Category Archives: SQLServerPedia Syndication

This category is for SQLServerPedia Syndication

SQL Saturday Jacksonville – May 14th, 2022

Hi Everyone,

This is your Austin SQL Server Consultant and I will be speaking at SQL Saturday Jacksonville. I am happy to get back out and, on the road, again. I know COVID has impacted everyone in multiple different ways. While I have enjoyed my time alone with the people who matter the most to me, I am also excited to get back out on the road and reconnect with friends and make new ones as well.

This weekend, I am happy to get do my first in-person conference in the past two years. I will be at SQL Saturday in Jacksonville talking about my favorite topic Making SQL Server Queries go Faster! My SQL Server Consulting company will be sponsoring as well. Make sure to come on over as we look forward to connect, share and learn all day long!

SQL Saturday Jacksonville is May 14th 2022 and I am speaking!

Sample Code from Presentations

Persist Sample Percent in SQL Server IS Fixed!

Hi Everyone, this is John Sterrett. I am a SQL Server Consultant in Austin, TX. Last year I blogged about a feature called Persist Sample Percent. It had a nasty bug that could negatively impact performance. I have great news to share. The fix is now rolled into SQL 2016 SP2 CU17 and SQL 2019 CU10. Pedro Lopes let me know that with the fix now queued for SQL 2017 CU26, this becomes fixed in all versions.

Breaking News… Persist Sample Percent is coming to SQL 2017 Soon!



Kudos to Pedro Lopes and the MSSQL Development team for this update. Make sure you are applying the latest updates so you can leverage all great enhancements, updates, and fixes.

Persist Sample Percent Matters

Okay, you might be wondering why should I consider utilizing persist sample percent? If you have large tables, auto update statistics might be hurting you instead of helping. Yup, that is not a typo. Also, if you update statistics and don’t provide a sample percent you can have the same problem. Worst case, you have a job that updates statistics with a good sample percent, data changes and auto-update uses a subpar percent.

By default, modern versions of SQL Server will utilize a smaller sample percent as your table row count grows. This can potentially give you bad execution plans.

Let’s take a look at the example below. It doesn’t take a whole lot of rows to get a sample percent under 10%

If Puff Daddy was a DBA he would say, “More Rows More Problems..”

If you want to identify if this is a potential problem in your environment I am including the script below that we utilize in our SQL Server Health Check.

;with cte as (SELECT CAST(((rows_sampled * 1.00)/ [rows] )*100.00 AS NUMERIC(5,2)) AS SamplePCT, OBJECT_NAME(s.object_id) as TableNAME, s.name StatsName,
    sp.*
    FROM sys.stats AS s
    OUTER APPLY sys.dm_db_stats_properties (s.[object_id], s.stats_id) AS sp
	JOIN sys.objects o on s.object_id = o.object_id and o.is_ms_shipped = 0
    WHERE 1=1)
	select * from cte where SamplePCT IS NULL or SamplePCT < 10
	order by SamplePCT

The following is an example of this occurring. The only change we made was updating stats with a fixed sample rate.

Does anyone want to guess when Stats was updated?

Persist Sample Percent Is Your Friend

You can utilize persist sample percent as long as you are utilizing one of the cumulative updates (CU’s) provided above or a newer CU. Persist sample percent will lock in your sample percent. You will no longer need to worry about an index rebuild removing the persisted sample percent which puts you back at the default sample percent.

You can follow this demo to test this out on your own.

If you enjoyed this blog post subscribe to our newsletter so we can make sure to send you more free tips and videos.

Workload Replay for Azure SQL Database and Amazon RDS

Want to make sure you don’t have errors, validate performance, and save money while making changes with Azure SQL Database, Azure SQL Managed Instance, SQL Server RDS in Amazon AWS? In this video, you will learn how to use the Data Experimentation Assistant to perform workload replay and compare your on-premise or cloud SQL Server workloads on-demand.

Replay Workloads is your Secret Weapon to being a Rockstar!
Replaying Workloads is your Secret Weapon to being a Rockstar!

Recommend Links

Persist Sample Percent in SQL Server IS NOT PERSISTED!

