Automatically convert contained database to non-contained for replication

This article was recently published on dev.getroadmap.com:

 
 
In one of my previous posts, I described how to setup replication to an Azure SQL database. This works like a charm, and I still highly recommend using this when you want to migrate data from an on-premise server (or Azure VM) to a Azure SQL db (PaaS).

But in our environment, we use SQL Server 2016 and contained databases for some of our datasets. Unfortunately (but totally understandable), you can’t setup replication from a contained database. So how do you deal with this? For our use-case, I’ve written a script to automatically change the database from contained to non-contained. And because I’m probably not the only one who needs to do this, I’d like to share it with you.

 
Steps to take
It might sound a bit difficult, but when you think about it, it’s actually quite easy. To go from a contained database with a user to a non-contained database with a login, you need to take the following steps:

 
1) Duplicate user as login on server level
2) Drop user on database level
3) Alter database to non-contained
4) Add login to database

 
The script
Before you start using this, I want to warn you. Using this script on your server(s) or in production is at your own risk. This worked for me, but maybe it doesn’t on your server(s)!

In order to let this script work, you must be able to stop applications and queries from executing on your database. In our case, we can just stop the service(s) that use a specific database (that’s the advantage of micro services). The reason for this is that you must be able to obtain an exclusive lock on the database, in order to switch from contained to non-contained.

If you can manage this, the script below could work for you too:

--====================================================================================================
/* 1 - Duplicate user on instance level as login */

USE master
GO

CREATE LOGIN [Login non-contained database] WITH PASSWORD=[Password]', DEFAULT_DATABASE=[master], CHECK_EXPIRATION=OFF, CHECK_POLICY=OFF
GO

--====================================================================================================
/* 2 - Drop user on database level */

USE [Contained database name]
GO

DROP USER [User contained database]
GO

--====================================================================================================
/* 3 - Alter database to non-contained */ 

USE master
GO

ALTER DATABASE [Contained database name] SET CONTAINMENT = NONE WITH NO_WAIT
GO

--====================================================================================================
/* 4 - Add login to database */

USE [Non-contained database name]
GO

CREATE USER [User non-contained database] FOR LOGIN [Login non-contained database]
GO

ALTER ROLE [db_datareader] ADD MEMBER [User non-contained database]
GO

ALTER ROLE [db_datawriter] ADD MEMBER [User non-contained database]
GO

--====================================================================================================

 
Or, to make it easier to read, an example with actual names:

--====================================================================================================
/* 1 - Duplicate user on instance level as login */

USE master
GO

CREATE LOGIN [Login_RW] WITH PASSWORD=N'Password123!', DEFAULT_DATABASE=[master], CHECK_EXPIRATION=OFF, CHECK_POLICY=OFF
GO

--====================================================================================================
/* 2 - Drop user on database level */

USE ContainedDatabase
GO

DROP USER Login_RW
GO

--====================================================================================================
/* 3 - Alter database to non-contained */ 

USE master
GO

ALTER DATABASE ContainedDatabase SET CONTAINMENT = NONE WITH NO_WAIT
GO

--====================================================================================================
/* 4 - Add login to database */

USE ContainedDatabase
GO

CREATE USER Login_RW FOR LOGIN Login_RW
GO

ALTER ROLE [db_datareader] ADD MEMBER Login_RW
GO

ALTER ROLE [db_datawriter] ADD MEMBER Login_RW
GO

--====================================================================================================

 
Conclusion
Even though I thought that using a contained database could be a big blocking factor for us in the process of migrating data to Azure, it really wasn’t that big of a deal. Especially now that we automated the process, it doesn’t add more than 5 minutes to the process of replicating data to Azure.

Build 2017: Administrating databases via Azure portal and Cloud Shell

This week I attended the Build conference in Seattle, and during the keynote on the first day (at around 1:01:00), Scott Hanselman (Blog | @shanselman) revealed the Cloud Shell integration in the Azure portal. This means that you can use Bash in the Azure portal as of the 10th of May 2017. If you ask me, that’s a HUGE addition to the portal, because now you can actually administer your Azure subscription by only using the portal instead of external tools!

