About two months back I ended up moving to another job, which has unfortunately kept me to be bit too occupied to find the time to blog, until now. Due to this previously mentioned career change, I have been working quite a bit with monitoring, and it gave me a spark to write this post about my favorite PerfMon counters.
Like most DBAs I rely quite a lot on monitoring, and like most DBAs I too have my own set of PerfMon counters that I rely on to provide me an accurate view of what’s happening in the environment I am administering. In this post, I’ll describe what are my favorite PerfMon counters.
Over the last two years I have been working lot on improving the tooling and processes related to database development at where I work. One concept born out of this work is what we have started calling a “Database-as-a-Code” model. Originally the idea was to introduce some of the good practices, such as version control, build automation and unit testing to database development. Over the time it has evolved to include even more, in an effort to break down traditional silos between software developers, database developers and the operations people.
In this post I’ll describe some of the decisions we made and the steps we have taken in our Database DevOps journey.
Dedicated Admin Connection is one of the easy-to-forget features in SQL Server that can really save your day. DAC (no relation to Data-Tier Applications, just shares the acronym), as it’s often called, is the way you can try to access a SQL Server instance that is in such a bad shape, that no normal connection is available. This can be due to resource exhaustion or if you happened to create a slightly wonky logon trigger. In this post we’ll look at how you enable Dedicated Admin Connection (for remote users) and how you connect to SQL Server using the DAC.
I recently had a discussion about the ability to offload DBCC CHECKDBs to a secondary database using Active Secondaries in SQL Server Availability Groups. While it is fully possible to run database consistency checks against secondary database (and there’s plenty of recommendations floating around for doing this), it needs to be pointed out that using Availability Groups for this does not equal of running checks on the primary database.
In my previous post about logical joins I wrote about the most common type of SQL Server join, the INNER JOIN. Naturally the logical follow-up is to look at the OUTER JOIN. Syntax for OUTER JOIN is bit different from the INNER JOIN, as you need to define either LEFT, RIGHT or FULL keyword (more about FULL in another post). Unlike the INNER JOIN which only returns rows from the tables if there are equal values in join column, OUTER JOIN will return all the rows from either LEFT or RIGHT even if there is no matching value in the table you are joining.
While back I wrote this blog post about SQL Server joins, the focus back then being in the physical ones. So I figured that now would be a good time to re-visit the topic and look at the logical joins also. Where the physical joins are decided by the SQL Server engine the logical joins are the ones we write. There are quite a few different kind of logical joins, so I will be writing multiple posts about this topic.
I was recently involved in a query tuning work where we used synthetic, rather than production data, to validate the results of our query and index tuning work. We faced some issues with the generated data that had quite a severe impact on our testing, and that prompted me into writing this blog post. Lets start by first defining what is synthetic data. In my view synthetic data is data that resembles actual production data, but is artificial/generated. I have seen similar (and also more detailed) definitions elsewhere and I think it is a good one.
I also like to point out that there are plenty of good reasons for using synthetic data in testing, as production data is often strictly regulated and not easily available for testing purposes. However, you need to be certain that the synthetic data you are using is similar to what you have in production.