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Curated SQL Posts

Working with Variables in Powershell

Published 2026-05-12 by Kevin Feasel

Garry Bargsley is a wild card:

Last week, we learned that quotes are not created equal. This week, we’re going deeper into the building blocks that make PowerShell scripts actually useful: variables.

If quotes are your lightsaber, variables are the Force itself. They carry information from one part of your script to another. They make the difference between a script that works on one server and a script that works on all of them. Get comfortable with variables, and you’ll look back at your pre-PowerShell DBA life the way Luke looked back at Tatooine – relieved to be moving on.

I can tell when Garry wrote this one.

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The Pain of Views in PostgreSQL

Published 2026-05-12 by Kevin Feasel

Radim Marik digs into views:

VIEWs should be the cleanest abstraction SQL, and therefore Postgres, has on offer. I love the concept. The promise of decoupling logical intent from physical storage is perfect on paper. In practice, few things in the database world trigger such a heated debate or carry as much historical baggage. VIEWs mix big promises with false hopes, and the promises rarely survive contact with production.

The appeal is straightforward. Abstract “active customer” once and reuse it everywhere. Every query, report and dashboard uses the same definition. The “active customer” then becomes the foundation of a “customer orders” view, which in turn powers an operational “customer summary” view.

Some of these pain points are PostgreSQL-specific, but others (especially around the performance of nested views) resonates with SQL Server as well.

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Optimized Locking in SQL Server 2025

Published 2026-05-11 by Kevin Feasel

John Deardurff enables a feature:

Modern SQL Server workloads demand high concurrency without sacrificing consistency. Traditionally, we relied on locking to enforce correctness, but that came with blocking and performance challenges. To resolve some of these issues, row versioning was used to reduce some of that contention overhead. But now with optimized locking in SQL Server 2025, we have a more efficient and scalable concurrency model.

Click through for a demo. My point of curiosity is, what’s the impact in a practical but busy environment? I don’t have one of those running SQL Server 2025, so I do wonder when we’ll get the tell-all post from someone in a very busy environment who has it on. (Or, if we already have and I’ve missed it, please do let me know!)

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Microsoft Fabric Eventhouse Caching and Retention

Published 2026-05-11 by Kevin Feasel

Nikola Ilic notes the ephemeral nature of life:

You spin up your first Eventhouse, ingest some IoT data, fire up a KQL query, and it runs fast. When I say fast, I mean embarrassingly fast. A few weeks later, you query data from a couple of months ago, and… it’s still fast, but maybe a tiny bit slower. A year later, the same query starts to feel sluggish. Two years later, you can’t find some of the data at all.

Welcome to the world of tiered storage in Real-Time Intelligence!

And when Nikola mentions how fast data in hot storage is, that’s no exaggeration. It is, to my knowledge, the fastest way of retrieving data in Microsoft Fabric.

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Power BI Maximum Allowable Memory Allocation Error

Published 2026-05-11 by Kevin Feasel

Chris Webb continues a series from a few years ago:

This is a very late addition to the series of posts I wrote back in 2024 and which started here on Power BI memory errors. It’s about a very rare error that is hard to deal with and often temporary but since people do run into it from time to time I decided to write about it so there is some useful information available about it online.

The error, which can occur when you refresh a semantic model or render a report, has two associated error messages:

Click through to see those two error messages, as well as a simplified version of the root cause and what you can do.

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Alternative Storage Engines for PostgreSQL

Published 2026-05-11 by Kevin Feasel

Cristophe Pettus puts together a list:

PostgreSQL 12 shipped the table access method API in October 2019, and the community spent the next six years figuring out what to do with it. The early prediction was that within a few releases we would have a thriving ecosystem of pluggable storage engines — columnar for analytics, undo-log for OLTP, in-memory for hot workloads — and the heap would become “the default, not the only choice.”

That is almost what happened. The ecosystem exists. It is thriving in the sense that there are more credible options now than at any point in the project’s history. It is also messier than the early vision implied, more concentrated around a couple of design philosophies than was expected, and littered with the bones of projects that ran out of funding or hit walls in the API itself. Before the next post in this series puts numbers on any of this, it is worth taking stock of what is actually out there, what each project is trying to do, and where the architectural fault lines run.

Click through for a survey of who’s tried what and what’s still around today.

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T-SQL Snapshot Backups on Hyper-V

Published 2026-05-11 by Kevin Feasel

Anthony Nocentino takes a backup:

If you’ve been following my T-SQL Snapshot Backup series, you’ve seen this technique work on bare-metal and standard VM deployments where database files live on volumes directly presented to the SQL Server OS. In this post, I’m bringing T-SQL Snapshot Backup into a Hyper-V cluster environment, with database files on VHDXs backed by a Pure Storage FlashArray Cluster Shared Volume (CSV). Hyper-V adds a few extra layers to manage at the hypervisor level, but the SQL Server side of the story is identical. Let’s walk through it.

Click through to see how it all works. It has taken me a long time to accept the idea that storage volume snapshots could fit the bill, and it’s been people like Anthony, Andy Yun, and Mark Wilkinson who have allayed my concerns over the years.

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Tips for Disaster Response

Published 2026-05-08 by Kevin Feasel

Christophe Pettus shares some advice:

  • Wind your watch.

No one has a watch that winds anymore, but the point is: take a deep breath. Give yourself a minute, or two, or five to gather data. A too-fast response is the main way a problem becomes a disaster.

Click through for all seven of them. I fully agree with doing drills. If you don’t practice in the easy times, you probably won’t respond well in the harder times. Also, I recommend having the process written down on a one-pager that everyone has a copy of. This should include the most important details around emergency response: how to escalate, what information to start capturing early on, etc.

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Page Splits and Readaheads on Clustered Tables

Published 2026-05-08 by Kevin Feasel

Erik Darling learns us some T-SQL:

But we can see that the number of page splits that have occurred on the server have gone up a bit here. We can see that that number has increased. So if we look back at the table itself now, and we look in here, right, we’re still not going to have any forwarded fetches because that’s never a thing.

But we do have a lot more pages in the table now, and the average page space used in percent has gone down dramatically. This was at 99-something percent. We are now below 50%.

We are at 46% full. Well, that doesn’t feel too good. What this means is that SQL Server has a lot more pages in the table now that are a whole lot less full, which means we are sort of like yesterday when we deleted a bunch of data from the heap and we still read empty pages.

Click through for the video.

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Cold Storage Data Archival

Published 2026-05-08 by Kevin Feasel

Brent Ozar answers a question:

At least once a month, I get this question from a client:

We have big data, and we want to save money. We want to move the older data down to some kind of archives that will cost us less.

I ask exactly one followup question:

Are you willing to modify the app that queries the data, or do you want the users to still be able to query the old data in their existing screens and reports?

Brent recommends trying a linked server approach. I’d also toss PolyBase in to the mix, though it’s going to have similar pros and cons to linked servers in this specific scenario. I have an old post on cold storage with PolyBase but the mechanisms haven’t changed much. I do have some sample scripts in my PolyBase talk’s GitHub repo, though fair warning that these are going away soon as I rebuild the talk for 2025.

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