Voice of the DBA

Author: way0utwest

  • Architecting Zero Downtime Deployment Resources–Albany Day of Data

    Thanks to those who attended my session at Day of Data Albany 2026. I’m posting this to help you find the resources from the talk.

    Slides: ArchitectZeroDowntime_SQLSatAlbany2026

    GitHub Repo: https://github.com/way0utwest/ZeroDowntime

    The GitHub repo has the code for the database setup and demos (and teardown) in the SQL folder. This creates the db, login/user, schema, and data.

    The VS project is in the DBClient folder. I don’t think this is updated beyond .Net 4.6 (?), but feel free to update it. There isn’t anything special about this project. It was developer in VS 2019, but I ran it from VS2022 on Saturday, and I believe I upgraded my local project to .NET 4.8 last week. I haven’t committed this back as I don’t want to push this forward yet.

    If you have questions, feel free to contact me.

  • The Quiet Part

    Apparently, Meta did what a lot of employees suspect their management will do: use AI to lay off people. In this case, there is a report (and lawsuit) that Meta used it’s AI-integrated HR platform to make decisions about who to let go in a layoff. The rumor is that the AI used productivity metrics to choose who was the target of the layoff. As with a lot of AI failures, this appears to be another case of poor communication guiding the AI, or the AI not actually taking individual situations into account.

    A number of the people terminated were on maternity/paternity leave, which is a protected activity. Others may have been on other medical leave, though for privacy reasons, the article doesn’t have firm data to prove this. The lawsuit will likely bring more of this out, but this appears to be a case not just of AI making decisions, but of poor behavior from management. Plaintiffs were discouraged from taking leave off, which is something sh****y humans have said to people for decades. We need you; your baby or family isn’t important, so don’t use your leave. It’s one aspect of working in the US that is way worse than overseas, where there are more employee protections.

    Meta disputes the case, saying the AI didn’t make decisions. However, that brings out an interesting point. If the AI recommends things, who’s responsible? The humans, right? They still have to sign off on the decision. If they don’t perform due diligence, they’re still responsible, correct? I think so; after all, I’m responsible for code the AI writes if I commit it. Even if an agent does the work, I have to oversee it and approve (or grant permissions for) the actions.

    I. Am. Responsible.

    Trusting an AI to do a lot of work is like trusting a lot of smart, but very inexperienced staffers to work in your environment. There are always inconsistencies and reasons why we might code something, configure something, or deploy something a certain way. What seems like a good way to tackle the situation from the outside doesn’t always make sense when you have experience. Humans often have experience that AI agents lack.

    LLMs are relatively stateless, and while we can provide context, give them guidance, and provide comprehensive codebases, they still sometimes do silly things. This can be problematic, especially with database changes, which are stateful and disruptive to rollback.

    AI agents can make mistakes much like humans, only faster. Much, much faster.

    Labor is one of the most expensive parts of many organizations’ budgets. Plenty of management would like to replace relatively expensive humans with cheaper tokens. That isn’t working out as well in practice, despite lots of experiments. Hopefully, other organizations realize that AI is a tool, not a replacement for humans, and we can’t trust it or even believe it’s outputs without some human judgment.

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.

  • Car Update: August 2026

    It’s been some time since I provided a car update, so I decided to showcase a few things I’ve learned about my cars since the last one. In this post:

    • A Second EV: Lucid Gravity
    • Efficiency and Tires on the Tesla Model Y
    • Serendipity with a Suburban

    This is part of a series of thoughts on cars, just for fun. These are my thoughts and opinions based on my experiences.

    Our Second EV: Lucid Gravity

    We bought  a Lucid Gravity after driving it one day. We’d driven a few other EVs and then stopped by the Lucid dealer because we had an hour and it was close. We weren’t expecting to be impressed by the car, but we were. In fact, we immediately stopped thinking of the other cars.

    After a few hours of debating it, we bought one in the airport while waiting for a flight. It’s amazing to see how much easier it is to buy from Tesla and Lucid than other deals.

    It’s the nicest car I’ve ever owned, and it’s an EV with great acceleration and handling. It’s also got a squashed steering wheel, which I am surprised I like. It’s easy to control and has a nice space to rest my wrists.

    Lucid squashed steering wheel

    There are some weird things with software, and I have already learned how to “reset” the car. It took me a few minutes to get it right, which is weird. I have to press the brake and then press two buttons on the steering wheel. This didn’t fix the issue I was having with switching profiles, but it was an interesting experience to reboot the car.

