Voice of the DBA

Category: Editorial

  • Comforting Habits

    I was chatting with a friend recently about routines and some of the helpful or silly things we do. I mentioned that when I played adult baseball, we often had Sunday morning games and a routine of mine was to drive to town, stop at a 7-11, and get a large cup of coffee and an apple fritter. It was a comforting habit that I still have today, often stopping when I have a morning flight to do the same thing in the way to the airport.

    Only in Denver though, not when I’m flying out of other cities and returning home.

    At another job, I rode my bike to work. However, I’d drive Monday morning and leave my car at work, taking it home Friday afternoon. In between I’d commute on a bicycle 10-12 miles each way. On Monday, I’d buy a half dozen bagels for the week, which I’d leave in my office. After riding in and showering, I’d toast a bagel and have that with my coffee. It was a nice way to start the day while I scanned email or had a morning meeting.

    Apparently, many of my routines revolve around food.

    I have other habits, like waking up and checking my email before doing anything else. Not always, but most days I’ll stir, give up on trying to sleep, and scan email from bed to see what is in store for the day. I don’t always get up and go to work, but I can mentally prepare myself for how the workday will go. I’ve often found this helps me ease the way into the day, and I’m less stressed when I’ve broken the question of the day or had some other issue. I am prepared for what I’ll face in the next hour after relaxing with my wife for a bit longer.

    Routines not only bring us comfort, but they ground us in the chaos of the real world. Even if we have lost control of our work, with others pressuring us to meet deadlines or fix a broken system, a routine helps us to react calmly and not panic.

    Even during security or failure incidents, which can be very stressful, I’d start the issue with a blank notebook page, writing a few known facts down on paper and starting to think forward as to how I’d manage staff. Often this exercise had me making initial decisions about who would work now and who would go home to come back later and relieve people. This was before I knew the scope or details of the problem. Even though I often had to change my plan, having a plan to change gave me comfort.

    Think about the repetitive things you do on a daily or weekly basis. Think about the things you do in response to periodic events. What comforting habits do you have that help you get through the day or just bring a smile to your face.

    Steve Jones

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

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  • Big Data or Small Data

    I went to San Francisco for Small Data SF, a conference sponsored by Mother Duck. The premise of the event was that smaller sets of data are both very useful and prevalent. The manifesto speaks to me, as I am a big fan of smaller sets of data for sure. I also think that most of the time we can use less data than we think we need, especially when it’s recent data. That often is more relevant and we end up with contorted queries that try to weight new or old data differently to reflect this. Maybe the best line for me is this one:

    Bigger data has an opportunity cost: Time.

    I think time is a very valuable commodity and large sets of data can slow you down. There’s also the chance that looking at too much data starts to blur the lines of understanding. We may start to miss information in our dataset, or we may find people arguing about different things the data means, because we have so much data that we can find support for any position somewhere in the vast sea of numbers, strings, and dates.

    Big data also has a real cost in resources, often money. One of the examples was from the organizer, who once gave a demo on stage, querying a PB of data.  That’s impressive, and lots of us would want to be able to query our very-large-but-less-than-PB-sized data in minutes. However, the thing that wasn’t disclosed in the demo was the query cost over USD$5k.

    I’ve heard from a number of customers and speakers that most people don’t have big data. Most of us have 100s-of-GB-sized working sets of data, sometimes with TB-sized archives in the same database that slow everything down. If we could easily extract out the useful data, we could query those hundreds of GB more efficiently.

    This is especially true in the era of small devices that can handle something close to a TB of data in a small form factor. With some of the columnar systems that compress data, a TB of raw data might be substantially compressed in Parquet files or an analysis system like DuckDB. In that case, we might realistically search and analyze 1TB of data on a laptop.

    I know that big data is relative, but many of us face challenges with data sizes and query performance. I know lots of you embrace the challenge and see working with TB (or larger) systems as a badge of honor. I also know the reality is that most of us struggle to separate our archive data from current working data in our systems. However, if we could, would most of you want to work with smaller data sets or do you enjoy large ones? I know which way I lean.

    Steve Jones

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

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

  • Learning to Grind

    When I was younger, I had a variety of jobs, but in most of the positions I had to work hard for stretches. Really hard, as in more than 8 hours a day or 40 hours a week. Often as I was starting a new position, it took some time for me to develop some understanding, some skill, and some muscle memory. In some jobs, especially in restaurants, I also had to build the physical skills to be on my feet for many hours.

    In technology, I’ve often found myself unsure of how to approach a new position, aware I had knowledge gaps about how things worked, and often, I was naïve or ignorant of some piece of technology my employer used. Even at jobs where I started as a developer or DBA on a known platform (ASP or SQL Server), I sometimes encountered some aspects of the technology that I hadn’t used in the past (like clustering).

