.

Because plan forcing is enabled, do we see a recompile every time this query is called? The answer to that question is slightly complex. Under normal circumstances, no. As long as that plan remains in cache, it’s simply reused. No other recompiles occur. A normal recompile event will cause another attempt at applying the invalid execution plan and we would see yet another query_store_plan_forcing_failed event for each recompile on the query. However, during testing, Joey D’Antoni (who was helping me play with this when we discussed what would happen when a plan was made invalid) had severe memory pressure on his server. He saw intermittent recompiles with a cause message that said plan forcing had failed. So if your server is under extreme stress and you cause this issue, you might see different messages. Just remember, the cause of the recompiles was not the plan forcing, but the memory pressure.

The fun thing is, as long as I don’t remove the plan forcing or take the query and plan out of the Query Store manually, if I recreate the index on my table with the same name and definition as that expected by the plan, the Query Store will simply reapply the plan and then successfully force it during any subsequent recompile situation. This is because Query Store is persisted with the database and barring outside activity, the information there will remain, just like the rest of the data in the database.

All of this means that Query Store works exactly the way we would expect, not forcing additional recompiles when you, or your junior DBA, inadvertently invalidate a plan. It also works as expected in that forcing a plan is stored with your database so that, assuming you don’t remove that plan from the Query Store, it will simply be reapplied after you fix the problem. It’s fun to see the thought that went behind the design of the behavior of Query Store. However, please, use plan forcing judiciously.

Jul 25 2016

Monitor Query Performance

Blog post #7 in support of Tim Ford’s (b|t) #iwanttohelp, #entrylevel. Read about it here.

Sooner or later when you’re working with SQL Server, someone is going to complain that the server is slow. I already pointed out the first place you should look when this comes up. But what if they’re more precise? What if, you know, or at least suspect, you have a problem with a query? How do you get information about how queries are behaving in SQL Server?

Choices For Query Metrics

It’s not enough to know that you have a slow query or queries. You need to know exactly how slow they are. You must measure. You need to know how long they take to run and you need to know how many resources are used while they run. You need to know these numbers in order to be able to determine if, after you do something to try to help the query, you’ll know whether or not you’ve improved performance. To measure the performance of queries, you have a number of choices. Each choice has positives and negatives associated with them. I’m going to run through my preferred mechanisms for measuring query performance and outline why. I’ll also list some of the other mechanisms you have available and tell you why I don’t like them. Let’s get started.

Dynamic Management Views

Since SQL Server 2005, Dynamic Management Views (DMV) and Functions (DMF) have been available for access all sorts of information about the server. Specifically there are a few DMVs that are focused on queries and query performance. If you go back through my blog, you can find tons of examples where I illustrate their use. You can also see them at work in commercial tools and free tools. Adam Machanic’s sp_WhoIsActive, a free tool, makes extensive use of DMVs. To learn more about DMVs, you can download a free book, Performance Tuning with SQL Server Dynamic Management Views. DMVs are available in Azure SQL Database, Azure SQL Data Warehouse, and all editions of SQL Server.

The information captured by DMVs is an aggregation of all the times the query has been run. This means you can’t find how long the query ran at 3PM yesterday. You can though see the minimum and maximum time the query took as well as the average. The ability to see this information is what makes DMVs useful. However, another important point about DMVs is that they only collect information while a query is in memory. As soon as it leaves the cache (the area of memory it is stored in), so does all the aggregated information about the query in the DMVs.

You use the DMVs for a general understanding of how a query is behaving. They’re not meant for detailed for long term collection of information about queries. For that we use other tools.

Extended Events

Introduced in SQL Server 2008, Extended Events (ExEvents) are a mechanism for capturing detailed information about SQL Server and the processes within. One of those processes is how queries behave. I have multiple examples on this blog on using ExEvents. You can’t go wrong reading about them on Jonathan Kehayias’ blog. Extended events are available in Azure SQL Database and all editions of SQL Server.

When you need to know every query against a database, or each time a particular query is called, and all the details associated with the query (reads, writes, duration), ExEvents are the way to go. ExEvents are very lightweight on the server (but not free) and can be filtered so that you capture just the information you need. The information is detailed and not aggregated. Instead it’s raw. The real issue with capturing this data is the amount of data you’ll be capturing. Testing and careful filtering to ensure you’re dealing with too much information is important. Prior to SQL Server 2012, there was no graphical user interface for reading ExEvent data, so you would have been forced to run queries against the XML that the information is captured within. With the tools available in SQL Server Management Studio, this is no longer the case.

You use ExEvents when you need specific and detailed information about a query. ExEvents are not so good for generalized monitoring.

Query Store

Introduced in Azure SQL Database, and first released in SQL Server with 2016, Query Store is another mechanism for capturing aggregated information about queries. As before, I have examples on how to work with Query Store on my blog. You can also find quite a bit on it over at Simple-Talk. Query Store is pretty specialized still and only available in Azure and SQL Server 2016, but it is in all editions of SQL Server 2016.

Query Store captures information similar to what is available in the DMVs. However, unlike the DMVs, the information that Query Store captures is kept around, even after a query ages out or is removed from cache. This persistence makes Query Store very exciting. You do have to choose to turn it on for each database you wish to capture queries for. It’s not automatic like DMVs. The capture processes are asynchronous, so they should be relatively light weight for most databases.

You use the Query Store when you need to capture query metrics over the long term, but you don’t need detailed information and aggregations works well for you.

Others

There are other ways to measure query performance. You can use the Profiler GUI, but that actually seriously negatively impacts the server. You can bring a server down by using it, so it should be avoided. Profiler generates scripts called trace, which can be used to monitor your server. However, they have a much higher impact than ExEvents and they’re on the deprecation list. Microsoft is not added new trace events for new functionality, so they’re becoming less and less useful with each release. You also can’t use trace against Azure. If you’re writing a query and you just want to see how long it takes to run, you can use SET STATISTICS TIME ON, to capture the execution time. This is a handy way to quickly measure performance. There is also the ability to capture reads and writes using SET STATISTICS IO ON, but, while this does capture the metrics we need, it adds considerable overhead to the query, skewing performance measurement. This is why I stick to ExEvents when I need an accurate measure.

Conclusion

Honest people can disagree about the best way to capture query performance. I have my preferences as you can see. However, I’m fairly certain that everyone would agree that it’s important to know how to capture performance metrics in order to be able to assert that performance has increased or decreased in a measured fashion. You don’t want to guess at query performance, you want to know.

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.