Update: Sept 14, 2021
Persist sample percent is fixed. The fix is now rolled into SQL 2016 SP2 CU17SQL 2019 CU10, and the future SQL 2017 CU26.


When a bug jumps out and surprises me I like to share it so others do not run into the same unexpected result. I don’t think there is any reason for a bug to bite multiple people in the butt. Therefore, I want to show you why persist sample percent IS NOT PERSISTED!

Why should I use Persist Sample Percent?

When your table grows and the rows multiply the default statistics sample percent used by SQL Server gets smaller and smaller. In theory, persist sample percent lets you update your statistic once specifying the percent it should use going forward when a sample percent is not specified. Unfortunately, this feature is broken, IMHO.

Persist Sample Percent Setup

To set up the scene so you can reproduce and learn. Below we create a single-column table that is an identity and also primary key. Therefore, an index is created which also will create statistics on our column.

DROP TABLE dbo.Test
CREATE TABLE dbo.Test (ID INT IDENTITY NOT NULL CONSTRAINT TestPK PRIMARY KEY);

 INSERT INTO dbo.Test DEFAULT VALUES
 GO 10000000 --100 million rows..

	CREATE PROCEDURE dbo.DemoStatsReview 
	AS BEGIN
	SELECT CAST(((rows_sampled * 1.00)/ [rows] )*100.00 AS NUMERIC(5,2)) AS SamplePCT,
	sp.*
	FROM sys.stats AS s
	OUTER APPLY sys.dm_db_stats_properties (s.[object_id], s.stats_id) AS sp
	WHERE s.[name] = N'TestPK';
	END

First, we will rebuild our index utilizing a fullscan. This is expected and normal activity for an index rebuild.

/* Index Rebuild uses 100% rows for sampling */
ALTER INDEX TestPK ON dbo.Test REBUILD WITH (STATISTICS_NORECOMPUTE = OFF)
EXEC dbo.DemoStatsReview 

Now, we will update statistics utilizing the new Persist Sample Percent feature. This should give us two benefits. One, auto stats updates will use this sample rate going forward on this statistic. Two, we no longer need to supply an sample percent if we update statistics manually or with our maintenance jobs.

/* Now lets update stats by using the PERSIST_SAMPLE_PERCENT */
UPDATE STATISTICS dbo.Test TestPK WITH SAMPLE 60 PERCENT, PERSIST_SAMPLE_PERCENT = ON;
EXEC dbo.DemoStatsReview 
We now have persisted sample percent to 60%. Now that we set it. We can forget it, right?? NOOOOO!
We now have persisted sample percent to 60%. Now that we set it. We can forget it, right??

Let’s go ahead and update statistics now without any sample percent specified. We will see that the persist sample percent is applied as expected.

/* Update stats to validate sample size is persisted */
UPDATE STATISTICS dbo.Test TestPK
EXEC dbo.DemoStatsReview 
Persist Sample Percent holds and the sample percent is still 60%
Persist Sample Percent holds and the sample percent at 60%

Let’s see what happens when we rebuild the index. We expect that a FULLSCAN is used to update the statistics behind the index. Did anything else change? OH THE SUSPENSE!

/* What happens if we rebuild an index that has its stats persisted?
Do we still use 100% rows for sample? */

ALTER INDEX TestPK ON dbo.Test REBUILD WITH (STATISTICS_NORECOMPUTE = OFF)
EXEC dbo.DemoStatsReview 
BOOM! Persisted Sample Percent is reset to ZERO on a Index Rebuild. Large tables have 1% or lower sample rate used going forward...
BOOM! Persisted Sample Percent is reset to ZERO on a Index Rebuild
/* Lets update Stats again. 
Remember presisted a sample size is 60%. */
UPDATE STATISTICS dbo.Test TestPK
EXEC dbo.DemoStatsReview 
We went from our desired 60% sample rate to 1%. This sample rate will only get lower as your data grows!
We went from our desired 60% sample rate to 1%. This sample rate will only get lower as your data grows!

There you have it. The persist sample percent not only went away on the Index Rebuild but because we updated statistics without forcing a sample percent on 100 million rows the sample percent went to 1%. I will add another blog post that focuses on this later. For now, if the late Notorious BIG was a DBA he would say, “More rows, more problems with stats you get!” If this didn’t make any sense. The more row the lower there sample rate when statistics get updated.