    One of the things I love is the cooled and massaging seats. I might get tired of this over time, but I have a bunch of different settings and there are a few I’ve enjoyed on our drives.

    If you think about a Lucid, use my referral link. We’ll both get some credit.

    Efficiency and Tires on the Tesla Model Y

    In about 4 years of driving the Tesla Model Y, covering almost 60k miles, I had a long term efficiency of around 250Wh/mi. That’s just a bit below the rating for the car. I think I did very well, driving mostly at 45mph and lower in chill mode.

    A friend sold me the spare 18” wheels from his Model 3 when he got rid of his car. I put them on mine, mostly because he had better tread than my summer tires. I didn’t think much of this until I checked the efficiency one day.

    I was in the 205Wh/mi range with the smaller tires. Now, the software doesn’t have a setting for 18” tires on the MYLR, so I left it at the 19” stock tires, so possibly there is a calculation error, but I got > 10% efficiency improvement from changing wheels and tires. Smaller, lighter tires, but still. That’s crazy.

    As we looked for new cars, I was aiming for the smallest tires I could get, both for replacement cost and efficiency.

    Serendipity with a Suburban

    In 2006, I bought a 2001 Chevy Suburban for $6500. The car had 101,000 miles on it and we were slightly nervous, but with a family of 5, we wanted a car that would carry all of us skiing and on trips. The AC died the next year, but that car has served the family driving all over CO, WY, and NM for many years. Each of my kids had driven it, one even banged into a concrete barrier in the snow.

    I felt this was the best car investment we’d made over the years. I sold it in 2026 with 250,000 miles on it. We’ve basically done regular maintenance (oil, brakes, tires) and replaced minor parts across the roughly 150,000 miles we’ve owned it. The car was old, had rust on it, and in the hot CO summers, it was a car my wife used carrying the dogs from client to client.

    2001 Suburban in 2026

    She wanted something better.

    I had been periodically looking for a good deal when I stumbled on a 2010 Suburban with 203,000 miles on it, It was a 3 owner car, no accidents, lots of records of service.

    It was listed $6500 (I paid $6200).

    I made my wife cut short work one Sat and we drove down to a dealer, getting there just before they closed. A bit of a test drive and we bought it that night. So far, about 8k miles in, it’s performed as well as the old one.

    And it has AC.

    Still feels like a good deal and a good choice. 20 years later, same price.

     

     

  • Are You Working More Hours?

    Recently, I noticed my son was coming home later and going to work earlier. He typically works a 9/80 schedule as a software engineer, but at times he might work extra hours on a deadline. His company tries hard to keep employees working a set schedule without overtime. Unless there is a need, after all, this is software. However, when they are on a deadline, management warns people, and they try to manage overtime to reasonable levels to avoid burning out employees.

    I asked him if he was extra busy or if AI was encouraging extra hours. He told me this was a short-term project with a few more hours, but mostly the team was decompressing a bit after work. I completely understood that as I’ve spent my share of time with co-workers at theend ofday, sometimes sharing a drink or meal nearby, sometimes just chatting in the office or the parking lot.

    There have been a number of studies and reports that AI is making people work more, not less. The time spent on a variety of tasks, especially those where we are reading or interacting with text, has grown. I suspect there is additional pressure from management as well to get more done since everyone has an AI assistant around. There is also the excitement of tackling new projects, which can lead people to spend more time at work. While I appreciate that AI can be exciting, and you might love it, don’t lose yourself in it at work. It’s still just a tool.

    And, of course, there’s the token spend. Some managers are measuring people on the amount of work their AI assistants do, rather than by what gets done. I’ve certainly seen plenty of people using AI to perform busy work just to be seen as using AI tools.

    At times, it seems people are using AI to process more information, which they then use AI to summarize. They post these summaries, which grow in number because the AI can produce more of them. Others use their AIs to read these summaries and produce their own summary of what they should be reading. It certainly seems like AI is producing more copies of information, summarized over and over, giving all of us more things to keep an eye on.

    That seems silly. While I like AI, I fall into that group that uses it lightly for targeted tasks. I certainly try not to create more reports, summaries, or even emails to send to others, especially large groups of people. We have enough to review without AI creating even more tasks to deal with every week.

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.