A Fine Slice Of SQL Server
Yihui Xie looks into an issue:
Running
quarto renderon a document with that single chunk took 35 seconds. The equivalentrmarkdown::render()finished in under half a second. As a side note in the issue, the reporter pinged me that the same problem existed in litedown. litedown is independent of both Quarto and knitr; it executes R code throughxfun::record(). That is where I started looking.
Click through for the discovery process, explanation, and fix.
Leave a CommentMarco Russo and Alberto Ferrari take a look back at calculation groups:
The introduction of user-defined functions (UDFs) in DAX changes the way we think about code reuse. Before UDFs existed, calculation groups were the only mechanism for sharing common logic across multiple calculations. Many developers adopted calculation groups not because they were the ideal tool for code reuse, but because there was no alternative.
Now that user-defined functions are available, it is time to revisit this practice. User-defined functions and calculation groups serve fundamentally different purposes. Understanding the distinction between the two is essential for building well-organized, efficient semantic models.
Click through for a dive into these two concepts and when to use each.
Leave a CommentKerry Kolosko digs into data visualization theory:
Power BI development is a relatively straight forward process when managed by one individual start to finish. But when the development process is shared among team members, ways of working need to be established and common work management frameworks such as agile, lean, HCD and UI/UX Design are adopted.
These frameworks can be useful for teams but as always, the rigid adoption and adherence to frameworks can cause project inefficiencies. It took a fair bit of corporate learning to acknowledge that applying Agile methodologies to a construction project and waterfall methodologies to a software project, weren’t effective.
There’s a lot in here around pros and cons of various tooling (like wireframing), visual selection, the grammar of graphics, and what smoothed lines actually represent. Smoothed lines is a bit of a hobby horse for me, as those smoothed lines represent a model of the data rather than the actual data, so if you show me the former, you’d better also show the latter.
Leave a CommentJens Vestergaard rethinks a metaphor:
In February 2025 I wrote about building an event-driven ETL system in Microsoft Fabric. The metaphor was air traffic control: notebooks as flights, Azure Service Bus as the control tower, the Bronze/Silver/Gold medallion layers as the runway sequence. The whole system existed because Fabric has core-based execution limits that throttle how many Spark jobs run simultaneously on a given capacity SKU.
The post was about working around a constraint. You could not just fire all your notebooks at once. You needed something to manage the queue.
More than a year on, it is worth being honest about what held up and what has changed.
Read on to see what has changed in this past year and how Jens thinks of it today.
Leave a CommentJeff Iannucci has some updates:
This month though there are mostly a few small updates for the tools, as next month’s updates should also include many of the issues noted in GitHub (thanks for those!) Most of this month’s updates were to sp_CheckSecurity, although we did add “Initial File Size” to the output of sp_CheckTempdb since that had been requested by a few folks.
We hope these new updates can help you, especially if you’ve never used these stored procedures. Here are the updates for March 2026, with links to the GitHub repositories where you can download the latest versions.
Click through for those links.
Leave a CommentLouis Davidson resurrects an old article:
When I wrote this post, I had a very strong belief that my OLTP databases are heavy writes and OLAP heavy reads. But in many cases this isn’t exactly the true story. Why? Because you have to read a lot of data to update, delete and display data. Even inserts have reads to look for duplicates, for example. OLAP systems should likely be read more times than write, but loading data can be a lot of data moved around and reads maybe less if the queries are efficient. When I was looking for stuff to post, this post stood out as one that I really liked (other than the aforementioned title!)
I came upon this idea when I found a post by Jason Massie (whose handle was statisticsio back then) that I had referenced, and Kendal Van Dyke alerted me this post at SQL Saturday in Atlanta (this would have been back in 2008 or 2009). After reading that (long gone) post, I wrote this post using
sys.dm_db_index_usage_stats, and added references tosys.dm_io_virtual_file_statsas well.
Click through for the script. My expectations going in would be that OLTP and OLAP servers would actually have fairly similar read-write ratios. We talk about OLTP being “write-heavy” and OLAP being “read-heavy” but in practice, you need a lot of write behavior to sustain a warehouse—especially one that isn’t just a “truncate and reload once a day” type of thing—and people care about reading the data from their OLTP systems. In practice, I see the split as more of optimizing for accuracy in data entry (OLTP) versus simplicity of reading data (OLAP).
Leave a CommentErik Darling has a pair of documentation videos for Performance Monitor. First up is the full dashboard installation:
In this video, I delve into the installation process of my free SQL Server performance monitoring tool, addressing a common issue where users have questions about setup despite having access to detailed documentation. I explain that there are two main installers: one for command line use and another with a graphical user interface (GUI). The command line installer offers various options such as reinstalling the database, choosing encryption levels, managing server certificates, and handling password input in different ways. Additionally, I cover how the tool runs through 52 installation scripts, including community dependencies like SP WhoIsActive and my own scripts for data collection and analysis.
After that, Erik shows off the user interface:
In this video, I delve into the exciting world of free SQL Server monitoring tools, specifically focusing on a dashboard I’ve developed called Performance Monitor. This tool is designed to make life easier for DBAs and developers by providing real-time insights into server performance, resource usage, blocking issues, and more. With a user-friendly interface that includes various tabs like an overview, resource metrics, and query analysis, the dashboard aims to simplify complex monitoring tasks. I also highlight some of its unique features such as the plan viewer, which leverages Microsoft’s SQL Server extension for Visual Studio Code to provide detailed execution plans with actionable insights. Whether you’re a seasoned DBA or just starting out, this tool can help you keep your SQL Server environment running smoothly and efficiently.
Click through to check out what Erik has to offer.
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