 
Creating storage account for Bash
When you log in to the Azure portal and click on the button for Cloud Shell (top right corner), you are asked to create a storage account to persist your “$Home” directory. There are some costs involved, but it’s needed to make the integrated tool work:

 
Once you clicked the “Create storage” button, your Cloud Shell will be created:

 
Connecting to a database
Now that your Cloud Shell is ready to go, you can start using Bash. This means you can also use sqlcmd from within Bash.

You can connect to a database with sqlcmd, by using the following command:

sqlcmd -S servername.database.windows.net -U username -P password

 
Once the connection to your database has been made, you can run queries against it. For example, request all the database names from your server/instance:

 
Support on mobile apps
The Cloud Shell can’t only be found in the Azure Portal, but they also announced that the feature is included in the mobile apps for Android and iPhone. This allows you to administer your resources when you’re on the road as well.

 
Conclusion
Now that Microsoft is supporting both Windows and Linux on their Azure platform, the integration and usability of the portal needed to be improved as well. By adding Cloud Shell to the web portal, they’ve taken a huge step in my opinion. And pretty soon they are going to support PowerShell as well:

 
Even though this is one of the smallest announcements on Build this year, I think this might have a big impact on the administrative part of a lot of people and jobs out there. Especially now that you can use a single tool (the portal) for everything you need to do. Another example shown by Scott is creating a list of resources from Bash. Now at least that’ll save me some time, and I can’t be the only one…

Replication: Snapshot Agent fails on date conversion

This article was recently published on dev.getroadmap.com:

 
 

In the previous post I wrote, I explained how to setup replication from an on-premise SQL Server instance to an Azure SQL database. While doing this, I came across a very strange issue (or maybe even bug) when setting up replication.

 
The problem child
After working on reproducing the issue for a day, and trying to reduce the issue to a small-scale problem, I came to the conclusion that the problem was (probably) caused by a single primary key on a table in the database:

CREATE TABLE dbo.BuggedTable
	([Day] DATE NOT NULL,
	 SomeId VARCHAR(50) NOT NULL,
	 Amount INT NOT NULL,
	 CONSTRAINT PK_BuggedTable PRIMARY KEY CLUSTERED 
		([Day] ASC,
		SomeId ASC
		)
	)
GO

 
Creating publication & subscriber
The setup of the publication and subscriber wasn’t that difficult. As I said before, there are a few things you need to configure differently then you would do for SQL Server to SQL Server replication.

So I won’t talk you through the whole process again, but refer you to the articles instead.

 
Generate initial snapshot
Once the publication and subscription are in place, it’s time to generate the initial snapshot. The snapshot agent prepares the snapshot that contains the schema and data, needed to initialize the subscriber(s):

 
In some cases it takes a while, but in the end, I found this “warning” on my screen:

 
When you dig into this by opening the agents tab in the replication monitor, you see the actual error:

 
The complete error states:

Error messages:
Message: Query for data failed
Stack: at Microsoft.SqlServer.Replication.Snapshot.SqlServer.NativeBcpOutProvider.ThrowNativeBcpOutException(CConnection* pNativeConnectionWrapper)
at Microsoft.SqlServer.Replication.Snapshot.SqlServer.NativeBcpOutProvider.BcpOut(String strBcpObjectName, String strBcpObjectOwner, String strBaseBcpObjectName, Boolean fUnicodeConversion, String strDataFile, String strLoadOrderingHint, String strWhereClause, Boolean useTableLockHint, Int32 bcpFileFormatVersion)
at Microsoft.SqlServer.Replication.Snapshot.SqlServer.BcpOutThreadProvider.DoWork(WorkItem workItem)
at Microsoft.SqlServer.Replication.WorkerThread.NonExceptionBasedAgentThreadProc()
at Microsoft.SqlServer.Replication.AgentCore.BaseAgentThread.AgentThreadProcWrapper() (Source: MSSQLServer, Error number: 0)
Get help: http://help/0
Message: Conversion failed when converting date and/or time from character string.
Stack: (Source: MSSQLServer, Error number: 241)
Get help: http://help/241

 
First I thought that the DATE column being part of the primary key was the problem. But then again, another table in a different database had the combination of a DATE and a UNIQUEIDENTIFIER as a primary key. So that couldn’t be it.