    In those situations, something I learned from my parents and a few youth coaches came to mind. I needed to bust my butt to be successful. The lessons I learned weren’t expressed so politely, but they boiled down to putting in extra time and focus, and continuing on that path until I was competent in the eyes of someone else, usually my boss.

    I’ve encountered many people in the last decade that have much to learn. I’ve met far too many that didn’t understand their environments as well as I’d expect them to as a manager. I have encountered far too many people who wish they could be more skilled in some way, but they haven’t made a commitment do the work to further that wish. I’ve met far too few people who are working to improve themselves on a regular basis.

    How do we teach people to grind away at something to improve themselves?

    I don’t know. I’ve tried to motivate people, I try to give them examples, I’ve tried to provide suggestions. It seems that many people have lost the drive to invest in themselves to prepare for the future. Too many want their boss to train them and then re-train them when they don’t use a skill and forget it. Or they want their time in a position to count as experience. Or they want their boss to give them time out of their 40 hours, without having to make their own investment of time at night or on weekends.

    Skill and experience don’t magically appear. They take work. They take grinding away, making mistakes, achieving small successes, taking a step backward, then driving forward in new ways. It’s effort, and it’s time. Read any story about a person who’s achieved success and you’ll find tales of study, work, practice on their own time.

    If you want something different in your career, or in life, you have to work at that thing. Make a plan, but then work at it. Give up some leisure time. Not all, but some. Give up something fun to achieve something else later. Learn to sharpen your saw, polish your craft, grow your marketability, whatever you want to call it.

    Just start doing it.

    If you want to read a few examples, I have a short series of posts on grinding away at life.

    Steve Jones

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

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

  • The AI/Human Spectrum

    I was asked this question recently: is it more likely that AI will replace humans or assist them in their work?

    It’s a good question. If you think about the way AI is being hyped in 2024, many people think AI is, or will soon be, replacing people and we need less of them in work. I guess the simplified view is that AI can do the jobs of many people, but I’m not sure the world is that simple. What I think is more likely is that AI becomes a lever that assists a few people in getting more work done and potentially replacing other, less knowledgable humans.

    Maybe it’s the ultimate, do-more-with-less pressure that the management in many organizations places on workers that has people looking to AI to help. Get more done this year, but we’re not adding staff, and potentially we’re removing a few staffers. Maybe AI can do your job?

    AI can be a lever, and I do think that there are tedious tasks that we might have AI take on for us. We’ll need more of an agent/proxy approach to AI systems, as now I can ask AI to do some things that relate to text/images, but not actually do work for me. I want an AI to actually set my Out-of-Office for me, not give me instructions on how to do it or write the message for respondents.

    The current Generative AI/LLM systems aren’t really smart or intelligent, but they do process vast amounts of data and mimic the responses that other humans might give. If you work in an area that can benefit from that type of interaction, maybe an AI works well as a lever and lets you get more done on any given day. For some jobs.

    However, getting more done might not be enough. If I pair program with someone else and they write a bunch of poorly performing code, it takes me time to judge the quality of what they’ve written and then additional time to fix it. Getting more done in that situation can be a burden because the additional code produced requires additional rework. I might get less work done in some cases if the code is low-quality and I use a lot of time to rewrite or improve the code.

    However, if I am tackling simple tedious tasks, perhaps basic CRUD work in an application, maybe an AI can generate enough SQL, web, C#, etc. code that the job is done quicker. Maybe not at the most efficient level, but how many of you think the code for your internal applications is amazing? Is it good enough? Can an AI do “good-enough” work?

    As with a lot of dramatic changes to technology, I find myself going back and forth on the value produced. Some days I think the tech is amazing and some days I think it’s akin to the stuff I shovel out of horse stalls. AI is in the same boat with me, and while I think there is potential, I know that there are also downfalls and potential detractions from its widespread use.

    Certainly, the need to evaluate and judge quality is a challenge with AI, which leans me towards the lever that assists talented humans and replaces less talented ones. The other issue is cost. The LLMs are expensive, and use a lot of compute power. I’ve seen some smaller models, perhaps tailored with RAG or other methods of refining (and limiting) their use will overcome that, but who knows. The current models, however, cost something to run and someone is going to have to pay for that. Is there enough ROI to do that?

    Lastly, trust. Can we really trust an AI to give us accurate responses, or even perform work on our behalf? We have that problem with other humans now, but they work slowly compared to a computer. Can you imagine the problems that a rogue computer system could create with access to change things in the real world?

    Answer my question today. Are AIs more likely to assist or replace people?

    Steve Jones

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

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