How do we fix this?

This is a bug inside of SQL Server. There is a feedback item that hasn’t received any feedback from Microsoft in two years since the bug was reported. Please upvote so this can get the focus of Microsoft so persist sample percent is actually persisted!

Did you enjoy this post?

If you enjoyed this post let us know in the comments. Also, go ahead and subscribe to our newsletter to get more free tips and videos.

Free SQL Data Compare

I am a consultant in Austin who can help make your data go fast, be secure and highly available. When I am engaged in a performance tuning project priority #1 isn’t to make sure your data go faster. Priority #1 is to make sure we get the same result sets while making your data go faster.

Free SQL Data Compare with T-SQL?

There are several tools out there that can be used to compare data. Today, I want to share how you can quickly do this on your own with T-SQL!

Let’s simplify the process. Our goal is to check two temp tables and validate if any of the data is different. This would include inserts, updates, and deletes. For this example, I will just do a dump of Sales.SalesOrderDetail in AdventureWorks into two temp tables as shown below.

SELECT * 
INTO #Tmp1
FROM Sales.SalesOrderDetail

SELECT * 
INTO #Tmp2
FROM Sales.SalesOrderDetail

Now we shouldn’t see any differences since we used the same table to create both temp tables. We are going to use two different SQL operators to compare these two temp tables while applying some data changes. We will focus on the UNION ALL and EXCEPT operators.

The Power of EXCEPT

Except is an underrated and underused SQL operation. In a nutshell, it will give you the results of the first query that are different from the next query. So, if the data of any column in #tmp1 is different from #tmp2 or if the row doesn’t exist in #tmp2 but is in #tmp1 it will get returned.

SELECT * FROM #Tmp1
EXCEPT 
SELECT * FROM #Tmp2

Let’s go ahead and modify a column in #Tmp1 so you can see how this works. We are going to set OrderQty to five when SalesOrderId is 45313 and SalesOrderDetailId is 6210. This will change just one column in one row. We will then select these columns from both temp tables to see the change.

This is how most people would start using T-SQL to identify changes in data.

UPDATE #Tmp1 SET OrderQty = 5 
WHERE SalesOrderID = 45313 
AND SalesOrderDetailID  = 6210

SELECT SalesOrderId, SalesOrderDetailID, 
OrderQty FROM #Tmp1 
WHERE SalesOrderID = 45313 
AND SalesOrderDetailID = 6210

SELECT SalesOrderId, SalesOrderDetailID, 
OrderQty FROM #Tmp2 
WHERE SalesOrderID = 45313 
AND SalesOrderDetailID = 6210

Data Compare is easy when we know what changed.
Data Compare is easy when we know what changed and not much changed. Just select it..

Data Compare is easy when we know what changed. Data Compare is easy when we know what changed and not much changed. Just select it..

Finding Data Changes The Easy Way

Selecting the two tables is easy if we know what change occurred and there aren’t many changes. This can get complicated quickly. Therefore, if we just want to quickly know if we have differences lets take a look at my goto method using EXCEPT. To make this example easier to read instead of using “SELECT *” I will just focus on columns that are changing. In a real example, I would want to know if any columns changed.

SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp1
EXCEPT 
SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp2
Data Compare using EXCEPT quickly lets us see that we had a data change
Data Compare using EXCEPT quickly lets us see that we had a data change

If an insert or a column change occurs in #tmp1 we will see it in our EXCEPT SQL statement. This isn’t true if the change is only in #tmp2.

For example, an insert in #tmp2 or delete in #tmp1 would not be shown. To see this we would have to switch the temp tables in the EXCEPT clause as shown below.

