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Day: June 15, 2026

Noise in CRAN Package Additions

Published 2026-06-15 by Kevin Feasel

Joseph Rickert shows a consequence of lowering the bar for application development:

If you are reading this post on R-bloggers, you will probably know that I have been publishing my selection of the “Top 40” new R packages on CRAN for quite some time. I did this first as part of my work at Revolution Analytics, then on R Views for RStudio and Posit, and now here on R Works. It used to take about a day’s worth of pleasurable work spread out over a month to select forty interesting packages. For a hundred or so packages, I could look at all of the package webpages, download and play with a small number of them. Now, the “Top 40” has become a real hamster-on-the-wheel project. The following plot shows my count of the number of new packages to make it to CRAN since I began publishing on R Works.

Click through to see what Joseph has laid out. The part that surprises me is, historically, CRAN was pretty difficult to get a package into and you typically needed to jump through a certain number of quality gates. I suppose that has to have changed given what Joseph notes around the lack of documentation in many of these new packages.. But it could be that my understanding of it was wrong H/T R-Bloggers.

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Jobs and Security Objects in Contained Availability Groups

Published 2026-06-15 by Kevin Feasel

John Morehouse moves some assets between availability group replicas:

In the first post, I introduced contained availability groups and how they bring contained versions of master and msdb along with the Availability Group. That matters because many applications depend on more than just user databases.

Two of the biggest wins are SQL Agent jobs and security objects.

Click through to see how they work.

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Vibe Coding and Maintenance

Published 2026-06-15 by Kevin Feasel

Buck Woody has an essay:

Artificial Intelligence constructs, from Large Language Models answering questions to Agentic AI that runs various workflows are fantastic, amazing, helpful tools in getting a job done. They aren’t quite completely automating entire tasks (The best ones as of this writing are correctly implementing around one out of three tasks accurately: https://llm-stats.com/benchmarks/apex-agents) but they are still a very helpful tool. “Vibe Coding” which means explaining to a model that can write code (or a Codex) what you want the code to do, trying it out, then correcting it until it does the thing, is prevalent everywhere now. And it’s easy to do.

But the code a Codex creates meets a single need: to ship.

This matches pretty well with what I’ve seen. You can definitely build something, which may be good enough for single-person use. But maintenance is a separate story altogether and raises the old adage that you can only maintain code less sophisticated than your knowledge level. Between that and cognitive overload, you can easily end up with a code base that you can’t understand.

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The Benfit of Disabling V-Order in Fabric Dataflows Gen2

Published 2026-06-15 by Kevin Feasel

Chris Webb covers a specific use case:

Quite a few new Dataflows Gen2 features were released recently without much fanfare, but that doesn’t mean they aren’t important. I will take a look at them all in my next few posts; in this first post I’ll look at the ability to disable V-Order on staged data.

As the (very detailed) documentation for this new feature describes, V-Order is a write-time optimisation for the parquet files that underpin the Delta tables that OneLake uses to store data. It slows down writing data to the tables but means that reading data from them, for example in Power BI Direct Lake mode, is much faster. 

Click through to see how disabling V-Order can make certain staging loads faster.

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Treating Query Store Abort Hints like a SQL Firewall

Published 2026-06-15 by Kevin Feasel

Emad Al-Mousa plays around a bit:

It’s a security mechanism designed to filter, and block unauthorized or malicious SQL query being executed against the database system before it reaches the database kernel itself. It acts as a specialized gatekeeper that ensures only “known good” queries are allowed to run. In a sense, you can compare it with WAF (web application firewall) in terms of protection mechanism.

SQL Firewall will provide protection against the following threats and attacks:

SQL Injection

Privilege Escalation

Data Exfiltration

Emad makes it very clear that this is not a viable technique, but it is also a fun enough idea that it’s worth checking out.

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