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Author: Kevin Feasel

Purging the SSIS Catalog

Published 2020-10-30 by Kevin Feasel

Peter Schott extends Tim Mitchell’s work:

I wrote about cleaning up the SSIS Catalog a while back, but needed to revisit this recently for a different use and needed something that can run in an ongoing manner. My earlier post still works, but I recently adapted some code from Tim Mitchell to create a stored procedure that can do that cleanup. Tim wrote the majority of this. I adapted it to wrap it in a stored procedure to handle varying batch sizes for the deletes as well as to ensure all of the tables are deleted in smaller sets of rows.

Here is the code to create the stored procedure. If your SSIS Catalog is not named SSISDB, adjust accordingly.

Click through for the script. It’s interesting to note how frequently cleanup processes for functionality in SQL Server is inadequate for the task at scale. I’ve regularly seen people write these sorts of things for SSISDB, Query Store, replication, ML Services (though that, at least, was changed), etc.

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SSAS Performance Counters to Monitor

Published 2020-10-30 by Kevin Feasel

Steven Wright takes us through a set of SQL Server Analysis Services performance counters we should track:

That said, I wanted to provide some updates with more of a focus on Tabular mode, as it has clearly become the new standard for how most organizations deploy SSAS. I recommend referring to that original series for a focus on Multidimensional mode, but much of the information provided in this blog post will be applicable to both modes. I recommend referencing Allen White’s blog post on SQL Server counters to learn more about many of the Windows-level counters that apply across the board, as I won’t speak to them here.

Let’s dive into the 15 SSAS performance counters you should be monitoring.

Click through for the set, as well as explanations for why.

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Recommendations for Hosting SQL Server on VMware

Published 2020-10-30 by Kevin Feasel

Michelle Gutzait walks us through recommendations on hosting SQL Server in Windows on VMware:

VMware has created a very detailed best-practice document for us, specifically for SQL Server. You may find the latest one here.

In case the link doesn’t work for you, or you have a different version of VMware, you can search for the proper SQL Server best practices on the VMware site.

Here are the main best practices VMware recommends, and the most important based on Pythian’s experience (SQL Server on Windows):

Click through for a detailed checklist.

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Installing an Always On Availability Group in SQL Server 2019

Published 2020-10-30 by Kevin Feasel

Ginger Daniel takes us through the process of setting up an Always On Availability Group in SQL Server 2019:

With SQL Server 2012 Microsoft introduced the AlwaysOn Availability Group feature, and since then many changes and improvements have been made.  This article is an update to our previous article https://www.sqlrx.com/steps-for-installing-sql-server-alwayson-availability-groups/ , and will cover the prerequisites and steps for installing AlwaysOn in your SQL Server 2019 environment.

Click through for a checklist of pre-requisites and installation + configuration steps.

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Understanding Digital Twins

Published 2020-10-29 by Kevin Feasel

Kurt Cagle explains a concept:

At the core of such digital twins is the notion of a model. A model, in the most basic definition of the word, is a proxy for a thing or process. A runway model, for instance, is a person who is intended to be a proxy for the viewer, showing off how a given garment looks. An artist’s model is a stand-in or proxy for the image, scene, or illustration that an artist is producing. An architectural model is a simulation of how a given building will look like when constructed, and with 3D rendering technology, such models can appear quite life-like. Additionally, though, the models can also simulate more than appearance – they can simulate structural integrity, strain analysis, and even chemistry interactions. We create models of stars, black holes, and neutron stars based upon our understanding of physics, and models of disease spread in the case of epidemics.

This is a really good explanation of the concept. Contrast with the explanation of, say, Azure Digital Twins. The first time I saw it, I thought one thing; then, when I read the intro page, I thought something different. Then, I walked through the demo and thought something yet again different. I might have just missed the part where it lays out exactly what a digital twin is and its importance but I do like Kurt’s explanation a lot more.

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Combining Two Survey Questions into a Graph

Published 2020-10-29 by Kevin Feasel

Stephanie Evergreen solves a challenge:

You’ve asked employees to rate a bunch of different aspects of their job. You want to know if they think that aspect is important AND how satisfied they are with that aspect of their job. So, naturally, you make two individual questions with response options like Not at all Important to Very Important and Not at all Satisfied to Very Satisfied. I would probably do the same thing.

But then you’ve got to show the data and, importantly, how those two variables – Importance and Satisfaction – relate to each other.

Click through for two methods of visualizing the results.

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Using Key Vault in Azure Databricks

Published 2020-10-29 by Kevin Feasel

Arun Sirpal shows us how easy it is to tie Azure Key Vault into Azure Databricks:

The key vault should always be a core component of your Azure design because we can store keys, secrets, certicates thus abstract / hide the true connection string within files. When working with databricks to mount storage to ingest your data and query it ideally you should be leveraging this to create secrets and secret scopes.

Click through for a demo.

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Detecting Spinlock Contention in SQL Server

Published 2020-10-29 by Kevin Feasel

Michael J. Swart walks us through spinlock contention:

When that occurred, the number of batch requests that the server could handle dropped significantly. So we saw more CPU use, but less work was getting done.

The high demand kept the CPU at 100% with no relief until the demand decreased. When that happened, the database seemed to recover. Throughput was restored and the database’s metrics became healthy again. During this trouble we looked at everything including the number of spins reported in the sys.dm_os_spinlock_stats dmv.

The spins and backoffs reported seemed extremely high, especially for the category “XVB_LIST”, but we didn’t really have a baseline to tell whether those numbers were problematic. Even after capturing the numbers and visualizing them we saw larger than linear increases as demand increased, but were those increases excessive?

Read on for the answer.

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