INSERT INTO #tmp2 (SalesOrderId, ProductID, 
SpecialOfferID, OrderQty, UnitPrice, 
UnitPriceDiscount,LineTotal, 
rowguid, ModifiedDate)
VALUES (45313, 1, 3, 1,1.25,0,
1.25*1, NEWID(), GETDATE())

DELETE FROM #Tmp1
WHERE SalesOrderID = 45313 
AND SalesOrderDetailID = 6211

SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp1
EXCEPT 
SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp2
/* We will now see our insert and delete */
SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp2
EXCEPT 
SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp1
Our first EXEMPT clause only shows the update that occurred in #tmp1. The delete in #tmp1 and insert in #tmp2 cannot be seen because the data doesn't exist in #tmp1.
Our first EXEMPT clause only shows the update that occurred in #tmp1. The delete in #tmp1 and insert in #tmp2 cannot be seen because the data doesn’t exist in #tmp1.
Our Second EXEMPT shows the insert in #tmp2, delete in #tmp1 and update on #tmp1 because the column is different on #tmp2
Our Second EXEMPT shows the insert in #tmp2, delete in #tmp1 and update on #tmp1 because the column is different on #tmp2

Our first except shows us data in #tmp1 that is not in #tmp2 because the OrderQty column changed in #tmp1. The second EXCEPT shows us data in #tmp2 that isn’t in #tmp1 because of our insert into #tmp2 and also our delete from #tmp1 would be found in #tmp2 but not #tmp1.

UNION ALL for the Win!

To wrap this up now we can include a UNION ALL operation between the two EXCEPT operations. This would get us any data changes to the columns selected from the temp tables.

SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp1
EXCEPT 
SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp2
UNION ALL
SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp2
EXCEPT 
SELECT SalesOrderId, SalesOrderDetailID, OrderQty 
FROM #Tmp1
UNION ALL and EXCEPT for the free Data Compare Win! Quickly shows rows that are different between the two tables.
UNION ALL and EXCEPT for the free Data Compare Win! Quickly shows rows that are different between the two tables.

Typically, I need to verify is the data before and after is the same. This is a quick and easy way to get that answer. Now I know you might want to take this to the next level. You might be thinking how do I just get the unique key for the table and columns that changed. I will leave that as an exercise for you.

If you enjoyed this post subscribe to get more free SQL tips.

Query Store for Workload Replays

Query Store for Workload Replays
Query Store for Workload Replays

UPDATE: 3/16/2020
The first script below was updated from setting QDS to “Read_Write” instead of “Read_Only”. Martin thank you for pointing this out in your comment below.

This month’s T-SQL Tuesday is hosted by Tracy Boggiano. Tracy invites us all to write about adopting Query Store. Today, I wanted to share my favorite but a very unique way I use the Query Store for Workload Replays.

You can read more about the invite in detail by clicking on the T-SQL Tuesday logo in this post.

Today, I wanted to talk about my least favorite part of replaying workloads. It’s having an extended event or server-side trace running during a workload replay only so we can compare the results at a query-level when the replay is finished. Now, this might seem like a trivial thing but when you have workloads over 10k batch requests/sec this can consume terabytes of data quickly. The worst part is waiting to read all the data, slice and dice the data for analysis.

Starting with SQL Server 2016 there is a better and faster way to go! You can replace your extended event or server-side trace with Query Store captured data. Today, I will show you how to use the Query Store for the same purpose.

Different Settings

Keep in mind our goal here is very different from the typical use case for using the Query Store. We want to capture metrics for all the queries executed during a workload replay. Nothing more and nothing less.

If we have the runtime results for multiple replays we can then easily compare the workload performance between the workload replays.

Most of our changes from the regular Query Store best practices are shown below:

  • Max Size (MB) – Need to make sure there is enough space to capture your whole workload. This size will vary by how much workload is being replayed.
  • Query Store Capture Mode set to All. Normally, not ideal, but remember we want to capture metrics for our whole workload being replayed.
  • Size Based Cleanup Mode set Off – Yup, we don’t want to lose our workload data that is capture until we persist in our ideal form. More on this later.

The Capture Process

Now, this is where you would use Database Experimentation Assistant (DEA), Distributed Replay or some other process to replay your consistent workload in an isolated non-production environment. This subject we will cover in another future post. For now, we will just have two replays called “Baseline” and “Change”. This simulates a baseline replay with no schema changes and then another change replay with a change introduced in the schema.

To capture our workload we just enable the Query store with our settings mentioned above and also clear out the query store right before our workload replay starts to help ensure we are just capturing our workload.