 
Digging deeper
During my investigation to this issue, I found the following:

– There was enough space on the disk for the snapshot
– Other database with DATE in primary key (PK) worked (even though combination was DATE + UNIQUEIDENTIFIER, instead of DATE + VARCHAR)
– Adding “-UseInprocLoader” to Snapshot Agent job doesn’t work

So after digging a bit deeper, and trying to reduce the issue to a small-scale problem, I found:

– Reproduced in an empty (new) database, with just the failing table as single object in the database –> STILL FAILS
– Changed object in test DB to use newly created IDENTITY(1,1) as PK, and added Unique Constraint to old PK columns –> WORKS
– Make DATE column part of different PK (together with IDENTITY), and no Unique Constraint –> WORKS
– Changing the PK to just the VARCHAR column –> WORKS
– Changing the PK to just the DATE column –> WORKS
– Setting database on local instance (SQL 2016 Dev) to comp. 110, just like on test –> WORKS

After a lot of different variables in the test-setup, I found out that it’s probably an old bug that wasn’t properly patched when upgrading the SQL Server engine to a newer version. Let me elaborate on that:

– The bug is reproducible on the test server, which is an upgraded engine from SQL 2012 or 2014 to SQL 2016 RTM
– The bug is reproducible on the production server, which is an upgraded engine from SQL 2014 to SQL 2016 RTM
– The bug is not reproducible on a clean install of SQL 2014
– The bug is not reproducible on a clean install of SQL 2016 RTM
– The bug is not reproducible on a clean install of SQL vNext CTP

 
Finding a work-around
Because I couldn’t find a work-around for this issue, I requested the help from Justing Langford (Blog | @JustinLangford) from Coeo. He pointed me to an article that describes a few possible work-arounds.

For me the row filter did the trick:

 
As described in the article mentioned above, adding the row filter disables the BCP partioning for this article (table), and the snapshot agent completed without any problem:

 
Conclusion
Although this bug should’ve been fixed ages ago, it looks like it (re)appeared again after an engine upgrade. I’m not sure how this happened, but all I know is that it took me about 3 days to find, reproduce, reduce and work around the problem.

So hopefully this article will save you that time!

Running maintenance on Azure SQL databases

This article was recently published on dev.getroadmap.com:

 

To keep your data healthy and topfit, we all know you need to run something called database maintenance on your databases. This prevents things like performance problems or unrecoverable data from happening, and that is sort of the core business of DBA’s. And when we look at how this can be performed on a regular basis, an on-premise instance is a bit easier to maintain than an Azure SQL database. That’s why I want to share our experience with you on that, hoping that it can save you some time when you start implementing this.

 
Platform setup
In order to get a better understanding of why certain choices were made, I want to share a very basic version of the setup of our environment.

Our platform consists of 2 mayor parts: a private cloud part (that we see as “on-premise”), and an Azure part. Those 2 parts combined make our platform, and this platform hosts all of our services, applications, databases, etc. The development approach we use is everything should be designed with cloud-first in mind, but only if it’s the right tool for the job, and with the exclusion of some data.

The databases we use in Azure are all what Microsoft calls “Azure SQL databases”. This means that there are no virtual machines of any kind are running on our Azure-part of the platform, also known as DBaas (Database as a Service).

When I draw this platform, and only focus on the data part, it will look something like this:

 
One of the advantages of this setup is that we can leverage the power of both parts of the platform. As you’ll see later on in this blog, there are some things that we (need to) run from the on-premise instances and some things fully on Azure.

 
Big shoutout to Ola
Before I’m going into detail, I want to give full kudos to Ola Hallengren (Website | @olahallengren). He has spend a lot of his time to build a SQL Server Maintenance Solution that is completely free for everyone to use. And he did such an excellent job a lot companies (also huge companies) use his solution to run maintenance tasks on their databases.

