August 22, 2016 2 Comments
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.
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.
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:
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.