USE [master]
GO
ALTER DATABASE [YourDatabase] SET QUERY_STORE = ON
GO
ALTER DATABASE [YourDatabase] 
SET QUERY_STORE (OPERATION_MODE = READ_WRITE,
 MAX_STORAGE_SIZE_MB = 10000, 
 QUERY_CAPTURE_MODE = AUTO, 
 SIZE_BASED_CLEANUP_MODE = OFF)
GO
ALTER DATABASE [YourDatabase] SET QUERY_STORE CLEAR
GO

Stop Capturing Query Store Data

Once your replay is finished we will want to disable the query store from writing data into the query store. We want the least amount of non-workload data inside of the Query Store when we are using it for the sole purpose of comparing workloads.

USE [master]
GO
ALTER DATABASE [YourDatabase] 
SET QUERY_STORE (OPERATION_MODE = READ_ONLY)
GO

Prepare Query Store Data for Long-Term Analysis

Now for smaller workloads, one might be happy with utilizing DBCC CLONEDATABASE to have a schema-copy of their workload with Query Store data persisted. This is perfectly fine. With bigger workloads being captured I have noticed there are ways to improve the performance of query store when doing analysis of the query store data. For example, clustered columnstore indexes can be very helpful for performance and compacity. Therefore, I like to have a schema for each replay and import the data. The following is a quick example of setting up a schema for a “baseline” replay and a “change” replay.

CREATE DATABASE [DBA]
GO
use [DBA]
GO
CREATE SCHEMA Baseline;
GO
CREATE SCHEMA Compare;
GO

Next, we will import our captured data from our baseline replay that’s in our read-only query store database. I also like to have a baked-in aggregate of metrics for reads, writes, duration and CPU at the query level.

use [YourDatabase]
GO
/* Load Data */
SELECT * INTO DBA.Baseline.query_store_runtime_stats 
FROM sys.query_store_runtime_stats;

SELECT * INTO DBA.Baseline.query_store_runtime_stats_interval 
from sys.query_store_runtime_stats_interval;

select * INTO DBA.Baseline.query_store_plan 
from sys.query_store_plan;

select * INTO DBA.Baseline.query_store_query
from sys.query_store_query;

select * INTO DBA.Baseline.query_store_query_text
from sys.query_store_query_text;
/* Addition for SQL 2017 */
select * INTO DBA.Baseline.query_store_wait_stats
 from sys.query_store_wait_stats

use [DBA]
GO

SELECT SUM(Count_executions) AS TotalExecutions,
SUM(Count_executions*avg_duration) AS TotalDuration,
SUM(Count_executions*avg_logical_io_reads) AS TotalReads,
SUM(Count_executions*avg_logical_io_writes) AS TotalWrites,
SUM(count_executions*avg_cpu_time) AS TotalCPU,
query_hash
INTO Baseline.QueryResults
FROM Baseline.query_store_runtime_stats rs
JOIN Baseline.query_store_plan p ON rs.plan_id = p.plan_id
JOIN Baseline.query_store_query q ON p.query_id = q.query_id
GROUP BY q.query_hash

Next, we would reset the database to our starting position and add our query store settings as mentioned above in this blog post and replay or workload again. This time, we would dump our data into the “change” schema

use [YourDatabase]
GO
/* Load Data */
SELECT * INTO DBA.Compare.query_store_runtime_stats 
FROM sys.query_store_runtime_stats;

SELECT * INTO DBA.Compare.query_store_runtime_stats_interval 
from sys.query_store_runtime_stats_interval;

select * INTO DBA.Compare.query_store_plan 
from sys.query_store_plan;

select * INTO DBA.Compare.query_store_query
from sys.query_store_query;

select * INTO DBA.Compare.query_store_query_text
from sys.query_store_query_text;

select * INTO DBA.Compare.query_store_wait_stats
 from sys.query_store_wait_stats

use [DBA]
GO

SELECT SUM(Count_executions) AS TotalExecutions,
SUM(Count_executions*avg_duration) AS TotalDuration,
SUM(Count_executions*avg_logical_io_reads) AS TotalReads,
SUM(Count_executions*avg_logical_io_writes) AS TotalWrites,
SUM(count_executions*avg_cpu_time) AS TotalCPU,
query_hash
INTO Compare.QueryResults
FROM Compare.query_store_runtime_stats rs
JOIN Compare.query_store_plan p ON rs.plan_id = p.plan_id
JOIN Compare.query_store_query q ON p.query_id = q.query_id
GROUP BY q.query_hash

Comparing Workload Results

Now that we have our two workloads imported we can now compare to see how the workload changed per query. I will break this down into two quick steps. First, get deltas per query. Second, get totals for how many times a query might be different in the query store. More on this a little later in the post.