None of the scripts below are written by me, but only small changes are made in order to make things more clear when the solution is deployed to an environment. The original scripts can be downloaded via the download page on Ola’s website.

 
Backups & Integrity check
Taking backups of your database and making sure there is no corruption in the datafiles is an essential part of the maintenance solution written by Ola. But because Azure SQL databases have a build-in maintenance solution (for example backups: full backups weekly, differentials hourly, and transaction log every 5 minutes, source), we don’t need to worry about that ourselves.

 
Index maintenance & Update Statistics
Indexes and statistics are the core of your performance-based maintenance processes. These make sure your queries run fast, and should provide you with a stable and predictable performance. This is especially needed on an Azure database, since you can’t monitor it like you would with an on-premise database.

Because Azure SQL databases are run on shared hardware that you can’t monitor yourself, Microsoft provides us with a number of different performance counters that we can use to check the status/health of our databases. The most important counters are CPU usage, Data IO, Log IO and DTU usage (a combination of the previously mentioned counters). The DTU counter is the most abstract (to me at least), because it’s explained by Microsoft as:

 

The Database Transaction Unit (DTU) is the unit of measure in SQL Database that represents the relative power of databases based on a real-world measure: the database transaction. We took a set of operations that are typical for an online transaction processing (OLTP) request, and then measured how many transactions could be completed per second under fully loaded conditions.

For example, a Premium P11 database with 1750 DTUs provides 350x more DTU compute power than a Basic database with 5 DTUs.

 
And for me, who is was used to monitoring physical hardware, that is a bit of a different approach when digging into performance-related issues. And it’s not that index and statistics maintenance isn’t important when you work on a on-premise database, but it’s a slightly bigger challenge to monitor the direct effects of a index rebuild or statistics update.

But because every Azure SQL database is a contained database, you need to deploy the stored procedures from Ola’s solution to every single database. So to keep it clear for everyone which table and stored procedures belong to the maintenance solution, I’ve changed Ola’s scripts slightly to create all objects in a specific schema named “dba”. So first of all, let’s create the schema:

CREATE SCHEMA dba
GO

 
And then create the used to log all of the maintenance commands and their outcome:

CREATE TABLE dba.CommandLog
	(ID INT IDENTITY(1,1) NOT NULL CONSTRAINT PK_CommandLog PRIMARY KEY CLUSTERED,
	 DatabaseName sysname NULL,
	 SchemaName sysname NULL,
	 ObjectName sysname NULL,
	 ObjectType CHAR(2) NULL,
	 IndexName sysname NULL,
	 IndexType TINYINT NULL,
	 StatisticsName sysname NULL,
	 PartitionNumber INT NULL,
	 ExtendedInfo XML NULL,
	 Command NVARCHAR(MAX) NOT NULL,
	 CommandType NVARCHAR(60) NOT NULL,
	 StartTime DATETIME NOT NULL,
	 EndTime DATETIME NULL,
	 ErrorNumber INT NULL,
	 ErrorMessage NVARCHAR(MAX) NULL)
GO

 
Now that these are created, you can create the stored procedure that execute the actual index and statistics maintenance. They are too long to post here as code snippet, but you can download the CommandExecute script here, and the IndexOptimze script here.

But because we want to schedule these procedures later on, I decided to create an additional stored procedure in every database, that is deployed alongside the maintenance objects:

CREATE PROCEDURE dba.ExecuteMaintenance
AS


EXECUTE dba.IndexOptimize
 @Databases = '<Insert database name>',
 @FragmentationLow = 'INDEX_REORGANIZE',
 @FragmentationMedium = 'INDEX_REORGANIZE,INDEX_REBUILD_ONLINE,INDEX_REBUILD_OFFLINE',
 @FragmentationHigh = 'INDEX_REBUILD_ONLINE,INDEX_REBUILD_OFFLINE',
 @FragmentationLevel1 = 5,
 @FragmentationLevel2 = 30,
 @UpdateStatistics = 'ALL',
 @LogToTable = 'Y',
 @Execute = 'Y'

GO

 
This allows us to run maintenance with specific options on the different databases.

