Voice of the DBA

Tag: software development

  • Building Great Software

    Most of us will work on software for our organization, and we might not want to or care about the end result being great. We do want it to work, and we want clients to find it useful. If we have external customers using our systems, maybe we want it to be great. In most of my experience, people are often proud of their work, sometimes ashamed, but not many people spend a lot of time making their corporate applications great.

    Often because we don’t have (or aren’t allowed) the time to do so.

    Basecamp is a popular SaaS project management solution, and Hey is a reimagined email service that many people love. One of the founders of the company wrote an interesting post on software being built that starts with this sentence: “The speed at which a product is developed doesn’t inherently make the product better or worse.”

    It’s a bit of a shot at AI, but it also goes into the fact that we often measure our software process in ways that aren’t about the output. Most organizations have abandoned lines of code as a metric, but I do see commits or PRs being used, as well as other metrics. Trying to decide if your developers are effective isn’t a horrible idea; after all, we should be ensuring that they are getting something done, but none of those metrics necessarily help us make better software.

    I see that at Redgate, as we incorporate AI into our work. A lot of the things that make software take time aren’t solved with AI. They’re solved with deep understanding of the problem space, what your customers need, and what helps them work well. AI accelerates some things, but we still need product people empathizing deeply with customers and designers watching for UX issues that create friction for customers. AI can help speed up the experiments and outputs in some ways, but just adding in chatbots or AI agents that write code isn’t necessarily useful.

    There are a lot of decisions in building software. What to do, what not to do, what’s more important than something else, and of course, what approach to take in the architecture. That’s before we even get to performance, which is something that far too often gets ignored, at least for the code being run against databases. Building great software is hard, but it can be done, and AI can help.

    You just need talented humans guiding the process.

    Steve Jones

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  • Imagine the Physical World

    Brent Ozar has a series of database animations posts, where he tries to explain what work is done by SQL Server during certain operations, such as Index Seeks and Page Splits. These show how the engine might need to read or update various pages as it tries to perform operations. Both experienced and novice SQL Server people might think that these are interesting, but not that useful.

    I think they’re great.

    For most of my IT career, I’ve drawn parallels between what I’ve asked the computer to do, and how this might play out in the real world if we weren’t working on digital systems. Most of the constructs we have, connecting to a system, sending a query, configuring a firewall, sorting data, etc. can be related to a physical action.

    If you want to understand page splits, imagine you had the index from a book printed out (or a TOC), and broken out across multiple pieces of paper. If I asked you to insert something in the middle, or to lengthen an entry that doesn’t fit, what would you have to do? You’d need a new piece of paper, you’d put it in a place between the others, you’d copy over some data, you’d erase some data from existing pages and more. Exactly what Brent’s animation shows.

    That’s real work.

    The more I think about the work the computer does, even if it’s measured in ms or ns, the more I can think about whether I can find an efficient way to complete my task. It’s not that I mind the computer reading 1mm rows (or 100mm), but if I can reduce the work, I reduce the latency, the computer, the disk, the cost, the everything. My customers are happy when I try to reduce the work and make everything run faster.

    This view has helped me look for and find better ways to implement solutions over the years. I know that not everything needs to be optimized, after all, sometimes we do more work in the physical world because of expediency, but we know the tradeoff. We can judge if it’s worth it.

    Too often I see people think the computer is doing the work and they dismiss the effort. It’s the same attitude people have in the real world when someone else has to move/lift/pay for something. It means less to them.

    Treat everything as if you had to do the work yourself. You’ll treat others better and appreciate the effort spent, whether it’s another human, a software program or an AI LLM. Your results will show better judgment, and hopefully, quality.

    Steve Jones

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  • 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

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  • Building Your Own Software

    Lots of AI usage has been spent inside companies on building new software. Sometimes people are trying to rebuild existing software. I’ve seen more than a few articles say that Slack is dead (it’s changing), or Monday is dead, or some other SaaS isn’t going to survive because people can vibe code their own replacement. I know a few people trying to do this, with very mixed success.

    I saw this post from Jason Friend of Basecamp, where he noted that most products wouldn’t exist if everyone were an entrepreneur. This is because the great products exist precisely because an entrepreneur had a great idea and followed it through. They made mistakes, they learned, and they spent a lot of time getting the product right. The coding was likely a part of this, but the mistakes, the changes of direction, the decisions on what to add or take away, those are time-consuming things not shortcut by AI. The coding isn’t the big delay in lots of products. A great response to the post notes this: there are many decisions that go into a product. Many people don’t think about all those decisions.

    Certainly, some people will use AI and do a great job coding a replacement for some existing product. They’ll make their own version of a product, likely learning a lot from existing software. They will shortcut some design and testing time since they know what they want. It’s the software they already use, and they know how it works. They don’t need to design it.

    However, the software is just part of the process, especially in the SaaS world. Infrastructure and operations are big. While a replacement Slack might not need quite the level of hardware, networking, DR, disks, etc., it needs a lot more than an individual can build and manage. An LLM might help you determine where your bottlenecks are and help you build the systems needed to manage more resources, but it’s going to take time. Especially if you don’t know what sorts of hardware and bottlenecks will occur as more people use your software.

    It’s also going to make mistakes.

    Imagine your LLM gives you a piece of bad code that doesn’t perform. We can refactor and fix that. Now imagine your LLM doesn’t configure DR correctly. Fixing that is a little harder, especially if you don’t realize DR is lacking until an incident occurs.

    Software is hard. One thing I’ve learned over the years in advocating for customers’ requests in Flyway, SQL Prompt, or Redgate Monitor is that the coding takes time and effort, but much less than the decisions on what and how to build something. Those eat up time and focus. Once you solve these, AI definitely helps the rest go quicker, especially if you have AI building tests. However, until you solve the problems around coding, it’s not necessarily faster or better.

    Steve Jones

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

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