/* Query Store Results */
use [DBA]
GO

SELECT DISTINCT  
c.TotalExecutions - b.TotalExecutions AS ExecutionDelta,
c.TotalExecutions AS CompareExecutions,
b.TotalExecutions AS BaselineExecutions,
c.TotalDuration - b.TotalDuration AS DurationDelta,
c.TotalCPU - b.TotalCPU AS CPUDelta,
c.TotalReads - b.TotalReads AS ReadDelta,
c.TotalWrites - b.TotalWrites AS WriteDelta,
c.TotalReads AS CompareReads,
b.TotalReads AS BaselineReads,
c.TotalCPU AS CompareCPU,
b.TotalCPU AS BaselineCPU,
c.TotalDuration AS CompareDuration,
b.TotalDuration AS BaselineDuration,
c.query_hash
--q.query_sql_text
INTO #CTE
FROM Baseline.QueryResults b
JOIN Compare.QueryResults c ON b.query_hash = c.query_hash


select COUNT(query_sql_text) AS QueryCount, 
MAX(query_sql_text) query_sql_text, 
MIN(query_id) MinQueryID, 
qsq.query_hash
INTO #Compare
from Compare.query_store_query qsq
JOIN Compare.query_store_query_text q 
	ON qsq.query_text_id = q.query_text_id 
where qsq.is_internal_query = 0
GROUP BY query_hash 

select COUNT(query_sql_text) AS QueryCount, 
MAX(query_sql_text) query_sql_text, 
MIN(query_id) MinQueryID, 
qsq.query_hash
INTO #Baseline
from Baseline.query_store_query qsq
JOIN Baseline.query_store_query_text q 
	ON qsq.query_text_id = q.query_text_id 
where qsq.is_internal_query = 0
GROUP BY query_hash 

select cte.*
, a.QueryCount AS Compare_QueryCount
, b.QueryCount AS Baseline_QueryCount
, a.MinQueryID AS Compare_MinQueryID
, b.MinQueryID AS Baseline_MinQueryID
, a.query_sql_text
FROM #CTE cte 
JOIN #Compare a on cte.query_hash = a.query_hash
JOIN #Baseline b on cte.query_hash = b.query_hash
WHERE 1=1
AND ExecutionDelta = 0
ORDER BY ReadDelta ASC

Query Store for Workload Replays

Query Store for Workload Replay gives you performance metrics to the query level.
Workload Replays compared down to the query execution level is priceless!

Lessons Learned Along the Way!

Initially, working with the query store I thought query_id was going to be my best friend. I quickly learned that my old friend query_hash is more helpful for multiple reasons. One, I can easily compare queries between different replays. That’s right now all workload replays get you the same query_id even when the workload is the exact same being replayed. Two, I can compare them with different databases as well. Finally, query_hash is very helpful with ad-hoc workloads as I can aggregate all the different query_ids that have the same query hash.

Need Performance Tuning Help?

Let us help you! Schedule a free 30-minute chat to see if we can point you the right direction. If you liked this blog post signup for our free newsletter! Also, take advantage of our free training videos!.

Import Databases to Azure SQL Database

I recently spoke at a conference and was asked what is the easiest way to import databases to Azure SQL Database. Therefore, I wanted to share how I do this with DBATools.io. You can use the same code to just export if you need a local copy of an Azure SQL database as well.