Scheduling
After this was all set up, I needed to come up with a way to run these procedures on a schedule. And as you might know, Azure SQL databases don’t have a SQL Server Agent so that’s were our on-premise platform comes in handy. Just for this I created a new virtual machine in our private cloud, and installed SQL Server on that machine to utilize the SQL Server Agent. This server (operations server) runs all of the scheduled operational jobs (including maintenance and some monitoring jobs) on our Azure environment.

But to run a proces from this operations machine on one of our Azure databases I needed to create a linked server first:

EXEC sp_addlinkedserver 
 @server=N'ServerName__DatabaseName', 
 @srvproduct=N'Azure SQL Db',
 @provider=N'SQLNCLI', 
 @datasrc=N'<SERVERNAME>,1433',
 @catalog='DatbaseName';
 GO

EXEC sp_addlinkedsrvlogin 
 @rmtsrvname = 'ServerName__DatabaseName', 
 @useself = 'FALSE', 
 @locallogin=NULL,
 @rmtuser = '<USERNAME>',
 @rmtpassword = '<PASSWORD>'
 GO

 
And all that’s left now is to create a SQL Server Agent job, that executes the “ExecuteMaintenance” stored procedure on the Azure database:

EXEC [<LinkedServerName>].<DatabaseName>.dba.ExecuteMaintenance

 
The reason I choose this approach is that I didn’t want to lose any time on figuring out how the Azure Scheduler or Automation works, at the moment I was implementing the maintenance solution.

But if you want to have a good resource on that, please check out Pieter Vanhove (Blog | @Pieter_Vanhove) blog, because he wrote a great blog post about that.

 
Conclusion
When I look at how this solution is set up, I’m the first one who admits that this isn’t a perfect or ideal solution. But in the end, this gives the rest of the team a clear solution when they need to start or restart the maintenance process: Just log in to the operations server, start the SQL Server Management Studio (SSMS), open the SQL Server Agent jobs, find the job associated with the database they want to run the maintenance on, and that’s it. But for future scalability and maintainability, we might need to implement another solution to do this.

SSRS Reporting automation with .NET

This article was recently published on dev.getroadmap.com in 2 separate posts:
SSRS Reporting automation with .NET
Application Authentication via https using NTLM:

 

SQL Server Reporting Services (SSRS) is a great way to create an overview or analysis of your data, that you can share with other people as a report. But what if you have a report that you need to share with a large group of people, but they need it with 50 different parameters (like CustomerID for example), and they want to receive it in Excel or PDF? Are you manually going to execute the report with 50 different parameters, export them to the specific file format, and email those files? I don’t think so. Automating this process is easy if you write a small tool for this, and if you use the “Report Server Web Service URL”.

 
ReportServers vs Reports
Before we’re diving into the .NET code, first let’s see what the difference is between the URL’s “http:// [servername] :80/ReportServer” and “http:// [servername] :80/Reports”. If you navigate to your SSRS server, you’ll be redirected to “http:// [servername] :80/Reports”. This is the default webinterface that you use to open reports, manage subscriptions, etc:

 
If you go to “http:// [servername] :80/ReportServer”, you’ll end up in the webservice of SSRS. This allows you to open reports, and as a bonus: add parameters to your http request, so you can automatically execute reports from a URL. This is also called the “SSRS Virtual Directory”:

 
Building a URL
Now that we know that we need to use the webservice, we can start building our URL. First, let’s start with the base-URL. I’ve created a folder in SSRS called “Test”, and a report called “TestReport”. So the base-URL will be: “http:// [servername] :80/ReportServer/Pages/ReportViewer.aspx?%2fTest%2fTestReport”. And because my report has 2 date-parameters (From and To), I need to add these to the URL: “&From=2015-12-01&To=2015-12-08”.

This URL doesn’t run the report yet, until you add the command for that to the URL: “&rs:Command=Render”. So your complete URL will look like: “http:// [servername] :80/ReportServer/Pages/ReportViewer.aspx?%2fTest%2fTestReport&From=2015-12-01&To=2015-12-08&rs:Command=Render”

One thing to keep in mind is that you need to add the dates in the URL in the correct format (yyyy-MM-dd). If you don’t do that, SSRS will throw an exception.