Import-Module dbatools -Force
<# Variables #>
$BackupPath = "C:\Demo\AzureSQL\Bacpac" #folder location for backups
$SourceInstance = "sql2019\sql2016"
$DBName = "AdventureWorksLT2012"
$AzureDestInstance = "procuresqlsc.database.windows.net"
$DBNameDest = $DBName

<# backpac options for import and export #>
$option = New-DbaDacOption -Type Bacpac -Action Export
$option.CommandTimeout = 0

$option2 = New-DbaDacOption -Type Bacpac -Action Publish
$option2.CommandTimeout = 0

<#  
The following assums Azure SQL Database exists and is empty 
Azure will create database by default if it doesn't exist 
#>
$bacpac = Export-DbaDacPackage -Type Bacpac -DacOption $option -Path `
$BackupPath -SqlInstance $SourceInstance -Database $DBName 
Publish-DbaDacPackage -Type Bacpac -SqlInstance `
$AzureDestInstance -Database $DBNameDest -Path $bacpac.path `
 -DacOption $option2 -SqlCredential username

What Is my Performance Tier?

Great question, as of 3/3/2020 if the database in Azure SQL Database does not exist then it will be created. When its created the following database uses the default performance tier. This is General Purpose (Gen5) with 2 vCores.

Default performance tier for a new Azure SQL Database costs $371.87 per month.
The default cost of a new Azure SQL Database is 371.87 per month.

How to create cheaper databases

Great question, you can import databases to Azure SQL Database cheaper using PowerShell. It is as simple as using the Azure PowerShell Module. The following example below I use my existing Azure SQL Database server and I end up creating a new database with the “S0” tier.

<# 
Install Azure Powershell module requires local admin
Install-Module -Name Az -AllowClobber -Scope AllUsers
#>

Import-Module Az -Force
$RGName = "<Your Existing Resource Group>"
$ServerName = "<Your Azure SQL Database Server>"
$Tenant = "<Add Tenant ID>"
$Subscription = "<Add your Subscription ID>"

Connect-AzAccount -Tenant $Tenant  -SubscriptionId $Subscription
$server = Get-AzSqlServer -ResourceGroupName $RGName -ServerName  $ServerName

$db = New-AzSqlDatabase -ResourceGroupName $RGName `
-ServerName $server.ServerName -DatabaseName $DBName `
-RequestedServiceObjectiveName "S0"

Your Homework Assignment

You got all the nuts and bolts to generate a script that can migrate all your databases on an instance. Then you can import databases to Azure SQL Database in one loop.

Need Help Moving to Azure?

Let us help you! Schedule a free 30-minute chat to see if we can point you the right direction. Take advantage of our free resources too.

If you liked this blog post signup for our free newsletter!

Allow users to start/stop Azure VMs

Today I wanted to cover how you can grant the least privilege required to stop, start or restart an Azure VM. This is also a fun great example of how you can create custom Azure Security Roles too. That’s right, we are killing two birds with one stone today.

Why Should you create a custom Role?

Where possible I like to grant security towards resource groups. Therefore, let’s assume we got multiple VM’s built for the developer group to do some testing. You want to grant people access to start, restart or stop any VM in that group. We can then grant access to the resource group to our custom role. As VMs come in and out of the resource group they would inherit our custom group.

Now, you might be wondering why don’t I just give them the “Contributor” role or the “Virtual Machine Contributor” role and be on our way? Well, if you were to do this on a resource group you just gave access to create VM’s and a whole lot more.

Least privileged roles are your best friend. Today, you will see they are also not that hard to create either.

How do we create custom roles?

Great question, first you need to identify what tasks do we need the role to complete. In our case, you have to be able to see a VM in order to take any action against the VM. Then we want to start, stop (deallocate) and restart the VM. Digging through IAM. I found the following security options.

 "Microsoft.Compute/*/read",
 "Microsoft.Compute/virtualMachines/start/action",
 "Microsoft.Compute/virtualMachines/restart/action",
 "Microsoft.Compute/virtualMachines/deallocate/action"

Now, we can create our custom JSON text file that we will then import using Azure CLI. Below you will find a sample JSON file to build our custom security role. You will need to add your subscription id(s). You can also change your name and description you would see in the Azure Portal.

  "Name": "VM Operator",
      "IsCustom": true,
      "Description": "Can start, restart and stop (deallocate) virtual machines.",
      "Actions": [
        "Microsoft.Compute/*/read",
        "Microsoft.Compute/virtualMachines/start/action",
        "Microsoft.Compute/virtualMachines/restart/action",
        "Microsoft.Compute/virtualMachines/deallocate/action"
      ],
      "NotActions": [


      ],
      "AssignableScopes": [
        "/subscriptions/<Subscription ID Goes Here>"
      ]
    }

How to Import Custom Security

Now that we are ready to go with our custom security role in a JSON file. We can then utilize Azure CLI to log in to the tenant and import our security role. First, we will log in to Azure with CLI as shown below.

az login --username <myEmailAddress> -t <customerTenantId-or-Domain>

Now we will load our saved JSON file. After a few minutes, we should then see our new security role in the Azure portal.

az role definition create --role-definition IAMRole-VMOperator.json

Now you can grant access to your custom role just like you would with any other role in Azure.

Previous SQL Server Updates

Hello everyone, this is John your Austin SQL Server Consultant here and today I am going to answer a question that comes up often so I wanted to blog about it for everyone. The question of the day is where can I download the previous SQL Server Updates?

The History towards Updates

Back in the day when we were young but not a kid anymore there were service packs and cumulative updates. We could download these separately and all of the updates were easy to find. Now today, if you click on a KB article to download an update you get pointed to the latest update as shown below.

Current SQL Server KB articles point to only the latest update.

How far is My SQL Server on Updates?

This is also another great question. My favorite place to find all the history of updates toward SQL Server is the SQL Server Build List Blog. You can cross-reference this towards your version by running the following query below.

You can use SELECT @@VERSION to get your current version number.
You can use SELECT @@VERSION to get your current version number.

I fully get exactly why Microsoft is trying to point everyone to the latest update. Normally, it makes perfect sense but let’s take a look at today Jan 9th, 2020. I am planning to update SQL Server 2017 to CU17. Its been out for two months. Today CU18 is released and if I wasn’t careful I would have downloaded a different update than expected.

SQL Server Blog List is a great resource for finding a list of all SQL Server Updates
SQL Server Blog List is a great resource for finding a list of all SQL Server Updates

Getting a previous SQL Server Update

So, on to the solution. It’s actually an easy one but also one that is easy to overlook as well. Let’s go back to the new standard update page for SQL Server updates.

That is right, the Microsoft Update Catalog is your best friend to find all your updates for Microsoft products including SQL Server. You can search for the product you want. For example, in this case, I am looking for SQL Server 2017 and can see all the previous updates for SQL Server.

All SQL Server Updates Can be Found in the Microsoft Update Catalog.
All the SQL Server Updates can be found in the Microsoft Update Catalog

If you enjoyed this tip and found it helped make sure to join our newsletter so you can enjoy more free tips, tricks, and video demos.

Free SQL Server Training Videos

Hello everyone! This is your SQL Server Consultant in Austin, TX and due to some posts on twitter about SQL PASS recordings costing $999 I wanted to share some of my favorite places to find free SQL Server training videos. I hope this helps make your data fast, secure and highly available in 2020 and beyond!

Where Is the Good Stuff Give Me Some More

Speaking on Migrating to Azure SQL Database at Ignite 2017
Attending conferences is nice but free recorded sessions are priceless!

Those who know me know I love music. I especially love the underground non-mainstream content. Therefore, my first recommendation is UserGroup.TV. As of December 27th, there are 127 videos tagged as SQL Saturday alone. Shawn goes around to almost every Tech conference he can find and brings his rig and records sessions for the community.

Are you in love with the new pop singles? Wish you could hear them before they hit the radio? If you like your tech like your music than Microsoft Ignite is for you. Every year Microsoft puts on a conference called, Ignite. This conference is usually where Microsoft will break its cutting edge tech. My favorite thing about the conference is that the content is available online for free. Midway through the page, you can search through the massive collection of free recorded sessions.

Next up, is the consistent greatest hits. Almost every session is a banger! This reminds me of my favorite Microsoft Data Platform conference. This is SQLBits and yes, their video content is also available for FREE.

Finally, Here is a collection of some great videos on performance tuning. Make SQL Server Go Faster, SQL Server Performance Improvements with SQL 2019. Another one is 7 Reasons your SQL Server Code is Slow! You can also find more great videos at Procure SQL Youtube Channel.