 
Where to find these URL’s
If you log on to your SSRS server, you can start the “Reporting Services Configuration Manager”. This is the configuration tool for your SSRS instance.

In this tool you can configure both the webinterface URL:

 
And the virtual directory:

 
Text parameter in URL
But SSRS can also have text-fields as input for your report. These can also be added to the URL. Just like the parameters above, you just add the parameter name and value to the URL: “http:// [servername] :80/ReportServer/Pages/ReportViewer.aspx?%2fTest%2fTestReport&From=2015-12-01&To=2015-12-08&FreeText=This is a test…&rs:Command=Render”.

After some testing I’ve found out that you can use any character in the text parameter you want to, except for the &-sign. If you use that, SSRS will think it’s a parameter or command and won’t accept the URL. And there’s also the (browser) limitation of the URL length. Testing proves that the limit is 7926-7931 characters. If your URL is below 7926 characters, it works like a charm. If you go above that (between 7926 and 7931) the behavior of SSRS gets buggy, and above 7931 characters SSRS will throw an exception.

 
Export to file
Exporting your report to file can also be added to the URL. By adding “&rs:Format=EXCEL” to the end of the URL tells SSRS to export your report to Excel: “http:// [servername] :80/ReportServer/Pages/ReportViewer.aspx?%2fTest%2fTestReport&From=2015-12-01&To=2015-12-08&FreeText=This is a test…&rs:Command=Render&rs:Format=EXCEL”.

This output can be used to automatically store this file on disk or email it with a .NET application.

 
Export formats
There are several export formats in the webinterface of SSRS:

 
The available output formats depend on the version of SSRS you’re using. In SQL Server 2016 you have all the same export formats as you have in SQL Server 2014, but they added PowerPoint to that list.

 
Creating the application
To automatically download an exported report, I’ve created a “Windows Forms Application”. In this applications we need to do 3 things:

– Determine variable values
– Build a URL
– Download/Export the report

To determine the variable values, I added 2 “DateTimePickers”to the form, and a “TextBox” for the CustomerID. Other than that, there are 2 buttons: 1 to get the URL (might come in handy for testing), and 1 to export the report in the selected format. There’s also a “TextBox” so that you can configure the drop-folder for the files:

 
Build URL
In order to build the URL we need 5 pieces:

– The SSRS servername or URL
– The folder of the report (if it’s not in the root)
– The report name
– The parameters needed for executing the report
– The export format

In my case the folder (“Test”) and report name (“SSRSAutomationTestReport”) are known, so I hard-coded them:

string ReportServer = 
    "http://"
    + ReportServerURL
    + "/ReportServer/Pages/ReportViewer.aspx?%2fTest%2fSSRSAutomationTestReport"
    + "&From="
    + DT_From.Value.Date.ToString("yyyy-MM-dd")
    + "&To="
    + DT_To.Value.Date.ToString("yyyy-MM-dd")
    + "&CustomerID="
    + TB_CustomerID.Text
    + "&rs:Command=Render";

if (RB_Excel.Checked)
{
    ReportServer += "&rs:Format=EXCEL";
}

if (RB_PDF.Checked)
{
    ReportServer += "&rs:Format=PDF";
}

 
This results in the URL that you can use to export the report to a specific file format (in my case either Excel or PDF).

Download the file
To download the file we need to use the “CredentialCache”, because when you use the SSRS webservice to execute a report, an NTLM challenge takes place. The “CredentialCache” will solve the 2-step authentication for you. After that, you can use “WebClient” to download the file. This will look like this:

var url = new Uri(ReportURL);

string FileExtension = ".pdf";

if (RB_Excel.Checked)
    FileExtension = ".xls";

var location = TB_Dropfolder.Text + "SSRSAutomationTestReport - Customer " + TB_CustomerID.Text + FileExtension;

// When calling for the url a NTLM challenge takes place
// Once this challenge takes place the GetCredentials will automagically be called via de CredentialCache
// This will resolve the 2 step authentication
// Requirement: the uri for the cache must be the Scheme + Host of the domain
var cc = new CredentialCache();
cc.Add(new Uri(string.Format("{0}://{1}", url.Scheme, url.Host)), "NTLM", new NetworkCredential(Username, Password, Domain));

using (var client = new WebClient())
{
    client.Credentials = cc;
    client.DownloadFile(url, location);

    MessageBox.Show("Report is exported");
}

 
Download the resources
To show you how I solved this, I’ve made the resources available for download. You can download the SSRS report here, and the Windows Forms application here.

Please feel free to download them, try them out for yourself, and let me know what you think.

Comparing execution plans with SSMS

In SQL Server 2016 (now available as CTP 3.0), a new feature is shipped: Execution Plan Comparison Tool. This new (and very cool) feature allows you to compare 2 execution plans within SQL Server Management Studio (SSMS). And according to Amit Banerjee (Blog | @banerjeeamit), this feature is also available in the “SSMS – September 2015” release (more info here). Let’s take a look at this new feature.

Creating resources
To generate an execution plan that we can compare later on, let’s create a table with some data first:

USE Sandbox
GO


CREATE TABLE dbo.T1
	(ID INT IDENTITY(1,1),
	 VAL VARCHAR(10))
GO


INSERT INTO T1
	(VAL)
VALUES
	('X')
GO 10

 
To generate some execution plans, run the queries below with the “actual execution plan” on, and safe these plans to disk:

SELECT *
FROM T1 AS T1
INNER JOIN T1 AS T2 ON T1.ID = T2.ID

 
and:

SELECT *
FROM T1 AS T1
INNER JOIN T1 AS T2 ON T1.ID = T2.ID
INNER JOIN T1 AS T3 ON T1.ID = T3.ID

 
I’ve also made the 2 plans that were generated available for download here and here.

 
Comparing execution plans
To compare execution plans in, you need to open the first execution plan from SSMS. In the plan-window, right-click and click on “Compare Showplan”:

 
Now a pop-up window will open, that asks you what plan you want to use to compare it with. Now open “Test2.sqlplan”. The compare window opens, and you can compare plans:

 
As you can see, parts of the plan that are the same in both execution plans, are colored the same. These colors are randomly chosen, and can be different every time you compare 2 plans. This makes it easy to determine where both plans are equal or differ.

If you click on one of the highlighted parts in the execution plan, the other plan will center its view on that part of the plan (unfortunately it’s a bit hard to see that with these small execution plans). If you click on a highlighted part, you can see that there is a blue rectangle drawn around the object:

 
Another really cool thing is the properties windows. If you don’t have that open by default, right-click on the first object in the execution plan (the SELECT part), and click on “Properties”. This shows 2 property-windows, in which you can compare the memory grant for both plans for example:

 
Conclusion
At this moment, you need to save both execution plans to disk in order to compare them. If you try to compare an execution plan of a query you just ran without saving it, it throws an exception:

 
But in my opinion this is just a minor issue, and I think this will be fixed in one of the next releases.

Comparing execution plans is definitely something I’ve been missing for years. Especially when you have 2 really big plans, this can really help you speed up the analysis and debug process.

Even though I’m pretty used to using SQL Sentry Plan Explorer to open execution plans, that doesn’t give me the option to compare plans (yet). At least not in the free version of the tool, that I always recommend to colleagues and friends to use.

This is another one of the really cool features in the overhauled SSMS, and I think the SQL Server team is rocking this new release.

Tracking query progress with Live Query Statistics

How frustrating is it, to run a query on a database and it seems to be “stuck”. We’ve all seen that happen right? But how can you tell what the problem is, without letting the query complete (which could take a long time)? Microsoft (or actually the SQL Server team) gave us a new toy to play with that can help us in situations like this, and this tool is called “Live Query Statistics”.

 
Creating resources
To show you how the Live Query Statistics work, let’s create a sample table first, and insert 1.000 rows:

CREATE TABLE LiveQueryStats
	(ID INT IDENTITY(1,1),
	 VALUE VARCHAR(10))
GO

INSERT INTO LiveQueryStats
	(VALUE)
VALUES
	('X')
GO 1000

 
Because we want to actually see something happen, let’s multiply the number of rows coming from the single table by using the query below:

SELECT *
FROM LiveQueryStats T1
CROSS APPLY LiveQueryStats T2
CROSS APPLY LiveQueryStats T3

 
The query will return 1 billion rows (1.000.000.000), and will run for a while. This gives you the opportunity to look at the different features without rushing or losing your running query.

 
Live execution plan
Before running the query above, you need to enable the Live Query Statistics just like you would do to the normal execution plan. This is an extra button added in the SQL Server 2016 SSMS (and the downloadable version of course). When that’s enabled, you can execute the query, and SQL Server Management Studio (SSMS) will automatically switch to the live execution plan.

In this execution plan, you’ll see the data flow through the components, so you can actually see what SQL Server is doing at a specific moment:

 
Query completion percentage
Another great addition is the overall completed percentage, that you can find at the bottom of your SSMS. This shows you the percentage of completion, that can help you estimate the time till completion:

 
Live rowcount
The properties window normally shows you the in-depth information of your query after completion (number of threads, memory grant, etc). With the live execution plan enabled, it can provide you with real-time statistics as well:

 
Live execution plan from Activity Monitor
From the Activity Monitor you can also open the live execution plan of running queries on your instance. In the Activity Monitor, you have an additional tab in SQL Server 2016 called “Active Expensive Queries”. In this tab you can right-click on a running query, and click on “Show Live Execution Plan”. This opens a new tab in SSMS with the execution plan:

 
Unfortunately this only works for queries that have the live statistics enabled before execution. I don’t expect this to change in the final product, mainly because of the negative performance impact this feature can have on your queries and instance.

 
Drawbacks, Limitations and Bugs
Because this is only a CTP version of SQL Server 2016, we can expect some bugs and limitation, so I’ll be the last one to judge. And I don’t think these limitations are a big drawback on the feature.

One of these bugs is a crashing SSMS. If you open a live execution plan from the Activity Monitor, and close that tab, SSMS crashes every now and then.

 
Another weird thing is that exiting SSMS by clicking on the close button (top right) when the live execution plan is open, causes the list of recently used SQL Servers (the list used when connecting object explorer for example) to be cleared for some reason.

This behavior is reproducible, so I think this is a small bug in SSMS, or the fact that I upgraded this instance from the first CTP version till the current version. But I’m sure this will be fixed by the SQL Server Team in the next releases.

 
The biggest pitfall (in my opinion) of this feature is also mentioned in the documentation. This feature is “primarily intended for troubleshooting purposes” and “can slow the overall query performance”. And I know, it look SO COOL to have this on all queries you’re running, but please be careful with this. Don’t enable this on every running query, but only use this to debug issues!

Another limitation for this feature (at least at the moment I’m writing this), is the use in combination with columnstore indexes, memory optimized tables and natively compiled stored procedures. You can read more about this here.

And I shouldn’t even have to mention this, but remember: you can only use Live Execution Plans when you have SHOWPLAN permissions on the database (same permissions you need to view normal execution plans).

 
Alternatives
One of the questions I had when I read about this: are there any alternative for this? One of the only things that come to mind is the “Track My Query” tool, written by Matan Yungman (Blog | @MatanYungman). This tool allows you to monitor your query, and it will show you what part of your query is currently running. For more in-depth information, I recommend the SQLBits session where he explains the inner-workings of this tool. I’ve seen this session in person, and it was an interesting session.

 
Conclusion
With all these awesome new features that will be shipped in SQL Server 2016, I think there’s a whole new way of looking at SQL Server as a product. I’ve always liked working with SQL Server, but this version is taking it to the next level.

With all these new features, debugging issues is going to be a bit easier, developing new stuff is going to be faster, and the overall usability is going to skyrocket. I can’t wait to get my hands on the finished product!